Advancing Predictive Hepatotoxicity at the Intersection of Experimental

Review Cite This: Chem. Res. Toxicol. 2018, 31, 412−430

pubs.acs.org/crt

Advancing Predictive Hepatotoxicity at the Intersection of Experimental, in Silico, and Artificial Intelligence Technologies Keith Fraser, Dylan M. Bruckner, and Jonathan S. Dordick*

Downloaded via KAOHSIUNG MEDICAL UNIV on June 20, 2018 at 19:15:12 (UTC). See https://pubs.acs.org/sharingguidelines for options on how to legitimately share published articles.

Department of Chemical and Biological Engineering and Department of Biological Sciences Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, New York 12180, United States ABSTRACT: Adverse drug reactions, particularly those that result in drug-induced liver injury (DILI), are a major cause of drug failure in clinical trials and drug withdrawals. Hepatotoxicity-mediated 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 two- and three-dimensional 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.

■

CONTENTS

1. Introduction 2. Mechanisms of DILI 3. Correlation between in Vitro Hepatotoxicity Studies and Observed Human Outcomes In Vitro Toxicology Platforms 4. Humanized Mouse Models 5. Computational Toxicology 5.1. Experimentally Derived Databases 5.2. Cheminformatics 6. Artificial Intelligence Applied to Toxicology 6.1. Functional Role of AI in Predicting Hepatotoxicity 6.2. AI in Extracting Human Toxicological Information from Patient Databases 7. Future Directions Author Information Corresponding Author ORCID Funding Notes Biographies References

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 effects of serum binding and drug clearance, and are unable to represent effective models for chronic toxicity mechanisms. Nonetheless, advances in multicellular three-dimensional (3D)

412 414 417 417 418 419 420 420 422 423 424 424 425 425 425 425 425 425 426

1. INTRODUCTION Despite the expenditure of billions of dollars over the last several decades, the development of successful new drugs and © 2018 American Chemical Society

Received: March 1, 2018 Published: May 3, 2018 412

DOI: 10.1021/acs.chemrestox.8b00054 Chem. Res. Toxicol. 2018, 31, 412−430

Chemical Research in Toxicology

Review

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 ⊥ 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: 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), P-glycoprotein (ABCB1), bile salt export pump (ABCB11), breast cancer resistance protein (ABCG2), cholesterol transporter (ABCG5/ABCG8), drug-metabolizing enzymes (DMEs), cytochromes P450 (CYP450s), 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 permeability transition (MPT), antigenpresenting cell (APC), Kupffer cell (KC), T-cell lymphocyte (T-cell).1,19,20,28

organoid “tissue-on-chip” 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 druginduced 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 absorption, distribution, metabolism, excretion, and toxicology (ADMET) studies. However, predictive models built on these data sets 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 data sets 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 inhibition,10 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 nontoxic compounds. Our inability to effectively capture the significance of these factors in defining the pharmacologic profile of administered drugs in existing 413



Review

activated and can prove to be harmful. Hepatic drug metabolism is mediated primarily by phase I enzymes (most notably cytochromes P450 (CYP450s), flavin-containing monooxygenases, and monoamine oxidases) and phase II enzymes (primarily UDP-glucuronosyltransferases, 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 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 of 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 derivatives, yet AMAP metabolites bind mainly to cytosolic and microsomal proteins while APAP metabolites bind preferentially to mitochondrial proteins, resulting in APAPmediated 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 interactions with chemically reactive metabolites and the contribution of these events to disruption of cellular homeostasis. Mitochondrial dysfunction generally occurs either through direct damage to the respiratory chain or through secondary mechanisms such as oxidative stress or lipid accumulation.23 Cellular oxidative stress activates mitogen-activated protein kinase (MAPK), ultimately resulting in phosphorylation of c-junN-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 inhibition of the synthesis of glutathione (GSH), an important scavenger of reduced oxygen and nitrogen species. Mitochondrial dysfunction is also known to de-energize cells via ATP depletion.25 Since ATP-dependent hepatic transporters are responsible for hepatobiliary excretion, reduction in hepatic ATP levels leads to accumulation of cytotoxic levels of bile acids within hepatocytes, either through transporter inhibition or reduction in transporter function, which 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 surfaces of hepatocytes play a central role in regulating the transit of a wide range of molecules into and out 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

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 potential and assess strategies by which computational methods can be used to better leverage publicly available curated and noncurated data sets 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 progress similar 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 OF 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, it is nonetheless important to provide some insight into the impact of disruption of these mechanisms and identification of pharmacologically relevant end points 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 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 glucocorticoid receptor (GR). In aggregate, these receptors promote gene activation that governs DME expression, bile and bilirubin production and elimination, and phospholipid excretion, among other activities.19 As described above, DMEs facilitate the conversion of xenobiotics to a range of intermediates, several of which are 414



Review

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 with permission from refs 47, 146, and 147. Copyright 2016 American Chemical Society and 2015 and 2014 Springer Nature, respectively.

ABC transporters transport bile acids into the blood.19 There is a growing body of evidence suggesting 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 DILI; this hypothesis is 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 with 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 yet been completely accepted, as some recent studies have suggested that BSEP inhibition in vitro is not a useful predictor 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 415



Review

Figure 3. Proteome analysis of HepG2 cells and primary hepatocytes cultured in 2D and 3D environments and compared to in vivo liver profiles. (a) Differential expression in HepG2 of proteins involved in drug metabolism, cell proliferation, adhesion, and angiogenesis in 2D monolayer cultures seeded with 5 × 104 cells/cm2 and 3D microarray cultures seeded with 6.7 × 106 cells/mL. (b) Heat map illustrating differentially expressed proteins (n = 574 proteins, p < 0.05, F test) that were detected in a whole-proteome analysis of primary human hepatocytes after 24 h and 7 days in 2D monolayer cultures and after 7 days of culture in 3D spheroids. (c) Principal component analysis used to classify the proteomes from 2D and 3D in vitro cell cultures of primary human hepatocytes and in vivo human liver. (d) Proteome analysis of in vivo phenotypes in 3D cultures highlighting that each of the 3D samples clustered with the representative human liver slice from the same donor. (e) Heat map representing proteins involved in drug absorption, distribution, metabolism, and excretion. These data indicate the tight correlation between the proteomes of primary human hepatocytes cultured in 3D clusters and the proteomes of in vivo liver models; primary hepatocytes cultured in 2D monolayers are less tightly correlated. Panel (a) adapted with permission from ref 30. Copyright 2012 Elsevier. Panels (b−e) adapted from ref 5 (open access). 416



Review

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 earlystage 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 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 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 spheroids 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 evidenced 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 versus 3D environments 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 that of hepatocytes cultured in 2D monolayers (Figure 3e). Vorrink et al.33 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 with 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. They employ confluent monolayers of primary hepatocytes attached to adsorbed or gelled rat tail collagen and then overlaid with another gelled extracellular matrix (ECM) (e.g., collagen, Matrigel, chitosan, hyaluronic acid, and polyelectrolytes, among others) to create

damage to hepatocytes, mitochondrial dysfunction, transporter inhibition, etc., idiosyncratic mechanisms involve activation of both the adaptive and innate immune systems 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 of idiosyncratic DILI. The discovery of an association of specific alleles of human leukocyte antigen (HLA) classes I and II with DILI (e.g., with amoxicillinclavulanate, 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, presentation of which on the surface of hepatocytes makes them recognizable to the immune system as damaged cells, 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 the development of methods focused on cellular approaches for assessing drug safety.4 In particular, the U.S. National Institutes of Health (NIH) and the U.S. 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, which are costly and 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 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 end points, 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 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 DMEs, transporters, molecular pathways, etc. Human primary hepatocytes, however, are typically pooled from multiple donors to obtain a sufficient number of cells for broad in vitro toxicology studies. In all cases, primary hepatocytes are subject to significant DME instability 417



Review

the so-called ECM “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 culturing hepatocytes 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 druginduced 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 However, it is possible 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 class of physiologically relevant in vitro platforms used to screen for hepatotoxicity are organs-on-a-chip, or microphysiological systems, which include micropatterned cocultures (MPCCs), perfused multiwell plates, and microfluidic liver and multiorgan chips based on specific tissue cell types. Advances in microfabrication have made it easier to maintain specific organlike function. Khetani and Bhatia36 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. 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 highcontent screen.42,43 Compared with 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 the 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 photo-cross-linkable 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 mimic 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 fluxes 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, Bavli et al.23 were able to demonstrate that troglitazone, a known hepatotoxic compound, shifts metabolic fluxes at concentrations previously 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 intertissue 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 coculture of gastrointestinal (GI) tract epithelium and 3D primary liver tissue described by Esch et al.53 that allows for interrogation of liver physiology and metabolic state. CYP450 activities in liver cells grown in coculture 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-physiological fluid flow, as would be needed for high-throughput drug screening. Integrated liver−gut combination systems have been developed that can be used to elucidate inflammatory intertissue crosstalk and aid in more detailed pharmacokinetic insight.54,55 As these and other more advanced integrated liver−gut 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.

4. HUMANIZED MOUSE MODELS The failure of existing preclinical systems to predict human pharmacokinetics and human-specific metabolic 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 nonhuman 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 418



Review

Table 1. Humanized Mouse Models Used in Drug Discovery animal model TK-NOG TK-NOG TK-NOG TK-NOG TK-NOG TK-NOG TK-NOG PXB-Mice

drug(s) tested

human relevance

furosemide diclofenac fialuridine valproic acid, carbapenem antibiotics clemizole, ritonavir

furosemide at a high dose is hepatotoxic in mice but safe in humanized mice141 humanized mice can generate human-specific glutathione-conjugated metabolites of diclofenac65 recapitulation of acute liver failure observed in humans62 recapitulation of the drug−drug interaction of valproic acid with carbapenems via coadministration of valproic acid and meropenem in chimeric humanized mice63

PXB-Mice

PF-04937319 benzydamine troglitazone, flutamide, diazepam mixture

FRG PIRF

lumiracoxib gefitinib, atazanavir

accurate prediction of human-specific metabolite of clemizole and prediction of drug−drug interaction after coadministration with the CYP3A4 inhibitor ritonavir56 prediction of human disposition of PF-04937319142 model of N-oxide and N-demethylated metabolites of benzydamine143 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 nonspecific binding of drugs and their hepatotoxic potential144 more accurate pharmacokinetic model of drug clearance and distribution generated from PXB-mice compared with monkeys and rats145 similar metabolic profiles for chimeric humanized mice and humans60 model for recapitulating human-relevant drug metabolism61

Figure 4. Summary of the ToxCast in vitro assay paradigm and how it has been used to screen a broad range of drugs and environmental chemicals.

exist among diverse human populations, which suggest that animal toxicology models are inherently flawed. Such models would be ill-suited as medicine advances toward 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 of human-specific drug toxicities relative to existing rodent and nonrodent models.62,63 Chimeric TK-NOG mice have been used in a range of studies, including prediction of 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 TKNOG mice revealed that mouse chimeras can generate metabolic profiles comparable to those of 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 to adopt this model for broader use in drug combination studies that employ screens for clinically relevant DDIs associated with 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 419



Review

predict human toxicity. In assessing the potential hepatotoxicity of chemical compounds, quantitative structure−activity relationship (QSAR) models are generated that take advantage of 2D and 3D chemical descriptors. A critical assumption here is that the chemical descriptors used incorporate all of the necessary information that describes specific interactions between the 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 knowledgebased systems, such as DEREK, Toxtree, ACToR, TOPKAT, and 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 U.S. in 2009), presumably with limited toxicity. The authors focused on trends in physiochemical characteristics, daily doses, structural alerts, evidence of reactive metabolite formation, and toxicity mechanism mediated by parent drugs. In this study 78− 86% of the toxic drugs contained structural alerts and evidence suggesting that reactive metabolite (RM) 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 of toxicity. Further analysis also revealed that approximately 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 with the higher daily doses of toxic drugs. In addition, competing detoxification pathways or alternate nonmetabolic 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 CYP450 metabolism can be used to predict the context-specific probability that a structural alert will be bioactivated in a given molecule. They focused on furan, phenol, nitroaromatic, and thiophene alerts, as these groups can produce 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 non-bioactivated furan-, phenol-, nitroaromatic-, and thiophene- containing drugs with area under the receiver operating characteristic curve (AUC) values of 100%, 73%, 93%, and 88%, respectively. This study illustrated how metabolism models can accurately predict whether alerts are bioactivated and thus can 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, k-nearest neighbor, and random forest ̈ Bayes analysis, algorithms, linear discriminant analysis, naive 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

properties, toxicity profiles, dosage, clearance, patient susceptibilities, and adverse outcomes associated with the use of therapeutics and environmental exposure to chemicals.67−70 The computational models generated are used to determine correlations across large data sets, many of which are publicly available. These toxicity models can be used to assess the hazard potential of drugs, their mechanism of action, and 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 the use of chemicals, particularly those used in the environment. Interestingly, ICCVAM and its European 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 data sets. 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 end points. 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 pathways,77 and enzyme inhibitors.78 The European Center for Ecotoxicology and Toxicology of Chemicals (ECETOC) developed a similar program to aggregate relevant human toxicity end points, resulting 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 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 420



Review

Figure 5. Mixed-learning workflow for predicting hepatotoxicity in humans. The pathology associated with DILI can be clustered into several preclinical and human hepatotoxicity end points (red M1−M8, various drug−molecule interactions; orange P1−P4, molecular pathways impacted by drugs and drug metabolites; green C1 and C2, cellular processes disrupted by drugs and drug metabolites; blue end point, pathological impact of drugs and drug metabolites). Features such as chemical descriptors of each of the compounds within the library being screened can be used to train machine learning (ML) algorithms (e.g., support vector machines and other generic algorithms), which can then be used to construct predictive models of the toxicity associated with various drugs. The concordance between the preclinical and human models is dependent on the toxicity end points used in initial model construction. Example 1, taken from Wu et al.,91 demonstrates the improvements in the accuracy of predictive hepatotoxicity models that can be had by moving away from standard QSAR models to models that integrate data on mode of action (MOA) of drugs (note: in the label permutation test, the DILI severity annotations of all drugs screened were shuffled, and then a predictive model was applied to the shuffled data). Example 2, adapted from Mulliner et al.,92 demonstrates the importance of selecting the features from available toxicity data when developing predictive hepatotoxicity models. Here end points for human clinical chemistry hepatocellular (H-CCHC) and hepatobiliary (H-CCHB) injury along with end points for morphological findings for hepatocellular (H-MFHC) and hepatobiliary (H-MFHB) injury were used to develop predictive models. In this example, the four models were used to predict the hepatotoxicity (P = positive, N = negative) associated with four triptan core derivatives with diverse pharmacological and toxicological features. The portion of the schematic illustration at the top center was adapted from ref 148 (open access).

these three validation data sets contained 483 unique drugs and had 54 drugs common to all three). In analyzing the performance of their QSAR model, Chen et al.86 observed a drop in the overall accuracy of the model (NCTR, 68.9%; Xu, 63.1%; Greene, 61.6%) along with a drop in the model sensitivity (percentage of toxic drugs predicted to be toxic) (NCTR, 66.3%; Xu, 60.6%; Greene, 58.4%)) across all three data sets. However, when the focus was 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

methods have driven the development of highly accurate models based on screening of relatively modest libraries (e.g., 71%

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 linear discriminant analysis predicting rat reproductive toxicity75

histopathology (hypertrophy, injury, proliferative lesions)

prediction accuracy mixed-learning method(s)

̈ Bayes, support vector linear discriminant analysis, naive machine, classification and regression trees, k-nearest neighbors, and an ensemble of classifiers

end points 422

study focus

Table 2. Mixed-Learning Methods for Predictive Toxicity

6. ARTIFICIAL INTELLIGENCE APPLIED TO TOXICOLOGY Predictive methods for human hepatotoxicity are difficult to develop, in part because of 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 data sets. This information represents a wealth of data, albeit from highly disparate sources. While complex, these multidimensional data sets are ideally suited to various artificial intelligence (AI) and machine learning (ML) technologies. When appropriately incorporated into the drug discovery process, AI and ML methods have the potential to revolutionize the predictions for DILI. 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 as well as 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 noncoding RNAs,107 and to perform 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

predicting hepatotoxicity8

automated text mining of the literature6 and by integrating the mode of action of a drug into model development.91 Such mixedlearning methods have been used to classify hepatotoxic drugs retrospectively 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 The reliability of in silico models is 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 end points can help to 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 data sets 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 and rat and human pregnane X receptor (PXR) activators (accuracy 89%);94 to assess success and failure of clinical trials;95 to predict estrogen bioactivity (accuracy 70−93%),96,97 and endocrine disruption;98 and to assess human health risk99 (Table 2). 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.

hybrid descriptors: hypertrophy, 0.84 ± 0.08; injury, 0.80 ± 0.09; proliferative lesions, 0.8 ± 0.10 76%

Review



Review

strength of a molecule’s reactivity with 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 identities 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 the 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 locations 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 by Hughes and Swamidass119 demonstrated how DCNNs could be tailored to predict the 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 predict the specific atom pairs within molecules that form quinones with an accuracy of 97.6% and to differentiate between quinone-forming and nonquinone-forming molecules with an accuracy of 88.2%. Building on the DCNN algorithm developed by Hughes and coworkers,112,119 Dang et al.120 trained a revised model to predict N-dealkylation by human liver microsomes (HLMs). 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 reactive metabolites quickly and accurately 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 AUC values 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 values 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

learning, a major hurdle in predicting concordant organ toxicities in humans from animal models and nonhuman 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 accurate prediction of metabolism-based DILI.113 6.1. Functional Role of 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 accurate identification of these compounds has taken on increased urgency. Deep learning methods have been proposed as one potential area of opportunity to address 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, DNNs have begun to be applied to solve problems in cheminformatics, 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 the data sets expand in size and complexity.113 More recently, Xu et al.113 showed that an undirected graph recursive neural network (UGRNN) can use available physicochemical data to predict DILI more accurately 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, and 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 to predict 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 the prediction accuracies of DILI prediction models using an UGRNN architecture and a model using a shallow neural network (SNN) architecture showed the extent to which DNNs outperformed models using SNNs (prediction accuracies: DNN (Mold2) 0.93 vs more conventional neural network (Mold2) 0.92; DNN (PaDEL) 0.90 vs more conventional neural network (PaDEL) 0.87; 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 described DILI prediction models, the method of Xu et al.113 represents a tool that can be used to more accurately identify important molecular features that are relevant to DILI, thus advancing our 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 for 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 423



Review

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 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 accompanying 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 events,136 and suicide risk.137 Archarya and co-workers138,139 demonstrated that data available in EHRs, such as electrocardiograms (ECGs) and electroencephalograms (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 ECGs and EEGs 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 and co-workers138,139 are that they negate the need for separate steps of feature extraction and selection and facilitate accurate mining of physiologically relevant end points from noisy data. Despite the progress made in these areas, the use of similar deep learning methods to predict 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 is not sufficient for accurate prediction of 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 of the hepatotoxic risk and off-target effects of a range of xenobiotics. Additionally, deeplearning-based methods can better incorporate the cornucopia of available “omic” data with other in vitro data sets to identify offtarget 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 variety of cellular models, a process that can be used to identify appropriate experiments to test unique toxicity phenotypes, targets, and susceptibilities.

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 potential to be used to predict reactive molecules and to demonstrate the location of reactive sites where further modification would reduce toxicity while retaining efficacy. In addition to the aforementioned examples, a great deal of work has been performed that leverages 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 protein 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 makes it possible to improve upon the poor concordance observed in hepatotoxicity studies among cell lines, in 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. 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 the 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 ensemble predictors of toxicity. Compared with other machine learning methods, such as linear regression functions, support vector machine, and random forest, DeepTox returned more accurate toxicity predictions (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 allows us to predict the biochemical toxicities associated with several nuclear receptor (estrogen receptor α, aromatase, aryl hydrocarbon receptor, androgen receptor, peroxisome proliferator-activated receptor γ) 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, multitask neural networks outperformed

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 In view of the success of AI in assimilating large data sets 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 424



Review

Figure 6. Illustration of how multidimensional data available today can be leveraged to better predict toxic phenotypes associated with DILI. Depicted here is how knowledge acquired from these predictions can be used to inform the development of improved in vitro strategies for screening of future compounds.

■

and in vivo approaches have underperformed in predicting human toxicity profiles, particularly hepatotoxicity, the adoption of in silico methods to predict 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 end points, 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 the use of next-generation cognitive computing platforms to extract value from these data would allow us to achieve the 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 would provide a level of detail that would move us much closer to truly personalized medicine.

AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. ORCID

Jonathan S. Dordick: 0000-0001-7802-3702 Funding

This work was partially supported by the National Institutes of Health (R01-ES020903), Taconic Biosciences, and a van Auken Postdoctoral Fellowship to K.F. 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 is studying 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. 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 425



Review

Metabolism: Sites, Products, SAR, P450 Enzyme Dynamics, and Mechanisms. J. Chem. Inf. Model. 52 (3), 617−648. (16) Doyle-Lindrud, S. (2015) Watson Will See You Now: A Supercomputer to Help Clinicians Make Informed Treatment Decisions. Clin. J. Oncol. Nurs. 19 (1), 31−32. (17) Urquhart, B. L., Tirona, R. G., and Kim, R. B. (2007) Nuclear Receptors and the Regulation of Drug-Metabolizing Enzymes and Drug Transporters: Implications for Interindividual Variability in Response to Drugs. J. Clin. Pharmacol. 47 (5), 566−578. (18) Thompson, R. A., Isin, E. M., Ogese, M. O., Mettetal, J. T., and Williams, D. P. (2016) Reactive Metabolites: Current and Emerging Risk and Hazard Assessments. Chem. Res. Toxicol. 29 (4), 505−533. (19) Cuperus, F. J. C., Claudel, T., Gautherot, J., Halilbasic, E., and Trauner, M. (2014) The Role of Canalicular ABC Transporters in Cholestasis. Drug Metab. Dispos. 42 (4), 546−560. (20) Waddington, J. C., Meng, X., Naisbitt, D. J., and Park, B. K. (2018) Immune Drug-Induced Liver Disease and Drugs. Curr. Opin. Toxicol. 10, 46−53. (21) Park, B. K., Laverty, H., Srivastava, A., Antoine, D. J., Naisbitt, D., and Williams, D. P. (2011) Drug Bioactivation and Protein Adduct Formation in the Pathogenesis of Drug-Induced Toxicity. Chem.-Biol. Interact. 192 (1−2), 30−36. (22) Hanzlik, R. P., Koen, Y. M., and Fang, J. (2013) Bioinformatic Analysis of 302 Reactive Metabolite Target Proteins. Which Ones Are Important for Cell Death? Toxicol. Sci. 135 (2), 390−401. (23) Bavli, D., Prill, S., Ezra, E., Levy, G., Cohen, M., Vinken, M., Vanfleteren, J., Jaeger, M., and Nahmias, Y. (2016) Real-Time Monitoring of Metabolic Function in Liver-on-Chip Microdevices Tracks the Dynamics of Mitochondrial Dysfunction. Proc. Natl. Acad. Sci. U. S. A. 113 (16), E2231−E2240. (24) Du, K., Farhood, A., and Jaeschke, H. (2017) MitochondriaTargeted Antioxidant Mito-Tempo Protects against Acetaminophen Hepatotoxicity. Arch. Toxicol. 91 (2), 761−773. (25) 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 (3), 1015−1022. (26) Chan, R., and Benet, L. Z. (2018) Measures of BSEP Inhibition In Vitro Are Not Useful Predictors of DILI. Toxicol. Sci. 162 (2), 499−508. (27) Chen, P., Lin, J.-J., Lu, C.-S., Ong, C.-T., Hsieh, P. F., Yang, C.-C., Tai, C.-T., Wu, S.-L., Lu, C.-H., Hsu, Y.-C., Yu, H.-Y., Ro, L.-S., Lu, C.-T., Chu, C.-C., Tsai, J.-J., Su, Y.-H., Lan, S.-H., Sung, S.-F., Lin, S.-Y., Chuang, H.-P., Huang, L.-C., Chen, Y.-J., Tsai, P.-J., Liao, H.-T., Lin, Y.H., Chen, C.-H., Chung, W.-H., Hung, S.-I., Wu, J.-Y., Chang, C.-F., Chen, L., Chen, Y.-T., and Shen, C.-Y. (2011) Carbamazepine-Induced Toxic Effects and HLA-B*1502 Screening in Taiwan. N. Engl. J. Med. 364 (12), 1126−1133. (28) Cho, T., and Uetrecht, J. (2017) How Reactive Metabolites Induce an Immune Response That Sometimes Leads to an Idiosyncratic Drug Reaction. Chem. Res. Toxicol. 30 (1), 295−314. (29) Judson, R., Houck, K., Martin, M., Knudsen, T., Thomas, R. S., Sipes, N., Shah, I., Wambaugh, J., and Crofton, K. (2014) In Vitro and Modelling Approaches to Risk Assessment from the U.S. Environmental Protection Agency ToxCast Programme. Basic Clin. Pharmacol. Toxicol. 115 (1), 69−76. (30) Meli, L., Jordan, E. T., Clark, D. S., Linhardt, R. J., and Dordick, J. S. (2012) Influence of a Three-Dimensional, Microarray Environment on Human Cell Culture in Drug Screening Systems. Biomaterials 33 (35), 9087−9096. (31) Malinen, M. M., Kanninen, L. K., Corlu, A., Isoniemi, H. M., Lou, Y. R., Yliperttula, M. L., and Urtti, A. O. (2014) Differentiation of Liver Progenitor Cell Line to Functional Organotypic Cultures in 3D Nanofibrillar Cellulose and Hyaluronan-Gelatin Hydrogels. Biomaterials 35 (19), 5110−5121. (32) Bell, C. C., Lauschke, V. M., Vorrink, S. U., Palmgren, H., Duffin, R., Andersson, T. B., and Ingelman-Sundberg, M. (2017) Transcriptional, Functional, and Mechanistic Comparisons of Stem Cell-Derived Hepatocytes, HepaRG Cells, and Three-Dimensional Human Hep-

understanding of biological principles and applying them to advance bioengineering and biomanufacturing, stem cell engineering, and drug discovery and toxicology.

■

REFERENCES

(1) Roth, A. D., and Lee, M.-Y. (2017) Idiosyncratic Drug-Induced Liver Injury (IDILI): Potential Mechanisms and Predictive Assays. BioMed Res. Int. 2017, 1−23. (2) Hay, M., Thomas, D. W., Craighead, J. L., Economides, C., and Rosenthal, J. (2014) Clinical Development Success Rates for Investigational Drugs. Nat. Biotechnol. 32 (1), 40−51. (3) Driessen, M., Vitins, A. P., Pennings, J. L. A., Kienhuis, A. S., van de Water, B., and van der Ven, L. T. M. (2015) A Transcriptomics-Based Hepatotoxicity Comparison between the Zebrafish Embryo and Established Human and Rodent in Vitro and in Vivo Models Using Cyclosporine A, Amiodarone and Acetaminophen. Toxicol. Lett. 232 (2), 403−412. (4) Schwartz, M. P., Hou, Z., Propson, N. E., Zhang, J., Engstrom, C. J., Costa, V. S., Jiang, P., Nguyen, B. K., Bolin, J. M., Daly, W., Wang, Y., Stewart, R., Page, C. D., Murphy, W. L., and Thomson, J. A. (2015) Human Pluripotent Stem Cell-Derived Neural Constructs for Predicting Neural Toxicity. Proc. Natl. Acad. Sci. U. S. A. 112 (40), 12516−12521. (5) Bell, C. C., Hendriks, D. F. G., Moro, S. M. L., Ellis, E., Walsh, J., Renblom, A., Fredriksson Puigvert, L., Dankers, A. C. A., Jacobs, F., Snoeys, J., Sison-Young, R. L., Jenkins, R. E., Nordling, A., Mkrtchian, S., Park, B. K., Kitteringham, N. R., Goldring, C. E. P., Lauschke, V. M., and Ingelman-Sundberg, M. (2016) Characterization of Primary Human Hepatocyte Spheroids as a Model System for Drug-Induced Liver Injury, Liver Function and Disease. Sci. Rep. 6 (1), 25187. (6) Fourches, D., Barnes, J. C., Day, N. C., Bradley, P., Reed, J. Z., and Tropsha, A. (2010) Cheminformatics Analysis of Assertions Mined from Literature That Describe Drug-Induced Liver Injury in Different Species. Chem. Res. Toxicol. 23 (1), 171−183. (7) Olson, H., Betton, G., Robinson, D., Thomas, K., Monro, A., Kolaja, G., Lilly, P., Sanders, J., Sipes, G., Bracken, W., Dorato, M., Van Deun, K., Smith, P., Berger, B., and Heller, A. (2000) Concordance of the Toxicity of Pharmaceuticals in Humans and in Animals. Regul. Toxicol. Pharmacol. 32 (1), 56−67. (8) Liu, J., Mansouri, K., Judson, R. S., Martin, M. T., Hong, H., Chen, M., Xu, X., Thomas, R. S., and Shah, I. (2015) Predicting Hepatotoxicity Using ToxCast in Vitro Bioactivity and Chemical Structure. Chem. Res. Toxicol. 28 (4), 738−751. (9) Zhang, J., Doshi, U., Suzuki, A., Chang, C. W., Borlak, J., Li, A. P., and Tong, W. (2016) Evaluation of Multiple Mechanism-Based Toxicity Endpoints in Primary Cultured Human Hepatocytes for the Identification of Drugs with Clinical Hepatotoxicity: Results from 152 Marketed Drugs with Known Liver Injury Profiles. Chem.-Biol. Interact. 255 (2016), 3−11. (10) Kotsampasakou, E., and Ecker, G. F. (2017) Predicting DrugInduced Cholestasis with the Help of Hepatic Transporters − An in Silico Modeling Approach. J. Chem. Inf. Model. 57 (3), 608−615. (11) Cheng, F., Yu, Y., Shen, J., Yang, L., Li, W., Liu, G., Lee, P. W., and Tang, Y. (2011) Classification of Cytochrome P450 Inhibitors and Noninhibitors Using Combined Classifiers. J. Chem. Inf. Model. 51 (5), 996−1011. (12) Dixit, V. A., and Deshpande, S. (2016) Advances in Computational Prediction of Regioselective and Isoform-Specific Drug Metabolism Catalyzed by CYP450s. ChemistrySelect 1 (20), 6571−6597. (13) Nath, A., Zientek, M. A., Burke, B. J., Jiang, Y., and Atkins, W. M. (2010) Quantifying and Predicting the Promiscuity and Isoform Specificity of Small-Molecule Cytochrome P450 Inhibitors. Drug Metab. Dispos. 38 (12), 2195−2203. (14) Su, B. H., Tu, Y. S., Lin, C., Shao, C. Y., Lin, O. A., and Tseng, Y. J. (2015) Rule-Based Prediction Models of Cytochrome P450 Inhibition. J. Chem. Inf. Model. 55 (7), 1426−1434. (15) Kirchmair, J., Williamson, M. J., Tyzack, J. D., Tan, L., Bond, P. J., Bender, A., and Glen, R. C. (2012) Computational Prediction of 426



Review

atocyte Spheroids as Predictive in Vitro Systems for Drug-Induced Liver Injury. Drug Metab. Dispos. 45 (4), 419−429. (33) Vorrink, S. U., Ullah, S., Schmidt, S., Nandania, J., Velagapudi, V., Beck, O., Ingelman-Sundberg, M., and Lauschke, V. M. (2017) Endogenous and Xenobiotic Metabolic Stability of Primary Human Hepatocytes in Long-Term 3D Spheroid Cultures Revealed by a Combination of Targeted and Untargeted Metabolomics. FASEB J. 31 (6), 2696−2708. (34) Khetani, S. R., Berger, D. R., Ballinger, K. R., Davidson, M. D., Lin, C., and Ware, B. R. (2015) Microengineered Liver Tissues for Drug Testing. J. Lab. Autom 20 (3), 216−250. (35) Godoy, P., Hewitt, N. J., Albrecht, U., Andersen, M. E., Ansari, N., Bhattacharya, S., Bode, J. G., Bolleyn, J., Borner, C., Böttger, J., Braeuning, A., Budinsky, R. A., Burkhardt, B., Cameron, N. R., Camussi, G., Cho, C.-S., Choi, Y.-J., Rowlands, J. C., Dahmen, U., Damm, G., Dirsch, O., Donato, M. T., Dong, J., Dooley, S., Drasdo, D., Eakins, R., Sá Ferreira, K., Fonsato, V., Fraczek, J., Gebhardt, R., Gibson, A., Glanemann, M., Goldring, C. E. P., Gómez-Lechón, M. J., Groothuis, G. M. M., Gustavsson, L., Guyot, C., Hallifax, D., Hammad, S., Hayward, A., Häussinger, D., Hellerbrand, C., Hewitt, P., Hoehme, S., Holzhütter, H.-G., Houston, J. B., Hrach, J., Ito, K., Jaeschke, H., Keitel, V., Kelm, J. M., Park, B. K., 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. K., Stieger, B., Stöber, 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 (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 (8), 1315−1530. (36) Khetani, S. R., and Bhatia, S. N. (2008) Microscale Culture of Human Liver Cells for Drug Development. Nat. Biotechnol. 26 (1), 120− 126. (37) Ghibellini, G., Vasist, L. S., Leslie, E. M., Heizer, W. D., Kowalsky, R. J., Calvo, B. F., and Brouwer, K. L. R. (2007) In Vitro-in Vivo Correlation of Hepatobiliary Drug Clearance in Humans. Clin. Pharmacol. Ther. 81 (3), 406−413. (38) LeCluyse, E. L., Witek, R. P., Andersen, M. E., and Powers, M. J. (2012) Organotypic Liver Culture Models: Meeting Current Challenges in Toxicity Testing. Crit. Rev. Toxicol. 42 (6), 501−548. (39) Gerets, H. H. J., Tilmant, K., Gerin, B., Chanteux, H., Depelchin, B. O., Dhalluin, S., and Atienzar, F. A. (2012) Characterization of Primary Human Hepatocytes, HepG2 Cells, and HepaRG Cells at the mRNA Level and CYP Activity in Response to Inducers and Their Predictivity for the Detection of Human Hepatotoxins. Cell Biol. Toxicol. 28 (2), 69−87. (40) Dalvie, D., Obach, R. S., Kang, P., Prakash, C., Loi, C., Hurst, S., Nedderman, A., Goulet, L., Smith, E., Bu, H.-Z., and Smith, D. A. (2009) Assessment of Three Human in Vitro Systems in the Generation of Major Human Excretory and Circulating Metabolites. Chem. Res. Toxicol. 22 (2), 357−368. (41) Wang, W. W., Khetani, S. R., Krzyzewski, S., Duignan, D. B., and Obach, R. S. (2010) Assessment of a Micropatterned Hepatocyte Coculture System to Generate Major Human Excretory and Circulating Drug Metabolites. Drug Metab. Dispos. 38 (10), 1900−1905. (42) Khetani, S. R., Kanchagar, C., Ukairo, O., Krzyzewski, S., Moore, A., Shi, J., Aoyama, S., Aleo, M., and Will, Y. (2013) Use of Micropatterned Cocultures to Detect Compounds That Cause DrugInduced Liver Injury in Humans. Toxicol. Sci. 132 (1), 107−117. (43) Persson, M., Løye, A. F., Jacquet, M., Mow, N. S., Thougaard, A. V., Mow, T., and Hornberg, J. J. (2014) High-Content Analysis/ screening for Predictive Toxicology: Application to Hepatotoxicity and Genotoxicity. Basic Clin. Pharmacol. Toxicol. 115 (1), 18−23. (44) Ballard, T. E., Wang, S., Cox, L. M., Moen, M. A., Krzyzewski, S., Ukairo, O., and Obach, R. S. (2016) Application of a Micropatterned

Cocultured Hepatocyte System To Predict Preclinical and HumanSpecific Drug Metabolism. Drug Metab. Dispos. 44 (2), 172−179. (45) Nguyen, T. V., Ukairo, O., Khetani, S. R., McVay, M., Kanchagar, C., Seghezzi, W., Ayanoglu, G., Irrechukwu, O., and Evers, R. (2015) Establishment of a Hepatocyte-Kupffer Cell Coculture Model for Assessment of Proinflammatory Cytokine Effects on Metabolizing Enzymes and Drug Transporters. Drug Metab. Dispos. 43 (5), 774−785. (46) Ware, B. R., Berger, D. R., and Khetani, S. R. (2015) Prediction of Drug-Induced Liver Injury in Micropatterned Co-Cultures Containing iPSC-Derived Human Hepatocytes. Toxicol. Sci. 145 (2), 252−262. (47) 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. (48) Domansky, K., Inman, W., Serdy, J., Dash, A., Lim, M. H. M., and Griffith, L. G. (2010) Perfused Multiwell Plate for 3D Liver Tissue Engineering. Lab Chip 10 (1), 51−58. (49) Bhise, N. S., Manoharan, V., Massa, S., Tamayol, A., Ghaderi, M., Miscuglio, M., Lang, Q., Shrike Zhang, Y., Shin, S. R., Calzone, G., Annabi, N., Shupe, T. D., Bishop, C. E., Atala, A., Dokmeci, M. R., and Khademhosseini, A. (2016) A Liver-on-a-Chip Platform with Bioprinted Hepatic Spheroids. Biofabrication 8 (1), 014101. (50) Vivares, A., Salle-Lefort, S., Arabeyre-Fabre, C., Ngo, R., Penarier, G., Bremond, M., Moliner, P., Gallas, J.-F., Fabre, G., and Klieber, S. (2015) Morphological Behaviour and Metabolic Capacity of Cryopreserved Human Primary Hepatocytes Cultivated in a Perfused Multiwell Device. Xenobiotica 45 (1), 29−44. (51) Oleaga, C., Bernabini, C., Smith, A. S. T., Srinivasan, B., Jackson, M., McLamb, W., Platt, V., Bridges, R., Cai, Y., Santhanam, N., Berry, B., Najjar, S., Akanda, N., Guo, X., Martin, C., Ekman, G., Esch, M. B., Langer, J., Ouedraogo, G., Cotovio, J., Breton, L., Shuler, M. L., and Hickman, J. (2016) Multi-Organ Toxicity Demonstration in a Functional Human in Vitro System Composed of Four Organs. Sci. Rep. 6 (1), 20030. (52) Choucha-Snouber, L., Aninat, C., Grsicom, L., Madalinski, G., Brochot, C., Poleni, P. E., Razan, F., Guillouzo, C. G., Legallais, C., Corlu, A., and Leclerc, E. (2013) Investigation of Ifosfamide Nephrotoxicity Induced in a Liver-Kidney Co-Culture Biochip. Biotechnol. Bioeng. 110 (2), 597−608. (53) Esch, M. B., Ueno, H., Applegate, D. R., and Shuler, M. L. (2016) Modular, Pumpless Body-on-a-Chip Platform for the Co-Culture of GI Tract Epithelium and 3D Primary Liver Tissue. Lab Chip 16 (14), 2719−2729. (54) Chen, W. L. K., Edington, C., Suter, E., Yu, J., Velazquez, J. J., Velazquez, J. G., Shockley, M., Large, E. M., Venkataramanan, R., Hughes, D. J., Stokes, C. L., Trumper, D. L., Carrier, R. L., Cirit, M., Griffith, L. G., and Lauffenburger, D. A. (2017) Integrated Gut/Liver Microphysiological Systems Elucidates Inflammatory Inter-Tissue Crosstalk. Biotechnol. Bioeng. 114 (11), 2648−2659. (55) Tsamandouras, N., Chen, W. L. K., Edington, C. D., Stokes, C. L., Griffith, L. G., and Cirit, M. (2017) Integrated Gut and Liver Microphysiological Systems for Quantitative In Vitro Pharmacokinetic Studies. AAPS J. 19 (5), 1499−1512. (56) Nishimura, T., Hu, Y., Wu, M., Pham, E., Suemizu, H., Elazar, M., Liu, M., Idilman, R., Yurdaydin, C., Angus, P., Stedman, C., Murphy, B., Glenn, J., Nakamura, M., Nomura, T., Chen, Y., Zheng, M., Fitch, W. L., and Peltz, G. (2013) Using Chimeric Mice with Humanized Livers To Predict Human Drug Metabolism and a Drug−Drug Interaction. J. Pharmacol. Exp. Ther. 344 (2), 388−396. (57) Anderson, S., Luffer-Atlas, D., and Knadler, M. P. (2009) Predicting Circulating Human Metabolites: How Good Are We? Chem. Res. Toxicol. 22 (2), 243−256. (58) Leclercq, L., Cuyckens, F., Mannens, G. S. J., De Vries, R., Timmerman, P., and Evans, D. C. (2009) Which Human Metabolites Have We MIST? Retrospective Analysis, Practical Aspects, and Perspectives for Metabolite Identification and Quantification in Pharmaceutical Development. Chem. Res. Toxicol. 22 (2), 280−293. 427



Review

(59) Hasegawa, M., Kawai, K., Mitsui, T., Taniguchi, K., Monnai, M., Wakui, M., Ito, M., Suematsu, M., Peltz, G., Nakamura, M., and Suemizu, H. (2011) The Reconstituted “humanized Liver” in TK-NOG Mice Is Mature and Functional. Biochem. Biophys. Res. Commun. 405 (3), 405− 410. (60) Dickie, A. P., Wilson, C. E., Schreiter, K., Wehr, R., Wilson, E. M., Bial, J., Scheer, N., Wilson, I. D., and Riley, R. J. (2017) The Pharmacokinetics and Metabolism of Lumiracoxib in Chimeric Humanized and Murinized FRG Mice. Biochem. Pharmacol. 135 (July), 139−150. (61) Barzi, M., Pankowicz, F. P., Zorman, B., Liu, X., Legras, X., Yang, D., Borowiak, M., Bissig-Choisat, B., Sumazin, P., Li, F., and Bissig, K.-D. (2017) A Novel Humanized Mouse Lacking Murine P450 Oxidoreductase for Studying Human Drug Metabolism. Nat. Commun. 8 (1), 39. (62) Xu, D., Nishimura, T., Nishimura, S., Zhang, H., Zheng, M., Guo, Y.-Y., Masek, M., Michie, S. A., Glenn, J., and Peltz, G. (2014) Fialuridine Induces Acute Liver Failure in Chimeric TK-NOG Mice: A Model for Detecting Hepatic Drug Toxicity prior to Human Testing. PLoS Med. 11 (4), e1001628. (63) Suzuki, E., Koyama, K., Nakai, D., Goda, R., Kuga, H., and Chiba, K. (2017) Observation of Clinically Relevant Drug Interaction in Chimeric Mice with Humanized Livers: The Case of Valproic Acid and Carbapenem Antibiotics. Eur. J. Drug Metab. Pharmacokinet. 42 (6), 965−972. (64) Hu, Y., Wu, M., Nishimura, T., Zheng, M., and Peltz, G. (2013) Human Pharmacogenetic Analysis in Chimeric Mice with “humanized Livers. Pharmacogenet. Genomics 23 (2), 78−83. (65) Kamimura, H., Ito, S., Nozawa, K., Nakamura, S., Chijiwa, H., Nagatsuka, S. I., Kuronuma, M., Ohnishi, Y., Suemizu, H., and Ninomiya, S. I. (2015) Formation of the Accumulative Human Metabolite and Human-Specific Glutathione Conjugate of Diclofenac in TK-NOG Chimeric Mice with Humanized Livers. Drug Metab. Dispos. 43 (3), 309−316. (66) Felmlee, M. A., Morris, M. E., and Mager, D. E. (2012) Mechanism-Based Pharmacodynamic Modeling. Methods Mol. Biol. 929, 583−600. (67) Sliwoski, G., Kothiwale, S., Meiler, J., and Lowe, E. W., Jr. (2014) Computational Methods in Drug Discovery. Pharmacol. Rev. 66 (1), 334−395. (68) Naven, R., and Louise-May, S. (2015) Computational Toxicology: Its Essential Role in Reducing Drug Attrition. Hum. Exp. Toxicol. 34 (12), 1304−1309. (69) Kirchmair, J., Göller, A. H., Lang, D., Kunze, J., Testa, B., Wilson, I. D., Glen, R. C., and Schneider, G. (2015) Predicting Drug Metabolism: Experiment And/or Computation? Nat. Rev. Drug Discovery 14 (6), 387−404. (70) Wang, Y., Xing, J., Xu, Y., Zhou, N., Peng, J., Xiong, Z., Liu, X., Luo, X., Luo, C., Chen, K., Zheng, M., and Jiang, H. (2015) In Silico ADME/T Modelling for Rational Drug Design. Q. Rev. Biophys. 48 (4), 488−515. (71) Xia, M., Huang, R., Witt, K. L., Southall, N., Fostel, J., Cho, M.-H., Jadhav, A., Smith, C. S., Inglese, J., Portier, C. J., Tice, R. R., and Austin, C. P. (2008) Compound Cytotoxicity Profiling Using Quantitative High-Throughput Screening. Environ. Health Perspect. 116 (3), 284− 291. (72) Tonnelier, A., Coecke, S., and Zaldívar, J. M. (2012) Screening of Chemicals for Human Bioaccumulative Potential with a Physiologically Based Toxicokinetic Model. Arch. Toxicol. 86 (3), 393−403. (73) Judson, R. S., Houck, K. A., Kavlock, R. J., Knudsen, T. B., Martin, M. T., Mortensen, H. M., Reif, D. M., Rotroff, D. M., Shah, I., Richard, A. M., and Dix, D. J. (2010) In Vitro Screening of Environmental Chemicals for Targeted Testing Prioritization: The ToxCast Project. Environ. Health Perspect. 118 (4), 485−492. (74) Martin, M. T., Dix, D. J., Judson, R. S., Kavlock, R. J., Reif, D. M., Richard, A. M., Rotroff, D. M., Romanov, S., Medvedev, A., Poltoratskaya, N., Gambarian, M., Moeser, M., Makarov, S. S., and Houck, K. A. (2010) Impact of Environmental Chemicals on Key

Transcription Regulators and Correlation to Toxicity End Points within EPA’s ToxCast Program. Chem. Res. Toxicol. 23 (3), 578−590. (75) Martin, M. T., Knudsen, T. B., Reif, D. M., Houck, K. A., Judson, R. S., Kavlock, R. J., and Dix, D. J. (2011) Predictive Model of Rat Reproductive Toxicity from ToxCast High Throughput Screening. Biol. Reprod. 85 (2), 327−339. (76) Attene-Ramos, M. S., Huang, R., Michael, S., Witt, K. L., Richard, A., Tice, R. R., Simeonov, A., Austin, C. P., and Xia, M. (2015) Profiling of the Tox21 Chemical Collection for Mitochondrial Function To Identify Compounds That Acutely Decrease Mitochondrial Membrane Potential. Environ. Health Perspect. 123 (1), 49−56. (77) Huang, R., Sakamuru, S., Martin, M. T., Reif, D. M., Judson, R. S., Houck, K. a, Casey, W., Hsieh, J.-H., Shockley, K. R., Ceger, P., Fostel, J., Witt, K. L., Tong, W., Rotroff, D. M., Zhao, T., Shinn, P., Simeonov, A., Dix, D. J., Austin, C. P., Kavlock, R. J., Tice, R. R., and Xia, M. (2015) Profiling of the Tox21 10K Compound Library for Agonists and Antagonists of the Estrogen Receptor Alpha Signaling Pathway. Sci. Rep. 4 (1), 5664. (78) Chen, S., Hsieh, J. H., Huang, R., Sakamuru, S., Hsin, L. Y., Xia, M., Shockley, K. R., Auerbach, S., Kanaya, N., Lu, H., Svoboda, D., Witt, K. L., Merrick, B. A., Teng, C. T., and Tice, R. R. (2015) Cell-Based HighThroughput Screening for Aromatase Inhibitors in the Tox21 10K Library. Toxicol. Sci. 147 (2), 446−457. (79) Daneshian, M., Kamp, H., Hengstler, J., Leist, M., and van de Water, B. (2016) Highlight Report: Launch of a Large Integrated European in Vitro Toxicology Project: EU-ToxRisk. Arch. Toxicol. 90 (5), 1021−1024. (80) Stepan, A. F., Walker, D. P., Bauman, J., Price, D. A., Baillie, T. A., Kalgutkar, A. S., and Aleo, M. D. (2011) Structural Alert/Reactive Metabolite Concept as Applied in Medicinal Chemistry To Mitigate the Risk of Idiosyncratic Drug Toxicity: A Perspective Based on the Critical Examination of Trends in the Top 200 Drugs Marketed in the United States. Chem. Res. Toxicol. 24 (9), 1345−1410. (81) Dang, N. L., Hughes, T. B., Miller, G. P., and Swamidass, S. J. (2017) Computational Approach to Structural Alerts: Furans, Phenols, Nitroaromatics, and Thiophenes. Chem. Res. Toxicol. 30 (4), 1046− 1059. (82) Judson, R., Richard, A., Dix, D., Houck, K., Elloumi, F., Martin, M., Cathey, T., Transue, T. R., Spencer, R., and Wolf, M. (2008) ACToR Aggregated Computational Toxicology Resource. Toxicol. Appl. Pharmacol. 233 (1), 7−13. (83) Snyder, R. D., Pearl, G. S., Mandakas, G., Choy, W. N., Goodsaid, F., and Rosenblum, I. Y. (2004) Assessment of the Sensitivity of the Computational Programs DEREK, TOPKAT, and MCASE in the Prediction of the Genotoxicity of Pharmaceutical Molecules. Environ. Mol. Mutagen. 43 (3), 143−158. (84) Judson, P. N., Marchant, C. A., and Vessey, J. D. (2003) Using Argumentation for Absolute Reasoning about the Potential Toxicity of Chemicals. J. Chem. Inf. Comput. Sci. 43 (5), 1364−1370. (85) Greene, N., Fisk, L., Naven, R. T., Note, R. R., Patel, M. L., and Pelletier, D. J. (2010) Developing Structure-Activity Relationships for the Prediction of Hepatotoxicity. Chem. Res. Toxicol. 23 (7), 1215− 1222. (86) Chen, M., Hong, H., Fang, H., Kelly, R., Zhou, G., Borlak, J., and Tong, W. (2013) Quantitative Structure−Activity Relationship Models for Predicting Drug-Induced Liver Injury Based on FDA-Approved Drug Labeling Annotation and Using a Large Collection of Drugs. Toxicol. Sci. 136 (1), 242−249. (87) Ekins, S., Williams, A. J., and Xu, J. J. (2010) A Predictive LigandBased Bayesian Model for Human Drug-Induced Liver Injury. Pharmacology 38 (12), 2302−2308. (88) Liew, C. Y., Lim, Y. C., and Yap, C. W. (2011) Mixed Learning Algorithms and Features Ensemble in Hepatotoxicity Prediction. J. Comput.-Aided Mol. Des. 25 (9), 855−871. (89) Liu, Z., Shi, Q., Ding, D., Kelly, R., Fang, H., and Tong, W. (2011) Translating Clinical Findings into Knowledge in Drug Safety Evaluation - Drug Induced Liver Injury Prediction System (DILIps). PLoS Comput. Biol. 7 (12), e1002310. 428



Review

(90) Xu, J. J., Henstock, P. V., Dunn, M. C., Smith, A. R., Chabot, J. R., and de Graaf, D. (2008) Cellular Imaging Predictions of Clinical DrugInduced Liver Injury. Toxicol. Sci. 105 (1), 97−105. (91) Wu, L., Liu, Z., Auerbach, S., Huang, R., Chen, M., McEuen, K., Xu, J., Fang, H., and Tong, W. (2017) Integrating Drug’s Mode of Action into Quantitative Structure-Activity Relationships for Improved Prediction of Drug-Induced Liver Injury. J. Chem. Inf. Model. 57 (4), 1000−1006. (92) Mulliner, D., Schmidt, F., Stolte, M., Spirkl, H. P., Czich, A., and Amberg, A. (2016) Computational Models for Human and Animal Hepatotoxicity with a Global Application Scope. Chem. Res. Toxicol. 29 (5), 757−767. (93) Sipes, N. S., Martin, M. T., Reif, D. M., Kleinstreuer, N. C., Judson, R. S., Singh, A. V., Chandler, K. J., Dix, D. J., Kavlock, R. J., and Knudsen, T. B. (2011) Predictive Models of Prenatal Developmental Toxicity from ToxCast High-Throughput Screening Data. Toxicol. Sci. 124 (1), 109−127. (94) AbdulHameed, M. D. M., Ippolito, D. L., and Wallqvist, A. (2016) Predicting Rat and Human Pregnane X Receptor Activators Using Bayesian Classification Models. Chem. Res. Toxicol. 29 (10), 1729−1740. (95) Gayvert, K. M., Madhukar, N. S., and Elemento, O. (2016) A Data-Driven Approach to Predicting Successes and Failures of Clinical Trials. Cell Chem. Biol. 23 (10), 1294−1301. (96) Browne, P., Judson, R. S., Casey, W. M., Kleinstreuer, N. C., and Thomas, R. S. (2015) Screening Chemicals for Estrogen Receptor Bioactivity Using a Computational Model. Environ. Sci. Technol. 49 (14), 8804−8814. (97) Ng, H. W., Doughty, S. W., Luo, H., Ye, H., Ge, W., Tong, W., and Hong, H. (2015) Development and Validation of Decision Forest Model for Estrogen Receptor Binding Prediction of Chemicals Using Large Data Sets. Chem. Res. Toxicol. 28 (12), 2343−2351. (98) Dreier, D. A., Denslow, N. D., and Martyniuk, C. J. (2017) Computational Analysis of the ToxCast Estrogen Receptor Agonist Assays to Predict Vitellogenin Induction by Chemicals in Male Fish. Environ. Toxicol. Pharmacol. 53 (July), 177−183. (99) Sipes, N. S., Wambaugh, J. F., Pearce, R., Auerbach, S. S., Wetmore, B. A., Hsieh, J.-H., Shapiro, A. J., Svoboda, D., DeVito, M. J., and Ferguson, S. S. (2017) An Intuitive Approach for Predicting Potential Human Health Risk with the Tox21 10k Library. Environ. Sci. Technol. 51 (18), 10786−10796. (100) Spencer, M., Eickholt, J., and Cheng, J. (2015) A Deep Learning Network Approach to ab Initio Protein Secondary Structure Prediction. IEEE/ACM Trans. Comput. Biol. Bioinf. 12 (1), 103−112. (101) Wang, C., Liu, J., Luo, F., Tan, Y., Deng, Z., and Hu, Q. N. (2014) Pairwise Input Neural Network for Target-Ligand Interaction Prediction. Proc. - 2014 IEEE Int. Conf. Bioinf. Biomed. IEEE BIBM 2014, 67−70. (102) Sun, T., Zhou, B., Lai, L., and Pei, J. (2017) Sequence-Based Prediction of Protein Protein Interaction Using a Deep-Learning Algorithm. BMC Bioinf. 18 (1), 277. (103) Du, X., Sun, S., Hu, C., Yao, Y., Yan, Y., and Zhang, Y. (2017) DeepPPI: Boosting Prediction of Protein-Protein Interactions with Deep Neural Networks. J. Chem. Inf. Model. 57 (6), 1499−1510. (104) Alipanahi, B., Delong, A., Weirauch, M. T., and Frey, B. J. (2015) Predicting the Sequence Specificities of DNA- and RNA-Binding Proteins by Deep Learning. Nat. Biotechnol. 33 (8), 831−838. (105) Zeng, T., Li, R., Mukkamala, R., Ye, J., and Ji, S. (2015) Deep Convolutional Neural Networks for Annotating Gene Expression Patterns in the Mouse Brain. BMC Bioinf. 16 (1), 147. (106) Leung, M. K. K., Xiong, H. Y., Lee, L. J., and Frey, B. J. (2014) Deep Learning of the Tissue-Regulated Splicing Code. Bioinformatics 30 (12), i121−i129. (107) Fan, X. N., and Zhang, S. W. (2015) lncRNA-MFDL: Identification of Human Long Non-Coding RNAs by Fusing Multiple Features and Using Deep Learning. Mol. BioSyst. 11 (3), 892−897. (108) Ditzler, G., Polikar, R., and Rosen, G. (2015) Multi-Layer and Recursive Neural Networks for Metagenomic Classification. IEEE Trans. Nanobioscience 14 (6), 608−616.

(109) Kelley, D. R., Snoek, J., and Rinn, J. L. (2016) Basset: Learning the Regulatory Code of the Accessible Genome with Deep Convolutional Neural Networks. Genome Res. 26 (7), 990−999. (110) Litjens, G., Sánchez, C. I., Timofeeva, N., Hermsen, M., Nagtegaal, I., Kovacs, I., Hulsbergen - van de Kaa, C., Bult, P., van Ginneken, B., and van der Laak, J. (2016) Deep Learning as a Tool for Increased Accuracy and Efficiency of Histopathological Diagnosis. Sci. Rep. 6 (1), 26286. (111) Chen, L., Cai, C., Chen, V., and Lu, X. (2015) Trans-Species Learning of Cellular Signaling Systems with Bimodal Deep Belief Networks. Bioinformatics 31 (18), 3008−3015. (112) Hughes, T. B., Miller, G. P., and Swamidass, S. J. (2015) Modeling Epoxidation of Drug-like Molecules with a Deep Machine Learning Network. ACS Cent. Sci. 1 (4), 168−180. (113) Xu, Y., Dai, Z., Chen, F., Gao, S., Pei, J., and Lai, L. (2015) Deep Learning for Drug-Induced Liver Injury. J. Chem. Inf. Model. 55 (10), 2085−2093. (114) Angermueller, C., Pärnamaa, T., Parts, L., and Stegle, O. (2016) Deep Learning for Computational Biology. Mol. Syst. Biol. 12 (7), 878. (115) Lusci, A., Pollastri, G., and Baldi, P. (2013) Deep Architectures and Deep Learning in Chemoinformatics: The Prediction of Aqueous Solubility for Drug-like Molecules. J. Chem. Inf. Model. 53 (7), 1563− 1575. (116) Kalgutkar, A. S., and Didiuk, M. T. (2009) Structural Alerts, Reactive Metabolites, and Protein Covalent Binding: How Reliable Are These Attributes as Predictors of Drug Toxicity? Chem. Biodiversity 6 (11), 2115−2137. (117) Wu, Z., Han, M., Chen, T., Yan, W., and Ning, Q. (2010) Acute Liver Failure: Mechanisms of Immune-Mediated Liver Injury. Liver Int. 30 (6), 782−794. (118) Hughes, T. B., Miller, G. P., and Swamidass, S. J. (2015) Site of Reactivity Models Predict Molecular Reactivity of Diverse Chemicals with Glutathione. Chem. Res. Toxicol. 28 (4), 797−809. (119) Hughes, T. B., and Swamidass, S. J. (2017) Deep Learning to Predict the Formation of Quinone Species in Drug Metabolism. Chem. Res. Toxicol. 30 (2), 642−656. (120) Dang, N. Le., Hughes, T. B., Miller, G. P., and Swamidass, S. J. (2018) Computationally Assessing the Bioactivation of Drugs by NDealkylation. Chem. Res. Toxicol. 31 (2), 68−80. (121) Hughes, T. B., Dang, N. L., Miller, G. P., and Swamidass, S. J. (2016) Modeling Reactivity to Biological Macromolecules with a Deep Multitask Network. ACS Cent. Sci. 2 (8), 529−537. (122) Lammert, C., Einarsson, S., Saha, C., Niklasson, A., Bjornsson, E., and Chalasani, N. (2008) Relationship between Daily Dose of Oral Medications and Idiosyncratic Drug-Induced Liver Injury: Search for Signals. Hepatology 47 (6), 2003−2009. (123) Cheng, A., and Dixon, S. L. (2003) In Silico Models for the Prediction of Dose-Dependent Human Hepatotoxicity. J. Comput.Aided Mol. Des. 17 (12), 811−823. (124) Chiba, M., Ishii, Y., and Sugiyama, Y. (2009) Prediction of Hepatic Clearance in Human From In Vitro Data for Successful Drug Development. AAPS J. 11 (2), 262−276. (125) Woodhead, J. L., Yang, K., Siler, S. Q., Watkins, P. B., Brouwer, K. L. R., Barton, H. A., and Howell, B. A. (2014) Exploring BSEP Inhibition-Mediated Toxicity with a Mechanistic Model of DrugInduced Liver Injury. Front. Pharmacol. 5 (November), 240. (126) Woodhead, J. L., Yang, K., Brouwer, K. L. R., Siler, S. Q., Stahl, S. H., Ambroso, J. L., Baker, D., Watkins, P. B., and Howell, B. A. (2014) Mechanistic Modeling Reveals the Critical Knowledge Gaps in Bile Acid-Mediated DILI. CPT: Pharmacometrics Syst. Pharmacol. 3 (July), e123. (127) Woodhead, J. L., Watkins, P. B., Howell, B. A., Siler, S. Q., and Shoda, L. K. M. (2017) The Role of Quantitative Systems Pharmacology Modeling in the Prediction and Explanation of Idiosyncratic DrugInduced Liver Injury. Drug Metab. Pharmacokinet. 32 (1), 40−45. (128) Woodhead, J. L., Brock, W. J., Roth, S. E., Shoaf, S. E., Brouwer, K. L. R., Church, R., Grammatopoulos, T. N., Stiles, L., Siler, S. Q., Howell, B. A., Mosedale, M., Watkins, P. B., and Shoda, L. K. M. (2017) Application of a Mechanistic Model to Evaluate Putative Mechanisms of 429



Review

Tolvaptan Drug-Induced Liver Injury and Identify Patient Susceptibility Factors. Toxicol. Sci. 155 (1), 61−74. (129) Chen, M., Suzuki, A., Borlak, J., Andrade, R. J., and Lucena, M. I. (2015) Drug-Induced Liver Injury: Interactions between Drug Properties and Host Factors. J. Hepatol. 63 (2), 503−514. (130) Shoda, L. K., Battista, C., Siler, S. Q., Pisetsky, D. S., Watkins, P. B., and Howell, B. A. (2017) Mechanistic Modelling of Drug-Induced Liver Injury: Investigating the Role of Innate Immune Responses. Gene Regul. Syst. Biol. 11, 117762501769607. (131) Jiménez, J., Doerr, S., Martínez-Rosell, G., Rose, A. S., and De Fabritiis, G. (2017) DeepSite: Protein-Binding Site Predictor Using 3DConvolutional Neural Networks. Bioinformatics 33 (19), 3036−3042. (132) Mayr, A., Klambauer, G., Unterthiner, T., and Hochreiter, S. (2016) DeepTox: Toxicity Prediction Using Deep Learning. Front. Environ. Sci. 3 (February), 80. (133) Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R. (2014) Dropout: A Simple Way to Prevent Neural Networks from Overfitting. J. Mach. Learn. Res. 15, 1929−1958. (134) Lasko, T. A., Denny, J. C., and Levy, M. A. (2013) Computational Phenotype Discovery Using Unsupervised Feature Learning over Noisy, Sparse, and Irregular Clinical Data. PLoS One 8 (6), e66341. (135) Nguyen, P., Tran, T., Wickramasinghe, N., and Venkatesh, S. (2017) Deepr: A Convolutional Net for Medical Records. IEEE J. Biomed. Heal. Informatics 21 (1), 22−30. (136) Miotto, R., Li, L., Kidd, B. A., and Dudley, J. T. (2016) Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records. Sci. Rep. 6 (1), 26094. (137) Tran, T., Nguyen, T. D., Phung, D., and Venkatesh, S. (2015) Learning Vector Representation of Medical Objects via EMR-Driven Nonnegative Restricted Boltzmann Machines (eNRBM). J. Biomed. Inf. 54 (April), 96−105. (138) Acharya, U. R., Fujita, H., Oh, S. L., Hagiwara, Y., Tan, J. H., and Adam, M. (2017) Application of Deep Convolutional Neural Network for Automated Detection of Myocardial Infarction Using ECG Signals. Inf. Sci. (N. Y.) 415−416 (November), 190−198. (139) Acharya, U. R., Oh, S. L., Hagiwara, Y., Tan, J. H., and Adeli, H. (2017) Deep Convolutional Neural Network for the Automated Detection and Diagnosis of Seizure Using EEG Signals. Comput. Biol. Med., DOI: 10.1016/j.compbiomed.2017.09.017. (140) Chen, Y., Argentinis, E., and Weber, G. (2016) IBM Watson: How Cognitive Computing Can Be Applied to Big Data Challenges in Life Sciences Research. Clin. Ther. 38 (4), 688−701. (141) Xu, D., Michie, S. A., Zheng, M., Takeda, S., Wu, M., and Peltz, G. (2015) Humanized Thymidine Kinase−NOG Mice Can Be Used to Identify Drugs That Cause Animal-Specific Hepatotoxicity: A Case Study with Furosemide. J. Pharmacol. Exp. Ther. 354 (1), 73−78. (142) Kamimura, H., Ito, S., Chijiwa, H., Okuzono, T., Ishiguro, T., Yamamoto, Y., Nishinoaki, S., Ninomiya, S.-I., Mitsui, M., Kalgutkar, A. S., Yamazaki, H., and Suemizu, H. (2017) Simulation of Human Plasma Concentration−time Profiles of the Partial Glucokinase Activator PF04937319 and Its Disproportionate N-Demethylated Metabolite Using Humanized Chimeric Mice and Semi-Physiological Pharmacokinetic Modeling. Xenobiotica 47 (5), 382−393. (143) Yamazaki-Nishioka, M., Shimizu, M., Suemizu, H., Nishiwaki, M., Mitsui, M., and Yamazaki, H. (2018) Human Plasma Metabolic Profiles of Benzydamine, a Flavin-Containing Monooxygenase Probe Substrate, Simulated with Pharmacokinetic Data from Control and Humanized-Liver Mice. Xenobiotica 48 (2), 117−123. (144) Yamazaki, H., Kuribayashi, S., Inoue, T., Honda, T., Tateno, C., Oofusa, K., Ninomiya, S., Ikeda, T., Izumi, T., and Horie, T. (2015) Zone Analysis by Two-Dimensional Electrophoresis with Accelerator Mass Spectrometry of in Vivo Protein Bindings of Idiosyncratic Hepatotoxicants Troglitazone and Flutamide Bioactivated in Chimeric Mice with Humanized Liver. Toxicol. Res. (Cambridge, U. K.) 4 (1), 106−111. (145) Miyamoto, M., Iwasaki, S., Chisaki, I., Nakagawa, S., Amano, N., and Hirabayashi, H. (2017) Comparison of Predictability for Human

Pharmacokinetics Parameters among Monkeys, Rats, and Chimeric Mice with Humanised Liver. Xenobiotica 47 (12), 1052−1063. (146) 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 Hepatotropic Pathogens. Nat. Protoc. 10 (12), 2027−2053. (147) 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) HighThroughput and Combinatorial Gene Expression on a Chip for Metabolism-Induced Toxicology Screening. Nat. Commun. 5, 3739. (148) Judson, R., Elloumi, F., Setzer, R. W., Li, Z., and Shah, I. (2008) A Comparison of Machine Learning Algorithms for Chemical Toxicity Classification Using a Simulated Multi-Scale Data Model. BMC Bioinf. 9 (1), 241.

430


Advancing Predictive Hepatotoxicity at the Intersection of Experimental

Recommend Documents