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Safety Lead Optimization and Candidate Identification: Integrating New Technologies into Decision-Making Donna M Dambach, Dinah Misner, Mathew Brock, Aaron Fullerton, William Proctor, Jonathan Maher, Dong Lee, Kevin A. Ford, and Dolores Diaz Chem. Res. Toxicol., Just Accepted Manuscript • DOI: 10.1021/acs.chemrestox.5b00396 • Publication Date (Web): 01 Dec 2015 Downloaded from http://pubs.acs.org on December 2, 2015

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Chemical Research in Toxicology

Safety Lead Optimization and Candidate Identification: Integrating New Technologies into Decision-Making Donna M. Dambach*, Dinah Misner, Mathew Brock, Aaron Fullerton, William Proctor, Jonathan Maher, Dong Lee, Kevin Ford, Dolores Diaz

Department of Safety Assessment, Genentech, Inc., 1 DNA Way, South San Francisco, CA 94080

ACS Paragon Plus Environment

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Table of Contents Graphic


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ABSTRACT Discovery Toxicology focuses on the identification of the most promising drug candidates through the development and implementation of lead optimization strategies and hypothesis-driven investigation of issues that enable rational and informed decisionmaking. The major goals are to [a] identify and progress the drug candidate with the best overall drug safety profile for a therapeutic area, [b] remove the most toxic drugs from the portfolio prior to entry into humans to reduce clinical attrition due to toxicity, and [c] establish a well-characterized hazard and translational risk profile to enable clinical trial designs. This is accomplished through a framework that balances the multiple considerations to identify a drug candidate with the overall best drug characteristics, and provides a cogent understanding of mechanisms of toxicity. The framework components include establishing a target candidate profile for each program that defines the qualities of a successful candidate based on the intended therapeutic area, including the risk tolerance for liabilities; evaluating potential liabilities that may result from engaging the therapeutic target (pharmacology-mediated or on-target) and that are chemical structure-mediated (off-target); and characterizing identified liabilities. Lead optimization and investigation relies upon the integrated use of a variety of technologies and models (in silico, in vitro and in vivo) that have achieved a sufficient level of qualification or validation to provide confidence in their use. We describe the strategic applications of various nonclinical models (established and new) for a holistic and integrated risk assessment that is used for rational decision-making. While this article focuses on strategies for small molecules, the overall concepts, approaches and technologies are generally applicable to biotherapeutics.


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1. INTRODUCTION

The discipline of Discovery Toxicology focuses on the identification of the most promising drug candidates, which is accomplished through the development and implementation of lead optimization strategies and hypothesis-driven investigation of issues that enable rational and informed decision-making. In particular, the major objectives of discovery toxicology are to [a] identify and progress the drug candidate with the best overall drug safety profile for a therapeutic area [b] remove the severely toxic drugs from the portfolio prior to entry into humans and reduce clinical attrition due to toxicity, and [c] establish a well-characterized hazard and translational risk profile to enable clinical trial designs.

In the discovery phase, project teams must make agile and well-informed decisions to identify a drug candidate for development in an environment that commonly has relatively limited information about target biology (which is often novel), as well as limited resources and time constraints. From a safety perspective, the major decisions during this phase encompass the suitability of the chosen pharmacological target; whether a drug candidate can be advanced based on the identified safety concerns; and whether there is a viable path for identification of an improved back-up molecule if a candidate molecule cannot be advanced. Because the environment is one of limited information, resources and time, discovery safety scientists typically must drive decisions based on building a weight-of-evidence (WOE) regarding the risk profile of a molecule. This is accomplished through the integrated implementation of in silico, in vitro and in vivo models that have achieved a sufficient level of qualification or validation to provide confidence in their use. It requires discovery toxicologists to continually evaluate new technologies and improved uses of currently available technologies. This, in turn, necessitates a systematic assessment of the ability of a new technology to predict nonclinical or clinical outcomes that involves benchmarking of the new models with the existing “gold standard” models to define the limitations of these models and to understand how to use them in the most meaningful way to inform decision-making. It is important to note that when attempting to utilize newer technologies or models, the time-


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sensitive and agile nature of decision-making needed in the discovery period to address unanticipated issues arise often constrains the time and resources needed to fully validate a model. Instead confidence in new models is often generated through a focused and limited ‘fit-for-purpose’ method development and qualification in the spirit described by Lee, et al.1 and as such in most cases these models are also run as non-good laboratory practice (GLP).

Herein, we describe the strategic framework used at Genentech for safety lead optimization and candidate identification as an example to demonstrate the application of various nonclinical models and integration of new technologies to enable holistic and integrated risk assessments. This framework balances the multiple considerations for identifying a drug candidate with the overall best drug characteristics, and provides a cogent understanding of mechanisms of toxicity that can be used for rational decisionmaking. This article will focus on strategies for small molecules; however, the concepts, approaches and technologies are generally also applicable to biotherapeutics.


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2. CANDIDATE IDENTIFICATION: FINDING THE RIGHT BALANCE OF DRUG CHARACTERISTICS

For small molecule therapeutics, the discovery phase is the period of intense medicinal chemistry design activity to identify a drug candidate with the optimal characteristics related to pharmacology, pharmaceutics, pharmacokinetics, and safety. Front-loading safety assessment activities in parallel with assessment of those other properties enables chemists to incorporate the best overall features of a molecule during the period of chemical design, eliminates the worst molecules early, and permits timely investigation and characterization of the underlying mechanisms of toxicity for identified liabilities. At this early stage, target candidate criteria are established for each therapeutic target (a.k.a. ‘target candidate profile’ or TCP) to guide the “lead optimization” strategy for the particular characteristics desired of a drug candidate.

From a nonclinical safety perspective, the candidate optimization strategy is customized for each target in the context of risk tolerance for the intended therapeutic area, e.g. advance, life-threatening/unmet medical need versus non-life threatening indications. The framework to identify a candidate includes activities that evaluate possible pharmacology-mediated (“on-target”) activity and chemical structure-mediated (“offtarget”) activity that may result in undesired effects. Commonly, the paradigm used for candidate selection utilizes a multi-endpoint, often tiered, approach in which higherthroughput computational (in silico) and/or in vitro assays are used as first and/or second tier assessments of one or more chemical series or for compounds-of-interest to ‘flag’ potential issues, followed by confirmation of findings in additional, more physiologically-relevant in vitro or in vivo models, or in a standard regulatory assay. The multi-endpoint data provided by these models are integrated and used to build a weightof-evidence (WOE) to characterize liabilities and to define their translatable clinical relevance. These data also inform the clinical development plans with regard to monitoring and establishing exclusion/inclusion criteria. The components of the framework are illustrated in Figure 1.


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3. DISCOVERY TOXICOLOGY “MINDSET” AND DECISION-MAKING There are numerous drivers of toxicology that the discovery toxicologist needs to consider and balance to identify an optimal drug candidate. The discovery toxicologist must have a scientific appreciation of the all desired characteristics of the candidate drug and have a ‘mindset’ that is not rigid or risk adverse, but oriented toward creative problem-solving to help project teams identify realistic paths forward. To this end, the overarching philosophy that drives candidate identification at Genentech is based in strong scientific rationale and data-driven decisions, and an in-depth understanding of the therapeutic target and patient needs so that strategies and risk tolerance can be tailored appropriately. Our model is full immersion of discovery toxicologists on early project teams to influence chemical design in a manner that puts an emphasis on hypothesisdriven assessment of toxicity over general screening paradigms that employ rigid cut-off values for decision-making. In this model, the tiered counter-screen approach is used to identify potential liabilities that trigger follow-up investigational activity to confirm the significance of the liability. Decisions to rank or remove compounds based on counterscreen assays are generally applied on a program-by-program basis when the utility of an assay has been confirmed for a particular program and are not applied across all therapeutic programs as a “one-size-fits-all” paradigm.

The decision-making process for candidate selection at Genentech is guided by criteria established for each program through generation of a TCP. The TCP outlines the desired characteristics of the candidate drug for efficacy, target class selectivity (e.g. versus other kinases, isoforms), pharmacokinetics, pharmacodynamics, biomarkers, and safety. The TCP proposed by the team is rigorously vetted for scientific merit by a scientific review committee, and any future changes to the TCP that the project team proposes resulting from generation of new data must also be defended for scientific merit. TCP criteria established for safety are guided by the “major objective” of discovery toxicology activities described earlier, i.e. progression of the drug candidate with the best overall drug safety profile for a therapeutic area with a well-characterized hazard and translational risk profile to inform clinical trial designs. To that end, discovery toxicologists at Genentech provide a framework of “safety guidelines” that provides clear


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expectations for a candidate drug, but is not overly prescriptive with regard to individual safety criteria, which may not be fully known or characterized in the early stages of a program. For example, the TCP language used as the basic guideline for all programs is that the target candidate has a safety profile that is “manageable, monitorable, and reversible upon discontinuation of the drug”. More specific criteria are when there is evidence for their utility and impact on identification of a successful drug candidate. For example, a criterium that is articulated is a safety margin for in vitro hERG IC50 values and the projected unbound efficacious Cmax at ≥ 30-fold based on the work of Redfern, et al.2. Additionally, for non-life threatening therapeutic indications, the criteria that a molecule is not mutagenic or clastogenic are also articulated. Finally, project-specific criteria may be added when there is knowledge of its importance to the success of the candidate. Using the TCP as the desired outcome, typically decision-making is iterative based on generating data that informs the overall characteristics of a molecule. When toxicity issues are identified they are rigorously investigated to determine if there is a path forward; decisions to terminate molecules or projects based on toxicology findings are only made when there is a significantly strong WOE for the inability to safely advance a molecule or engage a target.

To implement this framework for safety candidate optimization approach, we organize our activities into three major focus areas: •

Assessment of Target Safety and Pharmacology-related (On-Target) Liabilities

•

Assessment of Chemical-based Off-Target Liabilities

•

Investigational Approaches to De-risk and Assess Translatable Risk to Humans.

With these focus areas in mind, we identify and apply the type of technology or model that is most appropriate to properly address an issue or question. For either screening or investigative models, this may include well-established, validated models or novel models with limited, fit-for-purpose qualification. For the latter, some level of internal qualification is required to demonstrate utility to enable decision-making for a particular issue, which includes confirmatory assessments in additional in vitro or in vivo models. Furthermore, the decision to incorporate a novel or new technology is also dependent upon the added value that a platform holds to improve our decision-making, which is


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often is related to enhancing translational relevance of our models or improving our ability to better understand biological complexity of intended targets and toxicity. The diversity and number of new platform technologies and models are immense and a comprehensive description is beyond the scope of this work. For the most part, these platforms have been developed to address basic biology and pharmacology questions and thus are applicable to toxicology assessments. To give a flavor of how some of these new technologies or approaches are being incorporated into drug discovery at Genentech, considerations, example approaches and applications of new technologies will be described in the context of the three major focus areas for candidate optimization and identification and decision-making. 4. TARGET SAFETY ASSESSMENTS: EVALUATION OF UNDESIRED PHARMACOLOGY AND THERAPEUTIC AREA CONSIDERATIONS The purpose of a Target Safety Assessment (TSA) is to gain insight into the potential liabilities that may result from engaging a certain target and to understand the risk tolerance for liabilities based on the intended therapeutic area.3,4 The information derived from this analysis is used by nonclinical safety scientists to [a] educate themselves and to provide project teams with the safety considerations that are used to formulate the lead optimization and overall safety strategy for the targeted therapeutic, [b] generate a TCP that defines the key safety characteristics of the desired drug-like molecule, and [c] formulate the safety lead optimization activities customized to the target, including investigation of theoretical risks. The TSA is performed early in drug discovery, typically at initiation of the project, when there are oftentimes no appropriate prototype or “tool” molecules or drugs for testing. An example of pertinent information included in a TSA is shown in Table 1.

The key component of the TSA is evaluation of the biology of the identified molecular target and the potentially adverse effects of engaging that target, either through inhibition or activation, as it relates to the intended therapeutic area. This assessment is completed through review of information in the public domain (literature, databases) and leveraging


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internal company expertise. The biological assessment focuses on gaining understanding of characteristics of distribution, cross-species homology and functional effects. Followup interrogation of theoretical or risks identified by the TSA assessment is based on hypothesis-driven investigational activities, which may determine the need for the application of a specific counter-screen for candidate identification.

4.1. Distribution of Target Expression, Cross-species Homology and Biological Pathway Analysis.

Characterizing the distribution of target

expression is performed at both the cellular and organ level, as it is quite rare for a target to be expressed in a single tissue or cell type. This allows for confirmation of the intended target cell population and highlights potential unintended cross-reactivity with other tissues that may result in undesired pharmacological effects. Determining the expression of the different target isoforms, especially those most likely to be hit as undesired targets that may result in toxicity, is also a part of this evaluation. Computational tools are the major resource used for this evaluation. Public and commercial informatics tools and databases are commonly used understand aspects of distribution of target expression in normal and diseased tissues (human and animal) and assessing cross-species target homology; examples include National Center for Biotechnology Information (NCBI) resources and commercial databases/tools such as BioExpress® (Ocimum Biosolutions), ToxExpress® (Ocimum Biosolutions), and ToxWiz (Cambridge Cell Networks).

Concurrent evaluation of target expression in specific human tissue and tissues from nonclinical species is informative since differences in expression can lead to improper assessment of human risk, both in terms of over-predicting (if the target is not expressed in human tissues) or under-predicting toxicities (if the target is expressed in human tissue but not nonclinical species tissues). Target expression is classically assessed using molecular biology technologies that detect expression at the mRNA and/or protein level. For mRNA expression, quantitative and semi-quantitative PCR (polymerase chain reaction), transcriptional profiling, and in situ hybridization are the standard technologies performed on whole, homogenized tissues, tissue arrays, histological tissue preparations,


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or cell culture preparations. While these approaches are not new, recent technological developments have added efficiencies to these assessments. One advancement is the detection of mRNA expression with improved sensitivity and quality in formalin-fixed, paraffin-embedded tissues, which has enabled retrospective gene expression analysis on previously banked nonclinical or clinical study samples and normal human tissues.5 Next generation sequencing (NGS) technology is another significant advancement that has reduced the time and cost of sequencing due to miniaturization of sequencing reactions.6,7 NGS facilitates cost effective, high-throughput generation of comprehensive transcriptome profiles using RNA-Seq such that analysis of global gene expression is unhindered by the requirement of gene-specific probes. This approach is particularly useful in instances where the biology of novel drug targets may still be poorly understood, the relative expression of splice variants or other isoforms is not well characterized, or broader insights into mechanisms of toxicity are required.6,7 NGS is an alternative to microarray transcriptional analysis and requires a similar commitment to bioinformatics infrastructure and resources for data storage, quality evaluation and analysis. Concordance differences have been identified and may pose some challenges when attempting to compare expression data across these profiling platforms.8,9 We have used NGS to perform cross-species target assessments by generating transcriptome libraries of tissues from nonclinical species for comparison to the target expression distribution in human tissues; these can be used to prospectively query the expression of a desired pharmacological target across our nonclinical libraries to evaluate pharmacological relevance of each species and any target expression differences that may inform future toxicology findings in vivo.

Gaining insight into the known canonical, non-canonical, and inter-related pathways of the intended therapeutic target may help identify potentially undesired consequences of altering target function that may inform the application of counter-screens, as well as identification of potential pharmacodynamic biomarkers. Molecular pathway analysis is performed by review of the literature and the use of established public, private and commercial databases and computational tools. Pathway and network analysis tools that map metabolic and signaling pathways rely on reference databases, e.g. Kyoto


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Encyclopedia of Genes and Genomes (KEGG), Reactome, GenMAPP, ToxExpress®, BioExpress®, and TozWiz (to name a few), that require appropriate annotation and curation.10-12 A perspective on importance of the development of biological ontologies and how they are supporting predictive toxicology initiatives was recently published by Hardy, et al.13

4.2. Phenotypic Data Analysis. A critical addition to the evaluation of expression and systems pathways is the functional assessment of outcome of altered target function, with special focus on alterations that would mimic pharmacological intervention.

While evaluating protein sequence similarity is helpful, in particular with

regard to the drug-binding site, a phenotypic approach that assesses function using direct biochemical or cell-based readouts is more relevant. For example, assessing target potency across nonclinical species and humans can be extremely valuable in the discovery space to help clarify whether target liabilities are differentiated across species; to understand whether observed toxicities in nonclinical species are on- or off-target; and to inform the therapeutic index for target-related toxicities. When considerable shifts exist between target potency in nonclinical species and humans, these shifts can be taken into account for species selection (to ensure that at least one nonclinical species provides target coverage) and in the interpretation of toxicity findings and therapeutic indices. Phenotypic assessments can be accomplished early in discovery in vitro by generating gene constructs in transient or stable cell lines, through genetic manipulations of cellbased models in the relevant species, and in vivo through the application of genetically modified rodent models.

A useful technique used to interrogate the effects of loss of function of a candidate gene that may be of toxicological significance is sequence-specific gene expression silencing using small interfering RNA (siRNA).14 The siRNA technique is commonly used in vitro and provides an effective and efficient method to investigate specific questions about a potential target early in the discovery phase when there are no drugs available. Gene silencing can also be used in vivo. We have used this technique in vitro to investigate


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hypotheses generated from the TSA, and the data generated have been used by early discovery teams to inform potential program risks.

In vivo phenotypic assessments utilize data from the dosing of prototype or ‘tool’ compounds to a specific therapeutic target (if available) in rodents, as well as data from spontaneously occurring human genetic variants and genetically engineered rodent models, i.e. transgenic or gene knockout (KO). For some targets, it has been shown that there is a strong correlation between KO phenotypes and human pharmacology, which emphasizes the applicability of these phenotypes for human.15 In our experience, genetically engineered rodent models have been useful for evaluating target safety and investigating mechanisms of toxicity. Several types of genetically modified mouse and rat models can be leveraged in safety de-risking, and it is important to understand their advantages and limitations in order to select the most suitable model. A significant percentage of gene mutations in conventional, germline KOs result in embryonic lethality; however, such a finding does not necessarily imply that the target is not tractable in an adult population.16 Phenotypes derived from conventional gene deletion can still provide relevant information about target safety with regard to target-organ toxicity or potential reproductive and developmental effects16-18, but strategies that allow deletion of the gene in the adult organism are oftentimes very helpful to gain an understanding in the intended target population. Furthermore, germline KOs can underpredict toxicity if there are compensatory mechanisms that are present during development.19-21 To assess the potential effect of target inhibition in an adult population, the more relevant model may be an inducible KO model where gene deletion (for inhibitors) is triggered postpartum and the deletion is restricted to the binding domain of the target, thus still allowing expression of the inactive (for inhibitors) target protein. For inducible KOs, the gene of interest is inactivated at a given time point by use of a tamoxifen- or tetracyclinedriven Cre-lox system.22,23 Among these, active site-targeted models such as kinase-dead or ligand-binding-domain dead knock-in models, where effects on a protein’s contribution to multi-protein complexes are minimized, are the most relevant in terms of mimicking pharmacological inhibition.24, 25 When using these models it is important to


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design experiments that include control groups to differentiate effects of non-specific editing of Cre from the effects of knocking out the gene. Nuclease-based systems, e.g. the CRISPR/Cas9 system, represent a relatively recent technological development and a significant improvement over traditional embryonic stem cell (ESC) gene targeting. Nucleases create specific double-stranded breaks at the target locus, which trigger DNA repair mechanisms that facilitate constitutive KOs or knock-ins.26, 27 The main advantages of nuclease systems are speed and the potential for efficient multifunctional gene targeting, which can be particularly time-consuming to generate with traditional methods that require multiple crossings. Concerns about specificity and sequencedirected off-target cleavage have been raised28, 29 and several recent variations of the technology such as the use of nickases, short sgRNAs and Cas9 fused to Fokl, are promising in addressing this concern.30-32 Inducible KOs can bypass any developmental phenotypic influence (e.g. embryonic lethality), as well as preserve the scaffolding functions of the protein and as such, more closely resemble pharmacological engagement in an adult organism. Finally, since rats are the most commonly used rodent toxicology species, generating and characterizing the phenotypes of rat KOs can be extremely helpful in de-risking programs and in interpreting toxicity findings of compounds, and these models are increasingly being used in the pharmaceutical industry.33 The new, nuclease-based technologies have enabled the use of rats through improved efficiencies in generating these transgenics; an example of this approach includes studies with Braf KOs.34-36

Characterization of heterozygous animals can also provide useful and relevant information related to the incomplete loss of gene function. For example Bcl-xL heterozygous mice showed thrombocytopenia, which reflected the preclinical and clinical findings caused by dosing with Bcl-xL inhibitors.37-40 Tissue-directed KOs, which lie down-stream of a tissue-specific promotor, can bypass embryonic lethality and enable the investigation of gene function in particular tissues of interest. A good example of this is the array of different insulin receptor KOs.41-46


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Given the utility of rodent genetic models in assessing the safety of the target (especially novel targets) and the time line for model generation and characterization (typically 9-12 months), we trigger the generation of these models early in the life cycle of a project, typically in the early research space or hit-to-lead phase. Even when the findings might not be decisional for program fate, in our experience the information can be very useful in flagging safety watch-outs that might need further characterization in safety studies. At Genentech, we typically recommend generation of KO models for targets for which the biology is largely unknown, and in particular for targets in therapeutic indications with high safety bars (i.e. non-life threatening). The models that we find most useful are inducible, binding-site-dead KO mice (i.e. kinase dead). We also recommend the generation of KO rats in projects with novel therapeutic targets intended for non-life threatening indications as these have proven particularly useful in addressing mechanism of toxicity (on- or off-target) for potential findings in rats toxicology studies. In addition to short-term phenotyping, and particularly for therapeutic indications that require chronic (> 3 months) dosing, aging the mice and conducting long-term longitudinal phenotypic assessments can be useful in understanding potential long-term effects for the compound of interest.

KO models can be extremely helpful in the investigation of mechanisms of toxicity for findings that arise in animal studies or even in humans. When a particular toxicity is consistent with the phenotype of the KO, it is a strong sign that the toxicity is on-target, as illustrated by the lung toxicity caused by LRRK2 small molecule inhibitors in cynomolgous monkeys, which was morphologically similar to that seen in LRRK2 KO mice.46 Likewise, when a toxicity is not present in the absence of the target, this is strong evidence that the toxicity is target-related, as illustrated by the development of thyroid tumors and increased plasma calcitonin after dosing with GLP-R1 agonists, which were not observed when dosing GLP-1R KO mice under similar conditions.47 Alternatively, when a particular toxicity persists in the absence of the target, i.e. when dosing a compound to KO animals, this is a strong indication that the toxicity is off-target. Examples of this are genotoxicity caused by PLK2 inhibitors in rats,48 or retinal toxicity caused by BACE-2 small molecule inhibitors in rats.49


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In summary, the assessment of pharmacology-mediated, on-target toxicity is typically performed early in discovery before there is chemical matter. The initial analysis relies heavily on computational tools and databases and technologies to assess target expression. Confirmatory phenotypic assessments rely upon in vitro and in vivo models that often require genetic manipulation or expression of the target. The data gathered from these assessments are used to develop a customized candidate optimization strategy that may include investigation of theoretical liabilities and use of specialized counterscreening assays.

5. CHEMICAL STRUCTURE-RELATED OFF-TARGET LIABILITY ASSESSMENTS

The assessment of chemical structure-mediated (“off-target”) toxicities is a critical component of safety lead optimization strategies for small molecules as off-target effects are a significant cause of toxicity and attrition. Safety optimization activities are performed in parallel with other lead optimization activities for efficacy, pharmacokinetics, and pharmaceutics. This integrated approach is ideal because it allows for the incorporation of desired safety attributes early on in the chemical design, and it contributes to building the optimal overall characteristics of a candidate molecule. The tools and models used for safety lead optimization are identical to those used for the optimization of other molecule characteristics, namely, computational (in silico), in vitro, ex vivo and in vivo models, thus discovery project teams are well versed with the types of data generated by these types of approaches. Nonetheless, for optimal impact and to set expectations, the context regarding how these models will be used for decision-making around safety needs to be communicated to project teams, including clear articulation of the limitations of each model system. These efforts require thoughtful supervision, and cannot be a ‘check-box’ approach. Safety lead optimization strategies commonly employ what Kramer and colleagues50 described as “prospective” and “retrospective” approaches, components of which are described, as they are applied at Genentech, in Table 2. “Prospective” approaches are


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meant to ‘flag’ or “predict” a potential liability of a known or established mechanism of toxicity, e.g. genetic toxicity or engagement of endogenous ligands of clinical relevance, for which there is a level of confidence in the translational relevance. “Retrospective” approaches are traditionally applied to characterize a liability that has been identified, usually the result of a finding in nonclinical animal studies or in human clinical trials, or a theoretical risk identified by the TSA; this approach is based in hypothesis-driven investigative work.

For small molecules, the majority of prospective in silico and in vitro screening models are used to identify potential chemical structure-mediated, off-target effects, so that chemists can design away from unwanted features and choose the candidate with the overall best profile. There are various strategic approaches that different companies employ regarding prospective screening. One strategy is to invest in extensive high throughput platforms that can accommodate screening of large numbers of compounds across multiple endpoints to generate a compound profile of the major “druggable” features across physiochemical, pharmaceutics, pharmacokinetic and safety; project teams can make decisions around which risk factors are the most important regarding the advancement of a compound or chemical scaffold. An alternative approach is a more focused screening that may be influenced by resource constraints or other strategic considerations.51,52 Nevertheless, the key components of screening strategies encompass five core focus areas that are based on drivers of attrition that are common and well established, i.e. “known drivers of toxicity” (Table 2): [i] selectivity/promiscuity, [ii] secondary/safety pharmacology, [iii] intrinsic cytotoxicity, [iv] ADME-based drivers of toxicity, [v] genetic toxicity (genotoxicity)

At Genentech, we use the five focus areas as the basis of our off-target assessments with the desired outcome to identify highly selective molecules with minimal translatable in vivo effects, minimal intrinsic cytotoxicity, and acceptable ADME characteristics that


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minimize compound reactivity and accumulation. For non-life threatening indications, we avoid advancing candidate molecules that are mutagenic or clastogenic, but will consider advancement of aneugens depending upon the projected clinical safety margins. In general we rely heavily on well-established in vitro models as a first tier, but leverage computational assessments that add value when they are available (internally, publically, or commercially). When developing an off-target screening strategy, the temporal positioning of any one of the 5 focus area components is dependent upon the needs of a specific project team. Typically implementation of the screening strategy runs in parallel with investigational efforts driven by either the TSA or identification of in vivo liabilities from efficacy or pharmacokinetic studies or clinical trials with competitor compounds. Most often for a new program, our screening cascade begins with assessing chemical matter for selectivity and promiscuity using in vitro secondary pharmacology models to determine what potential acute liabilities that may manifest in vivo. We will ‘spot-check’ chemical series or compounds-of-interest for intrinsic cytotoxicity (in vitro) and genetic toxicity (in silico or vitro) and pay attention to the ADME characteristics that may be of toxicological significance. Further confirmation, characterization, or mechanistic evaluations are triggered by data generated by these screening activities. The models and technologies for such follow-up will depend upon the specific finding.

5.1. Selectivity, Promiscuity and Secondary Pharmacology Assessments. We put significant emphasis on selectivity and secondary pharmacology screening, which encompasses safety pharmacology screening, for chemical scaffolds and have established criteria that are used to guide follow-up activities and rank compounds as needed, which will be described in this section. This assessment lends itself to the classical tiered approach that uses first tier higher throughput in silico or in vitro binding assays, which may be followed by confirmatory or investigative in vitro or in vivo assessments. The in vitro approaches used for this assessment are well established and screening panels are commercially available. Although the general types of in silico and in vitro assays utilized have not significantly changed in recent years, we recognize that individual institutions may have developed advanced tools for internal decision-making. The most recent ‘advancement’ in this area has been the recognition by both chemists and


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toxicologists of the physiochemical parameters that can affect promiscuity and selectivity, which has aided in the identification and design of selective drug candidates.

The purpose of minimizing promiscuity and enhancing selectivity is to reduce the potential for off-target, secondary pharmacology effects. Highly promiscuous drugs have a higher failure rate when compared with successfully marketed drugs.53-55 Both promiscuity and selectivity should be considered independently, as there is an important distinction between these traits. Promiscuity is a measure of the propensity of a molecule to bind other targets, whereas while selectivity takes promiscuity into account, it is often used to indicate a biochemical safety margin with regard to an identified off-targets, i.e. a molecule is indicated to be X-fold selective between the intended target ligand and an undesired target ligand. These are important distinctions because a molecule may be considered highly selective over an intended therapeutic range, but during toxicology studies, the higher exposures evaluated may reach the range of off-target engagement and result in unintended effects; an understanding of these potential effects is important for interpretation of findings in toxicology studies.

Promiscuity and selectivity are assessed through an integrated evaluation of the physicochemical structures of a molecule, known structural-activity relationship alerts (usually via computational assessments), and measures engagement of secondary pharmacology targets of clinical or toxicological significance (e.g. receptors, ion channels, enzymes) that include in vitro ligand binding and cell-based function assays, and in vivo studies to establish translational exposure-effect relationships. The physicochemical parameters of high lipophilicity (cLogP > 3), ionization state (pKa >6) and molecular size (>500 Daltons) have been correlated with increased promiscuity and in vivo toxicity.56, 58 Compounds, like basic amines, with high pKa values, e.g., >6, are highly ionized at physiological pH and tend to interact with membrane phospholipids and can become trapped in acidic organelle compartments, i.e., mitochondrial intermembrane space and lysosomes, where they can cause dysfunction.58-61 Our approach is to have an awareness of the physicochemical ‘watch-outs’ that have been associated with promiscuity to help chemists and toxicologists trouble-shoot issues, as well as to design


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molecules to achieve a balance across all drug-like parameters. However, we do not have strict criteria to “not develop” a compound with less-than-desirable physiochemical characteristics, as these parameters alone do not predict toxicity. Likewise, our chemists have knowledge of the major toxicophores that have been historically associated with toxicity and are mindful of these when designing compounds.

We rely on in vitro secondary pharmacology screening panels as our typical first tier to evaluate promiscuity and selectivity. We will customize the ligand panels for a specific molecular target indication (e.g. kinase targets, proteases) or to evaluate specific organ effects, e.g. CNS targets for potential sucidality and abuse liability.61 While ligandbinding and phenotypic screens are commercially available, specific target screens are also commonly developed within companies based on their internal experience or therapeutic target space. A general assessment of compound promiscuity is based on the overall binding (“hit”) rate across the assay panel; insight into potential issues related to specific endogenous ligands that are bound is also evaluated using these screens.55 Azzaoui, et al.54 defined a compound’s “target hit rate” (THR) as the ratio of the number of targets bound (at >50% inhibition at 10 µM) to the total number of targets tested: “highly promiscuous” compounds have a THR of ≥20% and “selective” compounds have a THR of ≤5%. We use a baseline panel of ~40 diverse pharmacological targets known to be associated with significant clinical adverse effects to screen for both promiscuity and specific target engagement effects. The criteria we use to inform potential promiscuity risk across chemical series are similar to that described by Azzaoui, et al.54 Teams may decide to move forward with a compound that is defined as ‘promiscuous’ for other strategic reasons, in which case the discovery toxicologist would perform follow-up analyses on the specific targets identified in the binding assays. These analyses would include determining the pharmacological effects of engaging the offtarget and phenotypic assessment in cell-based, ex vivo tissue-based or in vivo studies to determine the type of activity, i.e. agonist versus antagonist, and exposure relationships.

The overall approach commonly used at Genentech with regard to promiscuity and selectivity assessments and decision-making for small molecules is a follows: chemists


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work with safety scientists to understand the physicochemical ‘watch-outs’ as they are designing their molecules to identify the balance across all drug-like parameters. Representative molecules of various chemical structural series are ‘screened’ using in vitro ligand-binding assays to assess ratio of overall ligand interactions (promiscuity assessment) and to evaluate the specific ligands with regard to their potential functional impact. Ligand-binding screens cannot inform phenotypic activity (agonistic versus antagonistic) and binding does not always translate into functional effects. So for molecules of interest, translational risk assessment is enabled with follow-up functional evaluation of ligands identified in the screen using cell-based assays, ex vivo organ assays (e.g. aortic ring contractility or hanging heart models), or in vivo studies if species translatability or relevance of the target is known. Furthermore, these follow-up assessments are used to determine projected safety margins related to in vitro biochemical potency or in vivo efficacy. As an example, we routinely utilize high throughput, 2-point patch clamp assessments for hERG, Nav1.5 and Cav1.2 cardiac ion channel inhibitory activity as a first tier screen and will follow-up in either an in vitro induced pluripotent stem cell-derived cardiomyocyte platform using multi-electrode arrays to identify integrated effects on action potentials or a hanging heart model as a functional assessment; telemetered models may be used to more fully assess cardiac and vascular effect and determine exposure-effect relationships. For situations in which offtarget engagement results in important, translatable functional effects, the in vitro binding assay for that target can then be incorporated earlier into the tiered screening paradigm to select molecules that lack the identified liability.62 We used this approach to optimize away from off-target opioid engagement and to build a pharmacophore model to elucidate structural drivers of engagement.63

The use of computational models, including pharmacophore models, varies by institution. In our experience, computational models are often customized for particular project needs; however, models that explore larger chemical space have been described. For example, commercial databases containing ligand–drug interaction information and computational models, e.g., BioPrint (Cerep, S.A.) have been developed based on structural similarity to predict off-target binding of drugs, and although potentially biased


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toward a particular chemical space (i.e., G-protein coupled receptor (GPCR) targets), they may be useful for informing chemical design.53 A recently described computational approach that expands the biological target space for potential off-target ligands beyond conventional off-target assessments may also prove useful in identifying unexpected offtargets, but confirmation of the potential off-targets requires mechanistic confirmation.64 A more project-based approach is to use secondary pharmacology binding data as part of multi-parameter optimization modeling and to identify structure-activity relationships using software, such as Stardrop™(Optibrium). Finally, transcriptional profiling has been used to assess potential off-target effects through a “class prediction” approach in which bioinformatic tools are used to profile a compound for potential toxicological effects based on the similarities between its gene expression profile and the profiles of a given toxicological target class; however, the use of this approach requires investment in or access to a comparative database. This approach can also be used to generate hypotheses when investigating mechanisms of toxicity.9, 65, 66

Our lead optimization strategy for selectivity/promiscuity assessments successfully identifies highly selective compounds for advancement, which has been confirmed based on the adverse event profiles for drugs currently in clinical development. Furthermore, any identified off-target effects for compounds that are advanced are well characterized phenotypically including exposure-effect relationships and identification of safety biomarkers.

5.2. Intrinsic Cytotoxicity Assessment. We assess intrinsic cytotoxicity on chemical scaffolds and compounds-of-interest as a potential indicator of in vivo toxicity and also as part of a multi-factoral hepatotoxicity risk assessment. Determining whether a compound is intrinsically cytotoxic is a useful parameter for assessing risk of toxicity in vivo.67-70 General cell viability, using in vitro models, can be used as a first tier assessment for comparing chemical scaffolds or candidate compounds. These in vitro models are also commonly used in mechanistic studies. The cell type chosen for these first tier assessments is dependent on both scientific rationale and practical logistics. Use of primary cells (e.g. cells derived directly from normal organs) provides information


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thought to most closely resemble “normal” in vivo cell functional states, e.g. metabolism. The application of cryopreserved cells has made the use of these primary cells amenable to high through put screening.71 Immortalized cells, typically derived from abnormal tissues (e.g. cancerous), are more readily available for screening applications than primary cells as they are able to readily proliferate; however, these cells may not represent a “normal adult” cell since they often lack or have altered cell functions, such as metabolic capabilities or over- /under-expression of receptors. Additionally, since immortalized cells tend to be highly proliferative, it is important to differentiated cytostatic from cytotoxic effects when using these models. Despite their limitations, it is common for immortalized cell lines to be used for the first tier, higher-throughput assessment of potential cytotoxicity.72-74 Our discovery teams may choose to assess cytotoxicity as part of a first tier, high throughput screening in HepG2 cells when identifying early chemical matter; however, our discovery toxicologists consider this first tier assessment as informative and not decisional. That is, a finding of cytotoxicity may trigger follow-up investigations, usually initiated in primary human hepatocytes, to determine the driver of toxicity using specific mechanistic endpoints models, e.g. mitochondrial effects, in a cell-based model75, 76 versus a decision not to consider the compound. In addition, for compounds or chemical series of interest, teams will separately assess the potential for mitochondrial toxicity that may not be apparent in primary human hepatocytes due to culture conditions.76

Mono-cell cultures remain a standard model for cytotoxicity assessments. However, mixed cell culture and microphysiology systems that employ either primary or induced pluripotent/embryonic stem cells and biomechanical forces are currently being evaluated to determine if the increased complexity due to enhanced cell interactions, application of endogenously relevant forces, and enhanced stability will result in better translation versus traditional monoculture in vitro models for predicting organ-based toxicity.77-80 To date no extensive utility assessments, qualifications or validations of the use of these microphysiology platforms for decision-making in the pharmaceutical industry that have been described in the public domain. Nonetheless, there are concerted efforts to drive the qualification and validation of mixed culture, 3-dimentional (3D), and “tissue chips” for


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drug development through cross-pharmaceutical collaborative efforts and governmentdirected efforts such as recent Defense Advanced Research Projects Agency (DARPA) grants81 and the National Center for the Advancement of Translational Science (NCATS)-led Tissue Chips for Drug Screening Program (http://www.ncats.nih.gov/tissuechip) that has recently begun collaboration with the International Consortium for Innovation and Quality in Pharmaceutical Development.

An important historical limitation of in vitro cytotoxicity assays has been the extrapolation of in vitro outcomes, e.g., IC50 (median inhibitor concentration) values, to in vivo exposure–effect relationships that can inform either animal studies or clinical trials. Currently there are no standardized methods to accomplish this in vitro–in vivo extrapolation. Typically, the output of these in vitro models is binary, i.e., toxic or not toxic based on a pre-defined IC50 cut-off, and is most commonly used for ranking compounds or compound series. As an example, with regard to our initial cytotoxicity screening (in either HepG2 or primary human hepatocytes), we use a binning system to guide teams on the relative cytotoxicity of a compound based on historical experience: compounds with an IC50 ≤ 10 µM are considered to have high cytotoxicity; compounds with in an IC50 ≥ 10 but ≤ 50 µM are considered moderately cytotoxic; and compounds with an IC50 ≥ 100 µM not cytotoxic. This binning allows teams to evaluate compounds based on in vitro or in vivo efficacy and make decisions regarding follow-up safety assessments, but is otherwise limited in its translational value. For example, we have used the in vitro cytotoxicity binning assessment to build a computational model, which implicated physiochemical drivers of cytotoxicity and enabled several project teams to design out those drivers and identify non-cytotoxic compounds.82

In addition, we have

related the in vitro cytotoxicity binning assessment to in vivo toxicity outcomes in nonclinical species in order to extrapolate the potential outcome in humans and used this model to both screen for non-cytotoxic compounds and to investigate the underlying driver of toxicity.58 Recently, potentially useful approaches have been proposed to build in vitro–in vivo exposure–effect extrapolations that may be helpful in predicting in vivo risk.67-71 For example, an exposure relationship comparing the ratio of a projected in vivo


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concentration, to an in vitro LC50 (median lethal concentration) could be used to predict a high risk potential (i.e., maximum serum concentration (Cmax)/LC50 > 1) for an adverse outcome versus a low risk potential (i.e., Cmax/LC50 < 0.01).68 Using an alternative method, drugs with an LC50 ≤ 50 µM were shown to have five-fold increased probability of demonstrating toxicity in vivo if a Cmax of 10 µM (total drug) was achieved in vivo when compared with a drug with an LC50 > 50 µM.69 By applying these approaches, the LC50 can be used to predict at what Cavg0-24 (average serum concentration, 0-24 hours) or Cmax toxicity may be observed compared with projected efficacious Cmax values, i.e., projected safety margin. These exposure-effect extrapolations will be more useful to make compound prioritization decisions, to set dose ranges in toxicology studies, and also to provide insights into translation compared to decisions made by binary or binning methods.

5.3. ADME-based Toxicity Considerations. While technologies to assess drug metabolism and pharmacokinetic (DMPK) parameters are not considered novel, what may be a ‘new’ approach is a more focused evaluation of the distribution and metabolism of drug candidates by toxicologists as potential risk factors for toxicity. Specifically, the distribution of a molecule at the cellular, organ and systemic levels needs to be considered with regard to the other toxicity risk factors described above (i.e. potential for secondary pharmacology interactions, level of intrinsic cytotoxicity and therapeutic target expression) and how together these may impact the development of toxicity. Physicochemical characteristics of a molecule, like lipophilicity, basicity and ionization state, are important determinants of distribution and potential accumulation. These characteristics are fixed and unique to each molecule and are key components for consideration in chemical design. Importantly, physicochemical characteristics can impact cellular and subcellular distribution and accumulation, e.g. high pKa and accumulation in cell membranes and acidic compartments versus low pKa and association for being a transporter substrate and systemic distribution, e.g. characteristics facilitating entry into the CNS via the blood brain barrier.83 Finally, integrated examination of parameters such a volume of distribution (Vd) and clearance (Cl) can identify risk for accumulation of drugs, which when put in context with other risk factors


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for toxicity (such as intrinsic cytotoxicity) may aid in the establishment of toxicity thresholds. For example, Poulin, et al.60 described an algorithm for identifying compounds with high Vd and low Cl that may have an increased potential for toxicity. Sutherland, et al.70 identified Vss (steady state) and intrinsic cytotoxicity in rat hepatocytes as key indicators of potential in vivo toxicity; in particular, the combination of high Vss and low IC50 for intrinsic cytotoxicity resulted in a 5-fold lower lowest observable adverse effect level (LOAEL). Chemical structure also determines engagement of various cellular transporters that may affect cellular concentrations and elimination of a drug, which may contribute to toxicity. In addition to affecting elimination of endogenous metabolic by-products (i.e. bile salts), inhibition of xenobiotic efflux transporters may result in accumulation of a drug within cells, resulting in toxicity for an intrinsically cytotoxic drug that would not have been predicted based on plasma drug concentrations. 84-88

There are several key metabolism considerations that discovery toxicologists need to keep in mind. These parameters include intrinsic clearance, metabolic phenotyping, metabolite prediction and reactive metabolite formation/covalent binding. In vitro intrinsic clearance estimated by primary hepatocytes or liver microsomes can indicate potential species differences in the rate of drug clearance and metabolic profiles that may be important in investigating the translatable relevance of toxicity findings in animals to humans. Furthermore, disconnects between intrinsic clearance estimated from liver microsomes versus hepatocytes or in vitro–in vivo clearance disconnects in nonclinical species may indicate other, non-CYP450 enzyme involvement in drug metabolism. Determining which non-CYP450 enzymes are involved in clearance may result in the identification of metabolizing enzymes with known polymorphisms or with species differences (e.g. epoxide hydrolase, aldehyde oxidase, N-acetyltransferase, or flavincontaining monooxygenase; FMO) that could impact outcomes of toxicity studies or clinical trials.89-91 Compound structure, metabolic profile, and metabolic pathway predictions may provide clues for assessing the potential impact of altering a major metabolic pathway, which may result in accumulation of a metabolite or shunting to another metabolic pathway that


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may be predicted to be toxic, and for potential formation of reactive substructures (toxicophores). Metabolic phenotyping to determine which CYP450 enzymes are important for the metabolism of a drug and whether a particular CYP450 may be responsible for the major metabolic route, will help determine the risk impact of inhibiting or inducing that particular enzyme, or whether there is a polymorphism that should be considered as a possible risk factor for toxicity. Inhibition of phase II enzymes should also be considered in this assessment. For example, inhibitors of uridine diphosphate-glucuronyl transferase (UGT) can potentially cause hyperbilirubinemia, as this is the conjugating enzyme for the endogenous processing of bilirubin.92, 93 Finally, the formation of reactive metabolites and potential for covalent binding to endogenous macromolecules is well recognized as an important risk factor for toxicity, especially drug induced liver injury (DILI).94-97 In addition to metabolic considerations above, a recently described computational assessment of the physiochemical and molecular properties of electrostatic potential and heat of formation/solvation has shown utility in predicting bioactivation and could be used as a first tier assessment of potential parent or metabolite reactivity; this also has direct application as a screening tool for DNA adduct formation and mutagenesis for genetic toxicity assessments.98 Taken together, these examples highlight the importance of re-focusing on integrating potential toxicology risk factors and DMPK properties in the toxicology risk evaluation of a compound.

The impact of transporters and reactive metabolites in toxicity will be covered in more detail in accompanying articles in this special edition.

5.4. Genetic Toxicity Assessments. For non-life threatening therapeutic indications, genetic toxicity assessments are a component of the lead optimization strategy, with the focus of removing any mutagenic and clastogenic molecules/chemical series or de-risking genotoxicity findings. For advanced, life-threatening therapeutic indications, although genotoxicity assessments may not be conducted, limited genotoxicity testing can still be performed in the discovery space to identify potential risk related to occupational exposures. With regard to lead optimization activities to assess genetic toxicity risk, the most commonly applied assays are those meant to predict the


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outcome of the regulatory assays for mutagenicity, clastogenicity and aneugenicity.99 This approach has been highly successful in reducing or eliminating the advancement of drugs with genotoxic risk, particularly with regard to drugs intended for non-life threatening therapeutic indications.99 As such, inclusion of this screening during the lead optimization phase is widely applied across the pharmaceutical industry.51, 52 The genetic toxicity safety lead optimization strategy is an example of a well-established tiered approach that is principally made up of both computational models and in vitro assays for mutagenicity, aneugenicity and clastogenicity that have minimal compound requirements and allow for relatively high throughput screening. There are several well-established computational models for genetic toxicity assessments. Computational models offer rapid delivery of results that can save time and resources by screening-out mutagenic compounds early in development, and enable testing of compounds of low solubility and antibiotics to be assessed for mutagenicity potential. Well-established computational models include QSAR (quantitiative structure activity relationship) and expert rules based models to ‘flag’ potential toxicophores or physicocohemical characteristics that are associated with DNA interaction.101 One of the newest developments in the prediction of mutagenicity is the use of flexible DNA-docking models.102, 103 These models have the advantage of offering medicinal chemists a three-dimensional visualization of the DNAdrug interaction, which may be useful for structure-activity based approaches to reduce mutagenicity potential.

Another recently described computational approach has been to

use the molecular properties of electrostatic potential and heat of formation/solvation to predict DNA adduct formation and mutagenesis for genetic toxicity assessments.98 Both of these newer models add complementary computational tools for chemists in the design of compounds.

In vitro screening models are also well entrenched in the early screening cascade to assess mutagenicity and potential aneugenicity or clastogenicity51, 52, 104-107 and a positive result in a screening assay that is not related to a known mechanism of action of the drug often leads to investigation of the cause, e.g. parent or metabolite, impurity, or technical cause.108, 109 Follow-up confirmatory assessments of genotoxic mechanism of action (i.e. aneugenic vs clastogenic) to determine risk are performed often using in vitro


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investigational (e.g. centromere labeling) or standard regulatory assays and/or in vivo mammalian assessments.100 This is an important assessment, because a compound that is aneugenic may potentially advance if there is a sufficient projected safety margin, while a clastogenic compound will likely be terminated. With regard to in vivo mutagenicity assessment, the Pig-a gene mutation assay is a more recent addition to genetic mutagenicity testing that appears to offer the advantages of accumulation of Pig-a mutatnt erythrocytes with repeat dosing and mutant erythrocytes that are not actively removed from the bloodstream of rodents as is the case for micronucleated erythrocytes. The basis of this assay is drug-induced mutation in the endogenous X-linked Pig-a gene, which results in the absence of antibody binding to cell surface glycosylphosphatidylinositol (GPI)-anchored proteins that can be detected by flowcytometry in peripheral blood.110, 111

6. TARGET ORGAN TOXICITY ASSESSMENTS The identification and characterization of drug-induced target organ toxicity is a key deliverable for nonclinical toxicology. Thus, both in vitro and in vivo models are commonly used to assess the potential toxicity in specific organs, particularly those associated with the most common causes of toxicity-related attrition: cardiovascular (electrophysiology and hemodynamic), hepatic and hematopoietic.112, 113 Models for other organ systems are warranted depending on the historical causes of attrition within a company or specific issues identified in a project; however, the availability of specific target organ models is variable and often limited to monoculture systems made up of cells from a specific organ. Likewise, the availability and utility of computational models to predict specific organ toxicities is also highly variable depending upon the breadth of understanding of mechanisms of toxicity, the quality and domain range of the data available to build the models and the scope of the models, i.e. mechanism-based or global organ toxicity. Finally the same target organ models may be used for candidate screening and to investigate mechanisms of toxicity identified either in nonclinical animal studies or during clinical trials as well.


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There have been recent technological advancements, as well as newer strategic approaches, related to the evaluation of cardiovascular and hepatic target organ toxicities that will be described here as examples of integrating new technologies to inform decision-making.

6.1. Cardiovascular Toxicity Models. The prediction and modeling of drug effects on cardiovascular electrophysiology and function represent mature examples of translationally impactful, integrated lead optimization approaches. For electrophysiology assessments, while various high through-put binding assays for the hERG channel are still routinely used, the lower cost and validation of high throughput, cell-based patch clamp assays for blockade of individual types of ion channel proteins (e.g. hERG, hNav1.5, hCav1.2) has enabled placement of these phenotypic assays as early tier assessments.114, 115 Furthermore, long-established, low-throughput ex vivo techniques using Langendorff-perfused rodent heart116 and preparations of isolated rodent cardiac tissue (including atrium, ventricle, and the Purkinje fiber)115 are still used as follow-up, integrated functional assessments; however, more recently, the development of human cardiomyocytes derived from stem cells provides a comprehensive functional human cardiac phenotype amenable to modern cell culture techniques and multi-well assays.117 In monolayer culture, these cells display highly regular, spontaneous action potentials with waveform features paralleling those of native tissues, as well as robust spontaneous contractility.118-120 Effects of drug candidates can be thus be monitored in platforms that measure either changes in electrical properties such as field potential using multielectrode arrays,121, 122 intracellular voltage,123, 124 and intracellular calcium,125, 126 or though analysis of contractility by video127-129 or electrical impedance across the cell monolayer.130 However, an important consideration with regard to stem cell-derived platforms is that these cells do not have an adult phenotype and they also demonstrate a mixture of atrial, ventricular and nodal action potentials, which may limit their predictivity of some drugs. Nonetheless, initial validation experiments with human stem cell-derived (SC-derived) cardiomyocytes have yielded encouraging concordance with nonclinical and clinical results for compounds know to be arrhythmogenic,121, 122, 131, 132 and have thereby spurred a consortium of scientists across industry, academia and


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government to propose inclusion of these cells in a new in vitro paradigm for assessing liability for clinical arrhythmia -- the Comprehensive In vitro Proarrhythmia Assay (CiPA).133, 134 Assays for drug effects on action or field potential in SC-derived cardiomyocytes would be coupled with patch clamp studies for multiple cardiac ion channels, and modeling of effects of ion channel blockade in silico. While this effort may ultimately trigger modification of in vivo and clinical safety regulations, the comprehensive mechanistic information provided, and experimental throughput, make the overall approach highly applicable to safety-related lead optimization during the drug discovery phase. With regard to computational models of cardiac electrophysiology, functional models of cardiac action potential that predict an integrated electrophysiological response have been developed to assess drug effects on QT prolongation when populated with in vitro ion channel concentration response data. Such action potential model can then be used to translate in vitro ion channel data into predictions of electrophysiological effects to better select compounds to advance in vivo assessments.133, 135, 136

In addition to the promise of human SC-derived cardiomyocytes for prediction of “acute” arrhythmia, the stability of beat properties over days to weeks in culture, when combined with label-free platforms such as multi-electrode arrays or impedance-based instruments, allows functional tracking of slowly-evolving “structural” cardiotoxicity, such as that sometimes observed with cancer drugs (e.g. kinase inhibitors).137-139 For further mechanistic insight, other toxicity endpoints related to mitochondrial stress, lipid metabolism, viability, gene expression changes, cellular biomarker release, and cell morphology (e.g. by high content imaging) can be measured in parallel assays,138, 140-143 or as direct follow-up measurements to label-free functional recording.139 We have revised our lead optimization strategy to minimize cardiovascular toxicity based on two of these technological advancements. Specifically, we use high throughput 2-point patch clamp assessments of the ion channel proteins, hERG, hNav1.5, hCav1.2, as the early indicator of possible blockage and are qualifying the use of stem cell-derived human and rodent cardiomyocytes with various phenotypic endpoints including MEA, beat rate and calcium exchange to enable more physiologically-relevant assessments prior to in vivo


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studies. We also use the cardiomyocyte culture systems, as well as established models, to investigate potential or observed cardiovascular toxicity. We have used data from these models make WOE decisions including the design of non-clinical in vivo cardiovascular investigation studies and GLP studies to support entry-into-humans, to identify improved back-up drug candidates, and to terminate a program with suspected on-target cardiotoxicity.

In vivo cardiovascular assessments remain the most physiologically relevant and technological improvements now allow for continuous data collection for several physiological parameters including cardiovascular and temperature during in vivo studies, such that more robust toxicokinetic/toxicodynamic data can be acquired to more fully inform exposure-activity relationships and thus decisions regarding safety biomarker use. The most notable examples include implantable probes that extensively measure cardiovascular function (including blood pressure, ECGs, and left ventricular pressure), external jacket ECG systems (e.g. JET) that can be combined with use of minimally invasive blood pressure devices and incorporated into repeat-dose toxicity studies, and inclusion of echocardiography into either single or repeat dose studies to characterize risk. Additionally, more extensive PK/PD modeling using this advanced technology in the nonclinical space has been demonstrated to enable more accurate predictions of clinical outcomes.144 Finally, the zebrafish may be an emerging animal model for assessing QT prolongation as these fish have action potentials that are similar in form to human action potentials and there has been reasonable correlation in the effects of drugs that cause prolongation in zebrafish compared to humans. They also offer an economy of scale that can accommodate drug-screening paradigms. However, differences have been observed in the sodium channel current and calcium handing between zebrafish and humans, so this may limit the utility of these animals beyond QT prolongation assessments.136

With regard to modeling effects on the vasculature, endothelial cell monoculture systems and ex vivo tissue preparations, such as aortic ring slices, remain commonly used models for investigation of issues and as potential counter-screens. In the last decade, modeling


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advancements have come in the form of bioengineered models that use mixed endothelial and smooth muscle cell culture systems and incorporate shear stress and impacts of hemodynamic flow into the in vitro systems in an attempt to include that physiological variable into toxicity assessments.145 These systems have been used to interrogate fenoldopam vasculititis using rat cells and to demonstrated similar biology and vascular response to the statin class of drugs when compared to human vasculature using a cynomoglus cell system.146, 147 Thus these new technology platforms can be used in the discovery period to investigate drug effects and to predict potential drug-related effects in animal species and humans.

6.2. Hepatotoxicity Modeling. With regard to screening for the potential for drug-induced hepatotoxicity (DILI), hepatocyte cultures (primary or immortalized) are still commonly used to “predict” possible hepatotoxicity in addition to animal studies. However, this approach when used alone has limited value aside from assessing intrinsic cytotoxicity, as the pathogenesis of DILI is multifactorial, i.e., likely the result of a combination of many risk factors. Likewise, although many computational tools to predict hepatotoxicity have been described, none has offered meaningful improvements for prediction (reviewed in148). To this end, what is newly evolving is an approach to assess potential DILI acknowledges that the pathogenesis of DILI is complex and multifactorial and employs the use multiple endpoints that are known clinical risk factors for DILI in the predictive algorithm.52, 97, 148

For example, in recent work, Aleo and

colleagues demonstrated that a high proportion of clinically severe hepatotoxicants were potent inhibitors of both mitochondrial function and bile salt export pump (BSEP) transporter.149

Shah and colleagues expanded on this work by finding that drugs

identified with three risk factors (intrinsic cytotoxicity, inhibition of mitochondrial function and BSEP inhibition) and a clinical Cmax (total) of ≥ 1.0 µM had a strong correlation with clinical DILI compared to drugs not associated with DILI; a potentially useful predictive exposure-effect relationship.149

Similarly, Schadt, et al.151 developed

a screening paradigm that uses four risk factors for DILI (generation of reactive metabolites, inhibition of the human BSEP, mitochondrial toxicity, and cytotoxicity) that when calibrated for human dose or exposure proved useful to prospectively assess DILI


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risk of drug candidates. The multiple factor assessment approach has the opportunity to implicate underlying drivers of toxicity that are also clinical risk factors that can be prospectively monitored in clinical populations and improve translational risk assessments.

Recent technological developments and commercialization of more phenotypically relevant hepatic in vitro models, including but not limited to 3D liver microspheres, 3D printed and self assembled hepatic organoids, and micropatterned hepatocyte and fibroblast co-culture platforms, appear promising tools for assessing risk for and mechanisms of DILI with regard to enhanced physiological relevance of these in vitro models. 52, 152, 153 Although each of these systems present various advantages over traditional plated hepatocyte assays, how these enhancements translate to increased predictability is generally unclear and under active evaluation. Recently, Kostakinova et al.154 reported that human and rat in vitro 3D liver co-culture systems containing primary parenchymal and non-parenchymal hepatic cells maintained liver function for up to 3 months, including the ability to respond to inflammatory stimuli, and had improved detection of in vivo drug-induced toxicity, including species-specific drug effects, when compared to monolayer hepatocyte cultures.

6.3. Other Target Organ Model Considerations. In vitro models exist in some form for most target organ platforms (e.g. hematopoietic, renal, retinal, epidermal, intestinal, neuronal, pulmonary) that vary from monocellular models to tissue slice preparations. Additionally, with regard to development and reproductive toxicity screening and investigation, newer, alternative computational, in vitro and in vivo models have been developed that can be used during the discovery period.155

Despite their limitations, in vitro monocellular models offer experimental flexibility and higher-throughput, and so are often applied to evaluate specific toxicologically-relevant physiological characteristics of the corresponding organ, e.g. electrophysiology, morphological changes, metabolism, proliferation, or unique homeostatic properties; these models may serve as informative surrogates for in vivo studies when used for


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decisions related to identifying improved compounds or for refining the design of in vivo studies. Historically there has been extensive effort put into developing models of various organs using monoculture systems and tissue slice methods, which is demonstrated by the extensive literature on the topic in the public domain. There continues to be significant effort in this area that is driven by the ever present need to improve the translational characterization of target organ toxicity and by continued technological developments, such as microphysiological systems that span mixed cell platforms and tissue chips. As mentioned earlier, the pharmaceutical industry is still gathering experience with these newer platforms and they have not been applied widely in the drug discovery space for screening. However, tissue chip platforms may be especially useful to build models for tissues with complex architectures or function, such as lung, central nervous system, and kidney.155-158 For example, Huh et al.158 created a microfluidic device that recapitulated the alveolar-capillary interface of the human lung consisting of channels lined by layers of human pulmonary epithelial and endothelial cells, air and fluid flow, and cyclic mechanical strain to mimic normal breathing motions. This model was able to reproduce interleukin-2 induced pulmonary edema at similar doses and over the same time frame that was observed in human cancer patients. Importantly, studies using this tissue-chip disease model revealed that mechanical forces associated with physiological breathing motions play a crucial role in the development of increased vascular leakage that leads to pulmonary edema. This model was then used to elucidate a molecular target of pulmonary edema and identify of potential new therapeutic agent.

As a general approach, if a target organ model is demonstrated to have translational value for a particular project, it may provide a tractable screening method that medicinal chemists can use to design away from series with observed toxicity, to rank chemicals, and identify lead compounds without having to resort to the use of in vivo screens that are resource intensive from chemistry; this approach is also in alignment with the “3Rs” perspectives of reducing, refining and replacing in vivo models. For example, we have used this approach assess potential species differences in vitro as a means to understand translation to humans and to identify an improved back-up molecule.58 Specifically,


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hepatotoxicity was observed in two non-clinical species; hepatocyte cultures from the two non-clinical species and humans were used to initially extrapolate the translatable risk to humans and then used to investigate the underlying drivers of toxicity. The hepatocyte model was then used to screen back-up compounds with altered physiochemical properties, which successfully identified an improved back-up compound that lacked hepatotoxicity.

To summarize, modeling aspects of target organs at the computational, in vitro and in vivo level is a significant activity in the discovery space for screening to identifying candidate drugs and also to investigate mechanisms of toxicity. The general approaches can be applied across any organ system, but the utility and limitations of each model needs to be addressed based on the intended use of the models. Refinement of these models will continue to be at the forefront of discovery activities across the pharmaceutical industry.

7. INVESTIGATIONAL APPROACHES TO DE-RISK AND ASSESS TRANSLATABLE RISK

Investigation or characterization of identified or potential liabilities is necessary to enable informed, rational decision-making (Table 2). As such, investment of time and resources in investigational activities, in our opinion, is a fundamental aspect of the work performed by toxicologists both the discovery and development periods. The desired outcomes of investigative safety work are two-fold: [a] identify the cause of the toxicity (i.e., on- versus off-target) to inform chemical design, a back-up strategy, or continued viability of the target, and [b] inform and characterize translatable risk to humans, which includes identification of potential safety biomarkers and exposure-effect relationships. Hypothesis-driven approaches are used to evaluate theoretical risks or target organ toxicities that have been identified (usually in vivo), or to evaluate the relevance an in silico or in vitro screening outcome as part of a WOE approach to inform decision-making. Applied models and technologies, whether established or new, must be properly qualified as to their ability to accurately predict the translatable outcome (either


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in animals or humans).1 However, these models are almost exclusively research-based (i.e. non-GLP) because time and resource constraints typically allow for fit-for-purpose and not full validation. There are numerous examples in the literature demonstrating the application of diverse investigative models to elucidate mechanisms of toxicity. Theoretic risks typically are identified during the target safety assessment (TSA) as either potential undesired pharmacology, or based on competitive intelligence regarding adverse effects observed non-clinically or clinically with a competitor compound or target/drug class. Alternatively, target organ toxicity can surface during efficacy, pharmacokinetic, or pilot toxicity studies. In any case, it is paramount that a hypothesis-driven research plan be developed that defines the essential questions to be addressed and how the data from these studies will impact decision-making.

Common themes in investigative approaches include determination of on- versus offtarget effects and mechanisms, cross-species translation, monitorability (identification of safety biomarkers) and reversibility assessments. To address on- versus off-target effects, structurally distinct “tool” molecules and/or structurally similar pharmacologically inactive molecules are often compared to the molecule of interest. As described earlier, evaluation of genetically engineered rodent models or cell-based systems can also be very useful in this regard. When target organ toxicity is identified in vivo, gaining an understanding of the potential translation to humans is a major focus, but it is typically a significant challenge. Such an investigation may rely upon the presence of the target organ toxicity in two species, as well as a cross-species susceptibility assessment for the target organ in vitro to assess the potential translation to humans. Often in vitro studies utilize a cell type identified histologically in the target organ to assess species translation and to investigate underlying cellular drivers of toxicity. This knowledge can be used to determine a possible structure-activity relationship or to identify risk factors for patients based on the cellular mechanisms, e.g. mitochondrial toxin. Investigational approaches demonstrated in the work of Tarrant, et al., 159 Zabka, et al., 160 Diaz, et al.,58 Uppal, et al. 161 and Leverson, et al. 162 are recent examples that employed the integration of cell-based models to investigate toxicity observed in animal or clinical studies, to evaluate the in vitro-to-in vivo translation, and


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cross-species sensitivity to an identified toxicant. The diversity of decisions and outcomes based on integration of this body of investigative work demonstrates the utility of these models. The outcomes included demonstration of organ selectivity for toxicity and use of an in vitro model to predict human toxicity that was not predicted in animal species;159 building a WOE to implicate an on-target toxicity that resulted in elimination of the therapeutic target from the portfolio160; elucidation of underlying physiochemical drivers of toxicity and the identification of back-up molecule that lacked the original toxicity58; clarify the mechanisms of drug-related thrombocytopenia and neutropenia that were used to support clinical trial designs and identify drug candidates to overcome clinical adverse effects. 161, 162

8. LOOKING FORWARD: COLLABORATION AND CLINICAL DATA ARE KEY ELEMENTS FOR IMPROVING NONCLINICAL MODELS

The application of new technologies in discovery toxicology will continue to drive progress towards the major objectives of safety lead optimization and candidate nomination. Namely, identification of the candidate with the best overall drug characteristics for a therapeutic area and from a safety perspective; removal of the most toxic drugs from the portfolio prior to entry into humans to reduce clinical attrition; and to establishment of a well-characterized hazard and translational risk profile to enable clinical trial designs. The foremost challenges that remain for all nonclinical models are to steadily improve the ability to accurately predict an in vivo or clinical liability, to meaningfully translate in vitro-in vivo exposure-effect relationships, and to understand differing species sensitivities. Refining our understanding and use of animal models is important because while humans are the target species for pharmaceuticals, the use of nonclinical species still remains the gold standard for physiologically-integrated risk assessments and will remain so until equally predictive alternative models can be identified and qualified or validated. Furthermore, enablement of the “3R’s” through expanded use of in silico or in vitro models requires a clear understanding of in vitro-in vivo translational and exposure relationships.


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A clear evolutionary direction of toxicology is to go beyond simple hazard identification toward informing the translatable risk of nonclinical findings to humans.163 Enabling accurate translatable risk assessments is dependent upon understanding of the concordance between nonclinical model outcomes and clinical outcomes they are attempting to model, greater focus on developing nonclinical models that address clinical drivers of toxicity (i.e. risk factors), and a better understanding of the underlying pathophysiology of drug-related adverse effects in humans. The application of new technologies alone cannot accomplish these tasks. What is also essential is a committed effort to better leverage nonclinical and clinical trial data to more accurately develop models that predict complex toxicity outcomes in humans.81, 163

The availability (i.e. sharing) of pertinent clinical data detailing information regarding the epidemiology of adverse effects and data that informs the translatability of nonclinical model findings to clinical outcomes is necessary to generate new knowledge to drive innovative model development in the nonclinical space.

Indeed there has been

considerable movement in this area including several drug companies making trial data from marketed or discontinued drugs available in a responsible manner upon request.164165

Clinical trial data availability is an immensely important first step in enabling the

improvements of nonclinical safety models; however, there are significant practical challenges regarding data variability that will make its use challenging. Nevertheless, there are several recent examples in which groups of researchers and regulators have come together to assess the concordance and predictivity of nonclinical model outcomes and clinical outcomes using shared data, which has been impactful toward understanding the best utility of nonclinical models as well as their limitations and in revising or influencing clinical trial design and regulatory guidances.133, 167-170

Finally, another opportunity to gather well-controlled clinical safety data that can inform the development of nonclinical models is to design clinical trials to enable prospective safety assessments based on the risk factors identified either non-clinically or clinically. For example the Drug-Induced Liver Injury Network (DILIN), a cooperative between the National Institutes of Health (NIH) and academic clinical centers, established prospective


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clinical studies that enrolled patients with clinically identified DILI to create a registry of clinical information and a repository of biological samples that can be used to evaluate the mechanisms of and risk factors for DILI.171 Alternatively, individual pharmaceutical companies have a similar opportunity to proactively obtain samples as part of clinical trial designs that could be used to investigate clinical toxicity, especially with regard to idiosyncratic events. These activities would constitute a new approach complementing the application of new technologies toward enhancing the goals of discovery toxicology.


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FUNDING SOURCES The authors are all employees of Genentech, Inc. and declare no competing financial interest. AUTHOR INFORMATION Corresponding Author *Phone: +1-650-225-8535. E-mail: [email protected].


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ABBREVIATIONS WOE, weight of evidence; 3R’s, reduction, refinement, replacement; TSA, target safety assessment; ADME, absorption, distribution, metabolism, excretion; PK, pharmacokinetics; mRNA, messenger RNA; PCR, polymerase chain reaction; NGS, next generation sequencing; KEGG, Kyoto Encyclopedia of Genes and Genomes; KO, knockout; ESC, embryonic stem cell; DNA , deoxyribonucleic acid; LRRK2, leucine-rich repeat kinase 2; GLP-R1, glucagon like peptide receptor 1; PLK2, polo like kinase 2; BACE-2, β-site amyloid precursor protein cleaving enzyme 1; CNS, central nervous system; THR, target hit rate; GPCR, G-protein coupled receptor; IC50 median inhibitory concentration; µM, micromolar; LD50, median lethal concentration; Cmax, maximum concentration; Cavg0-24, average concentration over 0-24 hours; AUC, area under the curve; DMPK, distribution, metabolism, pharmacokinetics; CYP, cytochrome; FMO, flavin-contraining monooxygenase; DILI, drug-induced liver injury; UGT, uridine diphosphate-glucuronyl transferase; Vd, volume of distribution; Vss, steady state volume of distribution; Cl, clearance; LOAEL, lowest adverse effect level; BSEP, bile salt export pump; QSAR, quantitative structure activity relationship; GPI, glycosylphosphatidylinositol; ICH, International Committee on Harmonisation; DARPA, Defense Advanced Research Projects Agency; NCATS, National Center for the Advancement of Translational Science; human ether-a-go-go; hNav, human sodium channel; hCav, human calcium channel; CiPA, Comprehensive in vivo Proarrythymia Assay; ECG, electrocardiograph; JET, jacketed external telemetry; PD, pharmacodynamics; GLP, good laboratory practice; DILIN, Drug Induced Liver Injury Network; NIH, National Institutes of Health


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REFERENCES 1. Lee, J.W., Devanaragan, V., Barrett, Y.C., Weiner, R., Allison, J., Fountain, S., Keller, S., Weinryb, F., Green, M., Duan, L., Rogers, J.A., Millham, R., O’Brien, P.J., Sailstad, J., Khan, M., Ray, C., and Wagner, J.A. (2006) Fit-for-purpose method development and validation for successful biomarker measurement. Pharmaceutical Res. 23, 312-328. 2. Redfern, W.S., Carlsson, L., Davis, A.S., Lynch, W.G., MacKenzie, I., Palethorpe, S., Seigl, P.K.S., Strang, I., Sullivan, A.T., Wallis, R., Camm, A.J. and Hammond, T.G. (2003) Relationships between preclinical cardiac electrophysiology, clinical QT interval prolongation and torsade de pointes for a broad range of q drugs: evidence for a provisional safety margin in drug development. Cardiovasc. Res. 58, 32-45. 3. Thomas, C.E. and Will, Y. (2012) The impact of assay technology as applied to safety assessment in reducing compound attrition in drug discovery. Expert Opin. Drug Discov. 7, 10-122. 4. Hornberg, J.J., Laursen, M., Brenden, N., Persson, M., Thougaard, A.V., Toft, D.B., Mow, T. (2014a) Exploratory toxicology as an integrated part of drug discovery. Part I: why and how. Drug Discov. Today, 19, 1131-1136. 5. McKinney, M.D., Moon, S.J., Kulesh, D.A., Larsen, T. and Schoepp, R.J. (2009) Detection of viral RNA from paraffin-embedded tissues after prolonged fixation. J. Clinic. Virol. 44, 39-42. 6. Woollard, P.M., Mehta, N.A., Vamathevan, J.J., Van Horn, S., Bonde, B.K., and Dow, D.J. (2011) The application of next-generation sequencing technologies to drug discovery and development. Drug Discov. Today 16, 512-519.


43


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 44 of 76

7. Khatoon, Z., Figler, B., Zhang, H., and Cheng, F. (2014) Introduction to RNA-Seq and its applications to drug discovery and development. Drug Dev. Res. 75, 324-330. 8. Su, Z., Ning, B., Fang, H., Perkins, R., Tong, W. and Shi, W. (2011) Next-generation sequencing and its applications in molecular diagnostics. Expert Rev. Mol. Diag. 11, 333-343. 9. Fielden, M., and Karbowski, C. (2012) The Application of Preclinical Toxicogenomics for Predicting and Understanding Drug-Induce Toxicity and Metabolism. In Encyclopedia of Drug Metabolism and Interactions (Lyubimov, A.V., Ed) 4, 1-29, John Wiley and Sons, New York. 10. Ganter, B., Zidek, N., Hewitt, P.R., Muller, D., and Vladiminova, A. (2008) Pathway analysis tools and toxicogenomics reference databases for risk assessment. Pharmacogenomics 9, 35-54. 11. Kelder, T., Conklin, B.R., Evelo, C.T., and Pico, A.R. (2010) Finding the right questions: exploratory pathway analysis to enhance biological discovery in large datasets. PLoS Biol. 8, e1000472. 12. Kramer, A., Green, J., Pollard, J., and Turgendreich, S. (2014). Causal analysis approaches in ingenuity pathway analysis. Bioinformatics 30, 523-536. 13. Hardy, B., Apic, G., Carthew, P., Clark, D., Cook, D., Dix, I., Escher, S., Hastings, J., Heard, D.J., Jeliazkova, N., Judson, P., Matis-Mitchell, S., Mitic, D., Myatt, G., Shah, I., Spiuth, O., Tcheremenskaia, O., Toldo, L., Watson, D., White, A., and Yang, C. (2012) Toxicology ontology perspectives. ALTEX 29, 139-156. 14. Christopher, P.D., Sandy, P., Nencioni, A., Kissler, S., Rubinson, D.A., and Parijs, L.V. (2006) Ann. Rev. Physiol. 67, 147-173.


44

Page 45 of 76

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


15. Zambrowicz, B. P., and Sands, A. T. (2003) Knockouts model the 100 best-selling drugs--will they model the next 100? Nature Rev. Drug Disc. 2, 38-51. 16. Papaioannou, V. E., and Behringer, R. R. (2012) Early embryonic lethality in genetically engineered mice: diagnosis and phenotypic analysis. Vet. Path. 49, 64-70. 17. Turgeon, B., and Meloche, S. (2009) Interpreting neonatal lethal phenotypes in mouse mutants: insights into gene function and human diseases. Phys. Rev. 89, 1-26. 18. Wu, H., Liu, X., Jaenisch, R., and Lodish, H. F. (1995) Generation of committed erythroid BFU-E and CFU-E progenitors does not require erythropoietin or the erythropoietin receptor. Cell 83, 59-67. 19. Pich, E. M., and Epping-Jordan, M. P. (1998) Transgenic mice in drug dependence research. Ann. Med. 30, 390-396. 20. Rocha, B. A., Scearce-Levie, K., Lucas, J. J., Hiroi, N., Castanon, N., Crabbe, J. C., Nestler, E. J., and Hen, R. (1998) Increased vulnerability to cocaine in mice lacking the serotonin-1B receptor. Nature 393, 175-178. 21. Scearce-Levie, K., Chen, J. P., Gardner, E., and Hen, R. (1999) 5-HT receptor knockout mice: pharmacological tools or models of psychiatric disorders. Ann. New York Acad. Sci. 868, 701-715. 22. Robbins, J. (1993) Gene targeting. The precise manipulation of the mammalian genome. Circ. Res. 73, 3-9. 23. Bronson, S. K., and Smithies, O. (1994) Altering mice by homologous recombination using embryonic stem cells. J. Biol. Chem. 269, 27155-27158. 24. Sacca, R., Engle, S. J., Qin, W., Stock, J. L., and McNeish, J. D. (2010) Genetically engineered mouse models in drug discovery research. Meth. Mol. Biol. 602, 37-54.


45


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 46 of 76

25. Doyle, A., McGarry, M. P., Lee, N. A., and Lee, J. J. (2012) The construction of transgenic and gene knockout/knockin mouse models of human disease. Transgen. Res. 21, 327-349. 26. Jinek, M., Chylinski, K., Fonfara, I., Hauer, M., Doudna, J. A., and Charpentier, E. (2012) A programmable dual-RNA-guided DNA endonuclease in adaptive bacterial immunity. Science 337, 816-821. 27. Wang, H., Yang, H., Shivalila, C. S., Dawlaty, M. M., Cheng, A. W., Zhang, F., and Jaenisch, R. (2013) One-step generation of mice carrying mutations in multiple genes by CRISPR/Cas-mediated genome engineering. Cell 153, 910-918. 28. Fu Y., Foden J.A., Khayter C., Maeder M.L., Reyon D., Joung J.K., and Sander J.D. (2013) High-frequency off-target mutagenesis induced by CRISPR-Cas nucleases in human cells. Nat. Biotechnol. 31, 822-826. 29. .Cho S.W., Kim S., Kim Y., Kweon J., Kim H.S., Bae S., and Kim J.S. (2014) Analysis of off-target effects of CRISPR/Cas-derived RNA-guided endonucleases and nickases. Genome Res. 24, 132-141. 30. Ran F.A., Hsu P.D., Lin C.Y., Gootenberg J.S., Konermann S., Trevino A.E., Scott D.A., Inoue A., Matoba S., Zhang Y., and Zhang, F. (2013) Double nicking by RNAguided CRISPR Cas9 for enhanced genome editing specificity. Cell 154, 1380-1389. 31. Fu Y., Sander J.D., Reyon D., Cascio V.M., and Joung J.K. (2014) Improving CRISPR-Cas nuclease specificity using truncated guide RNAs. Nat. Biotechnol. 32, 279-284.


46

Page 47 of 76

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


32. Tsai S.Q., Wyvekens N., Khayter C., Foden J.A., Thapar V., Reyon D., Goodwin M.J., Aryee M.J., and Joung J.K. (2014) Dimeric CRISPR RNA-guided FokI nucleases for highly specific genome editing. Nat. Biotechnol. 32, 569-576. 33. Ness, D., Ren, Z., Gardai, S., Sharpnack, D., Johnson, V. J., Brennan, R. J., Brigham, E. F., and Olaharski, A. J. (2013) Leucine-rich repeat kinase 2 (LRRK2)-deficient rats exhibit renal tubule injury and perturbations in metabolic and immunological homeostasis. PloS One 8, e66164. 34. Wojnowski, L., Zimmer, A. M., Beck, T. W., Hahn, H., Bernal, R., Rapp, U. R., and Zimmer, A. (1997) Endothelial apoptosis in Braf-deficient mice. Nat. Gen. 16, 293297. 35. Galabova-Kovacs, G., Matzen, D., Piazzolla, D., Meissl, K., Plyushch, T., Chen, A. P., Silva, A., and Baccarini, M. (2006) Essential role of B-Raf in ERK activation during extraembryonic development. Proc. Natl. Acad. Sci. 103, 1325-1330. 36. Blasco, R. B., Francoz, S., Santamaria, D., Canamero, M., Dubus, P., Charron, J., Baccarini, M., and Barbacid, M. (2011) c-Raf, but not B-Raf, is essential for development of K-Ras oncogene-driven non-small cell lung carcinoma. Cancer Cell 19, 652-663. 37. Mason, K. D., Carpinelli, M. R., Fletcher, J. I., Collinge, J. E., Hilton, A. A., Ellis, S., Kelly, P. N., Ekert, P. G., Metcalf, D., Roberts, A. W., Huang, D. C., and Kile, B. T. (2007) Programmed anuclear cell death delimits platelet life span. Cell 128, 11731186. 38. Tse, C., Shoemaker, A. R., Adickes, J., Anderson, M. G., Chen, J., Jin, S., Johnson, E. F., Marsh, K. C., Mitten, M. J., Nimmer, P., Roberts, L., Tahir, S. K., Xiao, Y.,


47


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 48 of 76

Yang, X., Zhang, H., Fesik, S., Rosenberg, S. H., and Elmore, S. W. (2008) ABT263: a potent and orally bioavailable Bcl-2 family inhibitor. Cancer Res. 68, 34213428. 39. Schoenwaelder, S. M., Jarman, K. E., Gardiner, E. E., Hua, M., Qiao, J., White, M. J., Josefsson, E. C., Alwis, I., Ono, A., Willcox, A., Andrews, R. K., Mason, K. D., Salem, H. H., Huang, D. C., Kile, B. T., Roberts, A. W., and Jackson, S. P. (2011) Bcl-xL-inhibitory BH3 mimetics can induce a transient thrombocytopathy that undermines the hemostatic function of platelets. Blood 118, 1663-1674. 40. Leverson, J. D., Phillips, D. C., Mitten, M. J., Boghaert, E. R., Diaz, D., Tahir, S. K., Belmont, L. D., Nimmer, P., Xiao, Y., Ma, X. M., Lowes, K. N., Kovar, P., Chen, J., Jin, S., Smith, M., Xue, J., Zhang, H., Oleksijew, A., Magoc, T. J., Vaidya, K. S., Albert, D. H., Tarrant, J. M., La, N., Wang, L., Tao, Z. F., Wendt, M. D., Sampath, D., Rosenberg, S. H., Tse, C., DC, S. H., Fairbrother, W. J., Elmore, S. W., and Souers, A. J. (2015) Exploiting selective BCL-2 family inhibitors to dissect cell survival dependencies and define improved strategies for cancer therapy. Sci. Transl. Med. 7, 279ra240. 41. Bruning, J. C., Michael, M. D., Winnay, J. N., Hayashi, T., Horsch, D., Accili, D., Goodyear, L. J., and Kahn, C. R. (1998) A muscle-specific insulin receptor knockout exhibits features of the metabolic syndrome of NIDDM without altering glucose tolerance. Mol. Cell 2, 559-569. 42. Kulkarni, R. N., Bruning, J. C., Winnay, J. N., Postic, C., Magnuson, M. A., and Kahn, C. R. (1999) Tissue-specific knockout of the insulin receptor in pancreatic beta


48

Page 49 of 76

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


cells creates an insulin secretory defect similar to that in type 2 diabetes. Cell 96, 329339. 43. Bruning, J. C., Gautam, D., Burks, D. J., Gillette, J., Schubert, M., Orban, P. C., Klein, R., Krone, W., Muller-Wieland, D., and Kahn, C. R. (2000) Role of brain insulin receptor in control of body weight and reproduction. Science 289, 2122-2125. 44. Michael, M. D., Kulkarni, R. N., Postic, C., Previs, S. F., Shulman, G. I., Magnuson, M. A., and Kahn, C. R. (2000) Loss of insulin signaling in hepatocytes leads to severe insulin resistance and progressive hepatic dysfunction. Mol. Cell 6, 87-97. 45. Guerra, C., Navarro, P., Valverde, A. M., Arribas, M., Bruning, J., Kozak, L. P., Kahn, C. R., and Benito, M. (2001) Brown adipose tissue-specific insulin receptor knockout shows diabetic phenotype without insulin resistance. J. Clin. Invest. 108, 1205-1213. 46. Fuji, R. N., Flagella, M., Baca, M., MA, S. B., Brodbeck, J., Chan, B. K., Fiske, B. K., Honigberg, L., Jubb, A. M., Katavolos, P., Lee, D. W., Lewin-Koh, S. C., Lin, T., Liu, X., Liu, S., Lyssikatos, J. P., O'Mahony, J., Reichelt, M., Roose-Girma, M., Sheng, Z., Sherer, T., Smith, A., Solon, M., Sweeney, Z. K., Tarrant, J., Urkowitz, A., Warming, S., Yaylaoglu, M., Zhang, S., Zhu, H., Estrada, A. A., and Watts, R. J. (2015) Effect of selective LRRK2 kinase inhibition on nonhuman primate lung. Sci. Transl. Med. 7, 273ra215. 47. Madsen, L. W., Knauf, J. A., Gotfredsen, C., Pilling, A., Sjogren, I., Andersen, S., Andersen, L., de Boer, A. S., Manova, K., Barlas, A., Vundavalli, S., Nyborg, N. C., Knudsen, L. B., Moelck, A. M., and Fagin, J. A. (2012) GLP-1 receptor agonists and


49


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 50 of 76

the thyroid: C-cell effects in mice are mediated via the GLP-1 receptor and not associated with RET activation. Endocrinology 153, 1538-1547. 48. Fitzgerald, K., Bergeron, M., Willits, C., Bowers, S., Aubele, D. L., Goldbach, E., Tonn, G., Ness, D., and Olaharski, A. (2013) Pharmacological inhibition of polo like kinase 2 (PLK2) does not cause chromosomal damage or result in the formation of micronuclei. Toxicol. Appl. Pharmacol. 269, 1-7. 49. May, P. C., Dean, R. A., Lowe, S. L., Martenyi, F., Sheehan, S. M., Boggs, L. N., Monk, S. A., Mathes, B. M., Mergott, D. J., Watson, B. M., Stout, S. L., Timm, D. E., Smith Labell, E., Gonzales, C. R., Nakano, M., Jhee, S. S., Yen, M., Ereshefsky, L., Lindstrom, T. D., Calligaro, D. O., Cocke, P. J., Greg Hall, D., Friedrich, S., Citron, M., and Audia, J. E. (2011) Robust central reduction of amyloid-beta in humans with an orally available, non-peptidic beta-secretase inhibitor. J. Neurosci. 31, 1650716516. 50. Kramer, J.A., Sagartz, J.E., and Morris, D.L. (2007) The application of discovery toxicology and pathology towards the design of safety pharmaceutical lead candidates. Nat. Rev. Drug Disc. 6, 636-649. 51. 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, 6978. 52. Hornberg, J.J., Laursen, M., Brenden, N., Persson, M., Thougaard, A.V., Toft, D.B., and Mow, T. (2014b) Exploratory toxicology as an integrated part of drug discovery. Part II: screening strategies. Drug Disc. Today 19, 1137-1144.


50

Page 51 of 76

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


53. Whitebread, S., Hamon, J., Bojanic, D., and Urban, L. (2005) In vitro safety pharmacology profiling: An essential tool for successful drug development. Drug Discovery Today 10,1421–1433. 54. 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. Chem. Med. Chem. 2, 874–888. 55. 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 Disc. 11, 909-922. 56. Leeson, P.D., and Springthorpe, B. (2007) The influence of drug-like concepts on decision makingin medicinal chemistry. Nat. Rev. Drug Disc. 6, 881–890. 57. Waring, M.J. (2010) Lipophilicity and drug discovery. Exp. Op. Drug Disc. 5, 235– 248. 58. Diaz, D., Ford, K.A., Hartley, D.P., Harstad, E.B., Cain, G.R., Achilles-Poon, K., Nguyen, T., Peng, J., Zheng, Z., Merchant, M., Sutherlin, D.P., Gaudino, J.J., Kaus, R., Lewin-Koh, S.C., Choo, E.F., Liederer, B.M., and Dambach, D.M. (2013) Pharmacokinetic drivers of toxicity for basic molecules: strategy to lower pKa results in decreased tissue exposure and toxicity for a small molecule Met inhibitor. Toxicol. Appl. Pharmacol. 266, 86-94. 59. Yokogawa, K., Ishizaki, J., Ohkuma, S., and Miyamoto, K. (2002) Influence of lipophilicity and lysosomal accumulation on tissue distribution kinetics of basic drugs: A physiologically based pharmacokinetic model. Meth. Find. Exp. Clin. Pharmacol. 24, 81–93.


51


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 52 of 76

60. Poulin, P., Dambach, D.M., Hartley, D.H., Ford, K., Theil, P., Harstad, E., Halladay, J., Choo, E., Boggs, J., Liederer, B., Dean, B., and Diaz, D. (2013) An algorithm for evaluating potential tissue drug distribution in toxicology studies from readily available pharmacokinetic parameters. J. Pharm. Sci. 102, 3816-3829. 61. Muller, P.Y., Dambach, D., Gemzik, B., Hartmann, A., Ratcliffe, S., Trendelenburg, C., and Urban, L. (2015) Integrated risk assessment of suicidal ideation and behavior in drug development. Drug Discovery Today 20, 1135-1142. 62. Papoian, T, Chiu, H-J, Elayan, I, Jagadeesh, G, Khan, A, Laniyonu, A.A., Xinguang Li, C, 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 Disc. 14, 294-297. 63. Diaz, D., Dambach, D., Siu, M., Hunt, K., Thomas, A., Lyssikatos, J., Liu, X., LewinKoh, S., McCray, B., and Ford, K. (2015) Mitigation of opioid off-target effects and identification of structural drivers of opioid receptor engagement for BACE-1 small molecule inhibitors. Toxicol. Mech. Methods 25, 478-486. 64. Lounkine, E., Keiser, M.J., Whitebread, S., Mikhailov, D., Hamon, J., Jenkins, J.L., Lavan, P.,Weber, E., Doak, A.K., Cote, S., Shoichet, B.K., and Urban, L. (2012) Large-scale prediction and testing of drug activity on side-effect targets. Nature 486, 361–367. 65. Maggioli, J., Hoover, A., and Weng, L. (2006) Toxicogenomic analysis methods for predictive toxicology. J. Pharmacol. Toxicol. Methods 53, 31-37. 66. Fabre, N., Anglade, A., and Vericat, J.A. (2009) Application of toxicogenomic tools in the drug research and development process. Toxicol. Lett. 186, 13017.


52

Page 53 of 76

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


67. Cockerell, G.L., McKim, J.M.,and Vonderrecht, S.L. (2002) Strategic importance of research support through pathology. Toxicol. Path. 30, 4–7. 68. 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. 69. Greene, N., Aleo, M.D., Louise-May, S., Price, D.A., and Will, Y. (2010) Using in vitro cytotoxicity assay to aid in compound selection for in vivo safety studies. Bioorg, Med. Chem. Lett. 20, 5308–5312. 70. 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. 71. Li, A.P. (2007) Human hepatocytes: Isolation, cryopreservation and applications in drug development. Chem. Bio. Interact. 168, 16–29. 72. Schoonen,W.G.E.J., de Roos, J.A.D.M.,Westerink,W.M.A., and Debiton, E. (2005) Cytotoxic effects of 110 reference compounds on HepG2 and of 60 compounds on HeLa. ECC-1 and CHO cells. II mechanistic assays on NAD(P)H, ATP and DNA contents. Toxicology 19, 491–503. 73. Schoonen,W.G.E.J., Westerink,W.M.A., de Roos, J.A.D.M., and Debiton, E. (2005) Cytotoxiceffects of 100 reference compounds on HepG2 and HeLa cells and of 60 compoundson ECC-1 and CHO cells. I mechanistic assays on ROS, glutathione depletion and calcein uptake. Toxicology 19, 505–516. 74. Lin, Z., and Will, Y. (2012) Evaluation of drugs with specific organ toxicities in organ-specific cell lines. Toxicol. Sci. 126, 114–127.


53


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 54 of 76

75. McKim, J.M. (2010) Building a tiered approach to in vitro predictive toxicity screening: a focus on assays with in vivo relevance. Comb. Chem. High Throughput Screening 13, 188-206. 76. 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. 77. Khetani, S., and Bhatia, S.N. (2008) Microscale culture of human liver cells for drug development. Nat. Biotech. 26, 120-126. 78. Sutherland, M.L., Fabre, K.M., and Tagle, D.A. (2013) The national institutes of health microophysiological systems program focuses on a critical challenge in the drug discovery pipeline. Stem Cell Res. Ther. 4, 1-5. 79. Kelm J.M., Timmins, N.E., Brown, C.J., Fussenegger, M., and Nielsen, L.K. (2013) Method for generation of homogeneous multicellular tumor spheroids applicable to a wide variety of cell types. Biotechnol. Bioeng. 83, 173–180. 80. Kelly, E.J., Wang, Z., Voellinger, J.L., Yeung, C.K., Shen, D.D., Thummel, K.E., Zheng, Y., Ligresti, G., Eaton, D.L., Muczynski, K.A., Duffield, J.S., Neumann, T., Tourovskaia, A., Fauver, M., Kramer, G., Asp, E., and Himmelfarb, J. (2013) Innovations in preclinical biology: ex vivo engineering of a human kidney tissue microperfusion system. Stem Cell Res. Ther. 4(Suppl. 1), S17. 81. Dambach, D.M. and Uppal, H. (2012) Improving risk assessment. Science Trans. Med. 4, 159ps22. 82. Pai, R., Wei, B.Q., Chang, J., Crawford, J., Young, W., Ortwine, D., Misner, D., and Dambach, D. (2013) Application of an in silico approach to predict intrinsic in vitro


54

Page 55 of 76

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


cytotoxicity for compounds in primary human hepatocytes during preclinical development. Toxicology 132, 189. 83. Chico, L.K., Van Eldik, L.J., and Watterson, D.M. (2009) Targeting protein kinases in central nervous system disorders. Nat. Rev. Drug Discovery 8, 892-909. 84. Ansede, J.H., Smith,W.R., Perry, C.H., St. Claire III, R.L., and Brouwer, K.R. (2010) An in vitro assay to assess transporter-based cholestatic hepatotoxicity using sandwich-cultured rat hepatocytes. Drug Metab. Dispos. 38, 276–280. 85. International Transporter Consortium. (2010) Membrane transporters in drug development. Nat. Rev. Drug Discovery 9, 215–236. 86. Morgan, R.E., Trauner, M., van Staden, C.J., Lee, P.H., Ramachandran, B., Eschenberg, M., Afshari, C.A., Qualls, C.W., Lightfoot-Dunn, R., and Hamadeh, H.K. (2010) Interference with bile salt export pump function is a susceptibility factor for human liver injury in drug development. Toxicol. Sci. 118, 485–500. 87. Morgan, R.E., van Staden, C.J., Chen, Y., Kaylanaraman, N., Kalanzi, J., Dunn II, 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. 88. Pfeifer, N.D., Hardwick, R.N., and Brouwer, K.L.R. (2014) Role of Hepatic Efflux Transporters in Regulating Systemic and Hepatocyte Exposure to Xenobiotics. Annu. Rev. Pharmacol. Toxicol. 54, 509–535. 89. O’Brien, P.J., Chan, K., and Silber, P.M. (2004) Human and animal hepatocytes in vitro with extrapolation in vivo. Chem. Biol. Interact. 150, 97–114.


55


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 56 of 76

90. Janmohamed, A., Hernandez, D., Phillips, I.R., and Shephand, E.A. (2004) Cell, tissue, sex, and developmental stage-specific expression of mouse flavin-containing monooxygenases (FMOs). Biochem. Pharmacol. 68, 73–83. 91. Beedham, C., Bruce, S.E., Critchley, D.J., Al-Tayib, Y., and Rance, D.J. (1987) Species variation in hepatic aldehyde oxidase activity. Eur. J. Drug Metab. Pharmacokinet. 12, 307–310. 92. Fevery, J. (2008) Bilirubin in clinical practice: A review. Liver Int. 28, 592–603. 93. Zucker, S.D., Qin, X., Rouster, S.D., Yu, F., Green, R.M., Keshavan, P., Feinberg, J., and Sherman, K.E. (2001) Mechanism of indinavir-induced hyperbilirubinemia. Proc. Natl. Acad. Sci. 98, 12671–12676. 94. Park, B.K., Boobis, A., Clarke, S., Goldring, C.E.P., 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. 95. Nakayama, S., Atsumi, R., Takakusa, H., Kobayashi, Y., Kurihara, A., Nagai, Y., Naiai, D., and Okazaki, O. (2009) A zone classification system for risk assessment of idiosyncratic drug toxicity using daily dose and covalent binding. Drug Met. Disp. 37, 1970–1977. 96. Sakatis, M.Z., Reese, M.J., Harrell, A.W., Taylor, M.A., Baines, I.A., Chen, L., Bloomer, J.C., Yang, E.Y., Ellens, H.M., Ambroso, J.L., Lovatt, C.A., Ayrton, A.D. and Clarke, S.E. (2012) Preclinical strategy to reduce clinical hepatotoxicity using in vitro bioactivation data for >200 compounds. Chem. Res. Toxicol. 27, 1537-1546.


56

Page 57 of 76

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


97. Thompson, R.A., Isin, E.M., Li, Y., Weaver, R., Weidolf, L., Wilson, I., Claesson, A., Page, K., Dolgos, H., and Kenna, J.G. (2011) Risk assessment and mitigation strategies for reactive metabolites in drug discovery and development. Chem. Biol. Interact. 192, 65–71. 98. Ford, K. (2013) Role of electrostatic potential in the in silico prediction of molecular bioactivation and mutagenesis. Mol. Pharmacol. 10, 1171-1182. 99. International Conference of Harmonisation. (2011) Guidance on genotoxicity testing and data interpretation for pharmaceuticals intended for human use S2(R1). http://www.ich.org/fileadmin/Public_Web_Site/ICH_Products/Guidelines/Safety/S2_ R1/Step4/S2R1_Step4.pdf. 100.

Kirkland, D., Reeve, L., Gatehouse, D., and Vanparys, P. (2011) A core in vitro

genotoxicity battery comprising the Ames test plus the in vitro micronucleus test is sufficient to detect rodent carcinogens and in vivo genotoxins. Mutat. Res. 721, 27-73. 101.

Hillebrecht, A., Muster, W., Brigo, A., Kansy, M., Weiser, T., and Singer, T.

(2011) Comparative evaluation of in silico systems for ames test mutagenicity prediction: scope and limitations. Chem. Res. Toxicol. 24, 843-854. 102.

Snyder, R. D., Holt, P. A., Maguire, J. M., and and Trent, J. O. (2013) Prediction

of noncovalent Drug/DNA interaction using computational docking models: studies with over 1350 launched drugs. Environ. Mol. Mutagen. 54, 668-681. 103.

Ford, K.A., Ryslik, G., Chan, B. K., Lewin-Koh, S.C., Almeida, D., Stokes, M.,

and Gomez, S. Comparative evaluation of eleven in silico models for the prediction of small molecule non- mutagenicity: role of steric hindrance and electronwithdrawing groups. Mutat. Res. In press.


57


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

104.

Page 58 of 76

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. Envir. Molec. Mutagen. 36, 72–77. 105.

Gee P., Sommers, C.H., Melick, A.S., Gidrol, X.M., Todd, M.D., Burris, R.B.,

Nelson, M.E., Klemm, R.C., and Zeiger, E. (1998) Comparison of responses of basespecific Salmonella tester strains with the traditional strains for identifying mutagens: The results of a validation study. Mutat. Res. 412, 115–130. 106.

Diehl, M.S., Willaby, S.L., and Synder, R.D. (2000) Comparison of the results of

a modified miniscreen and the standard bacterial reverse mutation assays. Environ. Mol. Mutagen. 35, 72–77. 107.

Diaz, D., Scott, A., Carmichael, P., Shi, W., and Cosatles, C. (2007) Evaluation

of an automated in vitro micronucleus assay in CHO-K1 cells. Mutat. Res. 630, 1– 13. 108.

Kirkland, D.J., Aardema, M., Banduhn, N., Carmichael, P., Fautz, R., Meunier, J-

R., and Pfuhler, S. (2007) In vitro approaches to develop weight of evidence (WoE) and mode of action (MoA) discussions with positive in vitro genotoxicity results. Mutagenesis 22, 161–175. 109.

Thybaud, V., Aardeama, M., Clements, J., Dearfield, K., Galloway, S., Hayashi,

M., Jacobson-Kram, D., Kirkland, D., MacGregor, J.T., Marzin, D., Ohyama, W., Schuler, M., Suzuki, H., and Zeiger, E. (2007) Strategy for genotoxicity testing: hazard identification and riskassessment in relation to in vitro testing. Mutat. Res. 627, 41-58.


58

Page 59 of 76

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


110.

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. Mut. Res. 783, 23-35. 111.

Heflich, R. H., Dobrovolsky, V. N., and Dertinger, S. D. (2015) Continued

progress in developing the Pig-a gene mutation assay. Mutagen. 30, 313-314. 112.

Stevens, J.L., and Baker, T.K. (2009) The future of drug safety testing: expanding

the view and narrowing the focus. Drug Disc. Today 14, 162-167. 113.

Laverty, H., Benson, C., Cartwright, E., Cross, M., Garland, C., Hammond, T.,

Holloway, C., McMahon, N., Milligan, J., Park, B., Pirmohamed, M., Pollard, C., Radford, J., Roome, N., Sager, P., Singh, S., Suter, T., Suter, W., Trafford, A., Volders, P., Wallis, R., Weaver, R., York, M., and Valentin, J. (2011) How can we improve our understanding of cardiovascular safety liabilities to develop safety medicines? Brit. J. Pharmacol. 163, 675-693. Pugsley, M.K., Curtis, M.J., and Hayes, E.S. (2015) Biophysics and molecular

114.

biology of cardiac ion channels for the safety pharmacologist. Handb. Exp. Pharmacol. 229, 149-203. 115.

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. 116.

Hamlin, R.L., Cruze, C.A., Mittelstadt, S.W., Kijtawornrat, A., Keene, B.W.,

Roche, B.M., Nakayama, T., Nakayama, H., Hamlin, D.M., and Arnold, T. (2004)


59


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 60 of 76

Sensitivity and specificity of isolated perfused guinea pig heart to test for druginduced lengthening of QTc. J. Pharmacol. Toxicol. Methods 49, 15-23. 117.

Kolaja K. (2014) Stem cells and stem cell-derived tissues and their use in safety

assessment. J. Biol. Chem. 289, 4555-4561. 118.

Peng, S., Lacerda, A.E., Kirsch, G.E., Brown, A.M., and Bruening-Wright, A.

(2010) The action potential and comparative pharmacology of stem cell-derived human cardiomyocytes. J. Pharmacol. Toxicol. Methods 6, 277-286. 119.

Ma, J., Guo, L., Fiene, S.J., Anson, B.D., Thomson, J.A., Kamp, T.J., Kolaja,

K.L., Swanson, B.J., and January, C.T. (2011) High purity human-induced pluripotent stem cell-derived cardiomyocytes: electrophysiological properties of action potentials and ionic currents. Am. J. Physiol. Heart Circ. Physiol. 301, H2006-2017. 120.

Guo, L., Abrams, R.M., Babiarz, J.E., Cohen, J.D., Kameoka, S., Sanders, M.J.,

Chiao, E., and Kolaja, K.L. (2011) Estimating the risk of drug-induced proarrythmia using human induced pluripotent stems cell-derived cardiomyocytes. Toxicol. Sci. 123, 281-289. 121.

Harris, K., Aylott, M., Cui, Y., Louttit, J.B., McMahon, N.C., and Sridhar, A.

(2013) Comparison of electrophysiological data from human-induced pluripotent stem cell-derived cardiomyocytes to functional preclinical safety assays. Toxicol. Sci. 134, 412-426. 122.

Clements, M., and Thomas, N. (2014) High-throughput multi-parameter profiling

of electrophysiological drug effects in human embryonic stem cell derived cardiomyocytes using multi-electrode arrays. Toxicol. Sci.140, 445-461.


60

Page 61 of 76

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


123.

Lee, P., Klos, M., Bollensdorff, C., Hou, L., Ewart, P., Kamp, T.J., Zhang, J.,

Bizy, A., Guerrero-Serna, G., Kohl, P., Jalife, J., and Herron, T.J. (2012) Simultaneous voltage and calcium mapping of genetically purified human induced pluripotent stem cell-derived cardiac myocyte monolayers. Circ. Res. 110, 15561563. 124.

Scheel, O., Frech, S., Amuzescu, B., Eisfeld, J., Lin, K.H., and Knott, T. (2014)

Action potential characterization of human induced pluripotent stem cellderived cardiomyocytes using automated patch-clamp technology. Assay Drug Dev. Technol. 12, 457-469. 125.

Sirenko, O., Cromwell, E.F., Crittenden, C., Wignall, J.A., Wright, F.A., and

Rusyn, I. (2013) Assessment of beating parameters in human induced pluripotent stem cells enables quantitative in vitro screening for cardiotoxicity. Toxicol. Appl. Pharmacol. 273, 500-507. 126.

Sirenko, O., Crittenden, C., Callamaras, N., Hesley, J., Chen, Y.W., Funes, C.,

Rusyn, I., Anson, B., and Cromwell,E.F. (2013) Multiparameter in vitro assessment of compound effects on cardiomyocyte physiology using iPSC cells. J. Biomol. Screening 18, 39-53. 127.

Hayakawa T, Kunihiro T, Ando T, Kobayashi S, Matsui E, Yada H, Kanda Y,

Kurokawa J, Furukawa T. (2014) Image-based evaluation of contraction-relaxation kinetics of human-induced pluripotent stem cell-derived cardiomyocytes: correlation and complementarity with extracellular electrophysiology. J. Mol. Cell Cardiol. 77, 178-191.


61


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

128.

Page 62 of 76

Huebsch, N., Loskill, P., Mandegar, M.A., Marks, N.C., Sheehan, A.S., Ma,

Z., Mathur, A., Nguyen, T.N., Yoo, J.C., Judge, L.M., Spencer, C.I., Chukka, A.C., Russell, C.R., So, P.L., Conklin, B.R., and Healy, K.E. (2015) Automated video-based analysis of contractility and calcium flux in human-induced pluripotent stem cell-derived cardiomyocytes cultured over different spatial scales. Tissue Eng., Part C 21, 467-479. 129.

Maddah, M., Heidmann, J.D., Mandegar, M.A., Walker, C.D., Bolouki, S.,

Conklin, B.R., and Loewke, K.E. (2015) A non-invasive platform for functional characterization of stem cell-derived cardiomyocytes with applications in cardiotoxicity testing. Stem Cell Rep. 14, 621-631. 130.

Peters, M.F., Lamore, S.D., Guo, L., Scott, C.W., and Kolaja, K.L. (2015) Human

stem cell-derived cardiomyocytes in cellular impedance assays: bringing cardiotoxicity screening to the front line. Cardiovasc. Toxicol. 15, 127-139. 131.

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. 13, 581-594. 132.

Scott, C.W., Zhang, X., Abi-Gerges, N., Lamore, S.D., Abassi, Y.A., Peters, M.F.

(2014) An impredance-based cellular assay using human iPSC-derived cardiomyocytes to quantify modulators of cardiac contractility. Toxicol. Sci. 142, 331-338. 133.

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.


62

Page 63 of 76

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


134.

Fermini, B., Hancox, J.C., Abi-Gerges, N., Bridgland-Taylor, M., Chaudhary,

K.W., Colatsky, T., Correll, K., Crumb, W., Damiano, B., Erdemli, G., Gintant, G., Imredy, J., Koerner, J., Kramer, J., Levesque, P., Li, Z., Lindqvist, A., Obejero-Paz, C.A., Rampe, D., Sawada, K., Strauss, D.G., and Vandenberg, J.I. (2015) A new perspective in the field of cardiac safety testing through the comprehensive in vitro proarrhythmia assay paradigm. J. Biomol. Screen. 13. In press. 135.

Beattie, K.A., Luscombe, C., Williams, G., Munoz-Muriedas, J., Gavaghan, D.J.,

Cui, Y., and Mirams, G.R. (2013) Evaluation of an in silico cardiac safety assay: Using ion channel screening data to predict QT interval changes in the rabbit ventricular wedge. J. Pharmacol. Toxicol. Meth. 68, 88-96. 136.

Heijman, J., Voigt, N., Carlsson, L.G., and Dobrev, D. (2014) Cardiac safety

assays. Curr. Opin. Pharmacol. 15, 16-21. 137.

Cohen, J.D., Babiarz, J.E., Abrams, R.M., Guo, L., Kameoka, S., Chiao, E.,

Taunton, J., and Kolaja, K.L. (2011) Use of human stem cell derived cardiomyocytes to examine sunitinib mediated cardiotoxicity and electrophysiological alterations. Toxicol. Appl. Pharmacol. 257, 74-83. 138.

Doherty, K.R., Wappel, R.L., Talbert, D.R., Trusk, P.B., Moran, D.M., Kramer,

J.W., Brown, A.M., Shell, S.A., and Bacus, S. (2013) Multi-parameter in vitro toxicity testing of crizotinib, sunitinib, erlotinib, and nilotinib in human cardiomyocytes. Toxicol. Appl. Pharmacol. 272, 245-255. 139.

Clements, M., Millar, V., Williams, A., and Kalinka, S. (2015) Bridging

functional and structural cardiotoxicity using human embryonic stem cell-derived cardiomyocytes for a more comprehensive risk assessment. Toxicol. Sci. 148, 241-


63


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 64 of 76

260. 140.

Rana, P., Anson, B., Engle, S., Will, Y. (2012) Characterization of human-

induced pluripotent stem cell-derived cardiomyocytes: bioenergetics and utilization in safety screening. Toxicol. Sci. 130, 117-131. 141.

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. 142.

Eldridge, S., Guo, L., Mussio, J., Furniss, M., Hamre, J. 3rd, and Davis, M.

(2014) Examining the protective role of ErbB2 modulation in human-induced pluripotent stem cell-derived cardiomyocytes. Toxicol. Sci. 141, 547-559. 143.

Guo, L., Eldridge, S., Furniss, M., Mussio, J., and Davis, M. (2015) Use of human

induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CM) to monitor compound effects on cardiac myocyte signaling pathways. Curr. Protoc. Chem. Biol. 7, 141-185. 144.

Caruso, A., France, N., Meille, C., Greiter-Wilke, A., Hillebrecht, A., and Lave,

T. (2014). Translational PK/PD modeling for cardiovascular safety assessment of drug candidates: Methods and examples in drug development. J. Pharmacol. Toxicol. Meth. 70, 73-85. 145.

Feaver, R.E., Gelfand, B.D., and Blackman, B.R. (2013) Human haemodynamic

frequency harmonics regulate the inflammatory phenotype of vascular endothelial cells. Nat. Commun. 4, 1525-1528.


64

Page 65 of 76

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


146.

Wamhoff, B.R., Simmers, M., Cole, B., Mackey, A., Manka, D., and Blackman,

B. (2014) Altered Hemodynamics Play a Critical Role in Fenoldopam Induced Mesenteric Vasculitis in the Rat. Toxicologist 138, 299. 147.

Cole, B.K., Simmers, M.B., Feaver, R., Qualls, C.W., Collado, M.S., Berzin, E.,

Figler, R.A., Pryor, A.W., Lawson, M., Mackey, A., Manka, D., Wamhoff, B.R., Turk, J.R., and Blackman, B.R. (2015) An In vitro Cynomolgus Vascular Surrogate System for Preclinical Drug Assessment and Human Translation. Arterioscler., Thromb., Vasc. Biol. 35, 2185-2195. 148.

Dambach, D.M. (2014) Drug-induced hepatotoxicity: advances in preclinical

predictive strategies and tools. In Predictive ADMET: Integrated Approaches in Drug Discovery and Development (Wang, J., and Urban, L., Eds) pp. 443-465, John Wiley and Sons, New York. 149.

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. 150.

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. 151.

Schadt, S., Simon, S., Kustermann, S., Boess, F., McGinnis, C., Brink, A.,

Lieven, R., Fowler, S., Youdim, K., Ullah, M., Marschmann, M., Zihlmann, C., Siegrist, Y.M., Cascais, A.C., Di Lenarda, E., Durr, E., Schaub, N., Ang, X., Starke, V., Singer, T., Alvarez-Sanchez, R., Roth, A.B., Schuler, F., and Funk, C. (2015) Minimizing DILI risk in drug discovery: a screening tool for drug candidates.


65


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 66 of 76

Toxicol. In Vitro. In press. 152.

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. 153.

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., Dahmen, U., Dahmen, 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., Hammand, 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., Park, K.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., Steiger, B., Stoeber, 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 sources and non-parenchymal liver cells and their use in investigating mechanisms of hepatotoxicity, cell signaling and ADME. Arch. Toxicol. 87, 1315–1530.


66

Page 67 of 76

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


154.

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. 155.

Piersma, A.H. (2013) Innovations in Testing Strategies in Reproductive

Toxicology In Teratogenicity Testing: Methods and Protocols, Methods in Molecular Biology (Barrow, P.C., Ed) 947, 327-342. 156.

Kim, S. and Takayama, S. (2015) Organ-on-a-chip and the kidney. Kidney

Research and Clinical Practice 34, 165-169. 157.

Materne, E.M., Ramme, A.P., Terrasso, A.P., Serra, M., Alves, P.M., Brito, C.,

Sakharov, D.A., Tonevitsky, A.G., Lauster, R., and Marx, U. (2015) A multi-organ chip co-culture of neurospheres and liver equivalents for long-term substance testing. J. Biotech. 205, 36-46. 158.

Huh, D., Leslie, D.C., Matthews, B.D., Fraser, J.P., Jurek, S., Hamilton, G.A.,

Thorneloe, K.S., McAlexander, M.A., and Ingber, D.E. (2012) A human disease model of drug toxicity–induced pulmonary edema in a lung-on-a-chip microdevice. Sci. Trans. Med. 4, 159ra147. 159.

Tarrant, J.M., Dhawan, P., Singh, J., Zabka, T.S., Clarke, E., DosSantos, E.,

Dragovich, P.S., Sampath, D., Lin, T., McCray, B., La, N., Nguyen, T., Kauss, A., Dambach, D., Misner, D.L., Diaz, D., and Uppal, H. (2015). Preclinical models of nicotinamide phosphoribosyltransferase inhibitor-mediated hematotoxicity and mitigation by co-treatment with nicotinic acid. Toxicol. Mech. Meth. 25, 1-11.


67


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

160.

Page 68 of 76

Zabka, T., 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. 161.

Uppal, H., Doudement E., Mahapatra, K., Darbonne, W.C., Bumbaca, D., Shen,

B.Q., Du, X., Saad, O., Bowles, K., Olsen, S., Lewis Phillips, G.D., Hartley, D., Sliwkowski, M.X., Girish, S., Dambach, D., and Ramakrishnan V. (2015) Potential mechanisms for thrombocytopenia development with trastuzumab emtansine (TDM1). Clin. Canc. Res. 21, 123-133. 162.

Leverson, J.D., Phillips, D.C., Mitten, M.J., Boghaert, E.R., Diaz, D., Tahir, S.K.,

Belmont, L.D., Nimmer, P., Xiao, Y., Ma, X.M., Lowes, K.N., Kovar, P., Chen, J., Smith, M., Xue, J., Zhang, H., Oleksijew, A., Magoc, T.J., Vaidya, K.S., Albert, D.H., Tarrant, J.M., La, N., Wang, L., Tao, Z.F., Wendt, M.D., Sampath, D., Rosenberg, S.H., Tse, C., Huang, D.C.S., Fairbrother, W.J., Elmore, S.W., and Souters, A. (2015) Exploiting selective BCL-2 family inhibitors to dissect cell survival dependencies and define improved strategies for cancer therapy. Sci. Trans. Med. 279, 279ra40. 163.

Sung, N. S., Crowley, W. F., Genel, M., Salber, P., Sandy, L., Sherwood, L. M.,

Johnson, S. B., Catanese, V., Tilson, H., Getz,K., Larson, E.L., Schienberg, D., Reece, E.A., Slavkin, H., Dobs, A., Grebb, J., Martinez, R.A., Korn, A., and Rimoin, D. (2003) Central challenges facing the national clinical research enterprise. JAMA, J. Am. Med. Assoc. 289, 1278–1287.


68

Page 69 of 76

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


164.

Ross, J.S., Lehman, R., and Gross, C.P. (2012) The Importance of Clinical Trial

Data Sharing: Toward More Open Science. Circulation: Cardiovascular Quality and Outcomes 5, 238-240. 165.

Mellow, M.M., Francer, J.K., Wilenzick, M., Teden, P., Bierer, B.E., and Barnes,

M. (2013) Preparing for Responsible Sharing of Clinical Trial Data. New Eng. J. Med. 369, 1651-1658. 166.

Institute of Medicine (IOM). 2015. Sharing clinical trial data: Maximizing

benefits, minimizing risk. Washington, DC: The National Academies Press. 167.

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, L.N., Teisman, A., Van Ammel, K., Vargus, 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. 168.

Pierson, J.B., Berridge, B.R., Brooks, M.B., Dreher, K., Koerner, J., Schultze,

A.E., Sarazan, R.D., Valentin, J.P., Vargas, H.M., and Pettit, S.D. (2013) A public– private consortium advances cardiac safety evaluation: achievements of the HESI cardiac safety technical committee. J. Pharmacol. Toxicol. Methods 68, 7-12. 169.

Monticello, T. (2014) Drug development and nonclinical to clinical translational

databases: past and current efforts. Toxicol. Pathol. 43, 57-61. 170.

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 Discovery 14, 475-486.


69


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171.

Page 70 of 76

Fontana, R.J., Watkins, P. B., Bonkovsky, H. L., Chalasani, N.,Davern, T.,

Serrano, J., and Rochon, J. (2009) DILIN Study Group, Drug-Induced Liver Injury Network (DILIN) prospective study: rationale, design and conduct. Drug Saf. 32, 55– 68.


70

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AUTHOR HEADSHOTS AND BIOGRAPHIES:

Donna Dambach earned a VMD from the University of Pennsylvania, completed a residency in comparative anatomic pathology and is a Diplomate of the American College of Veterinary Pathology. She holds a PhD in Toxicology from Rutgers University and the University of Medicine and Dentistry of New Jersey. Donna has 25 years of experience in academia and the pharmaceutical industry as a pathologist and toxicologist. Donna joined Genentech in 2006 and established the discovery and investigative toxicology capabilities; she is currently the Head of Toxicology, Department of Safety Assessment.

Dinah Misner holds a PhD in Biomedical Sciences from UCSD. After 10 years at Roche Palo Alto, and 2 years at Celgene leading discovery and investigative toxicology labs, she joined Genentech in 2012 as the group leader of the Investigative Toxicology group, and also serves as toxicologist on discovery and development projects for small molecule teams.

Mathew Brock earned his PhD in Neuroscience from Stanford University followed by a Gras Fellowship and postdoctoral training at NASA Ames Research Center researching using nanopore-based sensors to characterize DNA fragments. Mat joined Genentech in December 2014 in the Investigative Toxicology group leading the cardiotoxicology screening and investigative platforms.


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Aaron Fullerton earned his PhD in Pharmacology and Toxicology from Michigan State University followed by postdoctoral training at the NIH in the laboratory of Dr. Lance Pohl researching immune mechanisms of drug-induced liver injury. Aaron joined Genentech in December 2014 in the Investigative Toxicology group in Safety Assessment.

William Proctor earned his PhD in Pharmaceutical Sciences from the University of North Carolina at Chapel Hill followed by postdoctoral training at the NIH in the laboratory of Dr. Lance Pohl researching immune mechanisms of drug-induced liver injury. Will joined Genentech in December 2013 in the Investigative Toxicology group in Safety Assessment.

Jonathan Maher received his Ph.D. in 2005 in Toxicology from the University of Kansas Medical Center. He began his career at Abbott (now AbbVie) in 2009 in the investigative toxicology group, he then joined Genentech in 2013 and is currently a discovery toxicology project representative serving across multiple therapeutic areas.

Dong Lee holds a Ph.D. in Immunology from Harvard University. After five years as a lead Investigative Toxicology Scientist for Pfizer, he joined Genentech in 2012 as a Discovery Toxicologist supporting small molecule drug discovery projects in oncology and non-oncology therapeutic areas.


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Kevin Ford holds a Ph.D. in Molecular Toxicology from the University of California, Berkeley. He joined Genentech in 2009 as a Chemical and Computational Toxicologist, specializing in the elucidation of mechanisms of toxicity and genotoxic impurity assessments. He also supports drug discovery projects as a Discovery Toxicologist in and non-oncology therapeutic areas.

Dolo Diaz earned a Ph.D. in Toxicology from the University of Washington, and did post-doctoral work at the Fred Hutchinson Cancer Research Center. Dolo joined CEREP Inc. in 2003, where she built their in vitro toxicology screening group. Dolo has been at Genentech since 2007, where she is currently an Associate Director and leads the Small Molecule Discovery Toxicology group.


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Table 1: Target Safety Assessment (TSA) Elements

Assessment Component Target Pharmacology

Items for Consideration • • • • •

Therapeutic Area Patient Population

• • •

Competitive Landscape

• •

Core Lead Optimization Strategy Components (Small Molecule)

• • • • • • •

Example Evaluation Resources

Potential undesired ontarget pharmacology Distribution of the target across tissues Species homology of the target Target isoforms Transgenic (GERM*) strategy

• • • •

Level of safety required Unacceptable/Undesirable Clinical AEs Potential interactions with co-meds

• • •

Literature Databases Engage Clinical and Drug Safety representative

Safety of competitors (same target) Safety of competitors (same/related pathway)

• • • •

Literature Databases Competitive intelligence Internal assessment of competitors (biologics or small molecules)

Promiscuity and selectivity Intrinsic cytotoxicity Genetic toxicology (nonlife threatening) Target Organ assessments ADME assessments Target distribution Safety biomarker strategy

•

Physiocochemical risk profile; Secondary pharmacology binding assays; Target selectivity assays (e.g. kinase) Primary hepatocyte viability Ames II; in vitro micronucleus; GreenScreen, etc. Tissue-specific models (eg. CV, hematopoietic, hepatic) Toxicophore assessment; covalent binding/burden; metabolic stability; metabolite profile (reactive metabolite formation); metabolic phenotyping; transporter assessment; blood-brain-barrier assessment; tissue accumulation assessment

•

• • • •

Literature Databases Internal knowledge Genetic animal models (KO, cKO, Tg mice) Genetic mutations in humans

*GERM, genetically modified rodent models


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Table 2. Prospective and Retrospective Assessment Components

Prospective Assessment Core Components

Target Profile

Selectivity/Promiscuity

• • •

High selectivity Low promiscuity Acceptable functional effects (agonist/antagonist)

Intrinsic Cytotoxicity

•

Not present or minimal

ADME/PK Risk

• • •

Low intrinsic clearance No/minimal reactive metabolite formation/covalent binding (burden) Acceptable distribution profile (tissue/cellular)

Genetic Toxicity (non-life threatening indications)

• •

Eliminate mutagens and clastogens Minimize aneugens

Target Organ Toxicity Assessment

• • •

Acceptable electrophysiological and hemodynamic cardiovascular profiles Minimize risk factors for DILI* Acceptable profile for therapeutic area indication

•

Acceptable profiles for therapeutic area indication

Target-Specific Counter screens

Retrospective Assessments Hypothesis-driven Investigation

Drivers • •

Follow-up to TSA assessment of possible on-target risks and develop customized counter-screens Follow-up to observed toxicities during in vivo studies to determine drivers (on- versus off-target) and characterize translation risk and monitoring

*DILI, drug-induced liver injury


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FIGURE LEGEND Figure 1. Components of the Discovery Toxicology Safety Lead Optimization and Candidate Identification Framework


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