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Toxicology Strategies for Drug Discovery—Present and Future Eric Blomme, and Yvonne Will Chem. Res. Toxicol., Just Accepted Manuscript • DOI: 10.1021/acs.chemrestox.5b00407 • Publication Date (Web): 20 Nov 2015 Downloaded from http://pubs.acs.org on November 22, 2015

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Chemical Research in Toxicology

Toxicology Strategies for Drug Discovery—Present and Future

Eric A.G. Blomme, Global Preclinical Safety, AbbVie Inc., 1 North Waukegan Road, North Chicago, IL, USA Yvonne Will, Drug Safety Research and Development, Pfizer, Eastern Point Rd, Groton, CT, USA

Corresponding author: Eric Blomme, [email protected]

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Contents 1. 2. 3. 4. 5. 6.

Abstract Introduction Target Knowledge General Comments About Compound Profiling and Early Data Points Physicochemical Properties, Chemical Reactivity and Nonclinical Safety-Related Attrition In Silico Toxicological Evaluation a. Computational Models in Toxicology b. Specific In Silico Assessments c. Improving Toxicity Prediction and TSAs Through Protein-Ligand Interaction Network Analysis 7. In Vitro Toxicology a. In Vitro Genetic Toxicology b. Molecular Pharmacology Profiling c. Kinome Profiling d. In Vitro Cardiovascular Profiling e. High-Throughput Cytotoxicity Assays and Early Identification of Problematic Chemical Matter f. Mitochondrial Toxicity Screens g. Other In Vitro Toxicity Assays h. Zebrafish 8. Early In Vivo Toxicology Evaluation 9. Exploratory Toxicology Studies a. In Vivo Cardiovascular Assessment b. In Vivo Gene Mutation Tests c. Nonstandard In Vivo Models 10. Utility of Biomarkers Including The Omics Technologies in Early In Vivo Toxicology Assessment 11. An Eye to The Future a. Complex In Vitro Models b. Induced Pluripotent Stem Cells 12. Conclusions 13. Abbreviations 14. References

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Abstract Attrition due to nonclinical safety represents a major issue for the productivity of pharmaceutical research and development (R&D) organizations, especially during the compound optimization stages of drug discovery and the early stages of clinical development. Focusing on decreasing nonclinical safety-related attrition is not a new concept and various approaches have been experimented over the last two decades. Front-loading testing funnels in Discovery with in vitro toxicity assays designed to rapidly identify unfavorable molecules was the approach adopted by most pharmaceutical R&D organizations a few years ago. However, this approach has also a non-negligible opportunity cost. Hence, significant refinements to the “fail early, fail often” paradigm have been proposed recently to reflect the complexity of accurately categorizing compounds with early data points without taking into account other important contextual aspects, in particular efficacious systemic and tissue exposures. This review provides an overview of toxicology approaches and models that can be used in pharmaceutical Discovery at the series/lead identification and lead optimization stages to guide and inform chemistry efforts, as well as a personal view on how to best use them to meet nonclinical safety-related attrition objectives consistent with a sustainable pharmaceutical R&D model. The scope of this review is limited to small molecules, as large molecules are associated with challenges that are quite different. Finally, a perspective on how several emerging technologies may impact toxicity evaluation is also provided.

Keywords: Drug Discovery; Toxicology; Safety; Strategy; Attrition

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Introduction Pharmaceutical Research and Development (R&D) is a long, complex and expensive process that results in more failures than successes with approximately only 10% of molecules entering phase 1 clinical trials being ultimately approved by the United States Food and Drug Administration (FDA).1, 2 Failures at the Discovery stage are even more frequent.3 There are regularly published, quite variable estimates of the direct costs of bringing a new drug to the market, and irrespective of the estimates, these costs are largely over US$ 1 billion at the time of publication. Beyond challenging the sustainability of the pharmaceutical R&D model, this cost issue deters drug developers from focusing on several areas with significant unmet medical needs, and patients do not get access rapidly enough to new medicines with better efficacy and safety profiles.

The increased investments in R&D over the past 30 years have not translated to meaningful increases in drug approvals, leading to what has been referred to as the pharmaceutical R&D productivity gap: increased investments without corresponding increased success rates. There are many factors that explain this productivity gap, and those have been reviewed elsewhere in more details.1, 4 One of these factors is failure due to safety issues.3, 5 In particular, attrition due to nonclinical safety represents a major issue for R&D productivity, especially during the compound optimization stages of drug discovery and at the earlier stages of clinical development.3, 5 Reported attrition numbers vary according to company and reports.1 While these attrition numbers can be useful benchmarks, it is nonetheless important to analyze them with caution, as they depend on a large numbers of variables.1, 5 Firstly, these numbers vary Page | 5



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according to the time periods evaluated. Secondly, although often reported by large pharmaceutical companies, these analyses are based on relatively small numbers of molecules, numbers often too small for any robust statistical analysis. Thirdly, nonclinical safety-related attrition will vary according to therapeutic areas. For instance, nonclinical safety-related attrition is generally lower in oncology compared to indications where safety is a more critical component for success, such as in the areas of antiviral agents or metabolic diseases. Fourthly, companies have comparable, yet not similar governing processes, which typically dictate the fate of a compound or project, as well as different working principles, risk tolerance, culture and strategies.6 These organizational aspects impact when and how a project is labeled as terminated. Finally, a compound rarely fails due to a single reason and nonclinical toxicity has often been used as a default category. For example, a specific toxicity may be acceptable if observed at a comfortable predicted safety margin in an Investigational New Drug (IND)-enabling Good Laboratory Practice (GLP) toxicology study. However, if the human pharmacokinetic (PK) prediction is incorrect, the safety margin may shrink to an unacceptable level. In that situation, the termination will typically be labeled as related to nonclinical toxicity, while in fact one may argue that it was related to an incorrect human PK prediction. Conversely, a suboptimal toxicity profile may limit the ability to interrogate the pharmacodynamic (PD) effect or target engagement of an exploratory compound in a phase 1 clinical study. This may be categorized as an efficacy failure or as a nonclinical toxicity failure depending on the organization.

The main toxicities driving nonclinical safety-related attrition are variable depending on the company. Most companies are usually reporting liver and cardiac toxicity as two of the most Page | 6


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common toxicities.7-9 However, as already stated, these data are heavily influenced by current and past practices in individual organizations, as well as the therapeutic areas of interest. For instance, in the last decade, front-loading cardiovascular assessment with a battery of assays and models has significantly impacted the incidence of unexpected cardiovascular findings in pivotal safety pharmacology GLP studies or in early clinical trials.10, 11 In addition, other factors influence the types of development-limiting toxicities observed in an organization. For example, compounds targeting the central nervous system (CNS) will require particular physicochemical properties that are more frequently associated with some undesirable effects such as pharmacological promiscuity, while compounds targeting the kidney typically have quite different biodistributions. Finally, the chemical spaces vary depending on the company and likely influence the nature of the toxicities observed preclinically, although no good controlled studies have demonstrated that to be the case. At Abbvie, the main toxicities driving termination in nonclinical studies are quite variable and have evolved with time partly due to changes in therapeutic area focus, as well as integration of novel practices for nonclinical safety assessment.

Focusing on decreasing nonclinical safety-related attrition is not a new concept, and various approaches have been experimented over the last two decades. Fully understanding the impact that these approaches may have had is a difficult task, as it takes a few years to generate enough data to assess improvement and these data are usually too limited for any robust assessment. A decade or so ago, the mantra was “kill early, kill often” or “fail early, fail often”, the concept being that terminating bad molecules as early as possible in Discovery would result in substantial productivity increases. This was implemented by front-loading testing funnels in Page | 7



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Discovery with decision-enabling, often in vitro toxicity assays designed to rapidly identify these unfavorable molecules. While some may argue to the contrary, current evidence indicates that this approach has not resulted in meaningful improvements in R&D productivity in general and in non-clinical safety-related attrition in particular. One may even argue that it may have had a negative impact on productivity by limiting alternatives for chemistry (what one may consider as an opportunity cost). While technology advances have been considered a viable and potentially transforming solution for early safety prediction, it is fair to say that in the last two decades most technologies labeled as “new” or “emerging” have had limited impact on overall nonclinical safety-related attrition. Furthermore, the strategy adopted by the industry of front-loading with more toxicology assays to “kill early, kill often” has been recently challenged, since most assays (even good ones) have associated false positive rates and the combination of these early assays result in an overall low positive predictive value (PPV). This is partly explained by the fact that at this stage little information is available about efficacious exposures, such that data generated from these early assays can really only be used for hazard identification and not risk assessment. In other words, front-loading with more toxicology assays to “kill early, kill often” can result in discontinuation of too many “good” molecules (i.e., molecules with potential to succeed) and in increased cycle times at the lead optimization (LO) stage. This is mostly because few data points can be accurately interpreted in isolation without taking into account other contextual aspects. Others have also proposed that considering “what will not fail” may be a better way forward.8

This debate does not however negate the utility of focused, well-understood and validated assays to better guide and inform chemistry efforts. However, rather than using data from these assays as decision criteria to discontinue compounds, these data are probably better used as Page | 8


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“alerts” to influence the overall nonclinical safety testing strategy in a particular project. In other words, as nicely articulated by Hornberg et al., the strategy should be more about “testing the right things at the right time…instead of testing everything early”.8 This review provides an overview of the types of assays and approaches that have shown utility and a personal view on how to best use them to meet nonclinical safety-related attrition objectives consistent with a sustainable pharmaceutical R&D model. Figure 1 provides a high-level overview of the assays and studies that will be discussed in this review, as well as their timing during Discovery and early Development. The scope of this review is limited to small molecules, as large molecules are associated with challenges that are quite different. Finally, a perspective on how several emerging technologies may impact toxicity evaluation is also provided.

Target Knowledge Pharmacological modulation of the activity of some interesting therapeutic targets may result in unacceptable safety issues, such that therapeutic benefits cannot be easily separated from toxic effects. With discovery units investigating more unprecedented targets playing central roles in cellular homeostasis, this situation is probably more common than before. Being aware of the potential safety liabilities of targets and interrogating achievable therapeutic windows for these liabilities at an early stage become paramount for prioritizing work on targets more likely to succeed, for thinking early about creative ways to increase the therapeutic window for those targets, or for better predicting safety issues in humans. With this context in mind, most large pharmaceutical companies are routinely conducting an evaluation of the potential liabilities of new targets in Discovery. This exercise has different names depending on the company and may Page | 9



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include different components. But the overall principle of trying to learn earlier and being proactive with regards to target de-risking strategies is the same.

At Abbvie and Pfizer, we conduct what we refer to as Target Safety Assessments (TSAs) for all targets at the stage of target exploration. This assessment is based on a deep understanding of the biology of the target, the generation of a tissue expression map (mRNA as well as protein expression), the evaluation of a variety of data or information (such as human genetic data, phenotypes of available genetically engineered rodent models, on-going or past clinical trials targeting similar targets or pathways, or data from the patent literature), interrogation of biomedical literature databases facilitated by text mining strategies, as well as in silico simulation of the pharmacological consequences associated with changes of activity of the target or pathways (Figure 2). This collective knowledge is then used to generate a “TSA dashboard” that summarizes the lists of potential liabilities, their probability of occurrence (low, mid, high), their impact on development (low, mid, high), the proposed de-risking approaches (i.e. the activities that are recommended to rapidly interrogate the liability), as well as links to the most critical information. These TSAs are obviously living documents that require updates as new information (internal as well as external) becomes available. Ultimately TSAs represent the basis for the design of target/pathway-specific safety testing plans for target/pathway validation, hit identification and LO. Importantly, as they evolve and become more accurate during the life of a project, TSAs can serve as a basis for identifying populations that may be more sensitive to some adverse effects, or alternatively patient populations that may benefit therapeutically from a specific pharmacological mechanism. Page | 10


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For this approach to be successful, companies need to develop the appropriate infrastructure for scientists to access and mine the vast amount of information and data available to evaluate targets and pathways. At Abbvie, we worked with our knowledge management and information technology colleagues to develop customized tools for that purpose. Those include search engines that mine a vast array of internal and external databases, and provide information categorized in an appropriate manner and prioritized according to relevance for safety. In addition, chemoinformatics, pathway analysis and bioinformatics platforms allowing for in silico simulation of the effects associated with changes in target activity or pathway perturbations can be quite useful in spite of their current limitations.12 Some of these platforms are commercially or publicly available.12 As described later, those developed internally possess the advantages of incorporating internal experimental data that may be quite useful to also identify chemical matter as a basis for the synthesis of tool compounds; those can then be used for in vitro or in vivo experiments to interrogate the potential liabilities. For compounds identified through public information as potential modulators of the target of interest, a chemical safety review, including profiling in a battery of in vitro tests (e.g., cytotoxicity, molecular pharmacology, mitochondrial toxicity) may also be conducted.

The cardiotoxicity associated with targeting ErbB2 is a good and well documented example of a target-related safety liability. ErbB2 is a receptor tyrosine kinase also called HER2 (for human epidermal growth factor receptor 2) or HER2/neu. It is a validated target for cancer treatment, with Herceptin (trastuzumab, a monoclonal antibody or mAb) representing an Page | 11



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important therapeutic option for cancer treatment, especially breast cancer.13 Because of the role played by the ErbB2 pathway in cardiomyocyte differentiation, growth, repair and survival, Herceptin, as well as several other molecules impacting ErbB2 signaling, have been associated with cardiac dysfunction in the clinic. This obviously impacts the benefit:risk ratio of this class of drugs and limits their use in patients with pre-existing cardiac dysfunction. However, a deeper understanding of the mechanism of toxicity of these benchmark anti-ErbB2 molecules offers an opportunity to circumvent this undesirable toxic effect. For instance, recent data indicate that the cardiac liability of these molecules is related to their ability to prevent the assembly of the Neuregulin 1 b (NRG-1)/ErbB2/ErbB4 complex, which promotes cardiomyocyte survival.13 This mechanistic understanding provides a key starting point for the development of alternative antibodies targeting different epitopes to circumvent the cardiotoxic effect.

The epidermal growth factor receptor (EGFR) is another member of the ErbB family and a target for cancer that has received a lot of attention for its potential in oncology. Multiple companies have been targeting EGFR over the years using either small molecule receptor tyrosine kinase inhibitors (TKIs, including gefitinib, erlotinib, afatinib and dacomitinib) or mAbs (e.g., cetuximab and panitumumab). This approach has definite therapeutic benefits and EGFR inhibitors have become the standard of care for the treatment of advanced non-small-cell lung cancer. However, these agents are also associated with dermatologic side effects characterized by a severe papulopustular rash typically affecting the face, scalp, upper chest and back.14, 15 These target-related dermatological effects are due to the critical role of EGFR in the physiology and development of the epidermis, and interestingly, a positive correlation between skin rash and Page | 12


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clinical response has been observed.14 This rash may lead to secondary bacterial or viral infections, interference with daily activities, dose reduction or discontinuation.14 To address this on-target toxicity, AbbVie oncology discovery scientists and colleagues used a mAb that selectively targets a unique epitope of the EGFR, which is largely inaccessible when EGFR is expressed at normal physiological levels (i.e., levels observed in normal tissues) in contrast to levels in tumors.15 This tumor-specific binding property provides a scientific basis for eliminating or limiting the toxic skin rash observed with other anti-EGFR compounds.

The transient receptor potential vanilloid-1 (TRPV1) channel is expressed at high levels in sensory ganglia, is activated by multiple noxious stimuli, and has been an attractive target for pain management. However, selective TRPV1 antagonists have been associated with increased core body temperature in preclinical models and humans, while the natural TRPV1 agonist capsaicin causes dose-dependent hypothermia in animals.16 Those data complemented by additional experimental evidence suggest that modulation of TRPV1 would invariably affect core body temperature in vivo. Given the potential anti-nociceptive benefit of TRPV1 antagonists, additional efforts have been made to better understand this on-target effect. These efforts have led to the identification of modality-specific TRPV1 antagonists, which, in contrast to first-generation antagonists that inhibit all modes of TRPV1 activation, do not elevate core body temperature in preclinical models and are acid-sparing (i.e., only partially blocking TRPV1activation following acid treatment).17,18 The reason why acid-sparing TRPV1 antagonists are not associated with elevation in core body temperature is mostly unknown. The identification of these second-generation antagonists required adjustment to the testing plans in Page | 13



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order to front-load evaluation of the effects on core body temperature using appropriate in vivo models.

In summary, leveraging the constantly growing amount of target-specific information as well as in silico biology tools to generate TSAs has multiple benefits to a discovery-focused nonclinical safety organization. Firstly, these TSAs allow for optimal prioritization of experiments such that data-driven decisions on targets can be made more rapidly. Secondly, this exercise results in more complete, biology-driven data packages that ultimately should result in more comprehensive risk assessments of molecules in development. Thirdly, by being proactive and thinking of potential safety issues earlier, scientists from different functions can brainstorm to develop creative and innovative solutions to circumvent these issues. Fourthly, as serendipity is still a key aspect of drug discovery, discrepancies between predictions and outcomes may sometimes result in the identification of off-target effects with either safety concerns or therapeutic benefits. Finally, a thorough biological understanding of targets and pathways is an important step to ensure the safety of subjects in clinical trials. However, it is important to acknowledge the challenge associated with generating meaningful metrics to understand the impact of target knowledge activities, especially on safety-related drug attrition and/or clinically concerning adverse effects. Numerous variables can affect safety-related attrition in Discovery and Development, including the nature of therapeutic indications and targets, concurrent changes in practices, as well as overall proportion of small molecules versus large molecules in R&D portfolios.

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General Comments About Compound Profiling and Early Data Points Compound profiling offers the opportunity to triage compounds according to their inherent toxic properties, but also to optimize compounds for improved overall drug-like properties and lower probability of inducing toxicity. Compound profiling is widely used to improve the PK properties of compounds and, although this aspect is beyond the scope of this review, it is noteworthy that absorption, distribution, metabolism and excretion (ADME) profiling has been associated with better in vivo PK properties.19 A major reason for the success of ADME is that a consensus was reached amongst scientists in academia and industry on the best ways to tackle and optimize ADME properties. This consensus has led to a common agreed set of approaches to address the problem. In contrast, there is no consensus on the best ways to conduct early profiling for toxicity. This lack of consensus has definitely slowed down progress, although more efforts have been put toward that objective in the last few years. However, it should also be acknowledged that this lack of consensus is partly due to the complexity, lack of full characterization or understanding, and diversity of mechanisms of toxicity. Here we will focus on assays that can be used early in Discovery to profile compounds with the objective of decreasing their probability of causing development-limiting toxic effects in vivo. The key word here is “probability”. Although some of those assays or filters have arguably attractive performance characteristics, all have imperfect negative and positive predictive values, and few can be interpreted in isolation and out of context. These individual data points need to be interpreted in an integrated manner to increase the probability to identify compounds with better safety profiles. Furthermore, a significant proportion of profiling assays generate semiquantitative end points or binary outputs, which may facilitate decision-making in some situations, but also limit their usefulness in other instances. Page | 15



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An important aspect when trying to front-load toxicity assays or guidelines early in the hope of identifying and eliminating “bad players” is the multiple testing problem or falsediscovery rate (FDR). Simply stated, by trying to identify “bad” compounds using multiple parameters (even assays with good calculated performance characteristics), there is a high likelihood that most compounds will have at least one bad attribute by random chance alone. Since a single bad attribute, even if real, may not necessarily translate to a clinical significant safety issue, this strategy leads to the termination of a large numbers of compounds with likely acceptable safety profiles. In other words, there are significant limitations for early data points originating from in vitro assays or filters to predict in vivo toxicity.

The bile salt export pump (BSEP) membrane vesicle assay is a good illustration of the limitations of in vitro data interpreted in isolation. BSEP is a member of the ATP-binding cassette (ABC) family of transporters and is involved in the secretion of bile acids in the bile.20 Inhibition of BSEP has been suggested as a cause of cholestasis and hepatotoxicity (especially idiosyncratic) for several drugs, such as nefazodone and troglitazone.21 However, multiple widely prescribed drugs are also BSEP inhibitors and do not cause cholestasis clinically. For instance, the thiazolidinedione or glitazone class of compounds developed to treat type 2 diabetes mellitus are all BSEP inhibitors; yet, troglitazone is associated with idiosyncratic hepatotoxicity, while rosiglitazone has lower hepatotoxicity liabilities and pioglitazone is not considered hepatotoxic.22 Clearly, when interpreting these data, the IC50 values for inhibition of BSEP activity need to be interpreted in the context of exposure data (e.g., projected liver concentrations Page | 16


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of the compounds achieved at the therapeutic doses), as well as effects on the activity of other transporters involved in bile acid homeostasis (i.e., the multidrug resistance-associated proteins or MRPs).21, 23 The last aspect is particularly important, since in addition to being a cell-free assay system and therefore lacking important cellular contents, the BSEP membrane vesicle assay also does not interrogate other transporters, such as MRP2. This example also identifies an important gap for toxicology profiling to be successful: it is currently extremely difficult to estimate or simulate in vivo tissue exposure and the kinetics of compounds in tissues.

To identify potential hazards associated with molecules, three general categories of profiling assays can be used: profiling for specific physicochemical attributes and chemical reactivity, in vitro toxicity assays, and molecular pharmacology assays. It is beyond the scope of this review to discuss in details the technical aspects of these various assays and those will only be discussed when they can have an impact on data interpretation.

Physicochemical Properties, Chemical Reactivity and Nonclinical Safety-Related Attrition In the past decade, there have been multiple extensive efforts to understand the impact of various physicochemical properties on probability of success in general and toxicity in particular. This has been partly stimulated by the observation that current drug candidates have in general increased molecular weight and higher lipophilicity compared to historical benchmark data, a phenomenon that some have creatively labeled as “molecular obesity”.24 These changing attributes have generated challenges related to formulation and absorption, but also toxicity.19 Page | 17



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More than a decade ago, Lipinski and colleagues conducted a large assessment of the physiochemical properties of 2,245 drugs to understand the attributes that drive oral bioavailability of small molecules.25 Their assessment led to the well-known Lipinski’s “Rule of Five” that can be used to predict the potential of a compound to exhibit good oral absorption. The Rule of Five states that to be orally bioavailable, a small molecule should violate no more than one of the following criteria: having no more than 5 hydrogen bonds donor and 10 hydrogen bond acceptors; having a molecular mass < 500 Daltons; and a logP < 5.25

Beyond association with oral bioavailability, several groups have evaluated the physicochemical attributes that may be useful to guide medicinal chemists toward safer regions of the chemical space. In particular, a report by Pfizer scientists resulted in what is known as the 3/75 rule.26 In a large retrospective evaluation that included a diverse set of 245 proprietary compounds for which well curated early, repeat-dose in vivo toxicity data (studies of ≥ 4-day duration) were available, total polar surface area (TPSA) was the physicochemical descriptor with the strongest, most consistent correlation with incidence of adverse outcomes.26 In addition, calculated logP (ClogP) was another descriptor with a consistent link with adverse outcomes. Furthermore, compounds with a ClogP < 3 and a TPSA > 75 A2 were approximately 2.5 times more likely to be “clean” (i.e., not be associated with adverse outcomes at Cmax >10 µM) than “toxic”. In contrast, when both risk factors were present (i.e., ClogP > 3 and TPSA < 75 A2), compounds were approximately 2.5 times more likely to be “toxic” than “clean” in these shortterm exploratory toxicology studies at Cmax < 10 µM.26

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Interestingly, for 108 of these compounds, a full matrix of molecular pharmacology binding assay data (CEREP; 48-assay panel) was available, which allowed these investigators to interrogate the potential promiscuity of “toxic” versus “clean” compounds. Promiscuity was defined by whether a compound demonstrated over 50% activity at 10 µM in three or more of the 48 assays. Using this surrogate measure, promiscuity demonstrated a good correlation with higher ClogP and lower TPSA, suggesting that non-polar, lipophilic compounds may be associated with more adverse outcomes due to off-target effects.26 This observation that lipophilicity and low polarity may be important determinants of pharmacological promiscuity is consistent with that of others.27-29

Following this report, we conducted in 2009 a similar analysis at AbbVie to determine if these reported associations between toxicity and ClogP/TPSA was also reflected in our chemical space. Importantly, we tried to follow Pfizer’s approach as much as possible by using exclusively compounds for which well curated data from short-term exploratory toxicology studies were available, by exploring a set of 95 structurally and pharmacologically diverse compounds, and by using a similar definition of what is classified as “toxic” versus “clean” (with the exception that we used a 5 µM Cmax threshold compared to the 10 µM Cmax threshold used by Pfizer scientists as the practical desirable Cmax to achieve). The overall toxicity rate was 49% (47/95). Like Pfizer, compounds with ClogP > 3 and TPSA < 75 A2 were more likely to fail due to toxicity: approximately 60% of compounds violating the 3/75 rule were considered toxic in these exploratory toxicology studies.

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Further reports have provided similar evidence.30 However, it is noteworthy than other reports did not validate these findings, although the nature of data used was slightly different.31 Furthermore, other reports have suggested the influence of other molecular descriptors, such as molecular complexity.32 The inconsistencies between all these studies point to some of their limitations, which include, depending on the study: (1) a low number of well curated end points; (2) the variable nature of the data used (e.g., data from short-term exploratory studies versus data from robust, longer studies conducted under GLP conditions); (3) differences in the end points used or in the definition of these end points (e.g., differences in what is considered “toxic”) in the analysis; (4) an unavoidable bias due to the nature of the projects and chemical spaces of different companies; and (5) the overall weak, even if statistically significant, correlations reported. The latter point is especially noteworthy, as it relates to the concept of correlation inflation.33 The majority of these analyses partition data into bins prior to analysis in an effort to simplify their complexity. But this practice of binning continuous data and of averaging quantities by bin typically exaggerates the strength of any trend identified.33 In addition, most retrospective analyses do not control well for potential covariates (i.e., other attributes that may influence the same parameters) and therefore may generate spurious relationships.30 Given the relatively modest statistical significance reported in the analyses, it is important to be cautious when interpreting these results, but also to use the proposed rules appropriately. These rules, if valid, have the advantage of raising awareness of potential issues and parameters that may be associated with higher probability of success.

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To address some of these pitfalls, four companies (Astra-Zeneca, Eli Lilly, Glaxo-SmithKline and Pfizer) partnered to evaluate a larger data set composed of 812 oral compounds at various stages of Development.5 While this data set can clearly contribute to a robust analysis, it nonetheless was limited to compounds nominated for development: in other words, compounds that had already satisfied several relatively stringent criteria for toxicity properties (such as more extensive in vivo testing). With this data set, ClogP and TPSA did not influence in vivo toxicological outcomes. Collectively, these various analyses suggest that either these rules may not apply to all chemical spaces or that they are not useful for compounds that have already been evaluated in robust, dose-range finding toxicology studies. In other words, rules like the 3/75 rule may mostly be relevant during LO when compounds have not been extensively profiled in vivo. This last aspect is plausible, as the percentage of compounds predicted promiscuous is significantly higher at the LO stage compared to launched drugs.27

This latter point of view is the one we have adopted at AbbVie, since our internal analysis clearly showed an association between the 3/75 rule and probability of toxicology success. However, most of the debate resides more on how these rules are used. Clearly, some targets will require compounds with physicochemical properties that do not meet these criteria, just like not all orally bioavailable compounds comply with the Rule of Five. Likewise, an analysis of marketed drugs reveals a significant portion of drugs that violate the 3/75 rule, indicating that strictly adhering to that rule would result in a large number of missed opportunities (Figure 3). Consequently, these rules cannot accurately quantify probability of success for any specific project. However, they can still be useful when used creatively to more Page | 21



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rapidly advancing projects. For instance, in an early project with the majority of compounds violating the 3/75 rule, it may be very appropriate to rapidly initiate in vivo testing with a few early lead compounds to interrogate if these physicochemical attributes translate into negative in vivo outcomes, especially if these physicochemical properties are associated with other risk factors identified in vitro (e.g., cytotoxicity at low concentrations, mitochondrial toxicity, promiscuity in a molecular pharmacology panel). Likewise, when initiating work on a back-up compound, it may be wise to differentiate from the lead compound based on physicochemical properties like TPSA or ClogP. Finally, although correlations may not hold on an individual project, they may for an entire portfolio of drugs, and trying to maintain a portion of the portfolio into a more compliant physiochemical space may be advantageous.

Small molecules may contain structural components that are inherently reactive and toxic (often referred to as toxicophores) or that may be metabolized to reactive metabolites. Most toxicophores are typically identified early during hit identification and experienced chemists are well aware of chemical moieties to avoid. For example, chlorophenols have known association with mitochondrial uncoupling effects.34, 35 Computational methods are also available, as described later. In addition, there are established methods to detect reactive compounds. For instance, at AbbVie we use a La protein-based assay to detect reactive molecules by nuclear magnetic resonance (ALARM NMR); this assay can identify compounds that oxidize or form covalent adducts with protein thiols groups.36 In an evaluation of a large set of compounds that were considered reactive or non-reactive with the ALARM assay, reactive compounds had significantly increased risks of interacting with additional proteins (aldehyde dehydrogenase, Page | 22


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superoxide dismutase, and three cytochrome P450 enzymes), indicating that this reactivity was non-specific and likely to affect multiple proteins in cells and to produce toxicity.36

In Silico Toxicological Evaluation a. Computational Models in Toxicology A large variety of in silico methods have been developed for toxicology applications. Although most published models do not show performance characteristics sufficiently good for routine use, some theoretical methods for toxicology prediction can provide benefits to Discovery toxicologists. Computational methods vary in complexity and performance, and can be broadly categorized into three main categories: (1) grouping approaches such as read-across (a technique used to predict a specified end point for a chemical structure by using data on the same end point from another similar chemical structure) and chemical category formation; (2) quantitative structure-activity relationship (QSAR) approaches; and (3) expert systems (systems that can mimic human reasoning and formalize existing knowledge).37 All methods are based on the similarity principle (i.e., similar compounds have similar biological activities).37 Grouping approaches, specifically designed filters or look-up tables represent a simple approach to identify pharmacophores, toxicophores or problematic substructures, and are routinely used by medicinal chemists during LO.38, 39

Most computational toxicology models are not sufficiently performant to justify their use in drug discovery. One of the reasons is the complexity and large variety of mechanisms of Page | 23



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toxicity for any given tissue, as well as the extreme diversity of chemical structures associated with similar toxic end points. Because most toxic effects are due to interactions with a variety of proteins or protein binding sites and are caused by structurally dissimilar chemical structures, developing robust computational tools, which rely on the similarity principle to predict general end points (such as organ-specific toxic effects or broad mechanisms of toxicity), is a daunting and likely not achievable task. A more realistic approach is to focus on very specific end points, such as interactions with a specified target protein (e.g., a drug transporter or DNA) or a well characterized, narrowly defined mechanism of toxicity (e.g., phospholipidosis). Another limitation of computational toxicology models is related to the quality and quantity of the data points used as training sets. To develop a performant model, a large volume of robust data points is required, and most available data sets do not provide the necessary level of data quality/quantity and of chemical diversity. For instance, data sets often contain data points obtained under different, frequently inconsistent experimental conditions. Likewise, models are often built upon an irrelevant chemical space, for instance using pesticides or natural toxins. Models can also be developed with congeneric series or a narrow chemical space, such that their use applicability domain is limited to structurally related compounds.40 Such models are termed local or specific (in contrast to the more comprehensive global models) and can be quite useful when customized for use with chemical series associated with a specific, not pharmacologically driven toxic liability. However, the applicability domain of published models is typically not clearly defined and when it is defined, it is often not useful for drug discovery. Finally, published models are often not sufficiently validated using solid experimental evidence. However, in spite of their limitations, computational models offer obvious advantages in terms of cost and speed compared to “wet” experiments, which can be expensive, laborious and time-consuming without Page | 24


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even considering the need to synthesize compounds. As such, there is growing interest to develop these models for discovery toxicology, especially local, fit-for-purpose models.

b. Specific In Silico Assessments With this context in mind, some computational approaches have definite value in pharmaceutical R&D. In particular, in silico methods are widely used in genetic toxicology for identification of structural alerts, and their relatively good performance is related to the fact that they focus on a very well defined mechanism of toxicity (i.e., mutagenicity) that results from direct DNA interaction and depends partly on chemical electrophilicity. The performance of these systems is still variable, although a lot of the major reactive chemical structures associated with genotoxicity are known and incorporated into commercial or proprietary in silico models such as MultiCASE or DEREK.41-43 In addition, some models allow companies to integrate internal data to enhance their performance. Hence, for regulatory submissions during impurity qualification process for drug substances and products, the use a rule-based expert system complemented by either expert knowledge or a second QSAR model is recommended.43

Computational approaches are also quite attractive to predict interactions with drug transporters. Although crystal structures of some transporters, such as P-glycoprotein (P-gp), are available to enable structure-based homology models, the majority of in silico methodologies to predict interactions with transporters are based on common structural fragments, recognition elements or physicochemical descriptors.40 Several methods are available to predict substrates, Page | 25



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inhibitors or modulators of P-gp.44 Since the critical structure of P-gp has been deciphered, molecular docking models are also feasible in the future, although it is known that compounds interact with transporters at several binding nodes (a feature referred to as polyspecificity).44 Models also have been reported for other transporters, such as BSEP and the MRPs.40 However, these computational models have not to our knowledge been evaluated sufficiently to fully understand their performance and real utility.

Other well defined toxic end points may be amenable for prediction with computational models. For instance, phospholipidosis represents an excessive cytoplasmic accumulation of phospholipids, which are normal cellular components.45, 46 Drug-induced phospholipidosis is typically caused by cationic amphiphilic drugs, which are compounds containing a hydrophobic ring structure and a hydrophilic side chain with a charge amine group.45, 46 These types of compounds typically penetrate the brain very well, such that phospholipidosis is not an infrequent finding when developing compounds to treat neurological diseases. Because of the unique physicochemical properties (e.g., pKa and logP) of compounds associated with phospholipidosis in vivo, computational methods to assess the phospholipidosis-inducing potential of compounds have been published that rely on these calculated physiochemical descriptors.47 For example, compounds with pKa > 8.0 and ClogP > 1.0 tend to be associated with phospholipidosis.48 These models have been further refined by improving database curation49 and by incorporating additional features in the models, such as volume of distribution (Vd) to capture tissue accumulation50 or using QSAR models.51

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c. Improving Toxicity Prediction and TSAs Through Protein-Ligand Interaction Network Analysis Prediction of potential off-target effects has traditionally been based on the shared identity (or sequence homology) between the target of interest and other proteins. In other words, under that principle, high homology indicates a close relationship between two proteins, resulting in an increased likelihood that a compound with high affinity for a target will also exhibit affinity for proteins with some degree of homology. This conventional approach leads to establishment of selectivity screens against isoforms of the target or other proteins with high homology with the target. These selectivity screens often include members of the same family than the target of interest, especially if those are known to be associated with safety liabilities. This concept does have validity and utility, and is routine practice in the industry. For instance, in the realm of protein kinases, a general guideline is that any two kinases with over 60% sequence identity are likely to exhibit comparable affinities for the same chemical inhibitors.52 However, the converse cannot be generalized, as even very distantly related kinases (from a sequence perspective) can exhibit surprisingly high pharmacological similarity.53 These pharmacological relationships (defined as the ability to bind with similar affinities a range of chemical entities regardless of sequence similarity) can only be determined by testing sufficient compounds against an array of protein targets.

The recent explosion of public, commercial and internal databases coupled with the availability of appropriate information technology infrastructures and analysis tools has offered a unique opportunity to gain even more information on pharmacological relationships and Page | 27



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chemotype activity profiles. This growing knowledge when appropriately leveraged enables better prediction of probability of success for a target and a chemotype of interest, but also of key potential liabilities that could and should be interrogated early for specific targets.

For targets for which a sufficiently large number of compounds have been tested internally with some reasonable experimental consistency, AbbVie Cheminformatics scientists have developed tools to rapidly generate measures of pharmacological similarity (i.e., similarity in binding profile independent of protein sequence or a measure of the relatedness of targets based upon experimental profiling of potential ligands; Figure 4).53 Simply stated, this analysis allows for pharmacological grouping of targets that may have little or no structural similarity. These tools offer an unprecedented opportunity to rapidly generate a list of potential off-targets as long as enough end points are available. This approach has limitations similar to what we described for other computational models. Firstly, there is no a priori guidance as to what value constitutes a significant pharmacological link and experience with the approach is required to understand what values are significant and under which conditions. Secondly, the strength of the interaction predictions is dependent on the size and diversity of the data training set: for an ongoing project, predictions may strengthen or weaken as new data become available, hence the need to ensure continuous incorporation of these new data points into the model. Thirdly, simply observing a correlation of potency values between two target proteins does not in fact establish that a true causative pharmacological relationship exists. In other words, it is important to view the results of this analysis as probabilities. Fourthly, if the pharmacological link is based on a limited chemical space, the applicability domain is likely to be limited to that chemistry space. Conversely, if multiple chemical series have been tested, in addition of strengthening the Page | 28


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similarity measures, this information may identify chemical series with higher risk that may be either de-prioritized or interrogated earlier.

While it is premature to generalize on the utility of protein-ligand network analysis, one can envision several value-added applications, including: (1) the identification of off-target effects with known safety or drug-drug interaction (DDI) concerns (i.e., hitting toxicity hubs, such as hERG, PPARγ, 5-HT2b or PXR) that can then rapidly be interrogated using appropriate in vitro assays; (2) the identification of chemical series with higher risk based on polypharmacology; (3) the selection of the most appropriate in vitro assays to guide LO; and (4) the formulation of data-driven hypotheses to investigate mechanisms of toxicity.

In Vitro Toxicology In vitro toxicity assays are attractive, since they have high throughput, require minimal quantities of compounds, and reduce animal testing (in keeping with the 3R principles). In vitro toxicity tests can be useful in series selection and LO by guiding chemistry toward a chemical space with reduced toxic liabilities. However, to impact discovery productivity, in vitro methods need to generate data of a quality level sufficient to increase the probability of making a good molecular design decision. That implies data that can be used to infer the effects of compounds in humans and/or animal species that will be used in toxicology studies.54 While this statement seems obvious, it is noteworthy that a majority of current tests used in some organizations (and developed under significant imperatives to make an impact with in vitro technologies) have debatable utility, such that one wonders whether establishment of in vitro toxicity assays became

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an end objective, rather than the mean to a more relevant end objective (i.e., predicting toxicity and increasing probability of success).

Nevertheless, despite some recognized limitations, some assays are definite utility, such that they can confidently be used routinely during LO. For instance, in vitro genetic toxicity and safety pharmacology assays (e.g. assays for interaction with the hERG channel or molecular pharmacology profiling) are routinely used during LO and candidate selection in most companies using similar, yet not identical paradigms. In contrast, other in vitro toxicology assays are probably more useful when used on an as-needed basis to support SAR work around specific toxic end points. In addition, albeit desirable, it is not practical to set up screens for all possible toxic effects that may be observed in animals or humans. Hence, most companies tend to focus on the most common toxic events observed especially in humans, including hepatotoxicity, cardiovascular toxicity and CNS toxicity.8 Finally, previous institutional experience heavily (and maybe inappropriately) influences testing paradigms, resulting in the integration of unique assays in some companies. Consequently, the way in vitro toxicity assays are used in the industry varies quite a lot across companies, reflecting the complexity of successfully integrating in vitro toxicology assays during pharmaceutical Discovery, but also the fact that most organizations cannot develop accurate metrics to understand the positive or negative impact of any of those assays.

Understanding and refining experimental conditions for in vitro assays and screens are of outmost importance. For example, years of in vitro testing using rat and human hepatocytes have Page | 30


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advanced our understanding of the changes in expression and activity of CYP450 enzymes and transporters over time in culture and how these changes may affect experimental outcomes. However, most toxicology laboratories still culture hepatocytes in glucose media, even though a more physiological substrate would be lactate. Likewise, the bionergetic profile of induced pluripotent stem (iPS) cell-derived cardiomyocytes changes depending on the substrate used: cells grown in low glucose, galactose and fatty acids demonstrate increased aerobic metabolism in comparison to cells cultured in high glucose media.55 As discussed later, this has some functional consequences as illustrated by the effect of glucose concentrations in culture media on the detection of mitochondrial toxicants: cells grown in high glucose concentrations are resistant to mitochondrial toxicity, while cells grown in the presence of galactose die upon the insult.56 As most cells used in in vitro toxicology screens are of tumorigenic origin, care must be taken to understand how metabolism is affected and optimal experimental conditions.

Large scale experiments to predict organ toxicity (e.g., liver, kidney or heart) have clearly demonstrated that a cell line representative of the organ or tissue is not sufficient to predict organ toxicity, especially using general cell health parameters, such as ATP depletion. There are obvious reasons for that. Firstly, tissues are usually composed on more than a cell type. Secondly, cell lines cultured in monolayers cannot recapitulate the complexity of a tissue and the large number of biologically important cell-cell and cell-extracellular matrix (ECM) interactions. Even co-cultures of different cell types cannot reproduce the tissue architecture required for some cell responses. Thirdly, tissue-specific end points may be required for predicting a tissuespecific response. For instance, multiparametric assessment by high content screening (to simultaneously monitor, for example, mitochondrial membrane potential, DNA damage, Page | 31



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endoplasmic reticulum (ER) stress, and lipid accumulation) would not differentiate hepatotoxicants acting through BSEP inhibition or proarrythmogenic agents acting through cardiac ion channels. Finally, it is likely that most compounds cause toxicity in vitro by affecting common functions and structures of the cells (i.e., what is known as the basal cytotoxicity concept) and that in contrast to the in vivo situation, tissue-specific exposure cannot be recapitulated in vitro.57

a. In Vitro Genetic Toxicology Genetic toxicology testing is designed to identify compounds which may interact with and change the native structure of DNA, either through gene mutations (i.e., mutagenicity) or through chromosomal damage (i.e., clastogenicity). Compound-DNA interactions are considered good surrogate of carcinogenicity potential and are therefore testing requirements for compounds to progress in clinical trials. No single genetic toxicology test predicts all types of genetic damage. To support clinical trials, a battery of tests conducted to GLP standards is performed concurrently with other IND-enabling GLP toxicology studies. Typically, this battery consists of a bacterial mutation test (the Ames test) and an in vitro cytogenetic test for chromosome damage in mammalian cells (e.g., human lymphocytes) complemented by an in vivo cytogenetic test conducted in rodents (typically a bone marrow micronucleus test in rats). These assays are usually conducted at the same time as the other IND-enabling GLP studies, although in vivo genotoxicity studies are not required to support Phase 1 clinical studies. Except for oncology indications, a positive (genotoxic) result in one or more of these GLP tests would significantly impact further development. Hence, early identification of positive compounds and enabling SAR analysis on problematic series is extremely important. Page | 32


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Genotoxicity screening paradigms typically involve miniaturized genetic toxicology tests that are conducted during LO or earlier and that have high throughput, low cost and minimal compound requirements. The miniaturized versions of the Ames test (e.g., the micro-Ames and mini-Ames assays) have very good concordance with the regulatory GLP Ames assay, and are consequently very reliable for decision-making.58, 59 This is based on the fact that compounds positive in the Ames assay will face major, likely unsurmountable development hurdles for most therapeutic indications. However, it is important to recognize that the Ames assay has also some short comings. Firstly, it is an assay based on bacteria and not mammalian cells. Secondly, the assay is used as a hazard identification test and does not estimate the relative risk for mutagenic events under physiological conditions that would occur in whole animals. Thirdly, an exogenous metabolic activation system (most often an induced rodent microsome S9 fraction) is used to generate and assess metabolites, which may not be present under in vivo conditions (a consideration relevant to all in vitro test results). Therefore, more relevant in vivo approaches to evaluate mutagenicity would be useful to improve risk assessment.

Several screening assays for clastogenicity are available. For example, the mouse lymphoma assay evaluates forward mutations in the thymidine kinase locus of mouse lymphoma cells, allowing these cells to form colonies. The sizes of the colonies vary depending on whether the changes are caused by mutation events or chromosome damage. The in vitro chromosome aberration test uses various types of cells arrested in metaphase to identify by microscopy chromosomal breaks or rearrangements. This test is labor intensive and technically challenging. Page | 33



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Finally, the in vitro micronucleus assay is a simple option that utilizes various cell types to detect the presence of micronuclei (i.e., a small nucleus formed from a chromosome or a chromosome fragment).60 While easy to perform, traditional in vitro micronucleus assay protocols still require extended hands-on technical time, limiting their throughput. Adaptation to an automated image analysis or flow cytometry platform can significantly increase throughput and the numbers of cells evaluated per compound/dose combination, resulting in a more objective evaluation and decreased technician time.60-62 Other in vitro genotoxicity assays are available as early screens, such as the GreenScreen high-content screening assay, which has been shown to have robust performance characteristics.63-65 Our experience at AbbVie has been that the Ames test (in a format adapted to the Discovery stage) complemented with another assay in mammalian cells is sufficient to circumvent the vast majority of genotoxicity signals at later stages of R&D.

In vitro tests for clastogenicity have a high rate of false positive results. Thus, in contrast to Ames results, a positive signal in an in vitro clastogenicity test may not justify termination and mechanistic studies are occasionally warranted. Compounds that cause DNA damage in these assays may induce whole chromosome loss (aneugenicity) or chromosome breakage (clastogenicity). This distinction is important, because compounds acting as aneugens cause damage by indirect mechanisms (deregulation of chromosome segregation) and the damage may be absent below a certain threshold and, thus, may have no carcinogenicity liability at therapeutic exposures. To identify aneugens, a kinetochore assay can be used as a second-tier assay to interrogate in vitro micronucleus assay positive compounds for projects where the target is suspected to induce aneuploidy.66 In addition, flow cytometry-based assays have shown the potential to distinguish aneugens from clastogens in a higher throughput, first-tier in vitro Page | 34


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micronucleus assay.62, 67 It is also important to underscore that an in vitro clastogenic signal may not translate to an in vivo positive signal. Therefore, for these compounds, it may be useful to rapidly interrogate in an early in vivo rat repeat-dose toxicology study whether an in vivo correlate to the in vitro signal exists. This can be done by evaluating for the presence of micronuclei in bone marrow or peripheral blood.60 For these compounds, a negative in vivo result could support further advancement at a lower risk with a recommended early evaluation in the battery of GLP genetic toxicology tests supplemented by an additional in vivo genetic toxicology end point if the GLP in vitro cytogenetic test is positive, as indicated in ICH S2 (see: ICH Harmonized tripartite guideline guidance on genotoxicity testing and data interpretation for pharmaceuticals intended for human use).

b. Molecular Pharmacology Profiling The vast majority of pharmaceutical discovery organizations routinely screen compounds against a panel of safety relevant proteins (e.g., enzymes, receptors, ion channels) with the objective of identifying activity against these non-therapeutic targets (also called anti-targets) that are associated with undesirable adverse effects. These screens are conducted internally or in contract laboratories depending on the companies. Often, at least at the candidate selection stage, robust large panels composed of 50-80 anti-targets are used. These screens have traditionally been radioligand displacement assays, which do not distinguish activators from inhibitors and which are usually conducted using a single concentration of the test article (10 µM appears to be most widely used). Consequently, hits at pre-specified threshold (e.g., > 50% displacement at 10 µM) are commonly followed up with a functional assay in order to understand the activity better (inhibitor versus activator) and also to generate an IC50 or EC50 for better risk assessment. Page | 35



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In addition to identifying specific anti-targets, these assays, as discussed before, offer an opportunity to evaluate the pharmacological promiscuity of compounds. It should be stressed that there are no widely accepted criteria for what constitutes pharmacological promiscuity in these assays. Several criteria have been proposed in the literature. For example, Peters et al. in a recent analysis of the molecular properties associated with promiscuity used a hit rate > 5% in the Bioprint panel, which is composed of 131 safety relevant anti-targets.68 However, since there is no industry harmonization of panel compositions, such that each company uses a different set and a different number of anti-targets, it is not possible to derive criteria, and these criteria will therefore be company-specific. US FDA scientists, who review submissions from a wide variety of companies, reported that indeed the panels of targets used by sponsors vary widely and that often the rationale for the selection of the targets is unclear.69 This lack of obvious rationale likely reflects the fact that most companies use a standardized panel across all therapeutic indications without necessarily tailoring it according to the therapeutic target being pursued. Finally, polypharmacology may be an important part of driving efficacy. On average, a drug is estimated to modulate six target proteins, and for some indications some level of promiscuity may be required to achieve efficacy.70

Large, radioligand binding assays are costly and of relative low throughput, such that there are not really amenable for early testing. For that reason, a recent trend in the industry has been to complement these development candidate-stage evaluations with smaller sets of representative assays that can be used earlier in the Discovery process for both assessment of pharmacological promiscuity and detection of interactions with few specific anti-targets that Page | 36


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have clear development-limiting implications.68, 71 For instance, agonists of the 5hydroxytryptamine 2b (5-HT2b) receptor are clearly undesirable due to their known cardiovascular liability (i.e., valvulopathy).72 Likewise, activators of the peroxisome proliferator activated receptor gamma (PPARγ) are also typically avoided due to the safety liability associated with this pharmacological mechanism (i.e., cardiac toxicity due to plasma volume overload).73 At AbbVie, we have implemented a similar approach that we call Bioprofiling, which evaluates 21 anti-targets selected according to their known link to well-characterized in vivo adverse events. The main objective of the Bioprofiling approach is to flag compound liabilities as early as post-high-throughput screening (HTS), to prioritize the best candidate clusters for Hit-to-Lead (HTL), and to monitor through “spot checking” the profile of selected leads during LO and if applicable conduct SAR studies. In a study comparing the rationale, strategies and methodologies for pharmacological profiling from four large pharmaceutical companies (Astra-Zeneca, Glaxo-Smith-Kline, Novartis and Pfizer), variations in panel sizes, tactics and technologies were reported, but substantial overlap in the nature of the targets was also present.74 This analysis led to the recommendation of a 44-target panel for early assessment of potential hazard.74

There is a recent move in some R&D organizations toward focusing on smaller screening panels composed of functional assays rather than radioligand-based assays even for advanced compounds. The goal is to focus exclusively on anti-targets with good predictive value for adverse drug reactions, rather than to evaluate complete selectivity, which is arguably never completely feasible even with large panels.68, 71, 74 The rationale is based on the fact that a lot of hits in radioligand-based assays are associated with inhibitors and frequently have no safety Page | 37



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relevance. However, these hits need to be followed up with second-tier functional assays, which adds time and cost. For instance, across the industry, hits for the 5-HT2B receptor are frequent and most of these compounds are inhibitors panels (unpublished personal observation).68, 71 For example, in the Novartis assay panel, 5-HT2b was reported to be the most promiscuous receptor.71 Likewise, compounds with true safety issues due to activation of an anti-target may not be easily detected in these radioligand displacement assays, which are notoriously insensitive when the radioligand is an inhibitor.68

c. Kinome Profiling Because of the importance of protein kinases as therapeutic targets, but also as key regulators of essential cellular functions, it has become routine practice in drug discovery to screen small molecules for their kinase inhibition profiles for both safety and efficacy reasons. These screens use various panels of kinases representative of the complete kinome, which is estimated to contain 518 protein kinases.75 Kinome profiling is typically conducted internally in larger R&D organizations, but commercial profiling services exist as well. These high- to medium-throughput assays evaluate inhibition of enzyme activity (i.e., capability of compounds to decrease the phosphorylation activity of kinases), measurement of inhibitor binding, or cellular activity.76 It is noteworthy that a retrospective evaluation of four large collections of kinase profiles demonstrated that the concordance between the assay panels from the different sources was relatively modest, indicating that assay conditions substantially influence results, especially for compounds with activity.30 The composition and size of the panels are variable across companies and reflect to some extent institutional history (e.g., past kinase projects, experience with specific kinases associated with undesirable outcomes, literature reports linking Page | 38


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a kinase to a specific pathology), such that there are no simple criteria for what is an acceptable kinome profile from a safety perspective. The general assumption is that the more selective the compound, the better. Hence, the objective is to usually find compounds with the cleanest profiles against unintended kinases in these panels. Whether this assumption is always correct is often unknown, but without additional data to prove the contrary, this is a reasonable way to proceed.

Developing small molecule kinase inhibitors has been a very active area in medicinal chemistry in the last two decades. The majority of small molecule inhibitors are ATP-mimetic compounds that target the ATP binding site of the enzymes (those compounds are referred to as Type 1 and 2 kinase inhibitors). This site is located in a hydrophobic cleft between the two lobes of the kinase domain and is highly conserved. This structural conservation makes finding compounds with good selectivity for a specific kinase a challenge. Substantial progress in synthesizing kinase inhibitors with good selectivity profiles has been made and the regulatory approval of several small molecule kinase inhibitors for various indications (dominated by oncology, but also including immunology indications) is a testimony of these remarkable advances. However, one can argue that the vast majority of small molecule kinase inhibitors are not truly selective and that some degree of promiscuity exists, which may not necessarily translate into a safety issue. Because complex kinase/inhibitor relationships occur without relationships to their sequences, the kinase selectivity of the majority of exploratory compounds against the entire kinome is generally not known and cannot be reliably predicted using a small subset of the kinome.77, 78 Page | 39



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Another complicating factor is that little is still understood about the polypharmacology of kinase inhibition, especially for safety concerns. Several on-target effects against specific kinases are well described. For example, inhibition of the vascular endothelial growth factor receptor (VEGFR), especially the VEGFR2, is known to be associated with hypertension, hemorrhage, thrombosis, and proteinuria.79, 80 However, it is usually unknown if additional kinase interactions may worsen or ameliorate these on-target effects, given our current limited understanding of cellular signaling networks in various tissues. Furthermore, interactions with other less understood kinases may lead to effects that are not yet well understood. For example, cardiac toxicity (defined as drug-related deterioration in cardiac function and/or development of congestive heart failure) has been reported with kinase inhibitors. Although this is not surprising given that kinases play a critical role in cardiovascular energy and calcium homeostasis, it is not fully understood what combinations of kinase interactions really drive this side effect.81

Ideally, one would want to define the kinase inhibition profiles that are associated with specific toxicities. An example of this approach is the model published by Olaharsky et al. to predict whether a compound will test positive for clastogenicity in the in vitro micronucleus assay based on results of kinome profiling.82 Such safety predictive kinome “signatures” could then be used as more reliable criteria for decisions on compounds, but also possibly for SAR studies. As private and public data-rich databases linking phenotypic end points (i.e., undesirable effects) to large kinase interaction maps for a large number of diverse molecules are becoming more robust and abundant, this approach may become feasible and may contribute to the Page | 40


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knowledgebase necessary for developing these safety alerts. Without this fundamental biological understanding generated through data mining, the composition of kinome profiling panels and the decisions made from kinome profiles will remain mostly empirical. However, given the lack of concordance in activity results and related quantities, such as pharmacological similarities and target promiscuity, this approach will have definite limitations and will have to go through an extensive validation exercise in each institution using internal data.30 Furthermore, inconsistencies in results across platforms indicate that caution should be used when building or using in silico and chemoinformatics tools using data sets. Furthermore, these inconsistencies in results across platforms indicate that caution should be used at the current time when building or using in silico and chemoinformatics tools using datasets from different sources.

d. In Vitro Cardiovascular Profiling While compound profiling against cardiovascular effects belongs to the large category of molecular pharmacology profiling, it is useful to consider it separately because of its importance in the LO process, but also because it is often conducted separately from other molecular profiling assays. In addition, rapid changes have been and will be experienced in terms of evolution of the technologies and testing paradigms in this area. A decade or so ago, the hERG (human Ether-à-go-go-Related Gene) channel (a repolarizing potassium channel) was at the center and often the only part of in vitro cardiovascular safety tests conducted in Discovery, which included radioligand binding assay (e.g., dofetilide binding assay) sometimes complemented by the evaluation of action potential duration (APD) using canine Purkinje fibers.83, 84 These screens were then followed by a more thorough evaluation of hERG block to generate an accurate IC50 using manual in vitro electrophysiology patch clamp assays. The Page | 41



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rationale for this approach was clear and sound: hERG block by drugs is known to produce delayed repolarization and prolongation of the QT interval. Since QT prolongation is used as a surrogate marker of torsade de pointes (TdP), a potentially lethal cardiac arrhythmia, hERG blockade was considered an unacceptable attribute for exploratory compounds. However, as knowledge has evolved, there are reasons to challenge this simplistic approach. Firstly, hERG is a very promiscuous anti-target, meaning that a large proportion of compounds will be positive in a binding assay. Although theoretical safety margins are used (for example, ratio between IC50 for hERG and EC50 for the therapeutic target being pursued), many potentially safe compounds may be mistakenly deprioritized at that stage. Secondly, QT prolongation is a very sensitive end point and a weak indicator of risk for TdP because of poor specificity (i.e., several clinically safe drugs are associated with QT prolongation).84, 85 Thirdly, not all hERG blockers are associated with QT prolongation. This can be explained by the simultaneous effects on other ion channels in the heart that modulate the effects of hERG current block. For example, verapamil is the prototypical “false positive” in the hERG assay: it is a potent hERG blocker that is not torsadogenic because it also blocks calcium channels, thereby offsetting its effects on hERG.86, 87

As new equipments and methods for in vitro electrophysiology have become available, a shift in testing paradigm has occurred with most companies adopting automated electrophysiology patch clamp platforms and/or fluorescent dye-based assays (e.g., thallium influx evaluation with the FluxOR technology) to move away from binding assays.88 In our experience, in vitro electrophysiology assays adapted to different platforms yield generally comparable, yet not identical results. This has also enabled the parallel evaluation of the effects of compounds on several relevant cardiac ion channels (Nav1.5, a sodium channel, and Cav1.2, Page | 42


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an L-type calcium channel) to better identify hERG blockers without torsadogenic potential. This approach is referred to as MICE (Multiple Ion Channel Effects).89

A precompetitive effort is also currently evaluating a new paradigm to assess the proarrhythmic risk of small molecules and results from this collaboration will likely significantly impact screening paradigms for proarrythmic risk in the future. This collaboration is assessing the value of nonclinical in vitro human models based on current understanding of the mechanism of TdP, with the ultimate goal of shifting the current general approach which strongly relies on QT prolongation. The proposed paradigm is known as the Comprehensive In vitro Proarrythmia Assay (CiPA) and proposes to evaluate concurrently the functional effects of compounds on multiple cardiac ion channels with data interpreted with the help of in silico cellular simulations.90, 91 The computer electrophysiologic simulations will determine effects on the cardiac action potential and potential for development of aberrant rhythms. In addition, the paradigm would take advantage of the newly developed iPS cell-derived cardiomyocytes to serve as the basis for an integrated electrophysiological drug response.90

Other integrated models are available to assess the proarrhythmic potential of compounds, including the traditional Langendorff heart model, as well as the use of cell impedance assays or micro-electrode arrays (MEA) to measure the electrophysiological activities of cardiac myocytes or iPS-derived cardiomyocytes.92 The basis of MEA is the presence of substrate-integrated extracellular electrodes embedded in an array allowing for detection of field potentials and reconstruction of the shape and time course of the underlying action potential.87 These more integrated approaches also reflect the full range of mechanisms involved in cardiac Page | 43



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action potential regulation and limits the number of false positive compounds like verapamil in the hERG assay.87, 93 iPS-derived human cardiomyocytes have been proposed as the ideal cell type to conduct these in vitro electrophysiologic evaluations for two main reasons. Firstly, they are believed to be a more relevant model system than animal-derived cells. Secondly, they enable the integrated evaluation of the total effects of compounds on all ion channels in contrast to evaluating effects on each ion channel separately and using cell lines with heterogenous expression of human ion channels. Large numbers of publications have reported results with iPSderived human cardiomyocytes, but it is noteworthy that depending on the origin of the cells and methods of preparation, results vary because of phenotypic and functional differences and responses to reference agents may also differ from those obtained in established in vitro and in vivo models.92, 94, 95 No comprehensive effort to fully characterize these various models has been made, such that it is still unclear whether their performance characteristics will be superior to those of existing in vitro methods. In addition, iPS-derived cardiomyocytes have some recognized limitations, which complicate their use in in vitro systems. In particular, current culture protocols generate mostly immature cells with fetal-like morphology, ion channel expression and electrophysiological function, resulting in spontaneous beating and slow depolarization-repolarization speeds compared to primary cardiomyocytes.96, 97

e. High-Throughput Cytotoxicity Assays and Early Identification of Problematic Chemical Matter The use of non-mechanism specific cytotoxicity assays has traditionally been unrewarding and sometimes counterproductive by diverting limited resources toward efforts with no evident utility to guide medicinal chemistry decisions. This is mostly because of their lack of Page | 44


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well-defined relationships with specific molecular mechanisms of toxicity and because of the complexity of most toxic events in whole animals, which can rarely be fully recapitulated in simple in vitro models. Several reports have suggested that high-content mutiparametric cytotoxicity screening approaches can be used to predict specific toxicity in vitro, especially hepatotoxicity,98-100 but these approaches are also resource-intensive, often organ-specific and may not provide additional value compared to simpler, higher throughput, plate reader-based cytotoxicity assays, such as the CyQUANT assay.101

One major challenge facing the in vitro cytotoxicity approach is that expectations have probably been too high for what can be extracted from these types of data. Using a retrospective analysis of findings from rat exploratory toxicity studies, Pfizer has demonstrated that safety toleration can be roughly approximated using simple cytotoxicity values.102, 103 More specifically, this retrospective analysis used safety findings from rat exploratory toxicity studies (4-14 days) and in vitro cytotoxicity values using THLE cells (an SV40 large T-antigen immortalized hepatocellular cell line) generated in a 96-well plate ATP-based viability assay (Vialight) for 72 compounds that spanned a broad range of therapeutic targets (protease, transport, G-protein-coupled receptors, and kinase inhibitors, cGMP modulators). A composite safety score was calculated for each compound dose based on findings in each of the following categories: systemic toleration (mortality, food consumption, and adverse clinical signs), clinical chemistry/hematology parameters (deviations from normal ranges), and multi-organ pathology (necrosis or incidence/severity of histopathological change). A higher score was considered “more toxic”. Binning compounds into potent (LC50 100 µM) Page | 45



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in vitro toxicants showed that the potent in vitro toxicants were associated with higher overall severity scores at lower in vivo exposures. Correlating overall safety toleration for individual compounds was further refined using in vivo exposure data. When average plasma exposure (Cave) for a compound exceeded its LC50 (Cave/LC50 >1), higher overall safety scores were calculated compared to lower exposure margins (Cave/LC50 < 0.01).102 In the same year, Greene et al. expanded on the previous studies by showing how cytotoxicity data (using THLE cells and ATP as an end point) could be used to better predict in vivo adverse outcomes: compounds with a LC50 < 50 µM were 5 times more likely to be associated with toxicity findings in an exploratory rat study at a Cmax < 10 µM compared to compounds with an LC50 > 50 µM.103

This approach adopted by Pfizer offers several advantages. Firstly, it is not limited to evaluation of hepatotoxicity, which is probably realistic given that most liver-derived cell lines, including THLE cells, have limited hepatocellular characteristics and are probably more representative of poorly differentiated cells. Secondly, it provides a probability correlation to in vivo toleration (irrespective of its mechanism) at plasma concentrations representative of exposures achieved in toxicology studies, thereby providing a safety margin aspect. This probability measure can be used for compound or series discrimination, prioritization or selection at early stages. Thirdly, it established a robust probability-based cut-off value that allows for rapid analysis and decision-making in a Discovery environment. Others have also demonstrated that cytotoxicity data can be useful surrogates of in vivo toxicology outcomes. In a comprehensive evaluation, the combination of cytotoxicity data using rat primary hepatocytes and volume of distribution (Vd) was a good surrogate of in vivo toxicity, which as pointed out by Page | 46


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the authors, is intuitive given that the Vd represents a measure of tissue distribution while cytotoxicity addresses the intrinsic toxicity of compounds.30

Obviously, like any retrospective analysis using a binning approach, such findings can suffer from the phenomenon of correlation inflation.33 Therefore, in order to confirm that Pfizer’s observations would translate to another chemical space, AbbVie scientists conducted a similar retrospective study a few years ago (data not published). The high-throughput cytotoxicity assay used at that time within the Discovery profiling group was different from the one adopted by Pfizer: it was based on HepG2 cells, a 384-well plate format and the CyQUANT assay (a cell proliferation assay). However, these differences were not considered sufficient to modify the existing internal approach, since as previously mentioned, THLE cells are (like HepG2 cells) poorly differentiated toward a hepatocellular phenotype and since the use of another assay would be unlikely to change the in vivo-in vitro probability correlation outside of affecting the cut-off value to be used for decisions. These initial assumptions may not be completely correct, since recent data have shown that different cell lines have different sensitivities to compounds depending on their ionization state with HepG2 cells being more sensitive to basic compounds, whereas THLE cells have higher sensitivity for acidic and neutral compounds.104 These different sensitivity levels suggest a potential advantage of using multiple cell lines compared to a single cell line.104

To conduct this forward validation exercise, a set of 34 structurally and pharmacologically diverse compounds (all previously evaluated in 5-day rat exploratory studies at sufficient plasma exposure levels) was tested in the HepG2-based high-throughput Page | 47



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cytotoxicity assay in a dose-response format to determine an IC20 (defined as 20% inhibition of cell growth). Oncology compounds were eliminated from the analysis because they were expected to have low IC20. Compounds with clear in vivo toxic effects (based on in-life, clinical pathology and histopathology findings) at Cmax < 5 µM were classified as “toxic”, while compounds with no in vivo toxic effects at Cmax < 5 µM were classified as “non-toxic”. Figure 5 summarizes the results of this validation exercise. This retrospective evaluation confirmed Pfizer’s observation that compounds with cytotoxicity at low concentrations are more likely to be toxic in in vivo tolerability studies at relevant plasma exposures. Because AbbVie was using a different cell type, a different assay format and a different end point (IC20), the cut-off value was different and an IC20 of 30 µM appeared to be an appropriate cut-off value for decision-making.

Integrating these cytotoxicity data with results from physicochemical filters and pharmacological profiling and interpreting them in the context of other data available (e.g., ADME and potency data against the target of interest) can be useful for post-HTS and HTL prioritization decisions. In addition, compounds can be evaluated with this approach during LO for regular “spot-checking” and for compound optimization. However, low cytotoxicity values should just be viewed as a probability measure of in vivo tolerability issues. Hence, for interesting series and compounds with cytotoxicity at low concentrations, early evaluation in rodent exploratory toxicology studies may be justified to interrogate the in vivo translatability of this in vitro signal. If in vivo data confirm the presence of toxicity issues, then cytotoxicity data may prove extremely useful to guide chemistry toward higher quality chemical matter associated with increased in vivo safety margins in parallel with improvements in potency and ADME characteristics. Page | 48


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f. Mitochondrial Toxicity Screens Whereas the role of mitochondria in toxic injuries caused by chemicals and environmental components (e.g., insecticides and herbicides) has been studied in academic settings for nearly half a century, drug-induced mitochondrial toxicity was not investigated until much later. Pharmaceutical companies did not pay much attention to this issue until the withdrawal of troglitazone due to liver injury and cerivastatin due to rhabdomyolysis. Both compounds were postulated to have caused toxicity in humans at least in part through mitochondrial impairment.105, 106 Today, more than 200 drugs that cause severe liver injury (resulting in market withdrawal, black box warning or restricted usage) have been shown to impair mitochondrial function.107, 108 Furthermore, evidence strongly suggests that mitochondrial toxicity plays a particularly important role in initiating idiosyncratic toxicity.109

Mitochondria generate most of the energy used by cells in the form of ATP, which is required to execute all essential cellular functions. When mitochondrial function is impaired, cellular function is impaired, which can lead to organ failure and, in extreme circumstances, to death. Mitochondrial function can be inhibited in numerous ways. For many hepatotoxic drugs, direct inhibition of one or more of the electron transport chain proteins (e.g., cerivastatin, chloroquine, fenofibrate, nefazodone) and inhibition of fatty acid oxidation (e.g., salicylic acid, ibuprofen, pirprofen) have been demonstrated. Small molecules can also cause mitochondrial toxicity through uncoupling, a mechanism by which electron transport is separated from ATP synthesis, as the drugs cause inner membrane damage that makes protons pass back and forth in Page | 49



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a futile cycle, producing heat instead of ATP. In animals, this toxic mechanism translates into clinical signs of impaired respiration, decreased activity, drowsiness and elevation of rectal body temperature.110 Such compounds are typically weak acids and lipophilic cationic drugs. Tolcapone and nimesulide are two well-known examples of drugs that exhibit mitochondrial toxicity through uncoupling.110, 111 Other mechanisms of drug-induced mitochondrial injury, which have been demonstrated for several antiviral agents and antibiotics, include the inhibition of mitochondrial DNA (mtDNA) transcription and translation, respectively.112 The deleterious consequences of these effects are not of acute nature, but will manifest over time.113 Table 1 summarizes the effects of selected drugs on the various mitochondrial targets.

At Pfizer, during multiple mitochondrial studies conducted over the past decade, we have noted that many compounds inhibit more than one mitochondrial electron transport chain complex or mitochondrial protein target. For example, troglitazone was shown to potently inhibit complex IV, but also to a lesser extent complexes II/III and V.114 We observed similar trends for many other drugs, reflecting promiscuity for many pharmaceutical agents. Our in-house analysis of more than 500 marketed drugs exhibiting effects on mitochondrial toxicity revealed that the majority of these drugs had also unfavorable physicochemical properties, such as high lipophilicity (clogP >3), which can be expected to result in tissue accumulation and as noted before in promiscuity.115

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Since mitochondrial dysfunction can result in significant adverse functional consequences, it is desirable to select candidate compounds that do not exhibit this adverse property. This requires screening assays which ideally can be run in high throughput screening (HTS) format and can be used to define SAR. One HTS assay which is now used by many pharmaceutical companies is the “Glucose-Galactose” assay. This assay measures the ATP content of cells cultured with test compounds for 24 hr in media containing high concentrations of either glucose or galactose as an energy source.56, 116 Cells cultured in media containing high glucose concentrations are resistant to ATP depletion caused by mitochondrial toxicants since they can compensate for loss of mitochondrial function by glycolysis, whereas cells cultured in media containing galactose cannot do so. This leads to a marked (> 3-fold) shift between the potency for cellular ATP depletion caused by mitochondrial toxicants in glucose vs. galactose media. However, this shift will only occur with compounds for which mitochondrial toxicity is the dominant mechanism of toxicity. For molecules that cause toxicity through multiple mechanisms (e.g., BSEP inhibition, reactive oxygen species or ROS formation, ER stress) in addition to mitochondrial toxicity, no difference will be detected between cells cultured in glucose and galactose media.56 In fact, only about 2-5 % of mitochondrial toxicants can be detected using the Glucose-Galactose assay.117

A more precise HTS assay for mitochondrial toxicity that can be deployed in early drug discovery is the respiratory screening technology (RST), which uses soluble sensor molecules to measure oxygen consumption in isolated mitochondria in real time.117 Oxygen consumption measurements can be considered a surrogate readout for mitochondrial bioenergetic function. By Page | 51



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modulating the substrate which fuels the electron transport chain (e.g., by selecting glutamate/malate or succinate), insight into the site of mitochondrial impairment can also be obtained. It is also possible to distinguish between uncouplers (which increase oxygen consumption) and inhibition of the electron transport chain or inhibition of fatty oxidation (both of which decrease oxygen consumption). The RST assay is useful for SAR investigations and has also been used to derive an in silico tool for prediction of uncouplers.35 However, no SAR model has yet been built for inhibitors of mitochondrial function, even though more than 200 different drug classes are known to inhibit complex I alone. Mehta et al. have published a valuable review of drug classes and their mitochondrial targets.118

When chemists are unable to identify chemical matter devoided of mitochondrial liabilities through SAR studies, more complex assays that measure mitochondrial function in cells can be deployed to better assess the risk of toxicity that may arise in vivo. For example, both mitochondrial and glycolytic activity can be measured simultaneously using soluble or solid sensor technology.111, 117 In addition, cells can be permeabilized which allows testing of different substrates and provides insight into mechanisms of inhibition of bioenergetics and inhibition of fatty acid oxidation.119, 120

For many years, mitochondrial toxicity was studied in isolation as an initiating mechanism that could lead to liver injury. For a variety of drug classes, excellent correlations were observed between the severity of human drug-induced liver injury (DILI) observed in the Page | 52


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clinic and potency of mitochondrial toxicity. Examples include the thiazolidinediones,114 biguanides, 121 non-steroidal anti-inflammatory drugs (NSAIDs),111 and antidepressants.122 Not surprisingly, these retrospective evaluations also showed that in vitro data interpretation becomes more meaningful when systemic exposure was taken into consideration.111, 123 For example, Perceddu et al. using >200 drugs showed a correlation with human DILI of >80% for drugs with an in vitro IC50 value < 100-fold of the human maximum plasma drug exposure.107 Likewise, interpreting data from mitochondrial assays in the context of other in vitro data is more insightful. Mechanistic studies from Pfizer scientists on the toxicity of nefazodone provided good evidence that compounds can cause DILI by more than just mitochondrial toxicity.122 In the case of nefazodone, which exhibits clear mitochondria-mediated cytotoxicity, inhibition of BSEP also seemed to be an additional driver for toxicity.124 Years later, Aleo et al. confirmed that notion by showing that drugs which inhibit mitochondrial function also for the most part inhibit BSEP, and that inhibition of both processes correlates with severe DILI (necrosis, death).125 Moreover, many of the drugs investigated in the latter study have also been shown to form chemically reactive metabolites, which is another main contributor to DILI. Altogether, these observations have triggered attempts to derive cumulative scores from multiple in vitro assays to better predict toxicity, especially human DILI.126-128

g. Other In Vitro Toxicity Assays A large variety of in vitro toxicity assays have been proposed and described in the literature or conferences over the years. Several useful assays for LO are highlighted here. Because of the prevalence of hepatotoxicity in nonclinical and clinical drug development, Page | 53



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hepatocyte-based in vitro models are used extensively in Discovery toxicology. These assays range from monolayers of liver cell lines (e.g., HepG2, HepaRG, THLE cells) or primary isolated hepatocytes, co-cultures with other liver cells (e.g., Kupffer cells) to more complex three-dimensional (3D) culture systems, such as spheroids, bioreactors or bioartificial livers.129132

In general, 3D models offer the advantages of longer lifetime, enhanced and more chronic

functionality, and a more relevant microenvironment.130 In addition, these models are more amenable to co-cultures with non-parenchymal cells such as Kupffer cells, which play an important role in several mechanisms of hepatotoxicity. For primary hepatocyte cultures, human hepatocytes are more frequently used, but hepatocytes from other toxicology relevant species can have utility as well.133

Various parameters can be evaluated in vitro, ranging from single biochemical end points (e.g., gluthatione depletion), single or multiple mRNA transcripts (e.g., mRNA from cytochrome P450 3A (CYP3A) to assess metabolizing enzyme induction), mitochondrial function to multiparametric imaging approaches using high-content screens.100, 134, 135 A large numbers of in vitro assays have been proposed or described. Here, selected in vitro assays are discussed because of their potential impact on probability of success.

Hepatic transporters are critical for uptake and excretion of xenobiotics. Transporters regulate homeostasis, play an important role in bile formation and drug disposition, and their functionality can be critical for pharmacology and toxicology studies.136 Because interactions with hepatic drug transporters have been linked to toxicity (particularly cholestasis) as well as DDIs, various companies have integrated early evaluation of hepatic transporter-drug Page | 54


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interactions, especially for BSEP and MRP2.135 The jury is still open on whether these early evaluations provide value in addition to the evaluations required for the clinic. As discussed earlier, there are numerous examples of safe drugs interacting with hepatic transporters, especially BSEP, to suggest that this approach will result in termination of otherwise viable candidates. However, when the data generated with these assays are correctly interpreted in the context of other data, especially estimated exposure of hepatocytes or plasma exposures in humans, one may argue that such data can be used to reliably predict a safety concern in humans. Dawson et al. reported that in their assay out of 85 drugs evaluated, 40 showed BSEP inhibition, and out of these 40 drugs, 17 with an IC50 0.002 µM caused DILI.137 Likewise, the same group proposed an integrated approach using data generated from five assays (cytotoxicity using THLE cells with and without CYP450 3A4 activity, cytotoxicity using HepG2 cells in glucose and galactose media, BSEP inhibition, MRP2 inhibition) combined with an estimation of covalent binding to select development candidates with reduced propensity to cause idiosyncratic adverse drug reactions in humans.126 Their retrospective assessment of this approach using 36 drugs showed good specificity and sensitivity to detect drugs with a high idiosyncratic toxicity concern.126 However, generation of covalent binding data requires radiolabeled test agents, which are not typically available early during LO, and the interpretation of these data requires some understanding of daily dose in humans, which are often not available during compound optimization. Hence, the practical aspect of this approach is debatable.

A decade ago, gene expression profiling of cultured cells, especially hepatocytes, was proposed as a holistic approach to predict toxicity. Numerous literature reports have been published showing potential utility for various end points, including toxicologically relevant Page | 55



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nuclear receptor interactions (e.g., aryl hydrocarbon receptor (AhR) agonists and the PPARα agonists) or phospholipidosis.138-140

However, the applications of in vitro toxicogenomics to

prospectively evaluate compounds have been mostly limited to in vitro genomic biomarkers endpoints clearly linked to a single specific mechanism.54 This does not negate the utility of in vitro toxicogenomics to understand mechanisms of toxicity, to formulate hypotheses related to the mechanism of a toxic effect, or to characterize in vitro systems. However, the use of the technology has definitely been narrower in scope than originally hoped for.

Formation of reactive metabolites is well recognized as a risk factor for toxicity, especially idiosyncratic toxicity. Consequently, screens evaluating the formation of reactive metabolites have been integrated or experimented in most pharmaceutical R&D organizations.141 While this was routine practice a few years ago, it is unclear how consistently screens for reactive metabolites are currently used across companies. There are multiple reasons for this paradigm shift. Firstly, there is broad recognition that reactive metabolites alone cannot explain most cases of idiosyncratic toxicity and that multiple factors are involved in these adverse reactions.21, 142 In fact, the most important factor to limit idiosyncratic toxicity appears to be the total daily dose; most of the drugs in the top 200 oral drug list are administered at low daily doses.21 Secondly, while reactive metabolites may be identified in early screens, competing detoxification pathways and/or alternate clearance routes exist that can explain why some compounds generating reactive metabolites in those early assays may not cause safety issues in the clinic.21 Thirdly, extensive reactive metabolite investigations, including assessment of covalent binding using radiolabeled compounds, have been costly, not sufficiently informative and causing delays in projects. For example, Nakayama et al. evaluated covalent binding of 42 radiolabeled drugs using three test Page | 56


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systems (human liver microsomes, human hepatocytes, rat liver in vivo).143 These data were not sufficient to distinguish compounds with and without associated idiosyncratic toxicity unless interpreted in the context of the daily dose in humans (as a way to calculate an estimated body burden of covalent binding). In addition, data generated from hepatocytes were superior for prediction than data generated with human liver microsomes. This suggests that eliminating compounds on the sole basis that they are generating reactive metabolites under experimental conditions may be too simplistic without additional information (especially total daily dose in humans). Nevertheless, at the HTL or early LO stages, there may be still be value of minimizing bioactivation in molecules using bioactivation assays with appropriate throughput (e.g., glutathione or GSH trapping) to support rational drug design. However, these data, especially those generated using microsomal preparations, should not be used to predict idiosyncratic toxicities in isolation. In some instances, these data may also be useful to contribute to a mechanistic understanding of toxic effects (especially hepatotoxicity) observed in preclinical species.

h. Zebrafish Zebrafish have been used in ecotoxicity testing for a long time. For pharmaceutical R&D, this model has been proposed to bridge the gap between in vitro tests and in vivo studies because of its low compound requirements, low cost, amenability to automation and similarity of tissue architecture with mammals. Much of their value stems from unique advantages compared to mammalian models, including external fertilization, early optical transparency (allowing for noninvasive examination of internal organs), size (fit into microtiter plates), and ability to rapidly manipulate their well-characterized genome. Additionally, significant conservation exists Page | 57



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between zebrafish and human genes, signaling pathways, tissue architecture, as well as organ functions.144

The rapid development from a single cell stage to fully functional organ systems within 5 days makes multigenerational and reproductive toxicity evaluation feasible over relatively short periods of time. Consequently, the zebrafish model has been successfully deployed in many companies (including Pfizer) for developmental toxicity.145 However, the utility of zebrafish to predict organ toxicity in animals and humans is still a matter of debate. For example, we at Pfizer were able to show good concordance of zebrafish liver toxicity to human liver toxicity caused by NSAIDs, but were unable to demonstrate similar predictive value for other drug classes, such as antidepressants (data not published). In contrast, others have published encouraging reports. For example, Mesens et al. investigated whether zebrafish larvae were suitable for assessing the hepatotoxicity potential of drug candidates by measuring a liver specific fatty acid binding protein (lfabp10a).146 They found that lfabp10a was a valid marker with expression levels correlating with histopathological changes in the liver.146 Likewise, transcriptomics was used to compare rat/mouse liver and hepatocytes, human hepatocytes and zebrafish liver following exposure to cyclosporine A, amiodarone and acetaminophen, as prototypical compounds for cholestasis, steatosis and necrosis, respectively.147 Whereas analysis at the single gene level showed no clear concordance across models, concordance was found at the pathway level, suggesting that the zebrafish is comparable to traditional models for hepatotoxicity identification.147

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The use of zebrafish as a model for cardiovascular effects has received some interest in the last decade. Zebrafish express an orthologue of hERG, suggesting a potential utility for evaluation of drug-induced QT prolongation. Several studies have shown sensitivities of 78100% with specificities of 77-100% for that end point.148, 149 The biggest contributor to the false negative space was most likely the poor absorption of certain drugs because of their low solubility.148, 149 A large scale study of 100 compounds confirmed the high-throughput potential of the model, although the zebrafish model failed to be more predictive than the hERG assay itself in that much higher drug concentrations were required to achieve the reported effects.150 Additional non-invasive imaging methods have been developed to aid in cardiovascular assessment, potentially expanding the utility of the model.151, 152 Furthermore, in addition to QT prolongation, the zebrafish model has been adapted to interrogate other types of cardiotoxicity. For example, it has been used to model the cardiotoxicity observed with anthracyclins, which disturbed heart formation and development in the larvae.153 Similarly, in another study, seven cardiotoxicants and two non-cardiotoxicants were injected directly into the yolk sac, and using six phenotypic end points (heart rate, heart rhythm, pericardial edema, circulation, hemorrhage and thrombosis), all nine compounds could be categorized correctly.154

The zebrafish is also of interest for neurological toxicity assessment, since few cell-based assays are available and since animal models are difficult to establish. For example, Kosekia et al. used zebrafish to predict seizure liabilities of drugs and showed a 70% predictive value for 52 drugs evaluated using locomotor activity alone complemented with flash light simulation studies.155 Likewise, ocular toxicity can be assessed with the zebrafish model using the visual motor response (VMR; an assay which quantifies locomotor responses to light changes), the Page | 59



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optokinetic response (OKR; a behavioral assay that measures saccadic eye responses to rotating stimuli), and the touch response (a locomotor response to tactile stimuli). Overall, using six toxic compounds and 10 non-toxic compounds, the VMR had better performance than the OKR with 83% sensitivity, 100% specificity and 100% positive predictive value (PPV).156 Finally, the model is useful to predict the effects of drugs on gut transit time.157

In summary, the zebrafish has potential utility in toxicology screens to address some fo the current gaps in the toxicologist’s toolbox. While the model has many potential applications, expectations should nonetheless be realistic: false positive and false negative categories are influenced by various factors, including: (1) differences in metabolism and transporters across species; (2) basic differences in genetics, anatomy and physiology; (4) physicochemical properties of compounds impacting exposures (e.g., compounds with poor solubility may have limited exposure); and (4) complexity of the route of administration (e.g., exposure through oral ingestion, eye, skin, or gills). As such, the zebrafish model may be better used as a hazard identification tool than as a predictor of specific organ toxicity risk. At Pfizer, the model has been used for reproductive toxicology screening. At Abbvie, a small zebrafish laboratory is focused on developing fit-for-purpose assays to address key issues confronting specific programs and for which other approaches or assays are either not available or practical. In other words, rather than using the model to proactively screen compounds, the model is used when in vivo toxicity has been identified to conduct SAR and mechanistic studies and identify an appropriate back-up candidate. This approach has proven useful to address issues related to ocular, CNS or gastrointestinal toxicity for several projects. The utility comes from providing a robust model with rapid throughput, low compound requirement and low cost. Finally, it is noteworthy that Page | 60


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because of the simplicity and speed of generating genetically-engineered zebrafish models, this approach can be quite useful to interrogate via gene knockdown or knockout in an in vivo-like manner the safety liability of targets of interest. Likewise, models that incorporate fluorescent labeling of specific cell types (e.g., vasculature, heart, pancreatic cells) can be quite useful for pharmacological as well as toxicological assessment of compounds.

Early In Vivo Toxicology Evaluation a. Exploratory Toxicology Studies In order to decrease late-stage attrition related to toxicity, the current paradigm followed by most pharmaceutical R&D organizations involves the use of small, short-term (i.e., usually 37 days), and therefore compound-sparing animal toxicology studies during LO.158 These studies are typically referred to as exploratory, investigative or toleration studies. These exploratory toxicology studies with rodents or large animal species (dogs, mini-pigs or non-human primates) are not designed to generate an exhaustive assessment of the potential toxicologic liabilities of compounds for humans. Rather, their main objective is to generate early data that may signal development-limiting toxicities and guide molecular design decisions. Although most pharmaceutical R&D organizations use this general approach, there are significant differences across companies, often influenced by past institutional experience, available expertise, logistical aspects and the availability of complementary tools. Figure 6 illustrates the paradigm used at the Lake County AbbVie R&D site that was designed based on a strategic assessment of the needs of local project teams and available resources. This paradigm is tailored to individual project needs and to rapidly provide quality information to address current key issues of projects. Page | 61



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To maximize their impact on discovery projects, exploratory toxicology studies must meet several critical requirements. Firstly, they must minimize compound requirements, since compound availability is almost always limited at these stages of the Discovery process. While this varies depending on the project (e.g., nature of the chemistry, number of medicinal chemists supporting the project), most project teams can rapidly provide small quantities (< 2 g) of compounds with existing resources and enable rodent exploratory toxicology studies. Using mice provides obviously an advantage because of their smaller size. However, rat is typically the preferred species at that stage. Secondly, these studies need to provide appropriate, targeted and actionable data. Including at least one dosage group, in which dose-limiting toxicity and target organs can be identified and characterized, can increase certainty of downstream compound requirements and study design, while at least one dosage group should be considered in order to provide an estimate of existing safety margins. Thirdly, these studies must be conducted efficiently and results must be communicated as rapidly as possible to project teams, such that timely decisions can be made on compounds, molecular design and on overall project directions. For these various reasons, several large companies have established small, agile teams dedicated to that type of work.

Exploratory rodent toxicology studies have multiple applications in Discovery and those applications heavily influence their design. One obvious application is the rapid toxicological characterization of a lead compound to enable decisions. In that situation, the objective is to ensure that there is no development-limiting toxicity at insufficient safety margins. Therefore, several dosage levels are necessary to understand dose-response, but also to ensure that safety Page | 62


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margins can be appropriately estimated. Using a single or multiple studies, exploratory rodent toxicology studies can also be used to select the best lead compound(s) among several molecules for a project through the generation of comparable in vivo toleration data for all compounds. In that scenario, all that may be required is one comparable dose level for all compounds, since the goal is really about compound differentiation. Rodent exploratory studies are also a good starting place to interrogate whether potential target-related toxic issues anticipated based on a TSA are present. Finally, rodent exploratory studies are frequently used for mechanistic investigation of a toxic change, and their design needs to be adapted to the questions being asked. For example, the objective may be to understand time-course development of the toxic change, in which case several time points would be warranted.

A recognized limitation and critic of exploratory toxicology studies is that they cannot detect all toxic effects because of their short duration and reduced number of animals. Indeed, some toxic changes may not become apparent until after weeks of repeat dosing when using only traditional end points, such as serum chemistry, hematology or histopathology. The addition of more sensitive biomarkers or integration of new technologies to detect toxicity earlier can, at least in part, address this short coming. In addition, although nonclinical toxicology studies have been shown to be useful to protect the safety of humans enrolled in clinical trials, it is important to recognize that these studies cannot detect all clinical adverse events, in particular idiosyncratic toxic reactions. At AbbVie, we have developed and leveraged the predictive value of toxicogenomics for that purpose, as explained later.

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Most R&D organizations evaluate experimental compounds at the LO stage in rodents.135, 158, 159

The use of large animal species (e.g., dogs, non-human primates or mini-pigs) at these early

stages is more variable because of the larger compound requirements, but also because of animal welfare. Although few data exist in the current literature, AbbVie’s experience has been than this unbalanced use of rodents at early stages has resulted in a shift of attrition, such that the majority of development-limiting toxicities identified at the development candidate stage occur in large animal species. This intuitively makes sense, since compounds are mostly optimized in rodent studies. Consequently, it appears also important to include in vivo assessments in large animal species earlier to ensure that compound optimization considers toxicity profiles in non-rodent species without compromising animal welfare compliance. This can be done by using creative study designs (e.g., the use of non-terminal end points) and careful, data-driven dose escalation scheme. Like rodent studies, such approach requires a high level of flexibility and an ability to rapidly generate key data to inform study stakeholders. In our experience, this is best achieved by conducting these studies internally with the scientists accountable (i.e., study investigator, veterinary staff) closely involved in study conduct, including clinical evaluation. This close involvement allows for rapid decisions (e.g., dosing termination, veterinary treatment) and, if justified, adjustment of the study design (e.g., customization of end points, change of the initially intended dose or dosing frequency).

b. In Vivo Cardiovascular Assessment In addition to repeat-dose exploratory toxicity studies, an early in vivo safety pharmacology assessment can be useful to identify safety liabilities early.159 Safety pharmacology studies assess the presence of undesirable pharmacodynamic effects of Page | 64


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compounds on the major vital tissues, including but not limited to the cardiovascular, CNS, intestinal and respiratory systems.160 Out of these various systems, effects on the cardiovascular system are the most impactful and prevalent and will therefore be the focus here. Reviews covering tests and technologies available to monitor effects on other systems are available elsewhere.161

Early in vivo assessments of the hemodynamic and electrophysiological effects of compounds on the cardiovascular system are useful to confirm the significance of in vitro data (e.g., ion channel results), to detect cardiovascular safety liabilities with some chemical series and to guide chemistry. These early in vivo cardiovascular evaluations also are used to predict the results of the IND-enabling GLP safety pharmacology studies, thereby decreasing attrition after development candidate selection. Several models are available ranging from anesthetized to conscious (implanted or jacketed telemetry) models using various species (rodents, guinea pigs, ferrets, dogs, monkeys).161 Most pharmaceutical R&D organizations conduct these early in vivo cardiovascular evaluations, but there are differences in testing paradigms. At Abbvie, the in vivo cardiovascular testing plans are tailored to project needs and coordinated with other nonclinical safety tests and studies.

Testing plans need to be fit-for-purpose, because all available models have strengths and limitations. Anesthetized models are dosed with intravenous infusions and have the advantages of requiring lower amounts of compound, of being higher throughput and amenable to exposure modeling, and of not having the risk to cause pain or distress.162 However, for some projects, data from anesthetized models may not correlate with effects in conscious animals, which Page | 65



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ultimately will be used in the IND-enabling studies and may be more predictive of effects in the clinic.161, 163 Conversely, conscious models require larger quantities of compounds, significant capital and time investments, and are much more time- and resource-consuming.164 In addition, because implanted telemetry models are costly to generate and maintain, preliminary toxicity assessment in non-implanted animals is typically required to ensure lack of toxicity at the dose levels used. This can be partly addressed by including a cardiovascular evaluation generated by jacketed telemetry in early repeat-dose non-rodent toxicity studies, since the technology has improved considerably, although hemodynamics parameters (blood pressure and contractility) are not captured.164-166 Finally, some models are not useful to detect electrophysiological effects relevant to humans. For example, the rat does not express the hERG channel and therefore cannot be used for QT prolongation assessment.

c. In Vivo Gene Mutation Tests Despite the importance of identifying mutagenic events and given the limitations of the Ames assay described above, in vivo alternatives to determine the risk of compounds that test positive in the Ames test are limited. The transgenic rodent assay is available and can be used to evaluate most tissue, but cannot be integrated into standardized toxicology studies and is expensive and resource intensive (requiring 28 days of dosing) with a slow turnaround time.167 The Pig-A assay is a recently proposed model that uses mutation in the phosphatidylinositol glycan-A (Pig-A) gene as an end point for rodent in vivo mutations.168, 169 This assay relies on flow cytometry to detect erythrocytes or reticulocytes that do not express CD59, the protein encoded by the X-linked Pig-A gene.168 In addition, this assay offers a potential for a

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translational biomarker, since the Pig-A locus is well conserved across species.60 It is well understood that this assay still requires validation work before routine and regulatory use.169

d. Nonstandard In Vivo Models Nonstandard animal models may be useful in some projects, especially for hypothesisdriven studies designed to understand or confirm a specific mechanism of toxicity. A recent review outlined recommendations endorsed by the Society of Toxicologic Pathologists (STP) for the use of animal models of human disease for nonclinical safety assessment of pharmaceutical compounds.170 Given the large numbers of possible mechanisms of toxicity, it should come as no surprise that the range of nonstandard models available is large. It would be beyond the scope of this review to cover all possibilities. Table 2 lists selected examples of nonstandard animal models and their main use for toxicology. Rodent genetic diversity panels will be used to illustrate their utility.

Genetic make-up can influence individual responses to a compound and evaluating the influence of genetics with animal models can help develop a mechanistic understanding of human-specific toxic events. This mechanistic understanding can then represent the basis to identify patient populations at risk, to find biomarkers to monitor the toxicity clinically, or to identify backup compounds unlikely to have the liability. Rodent diversity panels have been proposed as tools to develop this understanding.171 These panels consist of first-generation hybrids derived from inbred stains of rats or mice, as well as other classical and wild-derived Page | 67



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strains, and as such covers a large portion of the genetic variance of rodents. For example, studies with a mouse diversity panel have yielded important insights into acetaminophen (APAP) toxicity in humans. In a controlled clinical study, a significant percentage of individuals taking therapeutic doses of APAP (4 g/day) experienced transient alanine aminotransferase (ALT) increases with a higher percentage of responders being of Hispanic ethnicity.172 Using a panel of 36 inbred mouse strains orally administered APAP (300 mg/kg or a range of additional APAP doses), genetic background was found to greatly influence the toxicity response and the difference in response was unlikely to be related to differences in drug metabolism.173, 174 Targeted DNA sequencing confirmed a positive association with a variant of the Cd44 gene in both mice and humans. Confidence in the association was strengthened by demonstrating increased toxicity in Cd44-knockout mice (on a C57BL/6J background) and by independent verification of an enrichment of the risk allele in clinical cases of acute APAP-induced liver failure.175, 176 Altogether, these data provide data useful to identify human population at risk for APAP hepatotoxicity.

Rodent genetic diversity panels can also be valuable to identify backup molecules not associated with a specific liability. For example, ritonavir (RTV) and other human immunodeficiency virus (HIV) protease inhibitors (PIs) cause elevated serum triglyceride and cholesterol levels in some patients that is not observed in the standard Sprague-Dawley rat. Using a rat diversity panel, increased serum triglyceride and cholesterol levels could be observed in two rat strains following RTV administration, and transcriptomic analysis of liver samples showed that this different response was, at least in part, due to changes in hepatic lipid biosynthesis and metabolism.171 These findings allowed for evaluation of backup compounds in Page | 68


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the appropriate rat strain to enable selection of lipid-neutral compounds, but also the identification of relevant biomarkers for this undesirable side effect.

Utility of Biomarkers Including The Omics Technologies in Early In Vivo Toxicology Assessment The value of preclinical toxicology studies can be significantly strengthened by the incorporation and/or development of biomarkers with improved sensitivity and mechanistic clarity against toxic changes. These types of biomarkers are especially very useful in exploratory toxicology studies, since their sensitivity enables better data interpretation and informed decisions on compounds and projects. In addition, biomarkers that are validated by the scientific community and are translatable to humans have the potential to allow progress into development of compounds associated with toxicity that cannot be monitored with traditional biomarkers. Indeed, one of the main reasons for compound termination during preclinical development is the presence of toxic changes that cannot be appropriately monitored in the clinic due to lack of valid biomarkers. This is a widely recognized gap and large numbers of comprehensive reviews and research articles on toxicity biomarkers have been published in recent years. Here, we limit our discussion to high levels concepts related to the applications of non-traditional biomarkers of toxicity in Discovery toxicology.

To address the recognized gaps in the current battery of toxicology biomarkers, several precompetitive efforts in the form of consortia have been on-going in the last decade. While

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these consortia have been mainly focused on qualifying biomarkers for use in regulated studies, their output has influenced Discovery toxicology more broadly. For instance, the Critical Path Institute’s Predictive Safety Testing Consortium (PSTC) is a consortium composed of partners from academia, industry, and regulatory authorities, and was organized to qualify toxicity biomarkers for preclinical and eventually clinical use. The work originating from that consortium has resulted in significant advances in our understanding of the performance of several urinary biomarkers of kidney toxicity (e.g., albumin, Kim-1, lipocalin-2/neutrophil gelatinase-associated lipocalin (NGAL), osteopontin), and this knowledge has enabled Discovery toxicology organizations to integrate them in their testing paradigm.177, 178 In our experience, these markers have been useful in exploratory rat toxicology studies to detect renal changes with experimental compounds, but also to generate a better understanding of safety margins for improved decisionmaking.

There has been significant interest by the scientific community to use micro-RNAs (miRNAs) or mRNAs as circulating biomarkers. miRNAs are especially attractive because of several unique attributes, including: (1) stability in body matrices such as blood, serum, urine or plasma; (2) tissue specificity, with some miRNAs having almost exclusive expression in a single tissue; (3) cross-species conservation, making them translatable to the clinic; and (4) ability to be measured using simple and inexpensive analytical methods (e.g., PCR-based methods).179, 180 Finally, their tissue-specific expression patterns offers the possibility of developing panels of miRNAs that can be quantified as specific biomarkers for various tissue injuries.180 While numerous reports have shown their potential utility for a variety of disease states, several proofPage | 70


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of-concept studies have also demonstrated that miRNAs can be useful as toxicity biomarkers. For example, miR-122 has been shown to be consistently increased with hepatotoxicity or with steatohepatitis in rodents.181, 182 However, there is still a lot of additional work required to decide whether miRNAs will have real utility as toxicity biomarkers. For example, a recent study has shown that miR-122 performs equivalently or better than the traditional hepatotoxicity biomarkers (i.e., ALT, AST and glutamate dehydrogenase or GLDH) in acute and sub-acute rat toxicity studies.180 However, the improvement in hepatotoxicity detection was modest, partly reflecting the fact that existing biomarkers do perform quite well under these experimental conditions.180 In addition, diagnostic miRNA biomarker profiling is still in its infancy, and there are many technical and analytical aspects that need to be further addressed.

The ‘omics technologies have been advocated as an approach to identify toxic changes in tissues at earlier stages than with traditional morphological and functional end points (e.g., histopathology, serum chemistry or hematology), but also to identify potential biomarkers or mechanisms of toxicity.54, 183 Most successful attempts have been achieved using gene expression profiling (i.e., toxicogenomics) applied to rat toxicology studies. Because of the importance of hepatotoxicity in drug development and the availability of large volume of hepatic gene expression profiles, the liver has been the tissue most used in toxicogenomics studies.54, 184, 185

When used in the context of other data, especially in vitro screening, hepatic gene expression

profiling data have also been proposed as useful to identify compounds at risk of inducing idiosyncratic hepatotoxicity.186 Despite smaller gene expression profile repositories, other tissues (e.g., kidney, heart, blood, or testis) are also amenable to the approach.187-191 It is fair to Page | 71



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acknowledge that the toxicogenomics approach has not delivered as much as initially hoped, but unrealistic expectations and over-enthusiasm are quite common when evaluating emerging technologies. It is beyond the scope of this article to conduct an in-depth review of the technology and readers are referred to recent reviews or books dedicated to the topic.192 Few comments on the use of the technology for the early in vivo evaluation of compounds are warranted.

Several reports have shown utility to predict carcinogens with toxicogenomics. 184, 193, 194 However, the most useful aspect of the technology for Discovery toxicology is, in our experience, when it is integrated as part of the exploratory toxicology paradigm. When appropriately used, toxicogenomics not only provides early signals of toxic liabilities, but also a data-rich context that often brings mechanistic clarity of the nature of various phenotypic changes and that contributes to better design of subsequent, longer-term studies.195 For example, gene expression profiling of liver samples can alert on potential interactions with nuclear receptors (i.e., PXR, CAR, AhR, PPARα) that could result in morphological changes in other tissues, such as thyroid glands.185 Likewise, these data can reveal unanticipated off-target effects that could be associated with side effects or explain pharmacological effects.196 Finally, integrating proprietary genomics-based predictive models for common toxic effects can enhance the detection of toxic changes in short-term rat studies. For example, a genomic biomarker to predict the onset of bile duct hyperplasia (a histopathological change that frequently occurs after days or weeks of exposure to test agents) was shown to be quite robust when using hepatic gene expression data from 5-day exploratory rat studies.54 Page | 72


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An Eye to The Future a. Complex In Vitro Models The predictivity of two-dimensional (2D) in vitro assays is probably at best around 50%, leaving a large false negative space (i.e., compounds not toxic in vitro but toxic in vivo, especially in humans). This performance limitation has been the main rationale to develop better models, particularly more complex models composed of multiple cell types, arranged in 3D space and sometimes leveraging the microfluidic technology. Some of these models are labeled as “organ-on-a-chip” to reflect their closer similarity to normal organs. Several companies and laboratories have surfaced in the past few years with prototypes, in part fueled by investments by the National Institute of Health (NIH)/FDA. The most advanced models include the liver, kidney and lung models. The liver model will be used here to illustrate the differences between simple and complex cell systems. Selected models for various organ-specific or multi-organ toxicities are listed in Table 3.

Monolayers of primary hepatocytes are still the gold standard model for in vitro drug metabolism and toxicity studies.197 Minor refinements in culture conditions (e.g., sandwich cultures using collagen or matrigel) can help maintain hepatocellular functionality for a few days and restore transporter-mediated cellular uptake and efflux, but all these monolayer cultures of primary hepatocytes suffer from limited life span with rapid progressive dedifferentiation of hepatocytes.129, 133 In contrast, 3D liver models typically maintain a stable phenotype in culture coupled with enhanced functionality of drug metabolizing enzymes and transporters. For Page | 73



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example, several studies have shown excellent long-term viability of human hepatocyte-based spheroids with preservation of the expression and activity of Phase I and Phase II metabolizing enzymes, as well as the expression of hepatocyte-specific markers and activity of hepatocyte transporters.198 These parameters can even be enhanced using physiological perfusion feeding regimens enabled by bioreactor systems.199 In addition, co-cultures with non-parenchymal cells (e.g., endothelial, stellate and Kupffer cells) provide an additional opportunity to generate a tissue microenvironment necessary to evaluate immune-mediated mechanisms of hepatotoxicity. Some of these 3D liver models are amenable to routine use and high-throughput adaptations, and are conveniently commercially available.130, 131, 200

Liver 3D models range in complexity from spheroids (sometimes also referred to as microtissues or hepatospheres) to scaffold-based systems and more advanced models using microfluidic in vitro systems. Spheroids in particular have been the most utilized in pharmnaceutical R&D organizations because of their simplicity of generation, maintenance and use. Spheroids can be constructed through spontaneous self-assembly under static conditions, agitation (rotary culture, rocked culture) or with the hanging drop method. They can be composed of primary hepatocytes (all preclinical species or humans) or liver cell lines such as HepG2 or HepaRG cells.201 Furthermore, hepatocytes can be cultured with other liver cell types (i.e., endothelial, stellate and Kupffer cells). As many toxic responses in vivo are mediated by the complex interplay among different cell types, the predictive performance of hepatocytes alone is limited. Spheroid systems containing rat hepatocytes, hepatic stellate cells, the HSC-T6 cell line, HUVEC cells or Kupffer cells have been developed and their superiority over the conventional hepatocyte model have been demonstrated for drug metabolism and toxicity studies.131, 202, 203 Page | 74


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More complex 3D liver models are slowly becoming part of the tool armamentarium of toxicology laboratories in pharmaceutical companies. The assumption is that specific technological improvements will further increase the performance of these models. In particular, incorporating fluidics into these 3D culture systems contributes to improved oxygen flow and nutrient diffusion, as well as more “in vivo-like” drug exposure and excretion. However, these models are still in their infancy, and it is important to maintain realistic expectations for what they may actually provide, at least in the near future. The assumption that higher complexity coupled with a more in vivo-like microenvironment will result in higher performance compared to simple 2D models is realistic. However, the next generation of 3D models will also take many years and likely several precompetitive efforts to reach a stage where their use becomes routine and productive. In addition, their throughput and cost may not necessarily make them amenable to HTS use and they may be more appropriate for second-tier types of screens.

b. Induced Pluripotent Stem Cells iPS cells are an attractive area of research for toxicologists because of their self-renewing capabilities and ability to be differentiated into about multiple cell types, including hepatocytes. In addition, iPS cells can be generated from a wide range of species, including humans and laboratory animal species used in toxicology. Cardiomyocytes, neuronal cells, endothelial cells or even hepatocytes have been generated using the iPS cell technology and, not surprisingly, numerous toxicological applications leveraging the technology have been published in recent Page | 75



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years. Table 4 lists selected published models and their proposed use, and also include some models based on embryonic stem cells. We already discussed iPS-derived cardiomyocytes in a previous section and will focus here on iPS-derived hepatocytes, since those are of utmost interest to toxicologists. In addition, it is noteworthy that the stem cell technology has interesting potential to detect teratogens and selected assays developed for that purpose are listed in Table 4.

iPS-derived hepatocytes demonstrate many hepatocellular functional properties, but irrespective of the differentiation protocols, drug metabolizing enzyme activities as well as the expression levels of other liver-specific transcripts are lower than those of liver or primary hepatocytes. Transcript and proteomics profiling of different sources of iPS-derived hepatocytes indicate a fetal rather than an adult phenotype.204, 205 Other investigators, however, showed that these cells have appropriate functional activity for many drug metabolizing enzymes and transporters, as well as transcriptional profiles more similar to those from neonatal and adult hepatocytes than those from fetal liver.204 Our experience with these cells is more consistent with fetal, rather than adult hepatocytes. The overall lack of maturity of iPS-derived hepatocytes is similar to what has been observed with iPS-derived cardiomyocytes and greatly limit their current use, as responses to specific mechanisms of toxicity may differ from those of primary hepatocytes.206 Various laboratories are trying to refine differentiation protocols or culture methods to generate more mature hepatocytes, and the use of advanced culture models (e.g., 3D models like spheroids or co-cultures) might improve overall functionality. However, given the functional state and cost of current iPS-derived hepatocytes, one may argue that it is premature to invest in the technology. Indeed, internal and unpublished data from our laboratory generated

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with a small set of prototypical compounds suggest that iPS-derived hepatocytes offer no advantage compared to human hepatocyte cell lines, such as HepG2 cells.

If successful, the derivation of iPS-derived hepatocytes with phenotypic features similar to those of primary hepatocytes would allow for the creation of several in vitro models useful for toxicity or ADME compound profiling. In particular, since a major gap in the current toxicology toolbox is the lack of reliable models to identify compounds with risk for idiosyncratic hepatotoxicity, one useful approach would be to leverage iPS cells originating from patients who have experienced idiosyncratic DILI. Recent experimental evidence indicates that iPS cellderived hepatocytes retained donor-specific CYP-mediated metabolism capacity and drug responsiveness.207 Such models would provide an unfathomable insight into the various potential genetic components contributing to idiosyncratic DILI. Likewise, these models would enable drug toxicity testing in different risk populations. For example, mtDNA variations including single nucleotide polymorphisms (SNPs) have been proposed to be involved in idiosyncratic drug reactions. However, current in vitro and in vivo models do not cover the genetic diversity seen in the human population. Hence, compounds with potential mitochondrial liability based on target assessment or results of in vitro screens could be evaluated in hepatocytes derived from a panel of genetically diverse humans with mitochondrial DNA (mtDNA) variations. Early experimental evidence with mouse cells suggests that this may be a realistic path forward, since strain-dependent differences in the response to drugs known to impair mitochondrial functions can be detected.208 This indicates that different mouse strains with distinct mtDNA SNPs have different mitochondrial bioenergetic profiles, which lead to different responses to drug-induced toxicity. Page | 77



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The unlimited availability of such hepatocytes could expand the range of applications beyond in vitro models. For example, those could be used instead of primary hepatocytes to replenish the liver of the chimeric liver mouse models. The advantages would be more consistency over time for these models, the generation of genetically diverse chimeric livers, and potentially because of their self-renewing and pluripotent capabilities, iPS cells may be able to reconstitute a liver with a more appropriate tissue architecture. Conclusion The last two decades have experienced several disruptive technological revolutions in multiple fields relevant to life sciences, such as the genomics, microfluidic or iPS cell technologies. However, the discipline of pharmaceutical toxicology has not yet fully benefited from these advances and has not substantially evolved. Furthermore, only small and insufficient improvements in nonclinical safety-related attrition rates have been observed. Some may argue that this reflects the conservative nature of the toxicology community. However, this mostly reflects the challenge of predicting the toxicity of compounds at early stages, especially during LO. During compound optimization, key pieces of information are often unknown or poorly estimated, in particular efficacious exposures or tissue concentrations. It is currently an extremely difficult and often inaccurate exercise to quantify or simulate in vivo exposure to compounds in tissues, and to estimate the kinetics of compounds within tissues. Interpreting quantitative data points generated with standard in vitro assays without this information is most often inaccurate and may lead to termination of potentially useful compounds (i.e., lost

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opportunities). To quote Paracelsus: "All things are poison and nothing is without poison; only the dose makes a thing not a poison."

On the bright side, significant improvements have regularly and favorably impacted the toxicology discipline in pharmaceutical R&D. Safety pharmacology was not too long ago a discipline in its infancy, but nowadays, robust models exist to predict QT prolongation, a once significant cause of attrition and safety issues in humans. Likewise, better risk assessment of compounds causing liver hypertrophy in animals has been enabled by a molecular understanding of the multiple mechanisms causing this effect and of their relevance to humans. Multiple additions to the toxicologist’s toolbox (e.g., new biomarkers, the omics’ technologies, immunosafety assays) have further contributed to refine the toxicological assessment of compounds at both early and late stages of characterization. Finally, information technology tools have facilitated the transformation of data into knowledge. However, making correct decisions (1) on whether or not to progress molecules from Discovery into Development, (2) on when to spend more time and resources in order to select a better candidate molecule, (3) and on when to terminate exploration of a new target or pathway is tremendously challenging. This obviously requires integration of multiple data types from diverse assays and different functions, as well as the leveraging of computational and decision-support tools. Furthermore, it is also heavily influenced by other factors internal or external to the R&D organization (factors that are often categorized as business-related).

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It is important to acknowledge the increased complexity that toxicologists focused on discovery compounds are currently facing. Firstly, the biology of some new, unprecedented targets is extremely complex and unlike that of the traditional receptor or enzyme targets. For example, albeit considered undruggable a few years ago, protein-protein interactions are being targeted with small molecules. This requires compounds from a different part of the chemical space; these compounds are often large and greasy with a high propensity for promiscuity, and defy all established rules. Likewise, off-target interactions of these compounds are likely to be very different from those of traditional small molecule inhibitors, and this may require adjustment in the composition of current screening panels (e.g., molecular pharmacology panels). Hence, a deeper understanding of target biology through TSAs is clearly becoming essential for any toxicologist or pathologist in the industry. Secondly, various formulation technologies developed by pharmaceutical scientists have now enabled the oral dosing of compounds with much different physicochemical properties than before. Not surprisingly, these compounds often behave differently than traditional Rule-of-Five compliant molecules. Advances in nanotechnology are likely to further influence that aspect as well as the biodistribution of molecules, which would have consequences on safety assessment. Finally, new pharmacology mechanisms are being explored, such as immuno-oncology, and we are still at a stage of trying to understand how nonclinical models will predict adverse events in humans caused by these mechanisms. This changing landscape requires some refinements in the toolbox, but also in the interpretation of data.

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There are also exciting developments in toxicology-relevant technologies. Sophisticated in vitro systems recapitulating multiple in vivo phenotypes are becoming readily available. Likewise, the iPS cell technology offers the promise of humanizing specific tissues in rodents to the point of potentially being able to conduct “personalized” toxicology studies in which genetic variance could be interrogated. However, these new models also need validation, which is time consuming and resource intensive. The recent trend for pharmaceutical companies to collaborate precompetitively in these validations will hopefully continue to help demonstrate the utility of these potentially transforming models.

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Abbreviations 2D, Two-dimensional 3D, Three-dimensional 5-HT2b, 5-Hydroxytryptamine 2b ABC, ATP-binding cassette ADME, Absorption, distribution, metabolism, excretion AhR, Aryl hydrocarbon receptor ALT, Alanine aminotransferase APAP, Acetaminophen APD, Action potential duration AST, Aspartate aminotransferase BSEP, Bile salt export pump CAR, Constitutive androstane receptor ClopP, Calculated logP CNS, Central nervous system CiPA, Comprehensive in vitro proarrythmia assay CYP3A, Cytochrome P450 3A DDI, Drug-drug interaction DILI, Drug-induced liver injury EAD, Early-after depolarization ECM, Extracellular matrix EGFR, Epidermal growth factor receptor ER, Endoplasmic reticulum FDA, Food and Drug Administration FDR, False discovery rate GLDH, Glutamate dehydrogenase GLP, Good Laboratory Practices GSH, Glutathione hERG, human Ether-à-go-go-Related Gene HIV, Human immunodeficiency virus HTL, Hit-to-lead HTS, High-throughput screening IND, Investigational New Drug iPS cell, induced pluripotent stem cell LO, Lead optimization LPS, Lipopolysaccharide mAb, Monoclonal antibody MEA, Multi-electrode arrays MICE, Multiple ion channel effects miRNA, Micro-RNA MRP, Multidrug resistance-associated protein mtDNA, Mitochondrial DNA NGAL, Neutrophil gelatinase-associated lipocalin NIH, National Institute of Health Page | 82


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NSAID, Nonsteroidal anti-inflammatory drug OKR, Optokinetic response PD, Pharmacodynamic P-gp, P-glycoprotein PI, Protease inhibitor PK, Pharmacokinetic PPAR, Peroxisome proliferator-activated receptor PPV, Positive predictive value PSTC, Predictive Safety Testing Consortium PXR, Pregnane X receptor QSAR, Quantitative structure-activity relationship R&D, Research and Development ROS, Reactive oxygen species RST, Respiratory screening technology RTV, Ritonavir SAR, Structure-activity relationship SNP, Single nucleotide polymorphism STP, Society of Toxicologic Pathologists TKI, Tyrosine kinase inhibitor TPSA< Total polar surface area TSA, Target safety assessment TdP, Torsade de pointes TRPV1, Transient receptor potential vanilloid-1 channel Vd, Volume of distribution VEGFR, Vascular endothelial growth factor receptor VMR, Visual motor response

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Funding Sources This presentation was sponsored by AbbVie and Pfizer. AbbVie and Pfizer contributed to the writing, reviewing, and approving the publication. Eric Blomme is an employee of AbbVie. Yvonne Will is an employee of Pfizer.

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References (1) Hay, M., Thomas, D. W., Craighead, J. L., Economides, C., and Rosenthal, J. (2014) Clinical development success rates for investigational drugs. Nat. Biotechnol. 32, 40-51. (2) Munos, B. (2009) Lessons from 60 years of pharmaceutical innovation. Nat. Rev. Drug Discov. 8, 959-968. (3) Cook, D., Brown, D., Alexander, R., March, R., Morgan, P., Satterthwaite, G., and Pangalos, M. N. (2014) Lessons learned from the fate of AstraZeneca's drug pipeline: a five-dimensional framework. Nat. Rev. Drug Discov. 13, 419-431. (4) DiMasi, J. A., Feldman, L., Seckler, A., and Wilson, A. (2010) Trends in risks associated with new drug development: success rates for investigational drugs. Clin. Pharmacol. Ther. 87, 272-277. (5) Waring, M. J., Arrowsmith, J., Leach, A. R., Leeson, P. D., Mandrell, S., Owen, R. M., Pairaudeau, G., Pennie, W. D., Pickett, S. D., Wang, J., Wallace, O., and Weir, A. (2015) An analysis of the attrition of drug candidates from four major pharmaceutical companies. Nat. Rev. Drug Discov. 14, 475-486. (6) Leeson, P. D., and St-Gallay, S. A. (2011) The influence of the 'organizational factor' on compound quality in drug discovery. Nat. Rev. Drug Discov. 10, 749-765. (7) Olson, H., Betton, G., Robinson, D., Thomas, K., Monro, A., Kolaja, G., Lilly, P., Sanders, J., Sipes, G., Bracken, W., Dorato, M., Van, D. K., Smith, P., Berger, B., and Heller, A. (2000) Concordance of the toxicity of pharmaceuticals in humans and in animals. Regul. Toxicol. Pharmacol. 32, 56-67. (8) Hornberg, J. J., Laursen, M., Brenden, N., Persson, M., Thougaard, A. V., Toft, D. B., and Mow, T. (2014) Exploratory toxicology as an integrated part of drug discovery. Part I: Why and how. Drug Discov. Today 19, 1131-1136. (9) Stevens, J. L., and Baker, T. K. (2009) The future of drug safety testing: expanding the view and narrowing the focus. Drug Discov. Today 14, 162-167. (10) Reinhart, G. A., Fryer, R. M., Osinski, M. A., Polakowski, J. S., Cox, B. F., and Gintant, G. A. (2005) Predictive, non-GLP models of secondary pharmacodynamics: putting the best compounds forward. Curr. Opin. Chem. Biol. 9, 392-399. (11) Ewart, L., Aylott, M., Deurinck, M., Engwall, M., Gallacher, D. J., Geys, H., Jarvis, P., Ju, H., Leishman, D., Leong, L., McMahon, N., Mead, A., Milliken, P., Suter, W., Teisman, A., Van, A. K., Vargas, H. M., Wallis, R., and Valentin, J. P. (2014) The concordance between nonclinical and phase I clinical cardiovascular assessment from a cross-company data sharing initiative. Toxicol. Sci. 142, 427-435. (12) Khatri, P., Sirota, M., and Butte, A. J. (2012) Ten years of pathway analysis: current approaches and outstanding challenges. PLoS. Comput. Biol. 8, e1002375. (13) Fedele, C., Riccio, G., Malara, A. E., D'Alessio, G., and De, L. C. (2012) Mechanisms of cardiotoxicity associated with ErbB2 inhibitors. Breast Cancer Res. Treat. 134, 595-602. (14) Melosky, B., Leighl, N. B., Rothenstein, J., Sangha, R., Stewart, D., and Papp, K. (2015) Management of egfr tki-induced dermatologic adverse events. Curr. Oncol. 22, 123-132. (15) Reilly, E. B., Phillips, A. C., Buchanan, F. G., Kingsbury, G., Zhang, Y., Meulbroek, J. A., Cole, T. B., DeVries, P. J., Falls, H. D., Beam, C., Gu, J., Digiammarino, E. L., Palma, J. P., Donawho, C. K., Goodwin, N. C., and Scott, A. M. (2015) Characterization of ABT-806, a Humanized Tumor-Specific Anti-EGFR Monoclonal Antibody. Mol. Cancer Ther. 14, 1141-1151. (16) Gavva, N. R. (2008) Body-temperature maintenance as the predominant function of the vanilloid receptor TRPV1. Trends Pharmacol. Sci. 29, 550-557. Page | 85



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 86 of 117

(17) Reilly, R. M., McDonald, H. A., Puttfarcken, P. S., Joshi, S. K., Lewis, L., Pai, M., Franklin, P. H., Segreti, J. A., Neelands, T. R., Han, P., Chen, J., Mantyh, P. W., Ghilardi, J. R., Turner, T. M., Voight, E. A., Daanen, J. F., Schmidt, R. G., Gomtsyan, A., Kort, M. E., Faltynek, C. R., and Kym, P. R. (2012) Pharmacology of modality-specific transient receptor potential vanilloid-1 antagonists that do not alter body temperature. J. Pharmacol. Exp. Ther. 342, 416-428. (18) Voight, E. A., Gomtsyan, A. R., Daanen, J. F., Perner, R. J., Schmidt, R. G., Bayburt, E. K., DiDomenico, S., McDonald, H. A., Puttfarcken, P. S., Chen, J., Neelands, T. R., Bianchi, B. R., Han, P., Reilly, R. M., Franklin, P. H., Segreti, J. A., Nelson, R. A., Su, Z., King, A. J., Polakowski, J. S., Baker, S. J., Gauvin, D. M., Lewis, L. R., Mikusa, J. P., Joshi, S. K., Faltynek, C. R., Kym, P. R., and Kort, M. E. (2014) Discovery of (R)-1-(7-chloro-2,2-bis(fluoromethyl)chroman-4-yl)-3-(3-methylisoquinolin-5-yl)ur ea (A1165442): a temperature-neutral transient receptor potential vanilloid-1 (TRPV1) antagonist with analgesic efficacy. J. Med. Chem. 57, 7412-7424. (19) Meanwell, N. A. (2011) Improving drug candidates by design: a focus on physicochemical properties as a means of improving compound disposition and safety. Chem. Res. Toxicol. 24, 14201456. (20) Stieger, B., Fattinger, K., Madon, J., Kullak-Ublick, G. A., and Meier, P. J. (2000) Drug- and estrogen-induced cholestasis through inhibition of the hepatocellular bile salt export pump (Bsep) of rat liver. Gastroenterology 118, 422-430. (21) Stepan, A. F., Walker, D. P., Bauman, J., Price, D. A., Baillie, T. A., Kalgutkar, A. S., and Aleo, M. D. (2011) Structural alert/reactive metabolite concept as applied in medicinal chemistry to mitigate the risk of idiosyncratic drug toxicity: a perspective based on the critical examination of trends in the top 200 drugs marketed in the United States. Chem. Res. Toxicol. 24, 1345-1410. (22) Snow, K. L., and Moseley, R. H. (2007) Effect of thiazolidinediones on bile acid transport in rat liver. Life Sci. 80, 732-740. (23) Morgan, R. E., van Staden, C. J., Chen, Y., Kalyanaraman, N., Kalanzi, J., Dunn, R. T., Afshari, C. A., and Hamadeh, H. K. (2013) A multifactorial approach to hepatobiliary transporter assessment enables improved therapeutic compound development. Toxicol. Sci. 136, 216-241. (24) Hann, M. M. (2011) Molecular obesity, potency and other addictions in drug discovery. MedChemComm 2, 349-355. (25) Lipinski, C. A., Lombardo, F., Dominy, B. W., and Feeney, P. J. (2001) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Deliv. Rev. 46, 3-26. (26) Hughes, J. D., Blagg, J., Price, D. A., Bailey, S., Decrescenzo, G. A., Devraj, R. V., Ellsworth, E., Fobian, Y. M., Gibbs, M. E., Gilles, R. W., Greene, N., Huang, E., Krieger-Burke, T., Loesel, J., Wager, T., Whiteley, L., and Zhang, Y. (2008) Physiochemical drug properties associated with in vivo toxicological outcomes. Bioorg. Med. Chem. Lett. 18, 4872-4875. (27) Azzaoui, K., Hamon, J., Faller, B., Whitebread, S., Jacoby, E., Bender, A., Jenkins, J. L., and Urban, L. (2007) Modeling promiscuity based on in vitro safety pharmacology profiling data. ChemMedChem 2, 874-880. (28) Leeson, P. D., and Springthorpe, B. (2007) The influence of drug-like concepts on decisionmaking in medicinal chemistry. Nat. Rev. Drug Discov. 6, 881-890. (29) Peters, J. U., Schnider, P., Mattei, P., and Kansy, M. (2009) Pharmacological promiscuity: dependence on compound properties and target specificity in a set of recent Roche compounds. ChemMedChem 4, 680-686. (30) Sutherland, J. J., Raymond, J. W., Stevens, J. L., Baker, T. K., and Watson, D. E. (2012) Relating molecular properties and in vitro assay results to in vivo drug disposition and toxicity outcomes. J. Med. Chem. 55, 6455-6466. Page | 86


Page 87 of 117

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


(31) Muthas, D., Boyer, S., and Hasselgren, C. (2013) A critical assessment of modeling safety-related drug attrition. MedChemComm 4, 1058-1065. (32) Lovering, F., Bikker, J., and Humblet, C. (2009) Escape from flatland: increasing saturation as an approach to improving clinical success. J. Med. Chem. 52, 6752-6756. (33) Kenny, P. W., and Montanari, C. A. (2013) Inflation of correlation in the pursuit of drug-likeness. J. Comput. Aided Mol. Des. 27, 1-13. (34) Argese, E., Bettiol, C., Giurin, G., and Miana, P. (1999) Quantitative structure-activity relationships for the toxicity of chlorophenols to mammalian submitochondrial particles. Chemosphere 38, 2281-2292. (35) Naven, R. T., Swiss, R., Klug-McLeod, J., Will, Y., and Greene, N. (2013) The development of structure-activity relationships for mitochondrial dysfunction: uncoupling of oxidative phosphorylation. Toxicol. Sci. 131, 271-278. (36) Huth, J. R., Song, D., Mendoza, R. R., Black-Schaefer, C. L., Mack, J. C., Dorwin, S. A., Ladror, U. S., Severin, J. M., Walter, K. A., Bartley, D. M., and Hajduk, P. J. (2007) Toxicological evaluation of thiolreactive compounds identified using a la assay to detect reactive molecules by nuclear magnetic resonance. Chem. Res. Toxicol. 20, 1752-1759. (37) Raunio, H. (2011) In silico toxicology - non-testing methods. Front Pharmacol. 2, 33. (38) Baell, J. B., and Holloway, G. A. (2010) New substructure filters for removal of pan assay interference compounds (PAINS) from screening libraries and for their exclusion in bioassays. J. Med. Chem. 53, 2719-2740. (39) Metz, J. T., Huth, J. R., and Hajduk, P. J. (2007) Enhancement of chemical rules for predicting compound reactivity towards protein thiol groups. J. Comput. Aided Mol. Des. 21, 139-144. (40) Pinto, M., Digles, D., and Ecker, G. F. (2014) Computational models for predicting the interaction with ABC transporters. Drug Discov. Today Technol. 12, e69-e77. (41) Snyder, R. D., and Smith, M. D. (2005) Computational prediction of genotoxicity: room for improvement. Drug Discov. Today 10, 1119-1124. (42) Snyder, R. D. (2010) Possible structural and functional determinants contributing to the clastogenicity of pharmaceuticals. Environ. Mol. Mutagen. 51, 800-814. (43) Sutter, A., Amberg, A., Boyer, S., Brigo, A., Contrera, J. F., Custer, L. L., Dobo, K. L., Gervais, V., Glowienke, S., van, G. J., Greene, N., Muster, W., Nicolette, J., Reddy, M. V., Thybaud, V., Vock, E., White, A. T., and Muller, L. (2013) Use of in silico systems and expert knowledge for structure-based assessment of potentially mutagenic impurities. Regul. Toxicol. Pharmacol. 67, 39-52. (44) Chen, L., Li, Y., Yu, H., Zhang, L., and Hou, T. (2012) Computational models for predicting substrates or inhibitors of P-glycoprotein. Drug Discov. Today 17, 343-351. (45) Halliwell, W. H. (1997) Cationic amphiphilic drug-induced phospholipidosis. Toxicol. Pathol. 25, 53-60. (46) Reasor, M. J., and Kacew, S. (2001) Drug-induced phospholipidosis: are there functional consequences? Exp. Biol. Med. 226, 825-830. (47) Pelletier, D. J., Gehlhaar, D., Tilloy-Ellul, A., Johnson, T. O., and Greene, N. (2007) Evaluation of a published in silico model and construction of a novel Bayesian model for predicting phospholipidosis inducing potential. J. Chem. Inf. Model. 47, 1196-1205. (48) Ploemen, J. P., Kelder, J., Hafmans, T., van de, S. H., van Burgsteden, J. A., Saleminki, P. J., and van, E. E. (2004) Use of physicochemical calculation of pKa and CLogP to predict phospholipidosisinducing potential: a case study with structurally related piperazines. Exp. Toxicol. Pathol. 55, 347-355. (49) Goracci, L., Ceccarelli, M., Bonelli, D., and Cruciani, G. (2013) Modeling phospholipidosis induction: reliability and warnings. J. Chem. Inf. Model. 53, 1436-1446. Page | 87



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 88 of 117

(50) Hanumegowda, U. M., Wenke, G., Regueiro-Ren, A., Yordanova, R., Corradi, J. P., and Adams, S. P. (2010) Phospholipidosis as a function of basicity, lipophilicity, and volume of distribution of compounds. Chem. Res. Toxicol. 23, 749-755. (51) Choi, S. S., Kim, J. S., Valerio, L. G., Jr., and Sadrieh, N. (2013) In silico modeling to predict druginduced phospholipidosis. Toxicol. Appl. Pharmacol. 269, 195-204. (52) Vieth, M., Sutherland, J. J., Robertson, D. H., and Campbell, R. M. (2005) Kinomics: characterizing the therapeutically validated kinase space. Drug Discov. Today 10, 839-846. (53) Metz, J. T., and Hajduk, P. J. (2010) Rational approaches to targeted polypharmacology: creating and navigating protein-ligand interaction networks. Curr. Opin. Chem. Biol. 14, 498-504. (54) Blomme, E. A., Yang, Y., and Waring, J. F. (2009) Use of toxicogenomics to understand mechanisms of drug-induced hepatotoxicity during drug discovery and development. Toxicol. Lett. 186, 22-31. (55) Rana, P., Anson, B., Engle, S., and Will, Y. (2012) Characterization of human-induced pluripotent stem cell-derived cardiomyocytes: bioenergetics and utilization in safety screening. Toxicol. Sci. 130, 117-131. (56) Marroquin, L. D., Hynes, J., Dykens, J. A., Jamieson, J. D., and Will, Y. (2007) Circumventing the Crabtree effect: replacing media glucose with galactose increases susceptibility of HepG2 cells to mitochondrial toxicants. Toxicol. Sci. 97, 539-547. (57) Ekwall, B., Barile, F. A., Castano, A., Clemedson, C., Clothier, R. H., Dierickx, P., Ekwall, B., Ferro, M., Fiskesjö, G., Garza-Ocañas, L., Gómez-Lechón, M. J., Gülden, M., Hall, T., Isomaa, B., Kahru, A., Kerszman, G., Kristen, U., Kunimoto, M., Kärenlampi, S., Lewan, L., Loukianov, A., Ohno, T., Persoone, G., Romert, L., Sawyer, T. W., Shrivastava, R., Segner, H., Stammati, A., Tanaka, N., Valentino, M., Walum, E., and Zucco, F. (1998) MEIC Evaluation of Acute Systemic Toxicity. Part VI. The Prediction of Human Toxicity by Rodent LD50 Values and Results From 61 In Vitro Methods. ALTA 26, 617-658. (58) Diehl, M. S., Willaby, S. L., and Snyder, R. D. (2000) Comparison of the results of a modified miniscreen and the standard bacterial reverse mutation assays. Environ. Mol. Mutagen. 36, 72-77. (59) Escobar, P. A., Kemper, R. A., Tarca, J., Nicolette, J., Kenyon, M., Glowienke, S., Sawant, S. G., Christensen, J., Johnson, T. E., McKnight, C., Ward, G., Galloway, S. M., Custer, L., Gocke, E., O'Donovan, M. R., Braun, K., Snyder, R. D., and Mahadevan, B. (2013) Bacterial mutagenicity screening in the pharmaceutical industry. Mutat. Res. 752, 99-118. (60) Nicolette, J., and Blomme, E. (2011) Applications of Flow Cytometry in Genetic Toxicology. Am. Drug Discov. 6, 52-55. (61) Decordier, I., Papine, A., Plas, G., Roesems, S., Vande, L. K., Moreno-Palomo, J., Cemeli, E., Anderson, D., Fucic, A., Marcos, R., Soussaline, F., and Kirsch-Volders, M. (2009) Automated image analysis of cytokinesis-blocked micronuclei: an adapted protocol and a validated scoring procedure for biomonitoring. Mutagenesis 24, 85-93. (62) Nicolette, J., Diehl, M., Sonders, P., Bryce, S., and Blomme, E. (2011) In vitro micronucleus screening of pharmaceutical candidates by flow cytometry in Chinese hamster V79 cells. Environ. Mol. Mutagen. 52, 355-362. (63) Olaharski, A., Albertini, S., Kirchner, S., Platz, S., Uppal, H., Lin, H., and Kolaja, K. (2009) Evaluation of the GreenScreen GADD45alpha-GFP indicator assay with non-proprietary and proprietary compounds. Mutat. Res. 672, 10-16. (64) Walmsley, R. M., and Tate, M. (2012) The GADD45a-GFP GreenScreen HC assay. Methods Mol. Biol. 817, 231-250. (65) Luzy, A. P., Orsini, N., Linget, J. M., and Bouvier, G. (2013) Evaluation of the GADD45alpha-GFP GreenScreen HC assay for rapid and reliable in vitro early genotoxicity screening. J. Appl. Toxicol. 33, 1303-1315. Page | 88


Page 89 of 117

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


(66) Krishna, G., Fiedler, R., and Theiss, J. C. (1992) Simultaneous evaluation of clastogenicity, aneugenicity and toxicity in the mouse micronucleus assay using immunofluorescence. Mutat. Res. 282, 159-167. (67) Bryce, S. M., Shi, J., Nicolette, J., Diehl, M., Sonders, P., Avlasevich, S., Raja, S., Bemis, J. C., and Dertinger, S. D. (2010) High content flow cytometric micronucleus scoring method is applicable to attachment cell lines. Environ. Mol. Mutagen. 51, 260-266. (68) Peters, J. U., Hert, J., Bissantz, C., Hillebrecht, A., Gerebtzoff, G., Bendels, S., Tillier, F., Migeon, J., Fischer, H., Guba, W., and Kansy, M. (2012) Can we discover pharmacological promiscuity early in the drug discovery process? Drug Discov. Today 17, 325-335. (69) Papoian, T., Chiu, H. J., Elayan, I., Jagadeesh, G., Khan, I., Laniyonu, A. A., Li, C. X., Saulnier, M., Simpson, N., and Yang, B. (2015) Secondary pharmacology data to assess potential off-target activity of new drugs: a regulatory perspective. Nat. Rev. Drug Discov. 14, 294. (70) Mestres, J., Gregori-Puigjane, E., Valverde, S., and Sole, R. V. (2008) Data completeness--the Achilles heel of drug-target networks. Nat. Biotechnol. 26, 983-984. (71) Whitebread, S., Hamon, J., Bojanic, D., and Urban, L. (2005) Keynote review: in vitro safety pharmacology profiling: an essential tool for successful drug development. Drug Discov. Today 10, 14211433. (72) Elangbam, C. S. (2010) Drug-induced valvulopathy: an update. Toxicol. Pathol. 38, 837-848. (73) Engle, S. K., Solter, P. F., Credille, K. M., Bull, C. M., Adams, S., Berna, M. J., Schultze, A. E., Rothstein, E. C., Cockman, M. D., Pritt, M. L., Liu, H., Lu, Y., Chiang, A. Y., and Watson, D. E. (2010) Detection of left ventricular hypertrophy in rats administered a peroxisome proliferator-activated receptor alpha/gamma dual agonist using natriuretic peptides and imaging. Toxicol. Sci. 114, 183-192. (74) Bowes, J., Brown, A. J., Hamon, J., Jarolimek, W., Sridhar, A., Waldron, G., and Whitebread, S. (2012) Reducing safety-related drug attrition: the use of in vitro pharmacological profiling. Nat. Rev. Drug Discov. 11, 909-922. (75) Manning, G., Whyte, D. B., Martinez, R., Hunter, T., and Sudarsanam, S. (2002) The protein kinase complement of the human genome. Science 298, 1912-1934. (76) Goldstein, D. M., Gray, N. S., and Zarrinkar, P. P. (2008) High-throughput kinase profiling as a platform for drug discovery. Nat. Rev. Drug Discov. 7, 391-397. (77) Paricharak, S., Klenka, T., Augustin, M., Patel, U. A., and Bender, A. (2013) Are phylogenetic trees suitable for chemogenomics analyses of bioactivity data sets: the importance of shared active compounds and choosing a suitable data embedding method, as exemplified on Kinases. J. Cheminform. 5, 49. (78) Ferre, F., Palmeri, A., and Helmer-Citterich, M. (2014) Computational methods for analysis and inference of kinase/inhibitor relationships. Front. Genet. 5, 196. (79) Bhargava, P. (2009) VEGF kinase inhibitors: how do they cause hypertension? Am. J. Physiol.: Regul., Integr. Comp. Physiol. 297, R1-R5. (80) Shah, D. R., Shah, R. R., and Morganroth, J. (2013) Tyrosine kinase inhibitors: their on-target toxicities as potential indicators of efficacy. Drug Saf. 36, 413-426. (81) Force, T., and Kolaja, K. L. (2011) Cardiotoxicity of kinase inhibitors: the prediction and translation of preclinical models to clinical outcomes. Nat. Rev. Drug Discov. 10, 111-126. (82) Olaharski, A. J., Gonzaludo, N., Bitter, H., Goldstein, D., Kirchner, S., Uppal, H., and Kolaja, K. (2009) Identification of a kinase profile that predicts chromosome damage induced by small molecule kinase inhibitors. PLoS. Comput. Biol. 5, e1000446. (83) Martin, R. L., McDermott, J. S., Salmen, H. J., Palmatier, J., Cox, B. F., and Gintant, G. A. (2004) The utility of hERG and repolarization assays in evaluating delayed cardiac repolarization: influence of multi-channel block. J. Cardiovasc. Pharmacol. 43, 369-379. Page | 89



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 90 of 117

(84) Rampe, D., and Brown, A. M. (2013) A history of the role of the hERG channel in cardiac risk assessment. J. Pharmacol. Toxicol. Methods 68, 13-22. (85) Park, E., Willard, J., Bi, D., Fiszman, M., Kozeli, D., and Koerner, J. (2013) The impact of drugrelated QT prolongation on FDA regulatory decisions. Int. J. Cardiol. 168, 4975-4976. (86) Zhang, S., Zhou, Z., Gong, Q., Makielski, J. C., and January, C. T. (1999) Mechanism of block and identification of the verapamil binding domain to HERG potassium channels. Circ. Res. 84, 989-998. (87) Meyer, T., Boven, K. H., Gunther, E., and Fejtl, M. (2004) Micro-electrode arrays in cardiac safety pharmacology: a novel tool to study QT interval prolongation. Drug Saf. 27, 763-772. (88) Beacham, D. W., Blackmer, T., O', G. M., and Hanson, G. T. (2010) Cell-based potassium ion channel screening using the FluxOR assay. J. Biomol. Screen. 15, 441-446. (89) Kramer, J., Obejero-Paz, C. A., Myatt, G., Kuryshev, Y. A., Bruening-Wright, A., Verducci, J. S., and Brown, A. M. (2013) MICE models: superior to the HERG model in predicting Torsade de Pointes. Sci. Rep. 3, 2100. (90) Sager, P. T., Gintant, G., Turner, J. R., Pettit, S., and Stockbridge, N. (2014) Rechanneling the cardiac proarrhythmia safety paradigm: a meeting report from the Cardiac Safety Research Consortium. Am. Heart J. 167, 292-300. (91) Williams, G., and Mirams, G. R. (2015) A web portal for in-silico action potential predictions. J. Pharmacol. Toxicol. Methods 75, 10-16. (92) Peters, M. F., Lamore, S. D., Guo, L., Scott, C. W., and Kolaja, K. L. (2015) Human stem cellderived cardiomyocytes in cellular impedance assays: bringing cardiotoxicity screening to the front line. Cardiovasc. Toxicol. 15, 127-139. (93) Asakura, K., Hayashi, S., Ojima, A., Taniguchi, T., Miyamoto, N., Nakamori, C., Nagasawa, C., Kitamura, T., Osada, T., Honda, Y., Kasai, C., Ando, H., Kanda, Y., Sekino, Y., and Sawada, K. (2015) Improvement of acquisition and analysis methods in multi-electrode array experiments with iPS cellderived cardiomyocytes. J. Pharmacol. Toxicol. Methods 75, 17-26. (94) Guo, L., Coyle, L., Abrams, R. M., Kemper, R., Chiao, E. T., and Kolaja, K. L. (2013) Refining the human iPSC-cardiomyocyte arrhythmic risk assessment model. Toxicol. Sci. 136, 581-594. (95) Himmel, H. M. (2013) Drug-induced functional cardiotoxicity screening in stem cell-derived human and mouse cardiomyocytes: effects of reference compounds. J. Pharmacol. Toxicol. Methods 68, 97-111. (96) Robertson, C., Tran, D. D., and George, S. C. (2013) Concise review: maturation phases of human pluripotent stem cell-derived cardiomyocytes. Stem Cells 31, 829-837. (97) Martins, A. M., Vunjak-Novakovic, G., and Reis, R. L. (2014) The current status of iPS cells in cardiac research and their potential for tissue engineering and regenerative medicine. Stem Cell Rev. 10, 177-190. (98) Xu, J. J., Diaz, D., and O'Brien, P. J. (2004) Applications of cytotoxicity assays and pre-lethal mechanistic assays for assessment of human hepatotoxicity potential. Chem. Biol. Interact. 150, 115128. (99) Tolosa, L., Gomez-Lechon, M. J., and Donato, M. T. (2015) High-content screening technology for studying drug-induced hepatotoxicity in cell models. Arch. Toxicol. 89, 1007-1022. (100) O'Brien, P. J., Irwin, W., Diaz, D., Howard-Cofield, E., Krejsa, C. M., Slaughter, M. R., Gao, B., Kaludercic, N., Angeline, A., Bernardi, P., Brain, P., and Hougham, C. (2006) High concordance of druginduced human hepatotoxicity with in vitro cytotoxicity measured in a novel cell-based model using high content screening. Arch. Toxicol. 80, 580-604. (101) Abraham, V. C., Towne, D. L., Waring, J. F., Warrior, U., and Burns, D. J. (2008) Application of a high-content multiparameter cytotoxicity assay to prioritize compounds based on toxicity potential in humans. J. Biomol. Screen. 13, 527-537. Page | 90


Page 91 of 117

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


(102) Benbow, J. W., Aubrecht, J., Banker, M. J., Nettleton, D., and Aleo, M. D. (2010) Predicting safety toleration of pharmaceutical chemical leads: cytotoxicity correlations to exploratory toxicity studies. Toxicol. Lett. 197, 175-182. (103) Greene, N., Aleo, M. D., Louise-May, S., Price, D. A., and Will, Y. (2010) Using an in vitro cytotoxicity assay to aid in compound selection for in vivo safety studies. Bioorg. Med. Chem. Lett. 20, 5308-5312. (104) Shah, F., Louise-May, S., and Greene, N. (2014) Chemotypes sensitivity and predictivity of in vivo outcomes for cytotoxic assays in THLE and HepG2 cell lines. Bioorg. Med. Chem. Lett. 24, 2753-2757. (105) Tirmenstein, M. A., Hu, C. X., Gales, T. L., Maleeff, B. E., Narayanan, P. K., Kurali, E., Hart, T. K., Thomas, H. C., and Schwartz, L. W. (2002) Effects of troglitazone on HepG2 viability and mitochondrial function. Toxicol. Sci. 69, 131-138. (106) Westwood, F. R., Bigley, A., Randall, K., Marsden, A. M., and Scott, R. C. (2005) Statin-induced muscle necrosis in the rat: distribution, development, and fibre selectivity. Toxicol. Pathol. 33, 246-257. (107) Porceddu, M., Buron, N., Roussel, C., Labbe, G., Fromenty, B., and Borgne-Sanchez, A. (2012) Prediction of liver injury induced by chemicals in human with a multiparametric assay on isolated mouse liver mitochondria. Toxicol. Sci. 129, 332-345. (108) Pessayre, D., Fromenty, B., Berson, A., Robin, M. A., Letteron, P., Moreau, R., and Mansouri, A. (2012) Central role of mitochondria in drug-induced liver injury. Drug Metab. Rev. 44, 34-87. (109) Boelsterli, U. A., and Lim, P. L. (2007) Mitochondrial abnormalities--a link to idiosyncratic drug hepatotoxicity? Toxicol. Appl. Pharmacol. 220, 92-107. (110) Haasio, K., Nissinen, E., Sopanen, L., and Heinonen, E. H. (2002) Different toxicological profile of two COMT inhibitors in vivo: the role of uncoupling effects. J. Neural Transm. 109, 1391-1401. (111) Nadanaciva, S., Rana, P., Beeson, G. C., Chen, D., Ferrick, D. A., Beeson, C. C., and Will, Y. (2012) Assessment of drug-induced mitochondrial dysfunction via altered cellular respiration and acidification measured in a 96-well platform. J. Bioenerg. Biomembr. 44, 421-437. (112) Nadanaciva, S., Willis, J. H., Barker, M. L., Gharaibeh, D., Capaldi, R. A., Marusich, M. F., and Will, Y. (2009) Lateral-flow immunoassay for detecting drug-induced inhibition of mitochondrial DNA replication and mtDNA-encoded protein synthesis. J. Immunol. Methods 343, 1-12. (113) Selvaraj, S., Ghebremichael, M., Li, M., Foli, Y., Langs-Barlow, A., Ogbuagu, A., Barakat, L., Tubridy, E., Edifor, R., Lam, W., Cheng, Y. C., and Paintsil, E. (2014) Antiretroviral therapy-induced mitochondrial toxicity: potential mechanisms beyond polymerase-gamma inhibition. Clin. Pharmacol. Ther. 96, 110-120. (114) Nadanaciva, S., Dykens, J. A., Bernal, A., Capaldi, R. A., and Will, Y. (2007) Mitochondrial impairment by PPAR agonists and statins identified via immunocaptured OXPHOS complex activities and respiration. Toxicol. Appl. Pharmacol. 223, 277-287. (115) Will, Y., and Dykens, J. (2014) Mitochondrial toxicity assessment in industry--a decade of technology development and insight. Expert Opin. Drug Metab. Toxicol. 10, 1061-1067. (116) Swiss, R., Niles, A., Cali, J. J., Nadanaciva, S., and Will, Y. (2013) Validation of a HTS-amenable assay to detect drug-induced mitochondrial toxicity in the absence and presence of cell death. Toxicol. In Vitro 27, 1789-1797. (117) Hynes, J., Nadanaciva, S., Swiss, R., Carey, C., Kirwan, S., and Will, Y. (2013) A high-throughput dual parameter assay for assessing drug-induced mitochondrial dysfunction provides additional predictivity over two established mitochondrial toxicity assays. Toxicol. In Vitro 27, 560-569. (118) Mehta, R., Chan, K., Lee, O., Tafazoli, S., and O'Brien, P., Drug-associated mitochondrial toxicity. In Drug-Induced Mitochondrial Dysfunction, Dykens, J.; Will, Y., Eds. John Wiley & Sons, Inc.: 2008; pp 71-126. Page | 91



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 92 of 117

(119) Divakaruni, A. S., Rogers, G. W., and Murphy, A. N. (2014) Measuring Mitochondrial Function in Permeabilized Cells Using the Seahorse XF Analyzer or a Clark-Type Oxygen Electrode. Curr. Protoc. Toxicol. 60, 25-25. (120) Rogers, G. W., Nadanaciva, S., Swiss, R., Divakaruni, A. S., and Will, Y. (2014) Assessment of Fatty Acid Beta oxidation in cells and isolated mitochondria. Curr. Protoc. Toxicol. 60, 25-25. (121) Dykens, J. A., Jamieson, J., Marroquin, L., Nadanaciva, S., Billis, P. A., and Will, Y. (2008) Biguanide-induced mitochondrial dysfunction yields increased lactate production and cytotoxicity of aerobically-poised HepG2 cells and human hepatocytes in vitro. Toxicol. Appl. Pharmacol. 233, 203-210. (122) Dykens, J. A., Jamieson, J. D., Marroquin, L. D., Nadanaciva, S., Xu, J. J., Dunn, M. C., Smith, A. R., and Will, Y. (2008) In vitro assessment of mitochondrial dysfunction and cytotoxicity of nefazodone, trazodone, and buspirone. Toxicol. Sci. 103, 335-345. (123) Xu, J. J., Henstock, P. V., Dunn, M. C., Smith, A. R., Chabot, J. R., and de, G. D. (2008) Cellular imaging predictions of clinical drug-induced liver injury. Toxicol. Sci. 105, 97-105. (124) Kostrubsky, S. E., Strom, S. C., Kalgutkar, A. S., Kulkarni, S., Atherton, J., Mireles, R., Feng, B., Kubik, R., Hanson, J., Urda, E., and Mutlib, A. E. (2006) Inhibition of hepatobiliary transport as a predictive method for clinical hepatotoxicity of nefazodone. Toxicol. Sci. 90, 451-459. (125) Aleo, M. D., Luo, Y., Swiss, R., Bonin, P. D., Potter, D. M., and Will, Y. (2014) Human drug-induced liver injury severity is highly associated with dual inhibition of liver mitochondrial function and bile salt export pump. Hepatology 60, 1015-1022. (126) Thompson, R. A., Isin, E. M., Li, Y., Weidolf, L., Page, K., Wilson, I., Swallow, S., Middleton, B., Stahl, S., Foster, A. J., Dolgos, H., Weaver, R., and Kenna, J. G. (2012) In vitro approach to assess the potential for risk of idiosyncratic adverse reactions caused by candidate drugs. Chem. Res. Toxicol. 25, 1616-1632. (127) Kenna, J. G., Stahl, S. H., Eakins, J. A., Foster, A. J., Andersson, L. C., Bergare, J., Billger, M., Elebring, M., Elmore, C. S., and Thompson, R. A. (2015) Multiple compound-related adverse properties contribute to liver injury caused by endothelin receptor antagonists. J. Pharmacol. Exp. Ther. 352, 28190. (128) Shah, F., Leung, L., Barton, H. A., Will, Y., Rodrigues, A. D., Greene, N., and Aleo, M. D. (2015) Setting Clinical Exposure Levels of Concern for Drug-Induced Liver Injury (DILI) Using Mechanistic in vitro Assays. Toxicol. Sci. 147, 500-514. (129) Meng, Q. (2010) Three-dimensional culture of hepatocytes for prediction of drug-induced hepatotoxicity. Expert Opin. Drug Metab. Toxicol. 6, 733-746. (130) Messner, S., Agarkova, I., Moritz, W., and Kelm, J. M. (2013) Multi-cell type human liver microtissues for hepatotoxicity testing. Arch. Toxicol. 87, 209-213. (131) Godoy, P., Hewitt, N. J., Albrecht, U., Andersen, M. E., Ansari, N., Bhattacharya, S., Bode, J. G., Bolleyn, J., Borner, C., Bottger, J., Braeuning, A., Budinsky, R. A., Burkhardt, B., Cameron, N. R., Camussi, G., Cho, C. S., Choi, Y. J., Craig, R. J., Dahmen, U., Damm, G., Dirsch, O., Donato, M. T., Dong, J., Dooley, S., Drasdo, D., Eakins, R., Ferreira, K. S., Fonsato, V., Fraczek, J., Gebhardt, R., Gibson, A., Glanemann, M., Goldring, C. E., Gomez-Lechon, M. J., Groothuis, G. M., Gustavsson, L., Guyot, C., Hallifax, D., Hammad, S., Hayward, A., Haussinger, D., Hellerbrand, C., Hewitt, P., Hoehme, S., Holzhutter, H. G., Houston, J. B., Hrach, J., Ito, K., Jaeschke, H., Keitel, V., Kelm, J. M., Kevin, P. B., Kordes, C., Kullak-Ublick, G. A., LeCluyse, E. L., Lu, P., Luebke-Wheeler, J., Lutz, A., Maltman, D. J., Matz-Soja, M., McMullen, P., Merfort, I., Messner, S., Meyer, C., Mwinyi, J., Naisbitt, D. J., Nussler, A. K., Olinga, P., Pampaloni, F., Pi, J., Pluta, L., Przyborski, S. A., Ramachandran, A., Rogiers, V., Rowe, C., Schelcher, C., Schmich, K., Schwarz, M., Singh, B., Stelzer, E. H., Stieger, B., Stober, R., Sugiyama, Y., Tetta, C., Thasler, W. E., Vanhaecke, T., Vinken, M., Weiss, T. S., Widera, A., Woods, C. G., Xu, J. J., Yarborough, K. M., and Hengstler, J. G. (2013) Recent advances in 2D and 3D in vitro systems using primary hepatocytes, alternative hepatocyte Page | 92


Page 93 of 117

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


sources and non-parenchymal liver cells and their use in investigating mechanisms of hepatotoxicity, cell signaling and ADME. Arch. Toxicol. 87, 1315-1530. (132) Takahashi, Y., Hori, Y., Yamamoto, T., Urashima, T., Ohara, Y., and Tanaka, H. (2015) 3D spheroid cultures improve the metabolic gene expression profiles of HepaRG cells. In press. Biosci. Rep. 35. (133) Ditewig, A. C., Cugier, D. J., Liguori, M. J., Yang, Y., and Blomme, E. A. (2013) Transcriptomic evaluation of canine suspension-shipped and pre-plated hepatocytes: comparison to liver. Toxicol. Mech. Methods 23, 479-490. (134) Persson, M., Loye, A. F., Mow, T., and Hornberg, J. J. (2013) A high content screening assay to predict human drug-induced liver injury during drug discovery. J. Pharmacol. Toxicol. Methods 68, 302313. (135) Hornberg, J. J., Laursen, M., Brenden, N., Persson, M., Thougaard, A. V., Toft, D. B., and Mow, T. (2014) Exploratory toxicology as an integrated part of drug discovery. Part II: Screening strategies. Drug Discov. Today 19, 1137-1144. (136) Chandra, P., and Brouwer, K. L. (2004) The complexities of hepatic drug transport: current knowledge and emerging concepts. Pharm. Res. 21, 719-735. (137) Dawson, S., Stahl, S., Paul, N., Barber, J., and Kenna, J. G. (2012) In vitro inhibition of the bile salt export pump correlates with risk of cholestatic drug-induced liver injury in humans. Drug Metab. Dispos. 40, 130-138. (138) Yang, Y., Abel, S. J., Ciurlionis, R., and Waring, J. F. (2006) Development of a toxicogenomics in vitro assay for the efficient characterization of compounds. Pharmacogenomics 7, 177-186. (139) Sawada, H., Takami, K., and Asahi, S. (2005) A toxicogenomic approach to drug-induced phospholipidosis: analysis of its induction mechanism and establishment of a novel in vitro screening system. Toxicol. Sci. 83, 282-292. (140) Atienzar, F., Gerets, H., Dufrane, S., Tilmant, K., Cornet, M., Dhalluin, S., Ruty, B., Rose, G., and Canning, M. (2007) Determination of phospholipidosis potential based on gene expression analysis in HepG2 cells. Toxicol. Sci. 96, 101-114. (141) Park, B. K., Boobis, A., Clarke, S., Goldring, C. E., Jones, D., Kenna, J. G., Lambert, C., Laverty, H. G., Naisbitt, D. J., Nelson, S., Nicoll-Griffith, D. A., Obach, R. S., Routledge, P., Smith, D. A., Tweedie, D. J., Vermeulen, N., Williams, D. P., Wilson, I. D., and Baillie, T. A. (2011) Managing the challenge of chemically reactive metabolites in drug development. Nat. Rev. Drug Discovery 10, 292-306. (142) Hussaini, S. H., and Farrington, E. A. (2014) Idiosyncratic drug-induced liver injury: an update on the 2007 overview. Expert. Opin. Drug Saf. 13, 67-81. (143) Nakayama, S., Atsumi, R., Takakusa, H., Kobayashi, Y., Kurihara, A., Nagai, Y., Nakai, D., and Okazaki, O. (2009) A zone classification system for risk assessment of idiosyncratic drug toxicity using daily dose and covalent binding. Drug Metab. Dispos. 37, 1970-1977. (144) Howe, K., Clark, M. D., Torroja, C. F., Torrance, J., Berthelot, C., Muffato, M., Collins, J. E., Humphray, S., McLaren, K., Matthews, L., McLaren, S., Sealy, I., Caccamo, M., Churcher, C., Scott, C., Barrett, J. C., Koch, R., Rauch, G. J., White, S., Chow, W., Kilian, B., Quintais, L. T., Guerra-Assuncao, J. A., Zhou, Y., Gu, Y., Yen, J., Vogel, J. H., Eyre, T., Redmond, S., Banerjee, R., Chi, J., Fu, B., Langley, E., Maguire, S. F., Laird, G. K., Lloyd, D., Kenyon, E., Donaldson, S., Sehra, H., meida-King, J., Loveland, J., Trevanion, S., Jones, M., Quail, M., Willey, D., Hunt, A., Burton, J., Sims, S., McLay, K., Plumb, B., Davis, J., Clee, C., Oliver, K., Clark, R., Riddle, C., Elliot, D., Threadgold, G., Harden, G., Ware, D., Begum, S., Mortimore, B., Kerry, G., Heath, P., Phillimore, B., Tracey, A., Corby, N., Dunn, M., Johnson, C., Wood, J., Clark, S., Pelan, S., Griffiths, G., Smith, M., Glithero, R., Howden, P., Barker, N., Lloyd, C., Stevens, C., Harley, J., Holt, K., Panagiotidis, G., Lovell, J., Beasley, H., Henderson, C., Gordon, D., Auger, K., Wright, D., Collins, J., Raisen, C., Dyer, L., Leung, K., Robertson, L., Ambridge, K., Leongamornlert, D., McGuire, S., Gilderthorp, R., Griffiths, C., Manthravadi, D., Nichol, S., Barker, G., Whitehead, S., Kay, M., Brown, J., Page | 93



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 94 of 117

Murnane, C., Gray, E., Humphries, M., Sycamore, N., Barker, D., Saunders, D., Wallis, J., Babbage, A., Hammond, S., Mashreghi-Mohammadi, M., Barr, L., Martin, S., Wray, P., Ellington, A., Matthews, N., Ellwood, M., Woodmansey, R., Clark, G., Cooper, J., Tromans, A., Grafham, D., Skuce, C., Pandian, R., Andrews, R., Harrison, E., Kimberley, A., Garnett, J., Fosker, N., Hall, R., Garner, P., Kelly, D., Bird, C., Palmer, S., Gehring, I., Berger, A., Dooley, C. M., Ersan-Urun, Z., Eser, C., Geiger, H., Geisler, M., Karotki, L., Kirn, A., Konantz, J., Konantz, M., Oberlander, M., Rudolph-Geiger, S., Teucke, M., Lanz, C., Raddatz, G., Osoegawa, K., Zhu, B., Rapp, A., Widaa, S., Langford, C., Yang, F., Schuster, S. C., Carter, N. P., Harrow, J., Ning, Z., Herrero, J., Searle, S. M., Enright, A., Geisler, R., Plasterk, R. H., Lee, C., Westerfield, M., de Jong, P. J., Zon, L. I., Postlethwait, J. H., Nusslein-Volhard, C., Hubbard, T. J., Roest, C. H., Rogers, J., and Stemple, D. L. (2013) The zebrafish reference genome sequence and its relationship to the human genome. Nature 496, 498-503. (145) Chapin, R., ugustine-Rauch, K., Beyer, B., Daston, G., Finnell, R., Flynn, T., Hunter, S., Mirkes, P., O'Shea, K. S., Piersma, A., Sandler, D., Vanparys, P., and Van Maele-Fabry, G. (2008) State of the art in developmental toxicity screening methods and a way forward: a meeting report addressing embryonic stem cells, whole embryo culture, and zebrafish. Birth Defects Res. B Dev. Reprod. Toxicol. 83, 446-456. (146) Mesens, N., Crawford, A. D., Menke, A., Hung, P. D., Van, G. F., Nuyts, R., Hansen, E., Wolterbeek, A., van, G. J., De, W. P., and Esguerra, C. V. (2015) Are zebrafish larvae suitable for assessing the hepatotoxicity potential of drug candidates? J. Appl. Toxicol. 35, 1017-1029. (147) Driessen, M., Vitins, A. P., Pennings, J. L., Kienhuis, A. S., Water, B., and van, d., V (2015) A transcriptomics-based hepatotoxicity comparison between the zebrafish embryo and established human and rodent in vitro and in vivo models using cyclosporine A, amiodarone and acetaminophen. Toxicol. Lett. 232, 403-412. (148) Milan, D. J., Peterson, T. A., Ruskin, J. N., Peterson, R. T., and MacRae, C. A. (2003) Drugs that induce repolarization abnormalities cause bradycardia in zebrafish. Circulation 107, 1355-1358. (149) Berghmans, S., Butler, P., Goldsmith, P., Waldron, G., Gardner, I., Golder, Z., Richards, F. M., Kimber, G., Roach, A., Alderton, W., and Fleming, A. (2008) Zebrafish based assays for the assessment of cardiac, visual and gut function--potential safety screens for early drug discovery. J. Pharmacol. Toxicol. Methods 58, 59-68. (150) Mittelstadt, S. W., Hemenway, C. L., Craig, M. P., and Hove, J. R. (2008) Evaluation of zebrafish embryos as a model for assessing inhibition of hERG. J. Pharmacol. Toxicol. Methods 57, 100-105. (151) Tsutsui, H., Higashijima, S., Miyawaki, A., and Okamura, Y. (2010) Visualizing voltage dynamics in zebrafish heart. J. Physiol. 588, 2017-2021. (152) Chan, P. K., Lin, C. C., and Cheng, S. H. (2009) Noninvasive technique for measurement of heartbeat regularity in zebrafish (Danio rerio) embryos. BMC Biotechnology 9, 11. (153) Han, Y., Zhang, J. P., Qian, J. Q., and Hu, C. Q. (2015) Cardiotoxicity evaluation of anthracyclines in zebrafish (Danio rerio). J. Appl. Toxicol. 35, 241-252. (154) Zhu, J. J., Xy, Y. Q., He, J. H., Yu, H. P., Huang, C. J., Gao, J. M., Dong, Q. X., Xuan, Y. X., and Li, C. Q. (2014) Human cardiotoxic drugs delivered by soaking and microinjection induce cardiovascular toxicity in zebrafish. J. Appl. Toxicol. 34, 139-148. (155) Koseki, N., Deguchi, J., Yamashita, A., Miyawaki, I., and Funabashi, H. (2014) Establishment of a novel experimental protocol for drug-induced seizure liability screening based on a locomotor activity assay in zebrafish. J. Toxicol. Sci. 39, 579-600. (156) Deeti, S., O'Farrell, S., and Kennedy, B. N. (2014) Early safety assessment of human oculotoxic drugs using the zebrafish visualmotor response. J. Pharmacol. Toxicol. Methods 69, 1-8. (157) Cassar, S., Huang, X., and Cole, T. (2015) A high-throughput method for predicting drug effects on gut transit time using larval zebrafish. J. Pharmacol. Toxicol. Methods 76, 72-75. Page | 94


Page 95 of 117

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


(158) Bass, A. S., Cartwright, M. E., Mahon, C., Morrison, R., Snyder, R., McNamara, P., Bradley, P., Zhou, Y. Y., and Hunter, J. (2009) Exploratory drug safety: a discovery strategy to reduce attrition in development. J. Pharmacol. Toxicol. Methods 60, 69-78. (159) Roberts, R. A., Kavanagh, S. L., Mellor, H. R., Pollard, C. E., Robinson, S., and Platz, S. J. (2014) Reducing attrition in drug development: smart loading preclinical safety assessment. Drug Discov. Today 19, 341-347. (160) Pugsley, M. K., Authier, S., Stonerook, M., and Curtis, M. J. (2015) The shifting landscape of safety pharmacology in 2015. J. Pharmacol. Toxicol. Methods 75, 5-9. (161) Hamdam, J., Sethu, S., Smith, T., Alfirevic, A., Alhaidari, M., Atkinson, J., Ayala, M., Box, H., Cross, M., Delaunois, A., Dermody, A., Govindappa, K., Guillon, J. M., Jenkins, R., Kenna, G., Lemmer, B., Meecham, K., Olayanju, A., Pestel, S., Rothfuss, A., Sidaway, J., Sison-Young, R., Smith, E., Stebbings, R., Tingle, Y., Valentin, J. P., Williams, A., Williams, D., Park, K., and Goldring, C. (2013) Safety pharmacology--current and emerging concepts. Toxicol. Appl. Pharmacol. 273, 229-241. (162) Van, D. B., Van, A. K., Somers, Y., Cools, F., Straetemans, R., van der Linde, H. J., and Gallacher, D. J. (2009) The fentanyl/etomidate-anaesthetised beagle (FEAB) dog: a versatile in vivo model in cardiovascular safety research. J. Pharmacol. Toxicol. Methods 60, 11-23. (163) Hanson, L. A., Bass, A. S., Gintant, G., Mittelstadt, S., Rampe, D., and Thomas, K. (2006) ILSI-HESI cardiovascular safety subcommittee initiative: evaluation of three non-clinical models of QT prolongation. J. Pharmacol. Toxicol. Methods 54, 116-129. (164) Derakhchan, K., Chui, R. W., Stevens, D., Gu, W., and Vargas, H. M. (2014) Detection of QTc interval prolongation using jacket telemetry in conscious non-human primates: comparison with implanted telemetry. Br. J. Pharmacol. 171, 509-522. (165) Chui, R. W., Fosdick, A., Conner, R., Jiang, J., Bruenner, B. A., and Vargas, H. M. (2009) Assessment of two external telemetry systems (PhysioJacket and JET) in beagle dogs with telemetry implants. J. Pharmacol. Toxicol. Methods 60, 58-68. (166) Authier, S., Vargas, H. M., Curtis, M. J., Holbrook, M., and Pugsley, M. K. (2013) Safety pharmacology investigations in toxicology studies: an industry survey. J. Pharmacol. Toxicol. Methods 68, 44-51. (167) Thybaud, V., Dean, S., Nohmi, T., de, B. J., Douglas, G. R., Glickman, B. W., Gorelick, N. J., Heddle, J. A., Heflich, R. H., Lambert, I., Martus, H. J., Mirsalis, J. C., Suzuki, T., and Yajima, N. (2003) In vivo transgenic mutation assays. Mutat. Res. 540, 141-151. (168) Dobrovolsky, V. N., Cao, X., Bhalli, J. A., and Heflich, R. H. (2014) Detection of Pig-a mutant erythrocytes in the peripheral blood of rats and mice. Methods Mol. Biol. 1105, 205-221. (169) Gollapudi, B. B., Lynch, A. M., Heflich, R. H., Dertinger, S. D., Dobrovolsky, V. N., Froetschl, R., Horibata, K., Kenyon, M. O., Kimoto, T., Lovell, D. P., Stankowski, L. F., Jr., White, P. A., Witt, K. L., and Tanir, J. Y. (2015) The in vivo Pig-a assay: A report of the International Workshop On Genotoxicity Testing (IWGT) Workgroup. Mutat. Res. Genet. Toxicol. Environ. Mutagen. 783, 23-35. (170) Morgan, S. J., Elangbam, C. S., Berens, S., Janovitz, E., Vitsky, A., Zabka, T., and Conour, L. (2013) Use of animal models of human disease for nonclinical safety assessment of novel pharmaceuticals. Toxicol. Pathol. 41, 508-518. (171) Yang, Y., hly-Vernon, A. J., Blomme, E. A., Lai-Zhang, J., Kempf, D. J., Marsh, K. C., Harrington, Y. A., Nye, S. H., Evans, D. L., Roman, R. J., Jacob, H. J., and Waring, J. F. (2010) Liver transcriptomic changes associated with ritonavir-induced hyperlipidemia in sensitive and resistant strains of rats. Vet. J. 185, 7582. (172) Watkins, P. B., Kaplowitz, N., Slattery, J. T., Colonese, C. R., Colucci, S. V., Stewart, P. W., and Harris, S. C. (2006) Aminotransferase elevations in healthy adults receiving 4 grams of acetaminophen daily: a randomized controlled trial. JAMA 296, 87-93. Page | 95



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 96 of 117

(173) Harrill, A. H., Ross, P. K., Gatti, D. M., Threadgill, D. W., and Rusyn, I. (2009) Population-based discovery of toxicogenomics biomarkers for hepatotoxicity using a laboratory strain diversity panel. Toxicol. Sci. 110, 235-243. (174) Howell, B. A., Yang, Y., Kumar, R., Woodhead, J. L., Harrill, A. H., Clewell, H. J., III, Andersen, M. E., Siler, S. Q., and Watkins, P. B. (2012) In vitro to in vivo extrapolation and species response comparisons for drug-induced liver injury (DILI) using DILIsym: a mechanistic, mathematical model of DILI. J. Pharmacokinet. Pharmacodyn. 39, 527-541. (175) Harrill, A. H., Watkins, P. B., Su, S., Ross, P. K., Harbourt, D. E., Stylianou, I. M., Boorman, G. A., Russo, M. W., Sackler, R. S., Harris, S. C., Smith, P. C., Tennant, R., Bogue, M., Paigen, K., Harris, C., Contractor, T., Wiltshire, T., Rusyn, I., and Threadgill, D. W. (2009) Mouse population-guided resequencing reveals that variants in CD44 contribute to acetaminophen-induced liver injury in humans. Genome Res. 19, 1507-1515. (176) Court, M. H., Peter, I., Hazarika, S., Vasiadi, M., Greenblatt, D. J., Lee, W. M., and Acute Liver Failure Study, G. (2014) Candidate gene polymorphisms in patients with acetaminophen-induced acute liver failure. Drug Metab. Dispos. 42, 28-32. (177) Dieterle, F., Sistare, F., Goodsaid, F., Papaluca, M., Ozer, J. S., Webb, C. P., Baer, W., Senagore, A., Schipper, M. J., Vonderscher, J., Sultana, S., Gerhold, D. L., Phillips, J. A., Maurer, G., Carl, K., Laurie, D., Harpur, E., Sonee, M., Ennulat, D., Holder, D., ndrews-Cleavenger, D., Gu, Y. Z., Thompson, K. L., Goering, P. L., Vidal, J. M., Abadie, E., Maciulaitis, R., Jacobson-Kram, D., Defelice, A. F., Hausner, E. A., Blank, M., Thompson, A., Harlow, P., Throckmorton, D., Xiao, S., Xu, N., Taylor, W., Vamvakas, S., Flamion, B., Lima, B. S., Kasper, P., Pasanen, M., Prasad, K., Troth, S., Bounous, D., Robinson-Gravatt, D., Betton, G., Davis, M. A., Akunda, J., McDuffie, J. E., Suter, L., Obert, L., Guffroy, M., Pinches, M., Jayadev, S., Blomme, E. A., Beushausen, S. A., Barlow, V. G., Collins, N., Waring, J., Honor, D., Snook, S., Lee, J., Rossi, P., Walker, E., and Mattes, W. (2010) Renal biomarker qualification submission: a dialog between the FDA-EMEA and Predictive Safety Testing Consortium. Nat. Biotechnol. 28, 455-462. (178) Blomme, E. A. (2011) Assessing renal function: some significant improvements on the horizon. Vet. J. 188, 128-129. (179) Mikaelian, I., Scicchitano, M., Mendes, O., Thomas, R. A., and Leroy, B. E. (2013) Frontiers in preclinical safety biomarkers: microRNAs and messenger RNAs. Toxicol. Pathol. 41, 18-31. (180) Sharapova, T., Devanarayan, V., LeRoy, B., Liguori, M. J., Blomme, E., Buck, W., and Maher, J. (2015) Evaluation of miR-122 as a Serum Biomarker for Hepatotoxicity in Investigative Rat Toxicology Studies. Vet. Pathol. In press. (181) Wang, K., Zhang, S., Marzolf, B., Troisch, P., Brightman, A., Hu, Z., Hood, L. E., and Galas, D. J. (2009) Circulating microRNAs, potential biomarkers for drug-induced liver injury. Proc. Natl. Acad. Sci. U.S.A 106, 4402-4407. (182) Clarke, J. D., Sharapova, T., Lake, A. D., Blomme, E., Maher, J., and Cherrington, N. J. (2014) Circulating microRNA 122 in the methionine and choline-deficient mouse model of non-alcoholic steatohepatitis. J. Appl. Toxicol. 34, 726-732. (183) Khan, S. R., Baghdasarian, A., Fahlman, R. P., Michail, K., and Siraki, A. G. (2014) Current status and future prospects of toxicogenomics in drug discovery. Drug Discov. Today 19, 562-578. (184) Fielden, M. R., Nie, A., McMillian, M., Elangbam, C. S., Trela, B. A., Yang, Y., Dunn, R. T., Dragan, Y., Fransson-Stehen, R., Bogdanffy, M., Adams, S. P., Foster, W. R., Chen, S. J., Rossi, P., Kasper, P., Jacobson-Kram, D., Tatsuoka, K. S., Wier, P. J., Gollub, J., Halbert, D. N., Roter, A., Young, J. K., Sina, J. F., Marlowe, J., Martus, H. J., Aubrecht, J., Olaharski, A. J., Roome, N., Nioi, P., Pardo, I., Snyder, R., Perry, R., Lord, P., Mattes, W., and Car, B. D. (2008) Interlaboratory evaluation of genomic signatures for predicting carcinogenicity in the rat. Toxicol. Sci. 103, 28-34. Page | 96


Page 97 of 117

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


(185) Yang, Y., Ciurlionis, R., Kowalkowski, K., Marsh, K. C., Bracken, W. M., and Blomme, E. A. (2012) N-vinylpyrrolidone dimer, a novel formulation excipient, causes hepatic and thyroid hypertrophy through the induction of hepatic microsomal enzymes in rats. Toxicol. Lett. 208, 82-91. (186) Laifenfeld, D., Qiu, L., Swiss, R., Park, J., Macoritto, M., Will, Y., Younis, H. S., and Lawton, M. (2014) Utilization of causal reasoning of hepatic gene expression in rats to identify molecular pathways of idiosyncratic drug-induced liver injury. Toxicol. Sci. 137, 234-248. (187) Fielden, M. R., Eynon, B. P., Natsoulis, G., Jarnagin, K., Banas, D., and Kolaja, K. L. (2005) A gene expression signature that predicts the future onset of drug-induced renal tubular toxicity. Toxicol. Pathol. 33, 675-683. (188) Blomme, E. A., Ciurlionis, R., Marsh, K. C., Waring, J. F., and Yang, Y. (2008) Evaluation of the effects of serial phlebotomy on the transcriptome of major tissues and on the response to toxicants in rats. Toxicol. Lett. 176, 138-148. (189) Gu, Y. G., Weitzberg, M., Clark, R. F., Xu, X., Li, Q., Lubbers, N. L., Yang, Y., Beno, D. W., Widomski, D. L., Zhang, T., Hansen, T. M., Keyes, R. F., Waring, J. F., Carroll, S. L., Wang, X., Wang, R., Healan-Greenberg, C. H., Blomme, E. A., Beutel, B. A., Sham, H. L., and Camp, H. S. (2007) N-(3-2-(4alkoxyphenoxy)thiazol-5-yl-1-methylprop-2-ynyl)carboxy derivatives as acetyl-coA carboxylase inhibitors--improvement of cardiovascular and neurological liabilities via structural modifications. J. Med. Chem. 50, 1078-1082. (190) Fricano, M. M., Ditewig, A. C., Jung, P. M., Liguori, M. J., Blomme, E. A., and Yang, Y. (2011) Global transcriptomic profiling using small volumes of whole blood: a cost-effective method for translational genomic biomarker identification in small animals. Int. J. Mol. Sci. 12, 2502-2517. (191) Nishimura, Y., Morikawa, Y., Kondo, C., Tonomura, Y., Fukushima, R., Torii, M., and Uehara, T. (2013) Genomic biomarkers for cardiotoxicity in rats as a sensitive tool in preclinical studies. J. Appl. Toxicol. 33, 1120-1130. (192) Semizarov, D., and Blomme, E., Genomics in Drug Discovery and Development. John Wyley and Sons, Inc.: Hoboken, N.J., 2008. (193) Kramer, J. A., Curtiss, S. W., Kolaja, K. L., Alden, C. L., Blomme, E. A., Curtiss, W. C., Davila, J. C., Jackson, C. J., and Bunch, R. T. (2004) Acute molecular markers of rodent hepatic carcinogenesis identified by transcription profiling. Chem. Res. Toxicol. 17, 463-470. (194) Ellinger-Ziegelbauer, H., Stuart, B., Wahle, B., Bomann, W., and Ahr, H. J. (2005) Comparison of the expression profiles induced by genotoxic and nongenotoxic carcinogens in rat liver. Mutat. Res. 575, 61-84. (195) Buck, W. R., Waring, J. F., and Blomme, E. A. (2008) Use of traditional end points and gene dysregulation to understand mechanisms of toxicity: toxicogenomics in mechanistic toxicology. Methods Mol. Biol. 460, 23-44. (196) Waring, J. F., Yang, Y., Healan-Greenberg, C. H., Adler, A. L., Dickinson, R., McNally, T., Wang, X., Weitzberg, M., Xu, X., Lisowski, A., Warder, S. E., Gu, Y. G., Zinker, B. A., Blomme, E. A., and Camp, H. S. (2008) Gene expression analysis in rats treated with experimental acetyl-coenzyme A carboxylase inhibitors suggests interactions with the peroxisome proliferator-activated receptor alpha pathway. J. Pharmacol. Exp. Ther. 324, 507-516. (197) Gomez-Lechon, M. J., Donato, M. T., Castell, J. V., and Jover, R. (2003) Human hepatocytes as a tool for studying toxicity and drug metabolism. Curr. Drug Metab. 4, 292-312. (198) Schyschka, L., Sanchez, J. J., Wang, Z., Burkhardt, B., Muller-Vieira, U., Zeilinger, K., Bachmann, A., Nadalin, S., Damm, G., and Nussler, A. K. (2013) Hepatic 3D cultures but not 2D cultures preserve specific transporter activity for acetaminophen-induced hepatotoxicity. Arch. Toxicol. 87, 1581-1593.

Page | 97



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(199) Tostoes, R. M., Leite, S. B., Miranda, J. P., Sousa, M., Wang, D. I., Carrondo, M. J., and Alves, P. M. (2011) Perfusion of 3D encapsulated hepatocytes--a synergistic effect enhancing long-term functionality in bioreactors. Biotechnol. Bioeng. 108, 41-49. (200) Gomez-Lechon, M. J., Tolosa, L., Conde, I., and Donato, M. T. (2014) Competency of different cell models to predict human hepatotoxic drugs. Expert Opin. Drug Metab. Toxicol. 10, 1553-1568. (201) Gunness, P., Mueller, D., Shevchenko, V., Heinzle, E., Ingelman-Sundberg, M., and Noor, F. (2013) 3D organotypic cultures of human HepaRG cells: a tool for in vitro toxicity studies. Toxicol. Sci. 133, 67-78. (202) Soldatow, V. Y., LeCluyse, E. L., Griffith, L. G., and Rusyn, I. (2013) models for liver toxicity testing. Toxicol. Res. 2, 23-39. (203) van Zijl, F., and Mikulits, W. (2010) Hepatospheres: Three dimensional cell cultures resemble physiological conditions of the liver. World J. Hepatol. 2, 1-7. (204) Lu, J., Einhorn, S., Venkatarangan, L., Miller, M., Mann, D. A., Watkins, P. B., and LeCluyse, E. (2015) Morphological and Functional Characterization and Assessment of iPSC-Derived Hepatocytes for In Vitro Toxicity Testing. Toxicol. Sci. 47, 39-54. (205) Baxter, M., Withey, S., Harrison, S., Segeritz, C. P., Zhang, F., tkinson-Dell, R., Rowe, C., Gerrard, D. T., Sison-Young, R., Jenkins, R., Henry, J., Berry, A. A., Mohamet, L., Best, M., Fenwick, S. W., Malik, H., Kitteringham, N. R., Goldring, C. E., Piper, H. K., Vallier, L., and Hanley, N. A. (2015) Phenotypic and functional analyses show stem cell-derived hepatocyte-like cells better mimic fetal rather than adult hepatocytes. J. Hepatol. 62, 581-589. (206) Sjogren, A. K., Liljevald, M., Glinghammar, B., Sagemark, J., Li, X. Q., Jonebring, A., Cotgreave, I., Brolen, G., and Andersson, T. B. (2014) Critical differences in toxicity mechanisms in induced pluripotent stem cell-derived hepatocytes, hepatic cell lines and primary hepatocytes. Arch. Toxicol. 88, 1427-1437. (207) Takayama, K., Morisaki, Y., Kuno, S., Nagamoto, Y., Harada, K., Furukawa, N., Ohtaka, M., Nishimura, K., Imagawa, K., Sakurai, F., Tachibana, M., Sumazaki, R., Noguchi, E., Nakanishi, M., Hirata, K., Kawabata, K., and Mizuguchi, H. (2014) Prediction of interindividual differences in hepatic functions and drug sensitivity by using human iPS-derived hepatocytes. Proc. Natl. Acad. Sci. U.S.A 111, 1677216777. (208) Pereira, C. V., Oliveira, P. J., Will, Y., and Nadanaciva, S. (2012) Mitochondrial bioenergetics and drug-induced toxicity in a panel of mouse embryonic fibroblasts with mitochondrial DNA single nucleotide polymorphisms. Toxicol. Appl. Pharmacol. 264, 167-181. (209) Klapczynski, M., Gagne, G. D., Morgan, S. J., Larson, K. J., Leroy, B. E., Blomme, E. A., Cox, B. F., and Shek, E. W. (2012) Computer-assisted imaging algorithms facilitate histomorphometric quantification of kidney damage in rodent renal failure models. J. Pathol. Inform. 3, 20. (210) Zabka, T. S., Singh, J., Dhawan, P., Liederer, B. M., Oeh, J., Kauss, M. A., Xiao, Y., Zak, M., Lin, T., McCray, B., La, N., Nguyen, T., Beyer, J., Farman, C., Uppal, H., Dragovich, P. S., O'Brien, T., Sampath, D., and Misner, D. L. (2015) Retinal toxicity, in vivo and in vitro, associated with inhibition of nicotinamide phosphoribosyltransferase. Toxicol. Sci. 144, 163-172. (211) Waring, J. F., Liguori, M. J., Luyendyk, J. P., Maddox, J. F., Ganey, P. E., Stachlewitz, R. F., North, C., Blomme, E. A., and Roth, R. A. (2006) Microarray analysis of lipopolysaccharide potentiation of trovafloxacin-induced liver injury in rats suggests a role for proinflammatory chemokines and neutrophils. J. Pharmacol. Exp. Ther. 316, 1080-1087. (212) Liguori, M. J., Ditewig, A. C., Maddox, J. F., Luyendyk, J. P., Lehman-McKeeman, L. D., Nelson, D. M., Bhaskaran, V. M., Waring, J. F., Ganey, P. E., Roth, R. A., and Blomme, E. A. (2010) Comparison of TNFalpha to lipopolysaccharide as an inflammagen to characterize the idiosyncratic hepatotoxicity potential of drugs: Trovafloxacin as an example. Int. J. Mol. Sci. 11, 4697-4714. Page | 98


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(213) Iyanagi, T., Watanabe, T., and Uchiyama, Y. (1989) The 3-methylcholanthrene-inducible UDPglucuronosyltransferase deficiency in the hyperbilirubinemic rat (Gunn rat) is caused by a -1 frameshift mutation. J. Biol. Chem. 264, 21302-21307. (214) Kempf, D. J., Waring, J. F., Morfitt, D. C., Werner, P., Ebert, B., Mitten, M., Nguyen, B., Randolph, J. T., Degoey, D. A., Klein, L. L., and Marsh, K. (2006) Practical preclinical model for assessing the potential for unconjugated hyperbilirubinemia produced by human immunodeficiency virus protease inhibitors. Antimicrob. Agents Chemother. 50, 762-764. (215) Scheer, N., and Wolf, C. R. (2014) Genetically humanized mouse models of drug metabolizing enzymes and transporters and their applications. Xenobiotica 44, 96-108. (216) Scheer, N., Kapelyukh, Y., Rode, A., Oswald, S., Busch, D., McLaughlin, L. A., Lin, Henderson, C. J., and Wolf, C. R. (2015) Defining Human Pathways of Drug Metabolism In Vivo through the Development of a Multiple Humanized Mouse Model. Drug Metab. Dispos. 43, 1679-1690. (217) Azuma, H., Paulk, N., Ranade, A., Dorrell, C., Al-Dhalimy, M., Ellis, E., Strom, S., Kay, M. A., Finegold, M., and Grompe, M. (2007) Robust expansion of human hepatocytes in Fah-/-/Rag2-/-/Il2rg-/mice. Nat. Biotechnol. 25, 903-910. (218) Peterson, R. A., Krull, D. L., Brown, H. R., and de, S. M. (2010) Morphologic characterization of PhoenixBio (uPA+/+/SCID) humanized liver chimeric mouse model. Drug Metab. Lett. 4, 180-184. (219) Blomme, E. A., Chinn, K. S., Hardy, M. M., Casler, J. J., Kim, S. H., Opsahl, A. C., Hall, W. A., Trajkovic, D., Khan, K. N., and Tripp, C. S. (2003) Selective cyclooxygenase-2 inhibition does not affect the healing of cutaneous full-thickness incisional wounds in SKH-1 mice. Br. J. Dermatol. 148, 211-223. (220) Hardy, M. M., Blomme, E. A., Lisowski, A., Chinn, K. S., Jones, A., Harmon, J. M., Opsahl, A., Ornberg, R. L., and Tripp, C. S. (2003) Selective cyclooxygenase-2 inhibition does not alter keratinocyte wound responses in the mouse epidermis after abrasion. J. Pharmacol. Exp. Ther. 304, 959-967. (221) Beauchamp, P., Moritz, W., Kelm, J. M., Ullrich, N. D., Agarkova, I., Anson, B. D., Suter, T. M., and Zuppinger, C. (2015) Development and Characterization of a Scaffold-Free 3D Spheroid Model of Induced Pluripotent Stem Cell-Derived Human Cardiomyocytes. Tissue Eng. Part C. Methods 21, 852861. (222) Jahnke, H. G., Steel, D., Fleischer, S., Seidel, D., Kurz, R., Vinz, S., Dahlenborg, K., Sartipy, P., and Robitzki, A. A. (2013) A novel 3D label-free monitoring system of hES-derived cardiomyocyte clusters: a step forward to in vitro cardiotoxicity testing. PLoS One 8, e68971. (223) Ramaiahgari, S. C., den Braver, M. W., Herpers, B., Terpstra, V., Commandeur, J. N., van de, W. B., and Price, L. S. (2014) A 3D in vitro model of differentiated HepG2 cell spheroids with improved liverlike properties for repeated dose high-throughput toxicity studies. Arch. Toxicol. 88, 1083-1095. (224) Burkhardt, B., Martinez-Sanchez, J. J., Bachmann, A., Ladurner, R., and Nussler, A. K. (2014) Long-term culture of primary hepatocytes: new matrices and microfluidic devices. Hepatol. Int. 8, 14-22. (225) Mueller, D., Kramer, L., Hoffmann, E., Klein, S., and Noor, F. (2014) 3D organotypic HepaRG cultures as in vitro model for acute and repeated dose toxicity studies. Toxicol. In Vitro 28, 104-112. (226) Kostadinova, R., Boess, F., Applegate, D., Suter, L., Weiser, T., Singer, T., Naughton, B., and Roth, A. (2013) A long-term three dimensional liver co-culture system for improved prediction of clinically relevant drug-induced hepatotoxicity. Toxicol. Appl. Pharmacol. 268, 1-16. (227) Pei, Y., Peng, J., Behl, M., Sipes, N. S., Shockley, K. R., Rao, M. S., Tice, R. R., and Zeng, X. (2015) Comparative neurotoxicity screening in human iPSC-derived neural stem cells, neurons and astrocytes. Brain Res. In press. (228) Sirenko, O., Hesley, J., Rusyn, I., and Cromwell, E. F. (2014) High-content high-throughput assays for characterizing the viability and morphology of human iPSC-derived neuronal cultures. Assay Drug Dev. Technol. 12, 536-547. Page | 99



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(229) Holmgren, G., Sjogren, A. K., Barragan, I., Sabirsh, A., Sartipy, P., Synnergren, J., Bjorquist, P., Ingelman-Sundberg, M., Andersson, T. B., and Edsbagge, J. (2014) Long-term chronic toxicity testing using human pluripotent stem cell-derived hepatocytes. Drug Metab. Dispos. 42, 1401-1406. (230) Sengupta, S., Johnson, B. P., Swanson, S. A., Stewart, R., Bradfield, C. A., and Thomson, J. A. (2014) Aggregate culture of human embryonic stem cell-derived hepatocytes in suspension are an improved in vitro model for drug metabolism and toxicity testing. Toxicol. Sci. 140, 236-245. (231) Sirenko, O., Hesley, J., Rusyn, I., and Cromwell, E. F. (2014) High-content assays for hepatotoxicity using induced pluripotent stem cell-derived cells. Assay Drug Dev. Technol. 12, 43-54. (232) Clements, M., and Thomas, N. (2014) High-throughput multi-parameter profiling of electrophysiological drug effects in human embryonic stem cell derived cardiomyocytes using multielectrode arrays. Toxicol. Sci. 140, 445-461. (233) Clements, M., Millar, V., Williams, A., and Kalinka, S. (2015) Bridging Functional and Structural Cardiotoxicity Assays using Human Embryonic Stem-Cell Derived Cardiomyocytes for a more Comprehensive Risk Assessment. Toxicol. Sci. 148, 241-260. (234) Doherty, K. R., Talbert, D. R., Trusk, P. B., Moran, D. M., Shell, S. A., and Bacus, S. (2015) Structural and functional screening in human induced-pluripotent stem cell-derived cardiomyocytes accurately identifies cardiotoxicity of multiple drug types. Toxicol. Appl. Pharmacol. 285, 51-60. (235) Scott, C. W., Zhang, X., bi-Gerges, N., Lamore, S. D., Abassi, Y. A., and Peters, M. F. (2014) An impedance-based cellular assay using human iPSC-derived cardiomyocytes to quantify modulators of cardiac contractility. Toxicol. Sci. 142, 331-338. (236) Nozaki, Y., Honda, Y., Tsujimoto, S., Watanabe, H., Kunimatsu, T., and Funabashi, H. (2014) Availability of human induced pluripotent stem cell-derived cardiomyocytes in assessment of drug potential for QT prolongation. Toxicol. Appl. Pharmacol. 278, 72-77. (237) Shinde, V., Klima, S., Sureshkumar, P. S., Meganathan, K., Jagtap, S., Rempel, E., Rahnenfuhrer, J., Hengstler, J. G., Waldmann, T., Hescheler, J., Leist, M., and Sachinidis, A. (2015) Human Pluripotent Stem Cell Based Developmental Toxicity Assays for Chemical Safety Screening and Systems Biology Data Generation. J. Vis. Exp., e52333. (238) Hou, Z., Zhang, J., Schwartz, M. P., Stewart, R., Page, C. D., Murphy, W. L., and Thomson, J. A. (2013) A human pluripotent stem cell platform for assessing developmental neural toxicity screening. Stem Cell Res. Ther. 4, S12. (239) Palmer, J. A., Smith, A. M., Egnash, L. A., Conard, K. R., West, P. R., Burrier, R. E., Donley, E. L., and Kirchner, F. R. (2013) Establishment and assessment of a new human embryonic stem cell-based biomarker assay for developmental toxicity screening. Birth Defects Res. B Dev. Reprod. Toxicol. 98, 343363. (240) Kameoka, S., Babiarz, J., Kolaja, K., and Chiao, E. (2014) A high-throughput screen for teratogens using human pluripotent stem cells. Toxicol. Sci. 137, 76-90.

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Dr. Eric Blomme obtained his Doctorate in Veterinary Medicine in Lyon, France, completed his pathology residency training at Cornell University, and obtained his Ph.D. at The Ohio State University. Eric has more than 17 years of experience in the pharmaceutical industry at Monsanto/Searle, Pharmacia, Abbott and now AbbVie, where he has held various positions in both Discovery and Development. Eric is Diplomate of the American College of Veterinary Pathologists. He is currently Senior Research Fellow, Director of Investigative Toxicology and Pathology at AbbVie, where he focusses on the selection and characterization of targets and compounds before Development candidate nomination.

Dr. Yvonne Will conducted undergraduate studies in Human Nutrition at the University of Bonn and obtained her PhD in Biochemistry and Biophysics from Oregon State University. She has more than 15 years of industry experience at MitoKor and Pfizer. She is currently Head of Science and Technology for Drug Safety at Pfizer. Dr. Will has been a pioneer in the evaluation and development of toxicity screening paradigms and technologies, in particular for druginduced mitochondrial toxicity. Dr. Will has published numerous books and journal articles, and Page | 101



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has presented numerous national and international lectures, workshops and seminars on Discovery toxicology.

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Table 1. Examples of drugs and their mitochondrial targets. It is noteworthy that many drugs have more than one mitochondrial target. For example, troglitazone is known to inhibit the mitochondrial electron transport chain, but also induces the mitochondrial permeability transition pore. In addition, many drugs cause organ toxicity though multiple mechanisms. For example, nefazodone and troglitazone, which both caused fatal liver injury, have been shown to not just inhibit mitochondrial function, but also to inhibit BSEP and to form reactive metabolites.

Mitochondrial Target

Example of Drugs

Potential Clinical manifestation

Electron transport chain

Tamoxifen, nefazodone, alpidem, troglitazone, propofol, sorafenib, bupivacaine, cerivastatin, NSAIDs, amiodarone, acetaminophen,

Organ toxicity/failure, lactate accumulation

Uncouplers of oxidative phosphorylation

Nimesulide, diclofenac, usnic acid

Thermogenesis, organ toxicity/failure

Fatty Acid oxidation

Valproate, amineptine, amiodarone, pirprofen, tamoxifen, disclofenac, ibuprofen

Acylcarnitine accumulation, impaired ketogenesis, hypoglycemia

mtDNA replication and Mitochondrial protein synthesis

Linezolid, zalcibatine, abacavir, stavudine, didanosine, tacrine


Induction of mitochondrial permeability transition Pore

Dimebon, nimesulide, alpidem, amiodarone, diclofenac, acetaminophen, troglitazone, valproic acid


Mitochondrial Potassium channel

Glibenclamide, paxillin, nicorandil

Organ toxicity/failure

Sodium-Hydrogen exchanger

Cariporide


Carnitine palmiotyltransferase

Perhexilline, etomoxir


complexes I-V


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Table 2. Non-Standard Animal Models for Toxicology Non-standard animal models Main use in safety assessment

Useful references

General pharmacology efficacy models

182 209

• •

Early hazard or on-target toxicity identification Effect of a disease state on a toxic response

Spontaneous Surgically induced (e.g., 5/6 nephrectomy rat model) • Chemically induced • Nutritionally induced (e.g., fatty diet in rodents) • Genetically engineered • Xenograft models for cancer Focused mechanistic LPS or TNFα rat or mouse investigation of the impact of model toxicity signals (e.g., reactive metabolites, mitochondrial function test, transporter interactions) on the potential to induce idiosyncratic hepatotoxicity • •

Gunn rat

Prediction of hyperbilirubinemia in humans

210

211, 212

213, 214

Selection of backup molecules

Rodent diversity panels

Identification of mechanism of human-specific toxicity

171, 173

Selection of backup molecules

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Genetically engineered rodent models for nuclear receptors (e.g., CAR, PXR, PPARα, AhR), drug metabolizing enzymes or transporters Knock-out models Knock-in models expressing the human homolog Humanized liver chimeric mice


Investigation of the human relevance of liver and metabolism findings, including liver carcinogenesis

215, 216

Investigation of humanspecific hepatotoxicity

217, 218

Assessment of the potential impact of compounds on skin would healing and reepithelialization

219, 220

• •

Skin wound healing rodent models

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Table 3. Selected complex in vitro three-dimensional models for prediction of organ toxicity

Organ Toxicity

Approach

Reference

Cardiac toxicity

Scaffold-free, iPS cell-derived cardiac microtissues/spheroids recapitulating vital cardiac functionality for detection of cardiotoxicity Human embryonic stem cellderived 3D model utilizing a label-free monitoring system for multi-parametric analysis (field potential recording, impedance spectroscopy, and optical readouts)

221

Spheroid model using HepG2

223

Cardiac toxicity

Liver toxicity

Liver toxicity

cells showing improved functional properties and amenable to repeated dose highthroughput toxicity studies. Hydrogel- and scaffold-based 3D models using primary hepatocytes coupled with microfluidic devices

222

224

Liver toxicity

Spheroid model using HepaRG cells showing improved functional properties and amenable to repeated dose highthroughput toxicity studies.

201, 225

Liver toxicity

3D co-culture systems of primary rat or human hepatocytes with non-parenchymal hepatic cells amenable to repeated drugtreatments for detection of in vivo relevant hepatotoxicity

226

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Table 4. Selected models using embryonic or induced pluripotent (iPS) stem cells

Organ Toxicity

Stem Cell Approach

References

Neurotoxicity

Neurotoxicity screening with human iPS cell-derived neural stem cells, neurons and astrocytes. High-content, high-throughput assays using iPS-derived neuronal cells. Readouts include quantitative characterization of neurite outgrowth and branching, cell number and viability, measures of mitochondrial integrity and membrane potential. Long-term in vitro testing with human iPS cell-derived hepatocytes.

227

Liver Toxicity

Aggregate cultures of human embryonic stem cell-derived hepatocytes in suspension for drug metabolism and toxicity testing.

230

Liver Toxicity

High-content, high-throughput assay using iPS cell-derived hepatocytes. Endpoints assessed were cell viability, nuclear shape, average and integrated cell area, mitochondrial membrane potential, phospholipid accumulation, cytoskeleton integrity, and apoptosis. High-throughput profiling of electrophysiological effects of compounds using embryonic stem cell-derived derived cardiomyocytes using multi-electrode arrays (MEA). Use of multi-parameter phenotypic profiling and clustering techniques to analyze the electrophysiology data obtained by MEA analysis Impedance-based in vitro toxicity assay with human-iPS cell-derived cardiomyocytes evaluating effect on overall cell health (actin cytoskeleton damage, troponin secretion), mitochondrial stress,

231

Neurotoxicity

Liver Toxicity

Cardiac Toxicity

Cardiac Toxicity

228

229

232, 233

234

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Cardiac Toxicity

Cardiac Toxicity

Developmental Toxicity




and function (cardiac cell beating). Impedance-based in vitro toxicity assay with human-iPS cell-derived cardiomyocytes to quantify modulators of cardiac contractility. MEA-based assay using human-iPS cellderived cardiomyocytes to detect QT prolongation induced by multichannel blockers. Also able to detect early-afterdepolarization (EAD) for drugs with potential to induce Torsades de Pointes. Description of two assays based on human embryonic stem cells to predict humanspecific early embryonic toxicity/teratogenicity or early developmental neurotoxicity and epigenetic changes induced by chemicals. Description of a human pluripotent (embryonic and induced) stem cell-based platform for developmental neural toxicity screens. Use machine learning tools for high sensitivity. Metabolic biomarker (ornithine and cystine)-based assay using human embryonic stem cells to identify human developmental toxicants. Commercially available by Stemina High-throughput assay using human embryonic stem cells to detect teratogens by measuring the reduction in nuclear translocation of the transcription factor SOX17 in mesendodermal cells.

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235

236

237

238

239

240

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Figure Legends Figure 1. Overview of the Main Assays and Studies Conducted in Discovery Toxicology. Figure 2. Target Safety Assessments (TSAs) are conducted for all targets at the stage of target exploration. Multiple sources of information and tools are available to toxicologists to conduct these TSAs. The figure illustrates the various components used at AbbVie. Evaluation of this large collection of data is facilitated by text mining strategies powered by search tools developed internally, as well as in silico simulation of the pharmacological consequences associated with changes of activity of the target or pathways.

Figure 3. Physicochemical Properties and Toxicity. Polar surface area (Y axis) and ClogP (X axis) of 501 marketed, orally available drugs as a benchmark (obtained from DrugBank). The lower right quadrant (in yellow) indicates the drugs that violate the 3/75 rule. It is clear that compounds can be successful despite suboptimal physicochemical properties. Consequently, the 3/75 rule should be used as a guide, but not necessarily as a cause for termination. It is important to note that no consideration of total dose or systemic exposure is used in this high-level analysis.

Fig. 4. Pharmacology Similarity Measures. Using interaction network analysis, several measures of pharmacological similarity can be generated. Illustrated here are the activity data (given in pKi units) of small molecules against two unrelated targets. The Pharmacology Interaction strength (Pij) represents the fraction of compounds tested against both targets that are within 10-fold in potency. The Tanimoto Tij is the fraction of compounds that exhibit sub-µM potency against both enzymes. The Pearson Rij is derived from a linear fit of the data, and measures the absolute correlation of potency values against the two targets.53 In this example, the pharmacology metrics indicate that compounds within the applicability domain are likely to interact with both targets.

Figure 5. High-throughput Cytotoxicity Assay: Correlation with In Vivo Outcomes. The retrospective study at AbbVie showed that in the 72 hr-ATP assay with HepG2 cells, compounds with an IC20 < 30 µM are 2.5 times more likely to be associated with tolerability issues in exploratory rat toxicology studies at Cmax < 5 µM.

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Figure 6. General Exploratory Toxicology Paradigm at AbbVie Lake County R&D Site. Project needs are evaluated based on a diverse set of available information, such as the therapeutic area, chemical diversity or potential target liabilities based on target safety assessments (TSAs). An exploratory toxicology plan is developed based on these needs. Studies are designed based on the plan (e.g., species, sex, duration, tissue list for histopathology examination, nonstandard biomarkers of toxicity, tissue collection for molecular analysis) and conducted as efficiently as possible. Data are rapidly interpreted and communicated to the team (generally within 2 weeks) in order to guide medicinal chemistry efforts or to make decisions on compounds. This process is iterative with new data resulting in adjustments to the plan.

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Target safety assessment (TSA)

The Right Target 1,000,000 Compounds 1‐5 Series of Compounds Lead Development

Cytotoxicity, physicochemical properties, promiscuity panels Cytotoxicity, physicochemical properties, promiscuity panels, tissue‐specific in vitro assays (e.g., cardiovascular, genetic toxicology), in silico models

Cytotoxicity, physicochemical properties, promiscuity panels, tissue‐specific in vitro assays (cardiovascular, genetic toxicology), in silico models, kinase and pharmacological profiling, other ad‐hoc in vitro assays, exploratory in vivo studies

1‐3 Candidate(s) DRF Rodent Studies

Exploratory in vivo studies, ad‐hoc use of non‐standard in vivo models and biomarkers

DRF Non‐rodent Studies GLP Studies

Figure 1 ACS Paragon Plus Environment


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Figure 2

Literature • • •

Biology, pathology, etc. Competitor information Other

Databases • Tissue expression/distribution • Gene disruption phenotype (KO mice, human diseases, etc) • Cross‐species homology • Competitor information

TSA Dashboard

Bioinformatics Tools

TSA • Living Document • Archived and accessible to all

Chemoinformatics Tools


• Toxicology Evaluation • Safety Pharmacology Evaluation

Powered by internal search tools

Other SME contributions

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Figure 3

Polar Surface Area

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48


ClogP



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48

Figure 4

Pij = 0.53 Rij = 0.53* Tij = 0.69*


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Figure 5

0.70

0.60

Fraction of Compounds Toxic in Rats at < 5 M

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48


0.50

0.40

4/6

0.30

0.20

7/15 3/15

0.10

0.00 Compounds withwith HTCytotox IC20 < 10 Compounds withwith HTCytotox 10 £ IC20 Compounds with HTCytotox IC20 > 30 Compds with HTCytotox Compds HTCytotox Compds HTCytotox mM £ 30 mM mM

IC20 < 10 M

IC20 > 30 M

IC20  10 M &  30 M

1



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Figure 6 Chemical Diversity Therapeutic Area

Target Safety Assessment

Program Maturity

Previous Issues Target Novelty

Evaluation of Project Needs

Design of Safety Testing Plan Pharmacokinetics Execution of Safety Testing Plan

Functional Assays Pharmacology Models

Data Interpretation and Communication

Recommendation


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Drug Discovery Toxicology ‐The Road to Safe Drugs‐ Receptors

First‐in‐Human Channels Safety

Enzymes IND

GLP Regulations

Nuclear Receptors

Target Identification and Selection Lead optimization Series Selection


Characterization

Toxicology Strategies for Drug Discovery: Present ... - ACS Publications

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