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Advancing Predictive Hepatotoxicity at the Intersection of Experimental, In Silico and Artificial Intelligence Technologies Keith Fraser, Dylan M. Bruckner, and Jonathan S. Dordick Chem. Res. Toxicol., Just Accepted Manuscript • DOI: 10.1021/acs.chemrestox.8b00054 • Publication Date (Web): 03 May 2018 Downloaded from http://pubs.acs.org on May 4, 2018

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Chemical Research in Toxicology

Advancing Predictive Hepatotoxicity at the Intersection of Experimental, In Silico and Artificial Intelligence Technologies

Keith Fraser, Dylan M. Bruckner, and Jonathan S. Dordick*

Department of Chemical and Biological Engineering, and Department of Biological Sciences Center for Biotechnology and Interdisciplinary Studies Rensselaer Polytechnic Institute Troy, NY 12180

*To whom correspondence should be addressed ([email protected])

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Chemical Research in Toxicology 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

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Abstract Adverse drug reactions (ADRs), particularly those that result in drug-induced liver injury (DILI) is a major cause of drug failure in clinical trials and drug withdrawals. Hepatotoxicitymediated drug attrition occurs despite substantial investments of time and money in developing cellular assays, animal models and computational models to predict its occurrence in humans. Underperformance in predicting hepatotoxicity associated with drugs and drug candidates has been attributed to existing gaps in our understanding of the mechanisms involved in driving hepatic injury after these compounds perfuse and are metabolized by the liver. Herein, we assess in vitro, in vivo (animal) and in silico strategies used to develop predictive DILI models. We address the effectiveness of several 2D and 3D in vitro cellular methods that are frequently employed in hepatotoxicity screens and how they can be used to predict DILI in humans. We also explore how humanized animal models can recapitulate human drug metabolic profiles and associated liver injury. Finally, we highlight the maturation of computational methods for predicting hepatotoxicity, the untapped potential of artificial intelligence for improving in silico DILI screens and how knowledge acquired from these predictions can shape the refinement of experimental methods.

Keywords: In vitro toxicology; hepatotoxicity; organ-on-chip; in silico toxicology; artificial intelligence and machine learning.

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1. Introduction Despite expenditure of billions of dollars over the last several decades, the development of successful new drugs and therapeutic strategies for disease mitigation remains a highly inefficient process. Adverse drug reactions (ADRs) remain among the five leading causes of preventable death in the United States and have become a more significant reason for the withdrawal of drugs from the market and termination of drug candidates in clinical trials.1,2 Despite higher research and development spending, even today approximately 90% of all compounds that enter clinical trials fail to gain regulatory approval as a result of unexpected poor efficacy and unpredicted toxicity.2 This has placed tremendous pressure on the pharmaceutical industry to develop newer, more efficient approaches to identify and eliminate drug candidates with poor safety profiles as early in development as possible. Animal models have largely underperformed in predicting potential drug and drug candidate toxicity because they fail to recapitulate many aspects of human physiology, and as a result, show poor concordance between species.3 In vitro cellular models that accurately reflect human physiology have shown the potential to be cost effective alternatives to animal models that improve the accuracy associated with predicting drug toxicity early in development.4,5 However, simple cellular models cannot incorporate pharmacological contributions to toxicity, including the effect of serum binding and drug clearance, and are unable to represent effective models for chronic toxicity mechanisms. Nonetheless, advances in multi-cellular, 3D organoid “tissue-onchip” techniques have led to improved predictions for acute drug toxicity. Since the liver is highly perfused and is also the “first-pass” organ for all orally administered drugs it represents a frequent site of xenobiotic toxicity in humans. Animal studies and in vitro assays have been instrumental in shedding some light on drug-induced hepatotoxicity and improving our understanding of how drug induced liver injury (DILI) drives cholestasis, jaundice, hepatitis, fibrosis and other disease phenotypes. A wealth of data exists from in vivo and in vitro 3


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ADMET (Absorption, Distribution, Metabolism, Excretion and Toxicology) studies. However, predictive models built on these datasets have limited applicability due in part to superficial correlations between in vitro data and adverse human outcomes and poor concordance of liver effects between species in in vivo studies and human subjects.6,7 These major pharmacological problems highlight a critical need to develop new methodologies that improve our understanding of ADRs, and resulting DILI. To overcome this fundamental challenge in drug discovery, there has been rapid growth in the development and use of in silico techniques. Coupled with acquisition of high-throughput human cellular data and cheminformatics, in silico models can be provided with the necessary data to generate training sets leading to improved predictive outcomes, particularly in DILI. Indeed, over the past decade significant effort has been directed toward developing computational methods capable of extracting relationships across disparate datasets to advance our understanding of the risk of DILI. These methods have been used to probe both acute and idiosyncratic causes of DILI by modeling the impact of drugs on nuclear receptor activation,8 mitochondrial function,8,9 transporter inhibition10 and metabolism11–14 in hepatocytes, as well as to predict structural sites of metabolism on drugs and drug candidates.15 Although these in silico methods have been instrumental in improving risk assessment of drugs and drug candidates, there is still room for improvement in elucidating the molecular phenotypes in hepatocytes that define highly toxic, mildly toxic and non-toxic compounds. Our inability to effectively capture the significance of these factors in defining the pharmacologic profile of administered drugs in existing models makes it difficult to conduct hazard assessment studies broadly, let alone in any personalized way. Herein, we summarize the state-of-the-art as it pertains to predictive DILI models associated with approved drugs and drug candidates. We discuss how these approaches can be used to advance better and safer drug leads into the clinic. Additionally, we highlight areas of untapped

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potential and assess strategies by which computational methods can be used to better leverage publicly available curated and non-curated datasets to transform the study of hepatotoxicity from a serendipitous endeavor to a more streamlined approach with predictable outcomes. Machine learning tools have been used with some success in diagnosing disease and shaping therapeutic strategies for patients.16 These successes are predicated on the availability of high quality clinical data used to build predictive disease and treatment models. Conversely, the availability of high quality clinical data for toxicology model development is limited. This impacts our ability to make similar progress to that observed with deep learning neural networks in oncology, neurology and a few other clinical areas. Finally, we assess the ability of integrated in silico–in vitro methods to bridge this divide. Such approaches are anticipated to help advance our mechanistic understanding of DILI and better inform future risk assessment strategies. 2. Mechanisms for DILI The mechanisms of DILI are both well established and idiosyncratic. With respect to the former, tissue injury by drugs and their reactive metabolites is initiated through several mechanisms that include transporter inhibition, oxidative stress, mitochondrial dysfunction, transcription disruption, adduct formation (covalent binding) and idiosyncratic means (Figure 1).1,17 While a detailed summary of mechanisms for DILI lie outside the scope of this review; nonetheless, it is important to provide some insight into the impact of disruption of these mechanisms and identification of pharmacologically relevant endpoints that can serve as the basis for evaluating the performance of predictive methods/models of hepatotoxicity. A major determinant of the pharmacological response of hepatocytes to drug uptake is the regulation of drug metabolizing enzymes (DMEs) and drug transporters.17 The expression and activity of these hepatic proteins determine systemic and tissue exposure to drugs,17 with dysfunction manifesting as varying forms of tissue injury. DMEs facilitate the transformation of xenobiotics to metabolites that are subsequently excreted in urine and bile, although in some 5


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cases, metabolites are cytotoxic, particularly as a result of highly reactive chemical species.18 Transporters mediate the influx of xenobiotics into hepatocytes and facilitate their efflux after transformation by DMEs. In addition to their complementary roles in regulating drug disposition, DMEs and transporters share similar molecular mechanisms that govern hepatic expression.17 Expression of DMEs and drug transporters in the liver is coordinated by several ligand activated transcription factors.17,19 Some of the more prominent nuclear receptors include the farnesoid X receptor (FXR), liver X receptor (LXR), constitutive androstane receptor (CAR), pregnane X receptor (PXR), peroxisome proliferator activated receptor α/γ (PPAR α/γ) and the glucocorticoid receptor (GR). In aggregate, these receptors promote gene activation that governs DME expression, bile and bilirubin production and elimination, phospholipid excretion, among other activities.19 As described above, DMEs facilitate the conversion of xenobiotics to a range of intermediates, several of which are activated and can prove to be harmful. Hepatic drug metabolism is mediated primarily by phase I (most notably cytochrome P450s, flavin-containing monooxygenases, and monoamine oxidases) and phase II enzymes (primarily UDPglucuronosyltransferases,

sulfotransferases,

glutathione

S-transferase,

and

N-acetyl

transferases).1 In some cases, reactions catalyzed by these DMEs generate reactive and conjugated metabolites, which can cause hepatotoxicity. Reactive metabolites generated by phase I enzymes contribute to several biochemical processes such as lipid peroxidation, protein damage, DNA damage, induction of the endoplasmic reticulum stress response pathway and the mitochondrial dysfunction (Figure 1).1 Conjugated metabolites (protein adducts) generated by phase II enzymes are also capable of initiating cell death in hepatocytes along with a hypersensitive immune response through T-cell recruitment to hepatocytes.20 Bioactivation on structurally similar molecules may be strikingly different.21 For example, acetaminophen (APAP) and its nontoxic regioisomer N-acetyl-m-aminophenol (AMAP) are both metabolized to quinone

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derivatives, yet AMAP metabolites bind mainly to cytosolic and microsomal proteins while APAP metabolites bind preferentially to mitochondrial proteins, resulting in APAP mediated mitochondrial damage and cell death.21 With a broader array of compounds, Hanzlik et al.22 analyzed 302 reactive metabolites that target a wide range of proteins and showed that the most toxicologically relevant protein interacting partners were heavily involved with intracellular signaling pathways, protein folding, unfolded protein response and regulation of apoptosis. This work was instrumental in improving our understanding of the impact of disrupting intracellular protein-protein interaction with chemically reactive metabolites and the contribution of these events to disrupting cellular homeostasis. Mitochondrial dysfunction generally occurs through either direct damage to the respiratory chain, or through secondary mechanisms such as oxidative stress or lipid accumulation.23 Cellular oxidative stress activates MAP kinase ultimately resulting in the phosphorylation of cjun-N-terminal kinase (JNK), which then translocates to the mitochondria and further amplifies the mitochondrial oxidant stress. Enhanced mitochondrial oxidative stress triggers the opening of the mitochondrial permeability transition (MPT) pore and cell necrosis.24 Such oxidative stress has been suggested to be critical in DILI progression through inhibiting the synthesis of GSH, an important scavenger of reduced oxygen and nitrogen species. Mitochondrial dysfunction is also known to de-energize cells via ATP depletion.25 Given that ATP dependent hepatic transporters are responsible for hepatobiliary excretion, reduction in hepatic ATP levels lead to accumulation of cytotoxic levels of bile acids within hepatocytes either through transporter inhibition or reduction in transporter function has been known to disrupt mitochondrial function. This mechanism may be critical in the observed cytotoxicity of nefazodone, benzbromarone, and troglitazone.25 Hepatic ATP-binding cassette (ABC) transporters at the basolateral and apical surface on hepatocytes play a central role in regulating the transit of a wide range of molecules into and out

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of hepatocytes,19 thereby resulting in bile acid homeostasis and coordinating adaptive response to cholestasis. Whereas canalicular ABC transporters are responsible for the transport of bile acids, bilirubin, phosphatidylcholine, cholesterol and drugs/xenobiotics across the bile canalicular membrane, basolateral ABC transporters transport bile acids into the blood.19 There is a growing body of evidence that suggests that cholestatic forms of DILI result from drug- or metabolite-mediated inhibition of hepatobiliary transporters.26 Inhibition of the bile salt export pump (BSEP) by drugs has been implicated as a risk factor for in DILI; a hypothesis based on evidence that there is a genetic predisposition to cholestatic DILI due to BSEP gene mutations. Specifically, reduced bile acid excretion was observed in patients with mutations in the BSEP gene in comparison to normal patients; with patients having the mutated BSEP gene developing liver injury suspected to be due to the accumulation of bile acids. However, this hypothesis has not been completely accepted as yet as some recent studies have suggested that BSEP inhibition in vitro are not useful predictors of DILI.26 Although the mechanism underlying idiosyncratic DILI, as expected, remains unclear, a great deal of evidence points to the activation of the immune system as a causative agent. Whereas non-immune mediated mechanisms of DILI include direct damage to hepatocytes, mitochondrial dysfunction, transporter inhibition, etc., idiosyncratic mechanisms involve activation of both the adaptive and innate immune system and their interaction with hepatic tissue.20 The recruitment of cytotoxic CD8 T-cells in the liver and circulating drug specific T-cells highlights the important role of cell mediated immunity in the underlying mechanisms if idiosyncratic DILI. The discovery that an association of specific alleles of human leukocyte antigen (HLA) class I and II with DILI (e.g., with amoxicillin-clavulanate, ticlopidine, ximelagatran, flucloxacillin, lumiracoxib, lapatinib, and carbamazepine) provides further evidence of the involvement of the adaptive immune system in idiosyncratic DILI.20,27,28 Hepatic protein adducts are capable of forming haptens, which when presented on the surface of

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hepatocytes, makes them recognizable to the immune system as a damaged cell, thus activating T-cell recruitment and making these hepatocytes more susceptible to T-cell toxicity.1 3. Correlation between in vitro hepatotoxicity studies and observed human outcomes To date in vivo animal toxicity studies have been an integral component in drug candidate safety assessment. However, despite their ubiquitous use in preclinical studies animal models have underperformed in identifying concordant target organ toxicities in humans.3,4 Consequently, significant emphasis has been placed on developing methods focused on cellular approaches for assessing drug safety.4 In particular, the US National Institutes of Health (NIH) and the US Environmental Protection Agency (EPA) have initiated programs focused on human cellular approaches for assessing the safety of drugs/drug candidates and environmental chemicals.4 Unlike animal models that are costly, time consuming and poorly recapitulate human physiology, in vitro cellular models that accurately reflect human physiology have the potential to function as cost-effective tools that improve the prediction of drug toxicity early in the development pipeline.4,5 The applicability of these in vitro models as a screening tool is broad and can be expanded to food additives, pesticides and industrial chemicals, and into areas where animal testing is prohibited such as cosmetics.29 3.1 In vitro toxicology platforms In vitro systems frequently employed in hepatotoxicity studies include 2D and 3D cellular assays that cover a broad range of toxicity mechanisms and can be applied across many areas of pharmacology. These in vitro platforms are summarized in Figure 2. In vitro cellular systems enable high-throughput screening of drugs, drug candidates and chemicals for toxicological endpoints, including fibrosis, cytotoxicity, mitochondrial toxicity and genotoxicity, among others. In addition, in vitro cellular models can be used to understand the molecular pathways of drug/chemical toxicity. Inherent in such studies is the need to understand the effect of drug

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metabolism and transport on cellular toxicity, which would better emulate the physiological processes involved in first-pass metabolism in the liver. To this end, most 2D hepatotoxicity models use cell cultures of rat, mice or human primary hepatocytes, or animal or human transformed/immortalized liver cell lines in flasks or microtiter plates. Animal primary hepatocytes can be standardized genetically with respect to drug metabolism enzymes (DMEs), transporters, molecular pathways, etc. Human primary hepatocytes, however, are typically pooled from multiple donors to achieve sufficient cells for broad in vitro toxicology studies. In all cases, primary hepatocytes are subject to significant DME instability that alters the toxicological response of hepatocyte cultures to drugs and drug candidates. Transformed hepatoma cell lines, e.g., human HepG2 and mouse Hepa1C1C7, and immortalized cell lines, e.g., HepaRG and THLE-2, are simpler to culture and in some cases, can be used to model different aspects of DILI such as transport, metabolism and viability. However, in most cases, the expression of DMEs is either low or variable, particularly in the 2D environments that have been employed routinely in early-stage screening programs. As opposed to 2D models, 3D cultures can be developed within microenvironments that can mimic at least some of the native environments found in vivo. A good example of this is the differential expression of proteins in HepG2 between 2D and 3D cultures (Figure 3). Meli et al.30 showed that HepG2 in a 3D alginate matrix at the microscale upregulates the expression of cytochromes P450 (CYP450s) as well as proteins involved in cell signaling (Figure 3a). This suggests that the 3D microenvironment can enhance physiological function even in a transformed cell line. More recently Malinen et al.31 showed that HepaRG liver progenitor cells in 3D environments are capable of differentiating into multicellular spheroids with apicobasal polarity and functional bile canaliculi-like structures. Bell et al.5 followed this up with a proteome analysis to better understand the molecular changes that confer the functional advantages of multicellular spheroids over hepatocytes cultured in 2D monolayers. In their work Bell et al.5

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reported a proteome analysis of primary human hepatocytes cultured in 2D monolayers and 3D spheroids in which the pattern of differentially expressed proteins of hepatocytes cultured in 3D clustered more tightly to proteins expressed in in vivo liver samples than hepatocytes cultured in 2D monolayers (Figure 3 b-c). Interestingly, the 3D cultures used in this study were able to preserve in vivo phenotypes, as evident by the extent to which the proteomic profile of 3D cultures clustered with the liver slices from the same donor (Figure 3d). Further analysis of the differences in protein expression profiles in two recent studies by Bell et al.5,32 revealed an even more striking disparity between cells grown in 2D vs 3D when emphasis was placed on proteins involved in absorption, distribution, metabolism and excretion with the proteome profile of hepatocytes cultured in 3D spheroids, again clustering more tightly to profiles from in vivo liver samples than hepatocytes cultured in 2D monolayers (Figure 3e). Vorrink et al33 used a 3D spheroid culture model in combination with high-resolution mass spectrometry to demonstrate that primary human hepatocytes grown in 3D spheroid systems are able to maintain metabolic stability for multiple weeks in comparison to primary human hepatocytes from the same donor cultured in 2D monolayers. These molecular features suggest that spheroid models may effectively function as robust models for studying the effect of chronic exposure of drugs on the liver. Other emerging 3D hepatocyte and hepatoma cultures, more so than 2D models, can be used in a range of architectures to predict hepatotoxicity. These 3D models include hepatocyte sandwich cultures, 3D bioreactors, 3D micropillar microarrays, microphysiological systems, and in the limit, ex vivo precision cut liver slices that can be used to study pharmacologic responses in a physiological milieu (Figure 2). Sandwich cultures are a popular manifestation of these methods, and employs confluent monolayers of primary hepatocytes attached to adsorbed or gelled rat tail collagen and then overlaid with another gelled ECM (these can include collagen, Matrigel, chitosan, hyaluronic acid, polyelectrolytes, among others) to create the so-called ECM

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“sandwich” culture model.34 Unlike simple monolayers of confluent hepatocytes, the ECM sandwich model does not prevent differentiation.35,36 Ghibellini et al.37 showed that hepatocytes cultured in the ECM-sandwich format allows for the formation of robust networks of bile canaliculi after 4-6 days. However, this extended time in culture results in downregulation of CYP450 activities.36,38 Consequently, sandwich culture models have been limited largely to the evaluation of parent drug efflux into the bile canaliculi and drug induced cholestasis associated with perturbation of this process. Because of this limitation conventional sandwich culture models have underperformed giving only ~30-50% prediction of clinical outcomes when used in broad drug toxicity screens and drug metabolite identification.39,40 It is possible, however, to improve the sensitivity of toxicity and metabolite detection by stabilizing the primary human hepatocyte phenotype using contact coculture with nonparenchymal cells (e.g., endothelial cells, Kupffer cells, and macrophages).41,42 This improvement is due in part to the ability to chronically dose stable primary human hepatocytes with drugs to mimic clinical drug dosing over 4-24 h in conventional formats. Another physiologically relevant in vitro platform used for screening for hepatotoxicity is the organ-on-a-chip, or microphysiological systems, which includes micropatterned cocultures (MPCCs), perfused multiwell plates along with microfluidic liver and multiorgan chips based on specific tissue cell types. Advances in microfabrication have made it easier to maintain specific organ-like function. Khetani and Bhatia capitalized on this concept and showed that primary human hepatocytes can be seeded on 2D islands of collagen-patterned substrate insulated by supporting mouse 3T3-J2 fibroblasts to generate MPCCs (Figure 2). These MPCCs enable hepatocytes to retain hepatic functions including metabolism, nuclear factor expression, transport, urea synthesis, albumin secretion and drug sensitivity.36 The use of a repeat dosing regimen with primary human hepatocytes cultured in this platform was shown to be more sensitive to hepatotoxicants than a comparative 2D HepG2 high-content screen.42,43 When

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compared to sandwich cultures created with hepatocytes from the same human donor and exposed to the same drug set, MPCCs were shown to be more sensitive than sandwich culture models. In cross-species studies the MPCC hepatocyte system was also able to better reflect the metabolic profiles of hepatotoxins observed in vivo.44 Other groups have also shown that the MPCC hepatocyte system is robust and can generate similar hepatotoxic sensitivities when primary human hepatocytes are replaced with induced pluripotent stem cell (iPSC) derived hepatocytes, and that the use of MPCCs can be expanded to study effects of inflammation on drug metabolism and transport.45,46 More broadly, microfluidic chip based systems further improve our ability to approximate physiological conditions in in vitro models by more accurately emulating mechanical stress, drug exposure and nutrient exchange.47 Domansky et al.48 developed a perfused array of bioreactors containing hundreds of 3D hepatic aggregates that can be used to screen for drug metabolism, pharmacokinetics, hepatotoxicity and normal liver function. More recently Bhise et al.49 developed a more advanced bioreactor system based on cultured 3D human HepG2/C3A spheroids encapsulated within photocrosslinkable gelatin methacryloyl (GelMA) hydrogel for use in screening hepatotoxic response relative to in vitro and animal models. These perfused bioreactors function effectively because of how well they mimicked in vivo architecture and their ability to maintain protein expression levels at close to physiological levels in culture.48–50 Other chip-based systems, such as the HepG2/C3A system developed by Bavli et al.23, allow for more dynamic monitoring of metabolic flux of glucose and lactose, providing real-time analysis of minute changes from oxidative phosphorylation to anaerobic glycolysis, an early indicator of mitochondrial stress. By quantifying the dynamics of cellular adaptation to mitochondrial damage and the resulting redistribution of ATP production during rotenone and troglitazone induced mitochondrial stress Bavil et al.23 were able to demonstrate that troglitazone, a known hepatotoxic compound shifts metabolic fluxes at concentrations previously

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regarded as safe, highlighting a possible mechanism of action for its observed effect in humans. More complex microfluidic platforms incorporate multiple organ systems on individual biochips to develop systems with improved physiological relevance. Oleaga et al.51 described how a perfused multiorgan system can be developed using cardiac, muscle, liver (HepG2/C3A) and neuronal units to screen the toxic potential of acetaminophen, valproic acid, doxorubicin and atorvastatin, which are known to affect each individual unit. These authors were further able to show that the data generated from these multiorgan systems were in line with in vivo data. Simpler configurations of these multiorgan systems can also be used to more effectively study inter-tissue communication and screen for off target toxicities. Choucha-Snouber et al.52 described a liver-kidney coculture chip with two culture microchambers containing a renal cell line (MDCK) and a hepatic cell line (HepG2 or HepaRG) that were used to demonstrate the nephrotoxic effect of the anticancer drug ifosfamide through the formation of a cytotoxic metabolite. Another example that highlights the effectiveness of simplified multiorgan systems is the modular, pumpless body-on-a-chip platform for the co-culture of GI tract epithelium and 3D primary liver tissue system described by Esch et al.53 that allows for interrogation of liver physiology and metabolic state. Cytochrome P450 activities in liver cells grown in co-culture with GI tract epithelium were induced to levels higher than those previously observed in microfluidic liver only systems. Additionally, Esch et al.53 showed how passive fluid control can be used to create near physiologic fluid flow, as would be needed for high throughput drug screening. Integrated liver-gut combination systems have been developed, which can be used to elucidate inflammatory inter-tissue crosstalk and aid in more detailed pharmacokinetic insight.54,55 As these and other more advanced integrated gut and liver microphysiological systems are developed they will make it easier to construct models of enterohepatic signaling and its role in unanticipated drug toxicities, an underexplored area in current drug discovery.

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4. Humanized mouse models The failure of existing preclinical systems to predict human pharmacokinetics and humanspecific metabolism pathways is exacerbated by interspecies differences in drug metabolism.56– 58

These differences produce qualitative and quantitative distinctions between metabolites

generated in humans and animals, making it difficult to identify accurately human-specific drug metabolites.56 As a result, it has been difficult to use mice, rats, dogs, and even non-human primates in preclinical toxicology studies to predict adverse outcomes as a result of human drug metabolism and human specific metabolites.57,58 Furthermore, there are ethical concerns in performing toxicological studies on more advanced animals. Critically, even substantial differences exist among diverse human populations, which suggest that animal toxicology models are inherently flawed. Such models would be ill-suited as medicine advances towards a more precision basis. To address existing gaps in our mechanistic understanding of metabolism-based DILI in humans and minimize the occurrence of future incidents of ADRs, chimeric mice (e.g., TK-NOG mice, PIRF, FRG) with humanized livers have been developed.59– 61

These mice have the potential to mediate human-specific drug metabolism that may be

relevant for preclinical screens and represent an improved platform for screening human specific drug toxicities relative to existing rodent and non-rodent models.62,63 Chimeric TK-NOG mice have been used in a range of studies that include predicting the pattern of human drug metabolism64,65 and prospective investigation of the occurrence of human drug-drug interactions (DDIs)56 (Table 1). Studies involving diclofenac65 and lumiracoxib60 in humanized TK-NOG mice revealed that mouse chimeras can generate metabolic profiles comparable to humans, paving the way for a number of future studies that have begun to expand our understanding of metabolism-based DILI. The use of humanized TK-NOG chimeric models in DDI studies highlights a unique opportunity for adopting this model for broader use in drug combination studies that employ screens for clinically relevant DDIs associated with

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hepatotoxicity, an issue affecting a large segment of the general population that uses multiple prescription drugs daily. Even with a relatively small number of chimeric mice, it is possible to enhance the predictive value of in vitro screens that employ the exact same primary human hepatocytes in both the mice and the in vitro platforms, which will avoid human genetic differences and serve as a validation tool for identifying relevant human drug toxicity. Indeed, a major opportunity with these chimeras is the ability to perform more advanced toxicity analyses involved with drug candidate pharmacokinetics and pharmacodynamics, which has the potential to improve the accuracy of quantitative in vitro-in vivo correlations. 5. Computational toxicology Computational toxicology can broadly be defined as the development and application of mathematical and computational models and algorithms to predict adverse effects of chemicals and understand their mechanisms of action.66 The field of computational toxicology is multidisciplinary and incorporates knowledge of molecular pathways, physicochemical properties, toxicity profiles, dosage, clearance, patient susceptibilities and adverse outcomes associated with the use of therapeutics and environmental exposure to chemicals.67–70 Computational models generated are used to determine correlations across large datasets, many of which are publicly available. These toxicity models can be used to assess the hazard potential of drugs, their mechanism of action, patient susceptibility, and inform tailored programs that can be used to develop appropriate experimental screens. 5.1 Experimentally derived databases An early attempt to correlate in vitro toxicology to the wealth of in vivo animal data involved the Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM), which was formed to establish guidelines, recommendations and possible regulations on use of chemicals, particularly those used in the environment. Interestingly, ICCVAM, and its European

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equivalent ECVAM, were focused on environmental chemicals, not pharmaceutical candidates, although many of the techniques employed mirrored those used in early stage drug toxicity screening. ICCVAM and ECVAM studies resulted in large databases of correlations of in vitro rodent and human cells (derived from liver, kidney, central nervous system, lung and skin) cytotoxicity IC50 data and corresponding mouse LD50 data.71,72 However, the dearth of human toxicity information required alternative strategies to extract value from these richer and more relevant datasets. To develop in vitro models that would be more predictive of relevant human adverse outcomes, the EPA developed the ToxCast program73 to evaluate experimentally the hazard potential of over 1800 compounds (Figure 4). Tox21, a successor to ToxCast, was established as a collaboration between the EPA, the NIH National Toxicology Program, and the NIH Chemical Genomics Center (NCGC), and comprised over 12,000 compounds. Since their inception, these programs have generated a broad spectrum of high-throughput/high-content biochemical and cell-based data74 that have begun to be used to derive biological and chemical profiles predictive of in vivo toxicity endpoints. A key goal of these high-throughput methods is to create a system from which the results generated could be used as the basis for new toxicity screening and prioritization approaches in retrospective studies aimed at identifying new mechanisms of toxicity and identification of key proteins and signaling/metabolism pathways involved in human toxicology. These studies have focused on assessing the impact of xenobiotics on transcriptional regulators,74 rat reproduction,75 mitochondrial function,76 signaling pathways77 and enzyme inhibitors.78 The European Center for Ecotoxicology and Toxicology of Chemicals (ECETOC) developed a similar program to aggregate relevant human toxicity endpoints, and resulted in the construction of the Human Exposure Assessment Tools Database (HEATDB). Additionally, the EU-ToxRisk program provided an integrated in vitro and in silico toxicity screening paradigm for use as an

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alternative-to-animal tool for chemical safety evaluation,79 which focused on repeat dosing systemic toxicity and developmental and reproductive toxicity. Data from these high-throughput screens are freely available. 5.2 Cheminformatics Cheminformatics-based screening methods are some of the more popular computational methods used to determine the potential hepatotoxicity of drugs and drug candidates. Cheminformatics takes advantage of advances in computer science, chemistry and toxicology to identify unique physicochemical characteristics that can be used to accurately predict human toxicity. In assessing the potential hepatotoxicity of chemical compounds, quantitative structureactivity relationships (QSARs) are generated that take advantage of 2D and 3D chemical descriptors. A critical assumption here is that the chemical descriptors used incorporate all the necessary information that describes specific interactions between drugs in question and unique biological targets. Structural alerts, one type of molecular descriptor, enables determination of whether a molecule in its parent form is likely to be toxic and can form toxic reactive metabolites.80,81 A number of knowledge based systems, such as DEREK, Toxtree, ACToR, TOPKAT, MCASE among others, have been developed to predict toxicities associated with xenobiotics using available structural information.82–85 However, this approach is not an effective sole predictor of toxicity. This was highlighted in a retrospective analysis by Stepan et al.80 of 68 drugs with known toxicity and 200 “approved” drugs (top 200 prescriptions and sales in the US in 2009), presumably with limited toxicity. The authors focused on trends in physiochemical characteristics, daily doses, structural alerts, evidence of reactive metabolite formation as well as toxicity mechanism mediated by parent drugs. In this study 78-86% of the toxic drugs contained structural alerts and evidence suggesting that reactive metabolite formation was a causative factor for toxicity (RM positive) in 62-69% of these molecules. In some cases, mitochondrial toxicity and BSEP inhibition mediated by parent drugs were also potential drivers

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of toxicity. Further analysis also revealed that approx. half of the top 200 drugs contained one or more structural alerts, with many found to be RM positive. Few compounds within this set were associated with idiosyncratic DILI despite years of patient use. This was attributed to the low daily dose of administration of the top 200 drugs in comparison to the higher daily doses of toxic drugs. In addition, competing detoxification pathways or alternate non-metabolic clearance routes provided suitable justification for the safety record of RM positive drugs in the top-200 category. Since structural alerts do not effectively model if, when and why metabolism renders a molecule toxic, Dang et al.81 developed a machine learning method that leverages detoxification pathways in assessing the toxicity risk of drug candidates. The authors found that machine learning models of cytochrome P450 metabolism can be used to predict the context-specific probability that a structural alert will be bioactivated in a given molecule. Dang et al.81 focused on furan, phenol, nitroaromatic and thiophene alerts, each capable of producing reactive metabolites through independent metabolic pathways. Models of epoxidation, quinone formation, reduction and S-oxidation were used to predict the bioactivation of drugs with these structural features. The resulting model was capable of separating bioactivated and nonbioactivated furan-, phenol-, nitroaromatic-, and thiophene- containing drugs with AUC of 100%, 73%, 93% and 88%, respectively. This study illustrated how metabolism models can accurately predict whether alerts are bioactivated, and thus, serve as a practical approach to improve the interpretability and usefulness of structural alerts. QSAR models capitalize on a range of chemical and molecular descriptors and machine learning algorithms, such as support vector machine learning, k-nearest neighbor, random forest, linear discriminant analysis, naïve Bayes analysis and neural networks. These computational approaches have begun to be used more frequently to assess the hepatotoxic risk of drugs and drug candidates.85,86 In fact, machine learning methods have driven the

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development of highly accurate models when screening relatively modest libraries (e.g., < 1000 compounds) but degenerate as libraries increase in size.86–89 This scale-based degeneration arises because of the inability of purely cheminformatic-based methods to leverage adequately biological changes that are representative of hepatotoxic phenotypes. A good example of this was described in Chen et al’s.86 work to develop a QSAR model to assess the hepatotoxic risk of a library of compounds. The model was initially trained on a set of 197 compounds from the National Center for Toxicological Research (NCTR) dataset and then tested on three validation datasets – NCTR validation set (190 drugs: hepatotoxic/non-toxic = 95/95), Xu dataset90 (241 drugs: hepatotoxic/non-toxic = 132/109), and Green dataset85 (328 drugs: hepatotoxic/non-toxic = 214/114) (Note: these three validation datasets contained 483 unique drugs and had 54 drugs common to all three). In analyzing the performance of the Chen et al.86 QSAR model, a drop in the overall accuracy of the model (NCTR: 68.9%, Xu: 63.1%, Green: 61.6%) along with a drop in the model sensitivity (percentage of toxic drugs predicted to be toxic (NCTR: 66.3%, Xu: 60.6%, Green: 58.4%)) was observed across all three datasets. However, when focused on drugs from therapeutic categories such as analgesics, antibacterials and antihistamines with high prediction confidence, the accuracy of this model increased to 73.6%, highlighting a unique applicability domain. To improve the predictive accuracy of toxicity models, several mixed learning methods have recently been proposed. Predictive QSAR models have been developed by combining physicochemical data with observational toxicity data generated by means of automated text mining of the literature,6 and integrate the mode of action of a drug into model development.91 Such mixed learning methods have been used to classify retrospectively hepatotoxic drugs according to their severity: 1) transient and asymptomatic liver function abnormalities; (2) liver function abnormalities and hyperbilirubinaemia; (3) hepatitis, jaundice and cholestasis; (4) fulminant hepatitis and liver failure; and (5) fatality (Figure 5).88

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The reliability of in silico models are at times challenged when predictions are not in agreement with follow-up in vitro and more importantly, in vivo studies,92 thereby highlighting further gaps in our understanding of DILI. To that end, more advanced in silico models built on pharmacologically relevant toxicology endpoints can help address such gaps by identifying areas where there is an existing lack of in vivo-to-human and in vitro-to-human translation.92 For example, mixed learning methods that utilize large biological datasets along with various chemical descriptors have been used to characterize rodent hepatotoxicants (accuracy: hypertrophy:

0.80±0.08,

injury:

0.80±0.09,

proliferative

lesions:

0.80±0.10),8

prenatal

developmental toxicity (accuracy >70%),93 rat reproductive toxicity (accuracy 76%),75 rat and human pregnane X receptor (PXR) activators (accuracy 89%),94, assessing success and failure of clinical trials,95 predicting estrogen bioactivity (accuracy 70-93%),96,97 predicting endocrine disruption98 and assessing human health risk (Table 2).99 In addition to these isolated instances of success, computational toxicology tools can address interspecies differences that can reduce the effectiveness of DILI predictions based on mouse or other animal in vivo data. As a result, there is an emerging need to develop computational methods that can enhance the value of in vitro/in silico data together with known human in vivo data in predicting DILI. 6. Artificial Intelligence Applied to Toxicology Predictive methods of human hepatotoxicity are difficult to develop due in part to the multidimensional nature of the data used in model development and validation. In addition, there is rapid growth in human adverse effects information from existing electronic health records and claims data, among other sources that consist largely of unstructured datasets. This information represents a wealth of data, albeit from highly disparate sources. While complex, these multidimensional datasets are ideally suited to various artificial intelligence (AI) and machine learning (ML) technologies. Appropriately incorporated into the drug discovery process, AI and ML methods have the potential to revolutionize the predictions for DILI.

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To date AI tools have been deployed with some success across broad segments of biology and with a profound impact on drug discovery. Deep neural networks (DNNs) are now being used extensively for protein structure prediction100 along with prediction of protein-ligand and protein-protein interactions.101–103 At the genomic level DNNs allow for the classification of DNA and RNA binding proteins, their structural features and their sequence specificities.104 Additionally, DNNs have been used to annotate gene expression patterns in animal tissue,105,106 to identify human long non-coding RNAs107 and metagenomic classification.108 The confluence of data now available makes it easier to understand the regulatory code of the accessible genome,109 thereby enhancing the diagnosis and classification of diseases such as cancer.110 Interestingly, genomic data can be leveraged with the use of Bimodal Deep Belief Networks to facilitate trans-species learning, a major hurdle in predicting concordant organ toxicities in humans from animal models and non-human cell lines.111 DNNs are also sufficiently flexible to be used to identify chemical molecular properties of toxic compounds and to model biotransformation of drug like molecules,112 factors integral to accurately predicting metabolismbased DILI.113 6.1 Functional role for AI in predicting hepatotoxicity As more drugs that cause DILI in humans without hepatotoxicity warnings in animals are identified, our ability to develop predictive in silico models for accurately identifying these compounds has taken on increased urgency. Deep learning methods have been proposed as one potential area of opportunity for addressing this problem. In developing deep learning architectures, artificial neural networks have proven to be quite helpful. Artificial neural networks were initially inspired by the neural networks found in the human brain and consist of layers of interconnected computing nodes.114 Because of their capacity to assimilate vast data sets, deep neural networks (DNNs) have begun to be applied to solve problems in cheminformatics,

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bioinformatics and drug discovery. In developing predictive toxicology models for drug discovery, patient health data, biological data and physicochemical data can all be used. Historically, QSAR models have performed well on test sets of DILI candidates but degenerate as datasets expand in size and complexity.113 More recently Xu et al.113 showed that an undirected graph recursive neural network (UGRNN) could use available physicochemical data to predict more accurately DILI than traditional QSAR methods, and can enable the identification of important structural features (count of atoms/bonds/functional groups, structural features such as the number of five-membered rings, chemical properties such as log P along with several others) related to DILI. Instead of using an autoencoder or convolutional architecture, Xu et al.113 used an UGRNN that was initially developed by Lusci et al.115 for molecular structure encoding and to predict physicochemical features such as water solubility. The choice of using an UGRNN for predicting DILI allowed Xu et al.113 to take advantage of the minimal reliance of UGRNNs to identify suitable molecular descriptors and capitalize on the automated learning capabilities of neural network architectures. Consequently, a comparison of prediction accuracy of DILI prediction models using an UGRNN architecture and a model using a shallow neural network architecture showed the extent to which deep neural networks outperformed models using shallow neural networks (prediction accuracy: DNN (Mold2): 0.93 vs. more conventional neural network (Mold2): 0.92, DNN (PaDEL): 0.90 vs more conventional neural network (PaDEL): 0.87. Note: Mold2 and PaDEL refer to methods used to calculate the molecular descriptors used in these studies). In addition to improving the prediction accuracy over previously describe DILI prediction models Xu et al’s.113 method represents a tool that can be used to more accurately identify important molecular features that are relevant to DILI, thus advancing out knowledge of the mechanisms that impact this process. Drug metabolism is a major driver of cellular toxicities, and thus, our ability to predict the reactions that lead to the formation of reactive metabolites and protein adducts is critical to

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accurately identifying ADRs associated with various drugs and drug candidates. To this end considerable effort has been focused on developing strategies that generate predictive models of sites of metabolism/modification, which aid in identifying potential ADRs resulting from reactive metabolites and informing structural modifications to produce safer drugs. The strength of a molecule’s reactivity with glutathione (GSH) historically has been used as an indicator of its toxicity. GSH is known to conjugate to specific atoms on drugs and metabolites (sites of reactivity), the identity of which can be invaluable in assessing the toxic potential of compounds.116,117 In a recent study Hughes et al.118 developed a predictive model of molecular reactivity with GSH by analyzing atoms within 1213 reactive molecules known to conjugate with GSH. Using a neural network, they were able to identify patterns in molecular structures to identify reactive sites within molecules with ~90% accuracy, and to separate reactive and unreactive molecules with ~80% accuracy. The success of this study has spurred development of additional methods that attempt to predict the sites of metabolism of drugs from reactions that generate potentially toxic compounds. Example reactions that lead to potential toxicity include dealkylation, epoxidation and quinone formation, all catalyzed by oxidative metabolizing enzymes (e.g., CYP450s) and often resulting in reactive metabolites. Hughes et al.112 built a deep convolution neural network (DCNN) to predict the specific location on various molecules that undergo epoxidation. A database of 702 epoxidation reactions was used to build a DCNN capable of predicting sites of epoxidation with an accuracy of 94.9% and separating epoxidized and non-epoxidized molecules with an accuracy of 79.3%. A more recent study Hughes et al.119 demonstrated how DCNNs could be tailored to predict formation of quinone species (reactive electrophilic Michael acceptors) in drug metabolism. The literature-derived Accelrys Metabolite Database was used to extract information on metabolic reactions that form quinones and topological descriptors to describe the chemical properties of each atom. The resulting DCNN leveraged these data to

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predict the specific atom pairs within molecules that form quinones with an accuracy of 97.6% and to differentiate between quinone forming and non-forming molecules with an accuracy of 88.2%. Building on the DCNN algorithm developed by Hughes et al.,112,119 Dang et al.120 trained a revised model to predict N-dealkylation by human liver microsomes (HLM). This model can predict the site of N-dealkylation within metabolized substrates with an accuracy of greater than 90% and represents an additional tool that could be used to assess the contribution of aldehyde intermediates to observed instances of hepatotoxicity. The formation of aldehyde, epoxide and quinone metabolites represents a small fraction of the factors that contribute to toxicity; hence, deep learning methods developed to predict the formation of toxic metabolites represent a stepping stone toward more effective toxicity management.112,119 There exist no simple and direct experimental approaches to detect quickly and accurately reactive metabolites and assess their potential to generate protein or DNA adducts. In an attempt to address this problem, Hughes et al.121 developed a DCNN capable of screening a large library of compounds for covalent binding potential and flagging problematic molecules. The authors trained a DCNN with curated literature data to accurately predict both the sites and probability of reactivity for molecules with GSH, cyanide, protein and DNA. At the site level, the model performed with an AUC of ~90% for DNA and ~94% for protein. This model was also able to separate molecules that electrophilically react with DNA and protein from nonreactive molecules with AUC performance of 79% and 80%, respectively. Additionally, Hughes et al.121 developed a selectivity score to assess preferential reactions with the macromolecules as opposed to currently available screening traps. For the entire data set of 2803 molecules, the selectivity score-based approach yielded 257 and 227 molecules predicted to be reactive with only DNA and protein, respectively; an indicator of those molecules that would be missed by standard reactivity screening experiments. This method has strong

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potential to be used to predict reactive molecules and to demonstrate location of reactive sites where further modification would reduce toxicity while retain efficacy. In addition to the aforementioned examples, a great deal of work has been performed that leverage machine learning and statistical analysis to demonstrate that drug dosage122,123 and hepatic clearance124–128 are major risk factors associated with idiosyncratic DILI. Host factors such as genetics, immune response and immune tolerance that impact the severity of DILI can be modeled/assessed with computational tools such as DILIsym®,129,130 a multiscale mechanistic modeling approach that uses a series of differential equations to shed light on DILI hazard posed by xenobiotics. Additionally, as mentioned previously, DNNs have been used to tackle several questions in biology, some of which are relevant to xenobiotic toxicities. An example of this is the DNN developed by Alipanahi et al.104 for the classification of DNA and RNA binding proteins specificities. The application of this or similar methods would be advantageous to study hepatotoxicity because it would aid in improved predictions/modeling of DME expression, a major determinant in the accumulation of toxic drugs and metabolites in the liver. Furthermore, adoption of DNN and the related pairwise input neural network developed by Jiménez et al.131 and Wang et al.101 for predicting protein-ligand interactions has the potential to expand our ability to predict CYP450 inhibition. The use of DNNs also make it possible to improve upon the poor concordance observed in hepatotoxicity studies among cell lines, animal models and in humans. Chen et al.111 developed a novel hierarchical DNN model to function as an encoding system of the signal transduction pathways of human and rat bronchial cells representative of cellular responses to different stimuli. Such a feasibility study showed the utility of an AI model, yet could be extended to studies focused on mechanisms that regulate gene expression profiles and how signaling pathways can be perturbed by extrinsic factors such as disease and xenobiotics.

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In an effort to more efficiently leverage the mechanistic data available via Tox21 Mayr et al.132 developed the DeepTox pipeline, a DNN that consists of multiple layers of rectified linear units. These units allow for use of sparse input representations, are robust against noise and have been shown to improve learning across layers in DNNs.133 DeepTox normalizes the chemical representations of the compounds and computes chemical descriptors that are used as input features for models. The resulting models generated in DeepTox were evaluated for quality with the best ones combined into ensembles predictors of toxicity. When compared against other machine learning methods, such as linear regression functions, support vector machine learning and random forest, DeepTox returned more accurate toxicity predictions compared to other methods (DeepTox: 0.84, support vector machine: 0.83, random forest: 0.82, linear regression: 0.80). In their efforts to develop DeepTox, Mayr et al.132 highlighted how multitask learning achieved with the use of DNNs allow us to predict the biochemical toxicities associated with several nuclear receptor (estrogen receptor alpha, aromatase, aryl hydrocarbon receptor, androgen receptor, peroxisome proliferator-activated receptor gamma) and stress response (nuclear factor-like 2 antioxidant responsive element, heat shock factor response element, mitochondrial membrane potential, p53 pathway) elements. For 10 of the 12 assays performed, multi-task neural networks outperformed single-task neural networks in predicting biological activities associated with toxic compounds. Like most of the DNN methods discussed here tools such as DeepTox have the potential to improve our ability to predict hepatotoxicity but require further refinement. 6.2 AI in extracting human toxicological information from patient databases Available patient health data in the form of electronic health records (EHRs) is a powerful resource that can be used to fill gaps in our understanding of the toxic effects of drugs on patients, a critical step in strengthening our ability to improve the accuracy of predictions of compounds in vivo. Indeed, curated data from patients provides critical information on actual

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outcomes of drugs, DDIs and other ADRs, that can be used to better understand and predict the impact of drug exposure in vivo. EHRs also consist of genomic data that accompanies more traditional data such as disease burden and patient lifestyle, thus making it easier to assess patient susceptibilities. To date several deep learning methods have been developed to use data available in EHRs for predicting disease onset,134 unplanned patient readmission and discharge,135 future clinical events136 and suicide risk.137 Archarya et al.138,139 demonstrated that data available in EHRs, such as electrocardiograms (ECGs) and encephalograms (EEGs), can be used to predict the occurrence of myocardial infarctions (93.5% accuracy) and seizures (88.7% accuracy). In both approaches, human data in the form of ECG and EEG are input into a DNN when developing predictive models of disease onset. The main advantages of the methods described in these two examples reported by Archarya et al.138,139 are that they negate the need for separate steps of feature extraction and selection and they facilitate accurate mining of physiologically relevant endpoints from noisy data. Despite the progress made in these areas the use of similar deep learning methods for predicting drug induced tissue injury, particularly hepatic injury from EHRs remains an understudied problem. The mapping of safe drug properties using QSAR methods or the quantification of protein ligand interactions using physicochemical-based models are not sufficient for accurately predicting toxicity outcomes in humans. However, incorporating deep learning methods allows us to expand the applicability of computational tools used in toxicology. This integrated approach has begun to pay dividends in disease diagnosis, drug prescription, drug metabolism and DILI, and has the potential to improve our ability to make predictions on the hepatotoxic risk and off target effects of a range of xenobiotics. Additionally, deep learning-based methods can better incorporate the cornucopia of available “omic” data with other in vitro datasets to identify off target effects of drugs and decipher adverse outcome pathways involved in hepatotoxicity. In silico discoveries made using these methods can then be validated with in vitro assays using a

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variety of cellular models, a process that can be used to identify appropriate experiments for testing unique toxicity phenotypes, targets and susceptibilities. 7. Future directions AI, ML and cognitive computing resources such as IBM’s Watson are being used today in more expansive roles in biomedicine.138,139,16,140 Given the success of AI in assimilating large datasets to make accurate predictions in disease diagnosis, drug repurposing, protein-small molecule interactions, biochemical pathways etc., there is increased interest in deploying these computational tools to address challenges in human toxicology. Because most existing in vitro and in vivo approaches have underperformed in predicting human toxicity profiles, particularly hepatotoxicity, the adoption of in silico methods for predicting hepatotoxicity has expanded. AI, ML and cognitive computing tools may be able to capitalize on the vast amounts of in vitro data available in repositories such as ToxCast and Tox21, and from animal models. In particular, AI, ML and cognitive computing tools can be used to fill the vast knowledge gap of how cellular responses in in vitro assays translate to hepatotoxic risk in humans. Additionally, the use of DNNs may allow us to elucidate species-specific differences responsible for the poor concordance currently observed between DILI in animal models and reported incidents of DILI in humans. Finally, AI, ML and cognitive computing methods can be used to leverage multidimensional patient data available in EHRs and claims data to aid in accurate patient assessments. The wealth of personalized human data available in EHRs would make it possible to predict incidents of acute hepatotoxicity and allow for more accurate assessments of patient susceptibilities for chronic DILI given other underlying conditions, genetic traits and interactions among prescription medications. In addition, the results of in silico predictions can be used to inform more focused in vitro experiments and necessary endpoints, an approach that drives model refinement, improves learning and could fill knowledge gaps associated with DILI (Figure 6). The construction of various neural networks and use of next generation cognitive computing 29


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platforms to extract value from these data would allow us to achieve a finer degree of granularity that is needed to identify individual patient differences in how drugs are metabolized in the body, and how effective these drugs are in treating individual patients, which provides a level of detail that moves us much closer to truly personalized medicine.

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§

AUTHOR INFORMATION

Funding This work was partially supported by the National Institutes of Health (R01-ES0209023), Taconic Biosciences, and a van Auken Postdoctoral Fellowship to KF. Notes The authors declare no competing financial interest. Biographies Keith Fraser obtained his B.S. and M.S. in Biological Sciences from St. John’s University and his Ph.D. in Biological Sciences from Rensselaer Polytechnic Institute. He is currently a van Auken Postdoctoral Research Associate in the Center for Biotechnology and Interdisciplinary Studies at Rensselaer where his research focuses on developing predictive in silico models of drug induced hepatotoxicity. Dylan M. Bruckner obtained his B.S. in Chemical and Biomolecular Engineering from Clemson University in 2014. He is currently a doctoral student in Chemical and Biological Engineering at Rensselaer Polytechnic Institute, where he studies the quantitative predictivity of in vitro hepatotoxicity models. Jonathan S. Dordick is the Howard P. Isermann professor of Chemical and Biological Engineering at Rensselaer Polytechnic Institute and holds joint faculty appointments in Biological Sciences and Biomedical Engineering. He received his B.A. degree in Biochemistry and Chemistry from Brandeis University and his Ph.D. in Biochemical Engineering from the Massachusetts Institute of Technology. Previous to Rensselaer, he was professor of Chemical and Biochemical Engineering at the University of Iowa. Prof. Dordick’s research group includes chemical engineers, bioengineers, materials scientists, biologists, chemists, microbiologists and computational and AI scientists all focused on gaining a quantitative understanding of biological principles and applying them to advance bioengineering and biomanufacturing, stem cell engineering, and drug discovery and toxicology.

Corresponding Author *E-mail: [email protected]

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Table 1: Humanized mouse models used in drug discovery. Animal Model TK-NOG

Drugs Tested Furosemide

TK-NOG

Diclofenac

Human Relevance Furosemide at a high dose is hepatotoxic in mice but safe in humanize mice 141 Humanized mice able to generate human specific glutathione conjugated metabolites of diclofenac 65

TK-NOG

Fialuridine

TK-NOG

Valproic acid, carbapenem antibiotics

TK-NOG

Clemizole, ritonavir

TK-NOG

PF-04937319

TK-NOG

Benzydamine

PXB-Mice®

Troglitazone, flutamide, diazepam

PXB-Mice®

Mixture

FRG

Lumiracoxib

PIRF

Gefitinib, Atazanavir

Recapitulate acute liver failure observed in humans 62 Recapitulation of the drug-drug interaction of valproic acid with carbapenems via coadministration of valproic acid and meropenem in chimeric humanized mice 63 Accurate prediction of human specific metabolite of clemizole and prediction of drug-drug interaction after co-administration with the CYP3A4 inhibitor ritonavir 56 Prediction of human disposition of PF-04937319 142

Model of N-oxide and N-demethylated metabolites of benzydamine 143 Drug metabolism in the liver and the resulting in vivo binding in combination with zone analysis of covalent binding to protein targets can be used to evaluate the relationship between non-specific binding of drugs and their hepatotoxic potential 144 More accurate pharmacokinetic model of drug clearance and distribution generated from PXBmice compared to monkeys and rats 145 Similar metabolic profile between chimeric humanized mice and humans 60 Model for recapitulating human relevant drug metabolism 61

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Table 2: Mixed learning methods for predictive toxicity Study focus Predicting hepatotoxicity 8

Predicting rat reproductive toxicity 75

Mixed learning method Linear discriminant analysis, Naïve Bayes, support vector machine, classification and regression trees, knearest neighbors and an ensemble of classifiers Linear discriminant analysis

Predicting prenatal developmental toxicity 93

Linear discriminant analysis

Predicting rat and human pregnane X receptor activators

Bayesian classification implemented in Pipeline Pilot

Estrogen bioactivity

ToxCast ER model

Estrogen bioactivity

Decision Forest

Endocrine disruption / Vitellogenin induction 98

ToxCast ER model agonist model

94

96

97

Endpoints Histopathology (hypertrophy, injury, proliferative lesions)

Prediction accuracy Hybrid descriptors: hypertrophy: 0.84±0.08, Injury: 0.80±0.09, proliferative lesions: 0.8±0.10

Reproductive performance (fertility, maturing, gestational interval), female and male reproductive tract defects (testis, epididymis, ovary, uterus pathology and weight, sperm measure and morphology), sexual development landmarks (preputial separation, vaginal opening, anogenital distance) Cleft palate, urogenital defects, renal defects, weight, microphthalmia, prenatal loss, maternal wastage Luciferase reporter system for PXR activation

76%

ER binding, ER dimerization, DNA binding, gene expression, cell proliferation ER binding, ER activation

86-93%

Vitellogenin induction, steroid hormone levels, secondary sex characteristics

84-93%

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>71%

89%

70-89%

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Legends to Figures Figure 1: Simplified schematic of the mechanisms that contribute to DILI. Illustrated here are avenues through which exposure to small molecule drugs can disrupt hepatic homeostasis. → refers to stimulator pathways and Ⱶ refers to inhibitory pathways. Highlighted in magenta are mechanisms that stimulate apoptosis and necrosis; in brown are antioxidants that protect against the buildup of ROS/RNS. Abbreviations used represent: Na+ taurocholate cotransporting polypeptide (NTCP), organic anion transporting polypeptide (OATP), organic cation transporter (OCT), organic solute transporter α/β (OSTα/β), multidrug resistant protein 1-4 (ABCC1-4), Pglycoprotein (ABCB1), bile salt export pump (ABCB11), breast cancer resistance protein (ABCG2), cholesterol transporter (ABCG5/ABCG8), drug metabolizing enzymes (DMEs), cytochromes P450 (CYP450), flavin-containing monooxygenase (FMO), monoamine oxidase (MAO), UDP-glucuronosyltransferase (UGT), sulfotransferase (SULT), glutathione S-transferase (GST), N-acetyl transferase (NAT), reactive metabolite (RM), reactive oxygen species (ROS), reactive nitrogen species (RNS), glutathione (GSH), glutathione peroxidase (GPx), superoxide dismutase (SOD), mitochondrial DNA (mtDNA), mitochondrial pore transition, antigenpresenting cell (APC), Kupffer cell (KC), T-cell lymphocyte (T-cell).1,19,20,28 Figure 2: Composite illustrating several 2D and 3D in vitro methods that are frequently employed for culturing hepatocytes, and transformed and immortalized human liver cell lines for use in hepatotoxicity assays. Adapted from V Lauschke, V. M., Hendriks, D. F. G., Bell, C. C., Andersson, T. B., and Ingelman-Sundberg, M. (2016) Novel 3D Culture Systems for Studies of Human Liver Function and Assessments of the Hepatotoxicity of Drugs and Drug Candidates. Chem. Res. Toxicol. 29 (12), 1936–1955, with permission of the American Chemical Society47; March, S., Ramanan, V., Trehan, K., Ng, S., Galstian, A., Gural, N., Scull, M. A., Shlomai, A., Mota, M. M., Fleming, H. E., Khetani, S. R., Rice, C. M., and Bhatia, S. N. (2015) Micropatterned Coculture of Primary Human Hepatocytes and Supportive Cells for the Study of

56



Hepatotropic Pathogens. Nat. Protoc. 10 (12), 2027–2053, with permission of the publisher Springer Nature146; and Kwon, S. J., Lee, D. W., Shah, D. A., Ku, B., Jeon, S. Y., Solanki, K., Ryan, J. D., Clark, D. S., Dordick, J. S., and Lee, M.-Y. (2014) High-Throughput and Combinatorial Gene Expression on a Chip for Metabolism-Induced Toxicology Screening. Nat. Commun. 5, 3759, with permission of the publisher Springer Nature.147 Figure 3: Proteome analysis of HepG2 cells and primary hepatocytes cultured in 2D and 3D environments and compared to in vivo liver profiles. a) The differential expression in HepG2 of proteins involved in drug metabolism, cell proliferation, adhesion and angiogenesis in 2D monolayer cultures seeded with 5 x 104 cells/cm2 and 3D microarray cultures seeded with 6.7 x 106 cells/ml. b) Heatmap illustrating differentially expressed proteins (n=574 proteins, p

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