Hepatotoxicity: A scheme for generating chemical categories for read-across, structural alerts and insights into mechanism(s) of action

Improved Detection of Drug-Induced Liver Injury by Integrating Predicted in vivo and in vitro Data

Drug-induced liver injury (DILI) has been significant challenge in drug discovery, often leading to clinical trial failures and necessitating drug withdrawals. The existing suite of in vitro proxy-DILI assays is generally effective at identifying compounds with hepatotoxicity. However, there is considerable interest in enhancing in silico prediction of DILI because it allows for the evaluation of large sets of compounds more quickly and cost-effectively, particularly in the early stages of projects. In this study, we aim to study ML models for DILI prediction that first predicts nine proxy-DILI labels and then uses them as features in addition to chemical structural features to predict DILI. The features include in vitro (e.g., mitochondrial toxicity, bile salt export pump inhibition) data, in vivo (e.g., preclinical rat hepatotoxicity studies) data, pharmacokinetic parameters of maximum concentration, structural fingerprints, and physicochemical parameters. We trained DILI-prediction models on 888 compounds from the DILIst dataset and tested on a held-out external test set of 223 compounds from DILIst dataset. The best model, DILIPredictor, attained an AUC-ROC of 0.79. This model enabled the detection of top 25 toxic compounds compared to models using only structural features (2.68 LR+ score). Using feature interpretation from DILIPredictor, we were able to identify the chemical substructures causing DILI as well as differentiate cases DILI is caused by compounds in animals but not in humans. For example, DILIPredictor correctly recognized 2-butoxyethanol as non-toxic in humans despite its hepatotoxicity in mice models. Overall, the DILIPredictor model improves the detection of compounds causing DILI with an improved differentiation between animal and human sensitivity as well as the potential for mechanism evaluation. DILIPredictor is publicly available at https://broad.io/DILIPredictor for use via web interface and with all code available for download and local implementation via https://pypi.org/project/dilipred/.

Design of OASIS 1 and 2: phase 3 clinical trials assessing the efficacy and safety of elinzanetant for the treatment of vasomotor symptoms associated with menopause

OBJECTIVE

Elinzanetant is a selective neurokinin-1,3 receptor antagonist in development for the treatment of vasomotor symptoms (VMS) associated with menopause. The pivotal, double-blind, randomized, placebo-controlled phase 3 studies Overall Assessment of efficacy and Safety of elinzanetant In patients with vasomotor Symptoms (OASIS) 1 and 2 will assess the efficacy and safety of elinzanetant in women with VMS.

METHODS

The OASIS 1 and 2 pivotal studies are designed in accordance with regulatory guidance. Postmenopausal women with moderate/severe VMS are randomized to receive 120 mg elinzanetant or placebo once daily for 12 weeks, followed by a 14-week active treatment extension. Primary endpoints are the mean change in frequency and severity of moderate/severe VMS from baseline to weeks 4 and 12. Key secondary endpoints will assess the onset of action and effects on sleep disturbance and menopause-related quality of life. Primary and key secondary endpoints will be analyzed using a mixed model with repeated measures. Feedback from postmenopausal women with VMS was used during protocol development.

RESULTS

Women confirmed the relevance of endpoints that assess the impact of VMS, sleep disturbance, and mood changes, and the need for new nonhormone treatments. Educational materials around study design, conduct and expected assessments and procedures were developed based on questions and concerns raised by women.

CONCLUSIONS

The OASIS 1 and 2 pivotal phase 3 studies will enable assessment of the efficacy and safety of elinzanetant as a treatment for VMS, together with its effect on sleep disturbances, depressive symptoms, and menopause-related quality of life. Feedback from postmenopausal women with VMS was used to maximize patient centricity in the trials.

GeoDILI: A Robust and Interpretable Model for Drug-Induced Liver Injury Prediction Using Graph Neural Network-Based Molecular Geometric Representation

Drug-induced liver injury (DILI) is a significant cause of drug failure and withdrawal due to liver damage. Accurate prediction of hepatotoxic compounds is crucial for safe drug development. Several DILI prediction models have been published, but they are built on different data sets, making it difficult to compare model performance. Moreover, most existing models are based on molecular fingerprints or descriptors, neglecting molecular geometric properties and lacking interpretability. To address these limitations, we developed GeoDILI, an interpretable graph neural network that uses a molecular geometric representation. First, we utilized a geometry-based pretrained molecular representation and optimized it on the DILI data set to improve predictive performance. Second, we leveraged gradient information to obtain high-precision atomic-level weights and deduce the dominant substructure. We benchmarked GeoDILI against recently published DILI prediction models, as well as popular GNN models and fingerprint-based machine learning models using the same data set, showing superior predictive performance of our proposed model. We applied the interpretable method in the DILI data set and derived seven precise and mechanistically elucidated structural alerts. Overall, GeoDILI provides a promising approach for accurate and interpretable DILI prediction with potential applications in drug discovery and safety assessment. The data and source code are available at GitHub repository (https://github.com/CSU-QJY/GeoDILI).

Identifying chemicals based on receptor binding/bioactivation/mechanistic explanation associated with potential to elicit hepatotoxicity and to support structure activity relationship-based read-across

The liver is the most common target organ in toxicology studies. The development of chemical structural alerts for identifying hepatotoxicity will play an important role in in silico model prediction and help strengthen the identification of analogs used in structure activity relationship (SAR)- based read-across. The aim of the current study is development of an SAR-based expert-system decision tree for screening of hepatotoxicants across a wide range of chemistry space and proposed modes of action for clustering of chemicals using defined core chemical categories based on receptor-binding or bioactivation. The decision tree is based on ∼ 1180 different chemicals that were reviewed for hepatotoxicity information. Knowledge of chemical receptor binding, metabolism and mechanistic information were used to group these chemicals into 16 different categories and 102 subcategories: four categories describe binders to 9 different receptors, 11 categories are associated with possible reactive metabolites (RMs) and there is one miscellaneous category. Each chemical subcategory has been associated with possible modes of action (MOAs) or similar key structural features. This decision tree can help to screen potential liver toxicants associated with core structural alerts of receptor binding and/or RMs and be used as a component of weight of evidence decisions based on SAR read-across, and to fill data gaps.

A KNIME Workflow to Assist the Analogue Identification for Read-Across, Applied to Aromatase Activity

The reduction and replacement of in vivo tests have become crucial in terms of resources and animal benefits. The read-across approach reduces the number of substances to be tested, exploiting existing experimental data to predict the properties of untested substances. Currently, several tools have been developed to perform read-across, but other approaches, such as computational workflows, can offer a more flexible and less prescriptive approach. In this paper, we are introducing a workflow to support analogue identification for read-across. The implementation of the workflow was performed using a database of azole chemicals with in vitro toxicity data for human aromatase enzymes. The workflow identified analogues based on three similarities: structural similarity (StrS), metabolic similarity (MtS), and mechanistic similarity (McS). Our results showed how multiple similarity metrics can be combined within a read-across assessment. The use of the similarity based on metabolism and toxicological mechanism improved the predictions in particular for sensitivity. Beyond the results predicting a large population of substances, practical examples illustrate the advantages of the proposed approach.

Supervised chemical graph mining improves drug-induced liver injury prediction

Drug-induced liver injury (DILI) is the main cause of drug failure in clinical trials. The characterization of toxic compounds in terms of chemical structure is important because compounds can be metabolized to toxic substances in the liver. Traditional machine learning approaches have had limited success in predicting DILI, and emerging deep graph neural network (GNN) models are yet powerful enough to predict DILI. In this study, we developed a completely different approach, supervised subgraph mining (SSM), a strategy to mine explicit subgraph features by iteratively updating individual graph transitions to maximize DILI fidelity. Our method outperformed previous methods including state-of-the-art GNN tools in classifying DILI on two different datasets: DILIst and TDC-benchmark. We also combined the subgraph features by using SMARTS-based frequent structural pattern matching and associated them with drugs' ATC code.

Quantitative evaluation of explainable graph neural networks for molecular property prediction

Graph neural networks (GNNs) have received increasing attention because of their expressive power on topological data, but they are still criticized for their lack of interpretability. To interpret GNN models, explainable artificial intelligence (XAI) methods have been developed. However, these methods are limited to qualitative analyses without quantitative assessments from the real-world datasets due to a lack of ground truths. In this study, we have established five XAI-specific molecular property benchmarks, including two synthetic and three experimental datasets. Through the datasets, we quantitatively assessed six XAI methods on four GNN models and made comparisons with seven medicinal chemists of different experience levels. The results demonstrated that XAI methods could deliver reliable and informative answers for medicinal chemists in identifying the key substructures. Moreover, the identified substructures were shown to complement existing classical fingerprints to improve molecular property predictions, and the improvements increased with the growth of training data.

Chemoinformatic Design and Profiling of Derivatives of Dasabuvir, Efavirenz, and Tipranavir as Potential Inhibitors of Zika Virus RNA-Dependent RNA Polymerase and Methyltransferase

Zika virus (ZIKV) infection is one of the mosquito-borne flaviviruses of human importance with more than 2 million suspected cases and more than 1 million people infected in about 30 countries. There are reported inhibitors of the zika virus replication machinery, but no approved effective antiviral therapy including vaccines directed against the virus for treatment or prevention is currently available. The study investigated the chemoinformatic design and profiling of derivatives of dasabuvir, efavirenz, and tipranavir as potential inhibitors of the zika virus RNA-dependent RNA polymerase (RdRP) and/or methyltransferase (MTase). The three-dimensional (3D) coordinates of dasabuvir, efavirenz, and tipranavir were obtained from the PubChem database, and their respective derivatives were designed with DataWarrior-5.2.1 using an evolutionary algorithm. Derivatives that were not mutagenic, tumorigenic, or irritant were selected; docked into RdRP and MTase; and further subjected to absorption, distribution, metabolism, excretion, and toxicity (ADMET) evaluation with Swiss-ADME and pkCSM web tools. Some of the designed compounds are Lipinski's rule-of-five compliant, with good synthetic accessibilities. Compounds 20d, 21d, 22d, and 1e are nontoxic with the only limitation of CYP1A2, CYP2C19, and/or CYP2C9 inhibition. Replacements of -CH₃ and -NH- in the methanesulfonamide moiety of dasabuvir with -OH and -CH₂- or -CH₂CH₂-, respectively, improved the safety/toxicity profile. Hepatotoxicity in 5d, 4d, and 18d is likely due to -NH- in their methanesulfonamide/sulfamic acid moieties. These compounds are potent inhibitors of N-7 and 2'-methylation activities of ZIKV methyltransferase and/or RNA synthesis through interactions with amino acid residues in the priming loop/"N-pocket" in the virus RdRP. Synthesis of these compounds and wet laboratory validation against ZIKV are recommended.

A scheme to evaluate structural alerts to predict toxicity - Assessing confidence by characterising uncertainties

Structure-activity relationships (SARs) in toxicology have enabled the formation of structural rules which, when coded as structural alerts, are essential tools in in silico toxicology. Whilst other in silico methods have approaches for their evaluation, there is no formal process to assess the confidence that may be associated with a structural alert. This investigation proposes twelve criteria to assess the uncertainty associated with structural alerts, allowing for an assessment of confidence. The criteria are based around the stated purpose, description of the chemistry, toxicology and mechanism, performance and coverage, as well as corroborating and supporting evidence of the alert. Alerts can be given a confidence assessment and score, enabling the identification of areas where more information may be beneficial. The scheme to evaluate structural alerts was placed in the context of various use cases for industrial and regulatory applications. The analysis of alerts, and consideration of the evaluation scheme, identifies the different characteristics an alert may have, such as being highly specific or generic. These characteristics may determine when an alert can be used for specific uses such as identification of analogues for read-across or hazard identification.

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Structural alerts are useful tools for predictive toxicology.

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12 criteria to evaluate structural alerts have been identified.

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A strategy to determine confidence of structural alerts is presented.

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Different use cases require different characteristics of structural alerts.

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A Scheme to Evaluate Structural Alerts to Predict Toxicity – Assessing Confidence By Characterising Uncertainties.

Development of binary classification models for assessment of drug-induced liver injury in humans using a large set of FDA-approved drugs

Drug-induced liver injury (DILI) has been identified as one of the major causes for drugs withdrawn from the market, and even termination during the late stages of development. Therefore, it is imperative to evaluate the DILI potential of lead compounds during the research and development process. Although various computational models have been developed to predict DILI, most of which applied the DILI data were extracted from preclinical sources. In this investigation, the in silico prediction models for DILI were constructed based on 1140 FDA-approved drugs by using naïve Bayes classifier approach. The genetic algorithm method was applied for the molecular descriptors selection. Among these established prediction models, the NB-11 model based on eight molecular descriptors combined with ECFP_18 showed the best prediction performance for DILI, which gave 91.7% overall prediction accuracy for the training set, and 68.9% concordance for the external test set. Therefore, the established NB-11 prediction model can be used as a reliable virtual screening tool to predict DILI adverse effect in the early stages of drug design. In addition, some new structural alters for DILI were identified, which could be used for structural optimization in the future drug design by medicinal chemists.

Exploration of structural requirements for azole chemicals towards human aromatase CYP19A1 activity: Classification modeling, structure-activity relationships and read-across study

Human aromatase, also called CYP19A1, plays a major role in the conversion of androgens into estrogens. Inhibition of aromatase is an important target for estrogen receptor (ER)-responsive breast cancer therapy. Use of azole compounds as aromatase inhibitors is widespread despite their low selectivity. A toxicological evaluation of commonly used azole-based drugs and agrochemicals with respect to CYP19A1is currently requested by the European Union- Registration, Evaluation, Authorization and Restriction of Chemicals (EU-REACH) regulations due to their potential as endocrine disruptors. In this connection, identification of structural alerts (SAs) is an effective strategy for the toxicological assessment and safe drug design. The present study describes the identification of SAs of azole-based chemicals as guiding experts to predict the aromatase activity. Total 21 SAs associated with aromatase activity were extracted from dataset of 326 azole-based drugs/chemicals obtained from Tox21 library. A cross-validated classification model having high accuracy (error rate 5%) was proposed which can precisely classify azole chemicals into active/inactive toward aromatase. In addition, mechanistic details and toxicological properties (agonism/antagonism) of azoles with respect to aromatase were explored by comparing active and inactive chemicals using structure-activity relationships (SAR). Lastly, few structural alerts were applied to form chemical categories for read-across applications.

Oxidative-stress and long-term hepatotoxicity: comparative study in Upcyte human hepatocytes and hepaRG cells

Drug-induced liver injury (DILI) is one of the most common and serious adverse drug reactions and a major cause of drug development failure and withdrawal. Although different molecular mechanisms are implicated in DILI, enhanced ROS levels have been described as a major mechanism. Human-derived cell models are increasingly used in preclinical safety assessment because they provide quick and relatively inexpensive information in early stages of drug development. We have analyzed and compared the phenotype and functionality of two liver cell models (Upcyte human hepatocytes and HepaRG cells) to demonstrate their suitability for long-term hepatotoxicity assessments and mechanistic studies. The transcriptomic and functional analysis revealed the maintenance of phase I and phase II enzymes, and antioxidant enzymes along time in culture, although the differences found between both test systems underlie the differential sensitivity to hepatotoxins. The evaluation of several mechanisms of cell toxicity, including oxidative stress, by high-content screening, demonstrated that, by combining the stable phenotype of liver cells and repeated-dose exposure regimes to 12 test compounds at clinically relevant concentrations, both Upcyte hepatocytes and HepaRG offer suitable properties to be used in routine screening assays for toxicological assessments during drug preclinical testing.

Sub-structure-based category formation for the prioritisation of genotoxicity hazard assessment for pesticide residues: Sulphonyl ureas

In dietary risk assessment, residues of pesticidal ingredients or their metabolites need to be evaluated for their genotoxic potential. The European Food Safety Authority recommend a tiered approach focussing assessment and testing on classes of similar chemicals. To characterise similarity and to identify structural alerts associated with genotoxic concern, a set of chemical sub-structures was derived for an example dataset of 74 sulphonyl urea agrochemicals for which either Ames, chromosomal aberration or micronucleus test results are publicly available. This analysis resulted in a set of seven structural alerts that define the chemical space, in terms of the common parent and metabolic scaffolds, associated with the sulphonyl urea chemical class. An analysis of the available profiling schemes for DNA and protein reactivity shows the importance of investigating the predictivity of such schemes within a well-defined area of structural space. Structural space alerts, covalent chemistry profiling and physico-chemistry properties were combined to develop chemical categories suitable for chemical prioritisation. The method is a robust and reproducible approach to such read-across predictions, with the potential to reduce unnecessary testing. The key challenge in the approach was identified as being the need for pesticide-class specific metabolism data as the basis for structural space alert development.

In Silico Models for Hepatotoxicity

In this chapter, we review the state of the art of predicting human hepatotoxicity using in silico techniques. There has been significant progress in this area over the past 20 years but there are still some challenges ahead. Principally, these challenges are our partial understanding of a very complex biochemical system and our ability to emulate that in a predictive capacity. Here, we provide an overview of the published modeling approaches in this area to date and discuss their design, strengths and weaknesses. It is interesting to note the diversity in modeling approaches, whether they be statistical algorithms or evidenced-based approaches including structural alerts and pharmacophore models. Irrespective of modeling approach, it appears a common theme of access to appropriate, relevant, and high-quality data is a limitation to all and is likely to continue to be the focus of future research.

COSMOS next generation - A public knowledge base leveraging chemical and biological data to support the regulatory assessment of chemicals

The COSMOS Database (DB) was originally established to provide reliable data for cosmetics-related chemicals within the COSMOS Project funded as part of the SEURAT-1 Research Initiative. The database has subsequently been maintained and developed further into COSMOS Next Generation (NG), a combination of database and in silico tools, essential components of a knowledge base. COSMOS DB provided a cosmetics inventory as well as other regulatory inventories, accompanied by assessment results and in vitro and in vivo toxicity data. In addition to data content curation, much effort was dedicated to data governance - data authorisation, characterisation of quality, documentation of meta information, and control of data use. Through this effort, COSMOS DB was able to merge and fuse data of various types from different sources. Building on the previous effort, the COSMOS Minimum Inclusion (MINIS) criteria for a toxicity database were further expanded to quantify the reliability of studies. COSMOS NG features multiple fingerprints for analysing structure similarity, and new tools to calculate molecular properties and screen chemicals with endpoint-related public profilers, such as DNA and protein binders, liver alerts and genotoxic alerts. The publicly available COSMOS NG enables users to compile information and execute analyses such as category formation and read-across. This paper provides a step-by-step guided workflow for a simple read-across case, starting from a target structure and culminating in an estimation of a NOAEL confidence interval. Given its strong technical foundation, inclusion of quality-reviewed data, and provision of tools designed to facilitate communication between users, COSMOS NG is a first step towards building a toxicological knowledge hub leveraging many public data systems for chemical safety evaluation. We continue to monitor the feedback from the user community at support@mn-am.com.

Metabolic activation of zolmitriptan mediated by CYP2D6

Zolmitriptan (ZOL), a member of triptans, has been used for the treatment of migraine with definite therapeutic effects. However, several cases of liver injury associated with ZOL have been reported and the underlying mechanisms remain unclear.The present study aimed to investigate the metabolic activation of ZOL in vitro and in vivo. ZOL-derived glutathione (GSH) and N-acetyl cysteine (NAC) conjugates were detected in rat liver microsomal incubations. In addition, the GSH and NAC conjugates were also found in bile and urine of rats given ZOL, respectively.ZOL-derived GSH conjugate M1 was also observed in ZOL-treated rat primary hepatocytes, and the formation of M1 was inhibited by pre-cultured with quinidine (a selective inhibitor of CYP2D6). Combining with recombinant P450 enzymes incubations, we found that CYP2D6 was the predominant enzyme responsible for the metabolic activation of ZOL.ZOL can be metabolized to an α,β-unsaturated imine intermediate by CYP2D6. Pre-treatment of primary hepatocytes with quinidine was able to reverse ZOL-induced cytotoxicity. The finding facilitates the understanding of the mechanisms involved in ZOL-associated liver adverse reactions.

Mechanism-Driven Read-Across of Chemical Hepatotoxicants Based on Chemical Structures and Biological Data

Hepatotoxicity is a leading cause of attrition in the drug development process. Traditional preclinical and clinical studies to evaluate hepatotoxicity liabilities are expensive and time consuming. With the advent of critical advancements in high-throughput screening, there has been a rapid accumulation of in vitro toxicity data available to inform the risk assessment of new pharmaceuticals and chemicals. To this end, we curated and merged all available in vivo hepatotoxicity data obtained from the literature and public resources, which yielded a comprehensive database of 4089 compounds that includes hepatotoxicity classifications. After dividing the original database of chemicals into modeling and test sets, PubChem assay data were automatically extracted using an in-house data mining tool and clustered based on relationships between structural fragments and cellular responses in in vitro assays. The resultant PubChem assay clusters were further investigated. During the cross-validation procedure, the biological data obtained from several assay clusters exhibited high predictivity of hepatotoxicity and these assays were selected to evaluate the test set compounds. The read-across results indicated that if a new compound contained specific identified chemical fragments (ie, Molecular Initiating Event) and showed active responses in the relevant selected PubChem assays, there was potential for the chemical to be hepatotoxic in vivo. Furthermore, several mechanisms that might contribute to toxicity were derived from the modeling results including alterations in nuclear receptor signaling and inhibition of DNA repair. This modeling strategy can be further applied to the investigation of other complex chemical toxicity phenomena (eg, developmental and reproductive toxicities) as well as drug efficacy.

Reactive Metabolites from Thiazole-Containing Drugs: Quantum Chemical Insights into Biotransformation and Toxicity

Drugs containing thiazole and aminothiazole groups are known to generate reactive metabolites (RMs) catalyzed by cytochrome P450s (CYPs). These RMs can covalently modify essential cellular macromolecules and lead to toxicity and induce idiosyncratic adverse drug reactions. Molecular docking and quantum chemical hybrid DFT study were carried out to explore the molecular mechanisms involved in the biotransformation of thiazole (TZ) and aminothiazole (ATZ) groups leading to RM epoxide, S-oxide, N-oxide, and oxaziridine. The energy barrier required for the epoxidation is 13.63 kcal/mol, that is lower than that of S-oxidation, N-oxidation, and oxaziridine formation (14.56, 17.90, and 20.20, kcal/mol respectively). The presence of the amino group in ATZ further facilitates all the metabolic pathways, for example, the barrier for the epoxidation reaction is reduced by ∼2.5 kcal/mol. Some of the RMs/their isomers are highly electrophilic and tend to form covalent bonds with nucleophilic amino acids, finally leading to the formation of metabolic intermediate complexes (MICs). The energy profiles of these competitive pathways have also been explored.

High-Content Screening for the Detection of Drug-Induced Oxidative Stress in Liver Cells

Drug-induced liver injury (DILI) remains a major cause of drug development failure, post-marketing warnings and restriction of use. An improved understanding of the mechanisms underlying DILI is required for better drug design and development. Enhanced reactive oxygen species (ROS) levels may cause a wide spectrum of oxidative damage, which has been described as a major mechanism implicated in DILI. Several cell-based assays have been developed as in vitro tools for early safety risk assessments. Among them, high-content screening technology has been used for the identification of modes of action, the determination of the level of injury and the discovery of predictive biomarkers for the safety assessment of compounds. In this paper, we review the value of in vitro high-content screening studies and evaluate how to assess oxidative stress induced by drugs in hepatic cells, demonstrating the detection of pre-lethal mechanisms of DILI as a powerful tool in human toxicology.

Development of a Battery of In Silico Prediction Tools for Drug-Induced Liver Injury from the Vantage Point of Translational Safety Assessment

Drug-induced liver injury (DILI) remains a challenge when translating knowledge from the preclinical stage to human use cases. Attempts to model human DILI directly based on the information from drug labels have had some success; however, the approach falls short of providing insights or addressing uncertainty due to the difficulty of decoupling the idiosyncratic nature of human DILI outcomes. Our approach in this comparative analysis is to leverage existing preclinical and clinical data as well as information on metabolism to better translate mammalian to human DILI. The human DILI knowledge base from the United States Food and Drug Administration (U.S. FDA) National Center for Toxicology Research contains 1036 pharmaceuticals from diverse therapeutic categories. A human DILI training set of 305 oral marketed drugs was prepared and a binary classification scheme applied. The second knowledge base consists of mammalian repeated dose toxicity with liver toxicity data from various regulatory sources. Within this knowledge base, we identified 278 pharmaceuticals containing 198 marketed or withdrawn oral drugs with data from the U.S. FDA new drug application and 98 active pharmaceutical ingredients from ToxCast. From this collection, a set of 225 oral drugs was prepared as the mammalian hepatotoxicity training set with particular end points of pathology findings in the liver and bile duct. Both human and mammalian data sets were processed using various learning algorithms, including artificial intelligence approaches. The external validations for both models were comparable to the training statistics. These data sets were also used to extract species-differentiating chemotypes that differentiate DILI effects on humans from mammals. A systematic workflow was devised to predict human DILI and provide mechanistic insights. For a given query molecule, both human and mammalian models are run. If the predictions are discordant, both metabolites and parents are investigated for quantitative structure-activity relationship and species-differentiating chemotypes. Their results are combined using the Dempster-Shafer decision theory to yield a final outcome prediction for human DILI with estimated uncertainty. Finally, these tools are implementable within an in silico platform for systematic evaluation.

A Robust, Mechanistically Based In Silico Structural Profiler for Hepatic Cholestasis

Owing to the primary role which it holds within metabolism of xenobiotics, the liver stands at heightened risk of exposure to, and injury from, potentially hazardous substances. A principal manifestation of liver dysfunction is cholestasis-the impairment of physiological bile circulation from its point of origin within the organ to the site of action in the small intestine. The capacity for early identification of compounds liable to exert cholestatic effects is of particular utility within the field of pharmaceutical development, where contribution toward candidate attrition is great. Shortcomings associated with the present in vitro methodologies forecasting cholestasis render their predictivity questionable, permitting scope for the adoption of computational toxicology techniques. As such, the intention of this study has been to construct an in silico profiler, founded upon clinical data, highlighting structural motifs most reliably associated with the end point. Drawing upon a list of >1500 small molecular drugs, compiled and annotated by Kotsampasakou, E. and Ecker, G. F. (J. Chem. Inf. Model. 2017, 57, 608-615), we have formulated a series of 15 structural alerts. These describe fragments intrinsic within distinct pharmaceutical classes including psychoactive tricyclics, β-lactam antimicrobials, and estrogenic/androgenic steroids. Description of the coverage and selectivity of each are provided, alongside consideration of the underlying reactive mechanisms and relevant structure-activity concerns. Provision of mechanistic anchoring ensures that potential exists for framing within the adverse outcome pathway paradigm-the chemistry conveyed through the alert, in particular enabling rationalization at the level of the molecular initiating event.

A Review of In Silico Tools as Alternatives to Animal Testing: Principles, Resources and Applications

Across the spectrum of industrial sectors, including pharmaceuticals, chemicals, personal care products, food additives and their associated regulatory agencies, there is a need to develop robust and reliable methods to reduce or replace animal testing. It is generally recognised that no single alternative method will be able to provide a one-to-one replacement for assays based on more complex toxicological endpoints. Hence, information from a combination of techniques is required. A greater understanding of the time and concentration-dependent mechanisms, underlying the interactions between chemicals and biological systems, and the sequence of events that can lead to apical effects, will help to move forward the science of reducing and replacing animal experiments. In silico modelling, in vitro assays, high-throughput screening, organ-on-a-chip technology, omics and mathematical biology, can provide complementary information to develop a complete picture of the potential response of an organism to a chemical stressor. Adverse outcome pathways (AOPs) and systems biology frameworks enable relevant information from diverse sources to be logically integrated. While individual researchers do not need to be experts across all disciplines, it is useful to have a fundamental understanding of what other areas of science have to offer, and how knowledge can be integrated with other disciplines. The purpose of this review is to provide those who are unfamiliar with predictive in silico tools, with a fundamental understanding of the underlying theory. Current applications, software, barriers to acceptance, new developments and the use of integrated approaches are all discussed, with additional resources being signposted for each of the topics.

The integrated use of in silico methods for the hepatotoxicity potential of Piper methysticum

Herbal products as supplements and therapeutic intervention have been used for centuries. However, their toxicities are not completely evaluated and the mechanisms are not clearly understood. Dried rhizome of the plant kava (Piper methysticum) is used for its anxiolytic, and sedative effects. The drug is also known for its hepatotoxicity potential. Major constituents of the plant were identified as kavalactones, alkaloids and chalcones in previous studies. Kava hepatotoxicity mechanism and the constituent that causes the toxicity have been debated for decades. In this paper, we illustrated the use of computational tools for the hepatotoxicity of kava constituents. The proposed mechanisms and major constituents that are most probably responsible for the toxicity have been scrutinized. According to the experimental and prediction results, the kava constituents play a substantial role in hepatotoxicity by some means or other via glutathione depletion, CYP inhibition, reactive metabolite formation, mitochondrial toxicity and cyclooxygenase activity. Some of the constituents, which have not been tested yet, were predicted to involve mitochondrial membrane potential, caspase-3 stimulation, and AhR activity. Since Nrf2 activation could be favorable for prevention of hepatotoxicity, we also suggest that these compounds should undergo testing given that they were predicted not to be activating Nrf2. Among the major constituents, alkaloids appear to be the least studied and the least toxic group in general. The outcomes of the study could help to appreciate the mechanisms and to prioritize the kava constituents for further testing.

Integrated In Silico Models for the Prediction of No-Observed-(Adverse)-Effect Levels and Lowest-Observed-(Adverse)-Effect Levels in Rats for Sub-chronic Repeated-Dose Toxicity

Repeated-dose toxicity (RDT) is a critical endpoint for hazard characterization of chemicals and is assessed to derive safe levels of exposure for human health. Here we present the first attempt to model simultaneously no-observed-(adverse)-effect level (NO(A)EL) and lowest-observed-(adverse)-effect level (LO(A)EL). Classification and regression models were derived based on rat sub-chronic repeated dose toxicity data for 327 compounds from the Fraunhofer RepDose database. Multi-category classification models were built for both NO(A)EL and LO(A)EL though a consensus of statistics- and fragment-based algorithms, while regression models were based on quantitative relationships between the endpoints and SMILES-based attributes. NO(A)EL and LO(A)EL models were integrated, and predictions were compared to exclude inconsistent values. This strategy improved the performance of single models, leading to R² greater than 0.70, root-mean-square error (RMSE) lower than 0.60 (for regression models), and accuracy of 0.61-0.73 (for classification models) on the validation set, based on the endpoint and the threshold applied for selecting predictions. This study confirms the effectiveness of the modeling strategy presented here for assessing RDT of chemicals using in silico models.

Machine-Learning Prediction of Oral Drug-Induced Liver Injury (DILI) via Multiple Features and Endpoints

Drug discovery is a costly process which usually takes more than 10 years and billions of dollars for one successful drug to enter the market. Despite all the safety tests, drugs may still cause adverse reactions and be restricted in use or even withdrawn from the market. Drug-induced liver injury (DILI) is one of the major adverse drug reactions, and computational models may be used to predict and reduce it. To assess the computational prediction performance of DILI, we curated DILI endpoints from three databases and prepared drug features including chemical descriptors, therapeutic classifications, gene expressions, and binding proteins. We trained machine-learning models to predict the various DILI endpoints using different drug features. Using the optimal feature sets, the top-performing models obtained areas under the receiver operating characteristic curve (AUC) around 0.8 for some DILI endpoints. We found that some features, including therapeutic classifications and proteins, have good prediction performance towards DILI. We also discovered that the severity of DILI endpoints as well as the selection of negative samples may significantly affect the prediction results. Overall, our study provided a comprehensive collection, curation, and prediction of DILI endpoints using various drug features, which may help the drug researchers to better understand and prevent DILI during the drug discovery process.

Using Machine Learning Methods and Structural Alerts for Prediction of Mitochondrial Toxicity

Over the last few years more and more organ and idiosyncratic toxicities were linked to mitochondrial toxicity. Despite well-established assays, such as the seahorse and Glucose/Galactose assay, an in silico approach to mitochondrial toxicity is still feasible, particularly when it comes to the assessment of large compound libraries. Therefore, in silico approaches could be very beneficial to indicate hazards early in the drug development pipeline. By combining multiple endpoints, we derived the largest so far published dataset on mitochondrial toxicity. A thorough data analysis shows that molecules causing mitochondrial toxicity can be distinguished by physicochemical properties. Finally, the combination of machine learning and structural alerts highlights the suitability for in silico risk assessment of mitochondrial toxicity.

Incorporation of a Hydrophilic Spacer Reduces Hepatic Uptake of HER2-Targeting Affibody-DM1 Drug Conjugates

Affibody molecules are small affinity-engineered scaffold proteins which can be engineered to bind to desired targets. The therapeutic potential of using an affibody molecule targeting HER2, fused to an albumin-binding domain (ABD) and conjugated with the cytotoxic maytansine derivate MC-DM1 (AffiDC), has been validated. Biodistribution studies in mice revealed an elevated hepatic uptake of the AffiDC, but histopathological examination of livers showed no major signs of toxicity. However, previous clinical experience with antibody drug conjugates have revealed a moderate- to high-grade hepatotoxicity in treated patients, which merits efforts to also minimize hepatic uptake of the AffiDCs. In this study, the aim was to reduce the hepatic uptake of AffiDCs and optimize their in vivo targeting properties. We have investigated if incorporation of hydrophilic glutamate-based spacers adjacent to MC-DM1 in the AffiDC, (Z_HER2:2891)₂-ABD-MC-DM1, would counteract the hydrophobic nature of MC-DM1 and, hence, reduce hepatic uptake. Two new AffiDCs including either a triglutamate-spacer-, (Z_HER2:2891)₂-ABD-E₃-MC-DM1, or a hexaglutamate-spacer-, (Z_HER2:2891)₂-ABD-E₆-MC-DM1 next to the site of MC-DM1 conjugation were designed. We radiolabeled the hydrophilized AffiDCs and compared them, both in vitro and in vivo, with the previously investigated (Z_HER2:2891)₂-ABD-MC-DM1 drug conjugate containing no glutamate spacer. All three AffiDCs demonstrated specific binding to HER2 and comparable in vitro cytotoxicity. A comparative biodistribution study of the three radiolabeled AffiDCs showed that the addition of glutamates reduced drug accumulation in the liver while preserving the tumor uptake. These results confirmed the relation between DM1 hydrophobicity and liver accumulation. We believe that the drug development approach described here may also be useful for other affinity protein-based drug conjugates to further improve their in vivo properties and facilitate their clinical translatability.

Cordycepin activates autophagy through AMPK phosphorylation to reduce abnormalities in Machado-Joseph disease models

Machado-Joseph disease (MJD) is a neurodegenerative disorder caused by an abnormal expansion of citosine-adenine-guanine trinucleotide repeats in the disease-causing gene. This mutation leads to an abnormal polyglutamine tract in the protein ataxin-3 (Atx3), resulting in formation of mutant Atx3 aggregates. Despite several attempts to develop a therapeutic option for MJD, currently there are no available therapies capable of delaying or stopping disease progression. Recently, our group reported that reducing the expression levels of mutant Atx3 lead to a mitigation of several MJD-related behavior and neuropathological abnormalities. Aiming a more rapid translation to the human clinics, in this study we investigate a pharmacological inhibitor of translation-cordycepin-in several preclinical models. We found that cordycepin treatment significantly reduced (i) the levels of mutant Atx3, (ii) the neuropathological abnormalities in a lentiviral mouse model, (iii) the motor and neuropathological deficits in a transgenic mouse model and (iv) the number of ubiquitin aggregates in a human neural model. We hypothesize that the effect of cordycepin is mediated by the increase of phosphorylated adenosine monophosphate-activated protein kinase (AMPK) levels, which is accompanied by a reduction in the global translation levels and by a significant activation of the autophagy pathway. Overall, this study suggests that cordycepin might constitute an effective and safe therapeutic approach for MJD, and probably for the other polyglutamine diseases.

Models of Drug Induced Liver Injury (DILI) - Current Issues and Future Perspectives

Background:

Drug-induced Liver Injury (DILI) is an important cause of acute liver failure cases in the United States, and remains a common cause of withdrawal of drugs in both preclinical and clinical phases.

Methods:

A structured search of bibliographic databases – Web of Science Core Collection, Scopus and Medline for peer-reviewed articles on models of DILI was performed. The reference lists of relevant studies was prepared and a citation search for the included studies was carried out. In addition, the characteristics of screened studies were described.

Results:

One hundred and six articles about the existing knowledge of appropriate models to study DILI in vitro and in vivo with special focus on hepatic cell models, variations of 3D co-cultures, animal models, databases and predictive modeling and translational biomarkers developed to understand the mechanisms and pathophysiology of DILI are described.

Conclusion:

Besides descriptions of current applications of existing modeling systems, associated advantages and limitations of each modeling system and future directions for research development are discussed as well.

Predictive Systems Toxicology

In this review we address to what extent computational techniques can augment our ability to predict toxicity. The first section provides a brief history of empirical observations on toxicity dating back to the dawn of Sumerian civilization. Interestingly, the concept of dose emerged very early on, leading up to the modern emphasis on kinetic properties, which in turn encodes the insight that toxicity is not solely a property of a compound but instead depends on the interaction with the host organism. The next logical step is the current conception of evaluating drugs from a personalized medicine point of view. We review recent work on integrating what could be referred to as classical pharmacokinetic analysis with emerging systems biology approaches incorporating multiple omics data. These systems approaches employ advanced statistical analytical data processing complemented with machine learning techniques and use both pharmacokinetic and omics data. We find that such integrated approaches not only provide improved predictions of toxicity but also enable mechanistic interpretations of the molecular mechanisms underpinning toxicity and drug resistance. We conclude the chapter by discussing some of the main challenges, such as how to balance the inherent tension between the predicitive capacity of models, which in practice amounts to constraining the number of features in the models versus allowing for rich mechanistic interpretability, i.e., equipping models with numerous molecular features. This challenge also requires patient-specific predictions on toxicity, which in turn requires proper stratification of patients as regards how they respond, with or without adverse toxic effects. In summary, the transformation of the ancient concept of dose is currently successfully operationalized using rich integrative data encoded in patient-specific models.

Ab initio chemical safety assessment: A workflow based on exposure considerations and non-animal methods

We describe and illustrate a workflow for chemical safety assessment that completely avoids animal testing. The workflow, which was developed within the SEURAT-1 initiative, is designed to be applicable to cosmetic ingredients as well as to other types of chemicals, e.g. active ingredients in plant protection products, biocides or pharmaceuticals. The aim of this work was to develop a workflow to assess chemical safety without relying on any animal testing, but instead constructing a hypothesis based on existing data, in silico modelling, biokinetic considerations and then by targeted non-animal testing. For illustrative purposes, we consider a hypothetical new ingredient x as a new component in a body lotion formulation. The workflow is divided into tiers in which points of departure are established through in vitro testing and in silico prediction, as the basis for estimating a safe external dose in a repeated use scenario. The workflow includes a series of possible exit (decision) points, with increasing levels of confidence, based on the sequential application of the Threshold of Toxicological (TTC) approach, read-across, followed by an "ab initio" assessment, in which chemical safety is determined entirely by new in vitro testing and in vitro to in vivo extrapolation by means of mathematical modelling. We believe that this workflow could be applied as a tool to inform targeted and toxicologically relevant in vitro testing, where necessary, and to gain confidence in safety decision making without the need for animal testing.

In Silico Prediction of Organ Level Toxicity: Linking Chemistry to Adverse Effects

In silico methods to predict toxicity include the use of (Quantitative) Structure-Activity Relationships ((Q)SARs) as well as grouping (category formation) allowing for read-across. A challenging area for in silico modelling is the prediction of chronic toxicity and the No Observed (Adverse) Effect Level (NO(A)EL) in particular. A proposed solution to the prediction of chronic toxicity is to consider organ level effects, as opposed to modelling the NO(A)EL itself. This review has focussed on the use of structural alerts to identify potential liver toxicants. In silico profilers, or groups of structural alerts, have been developed based on mechanisms of action and informed by current knowledge of Adverse Outcome Pathways. These profilers are robust and can be coded computationally to allow for prediction. However, they do not cover all mechanisms or modes of liver toxicity and recommendations for the improvement of these approaches are given.

Current nonclinical testing paradigms in support of safe clinical trials: An IQ Consortium DruSafe perspective

The transition from nonclinical to First-in-Human (FIH) testing is one of the most challenging steps in drug development. In response to serious outcomes in a recent Phase 1 trial (sponsored by Bial), IQ Consortium/DruSafe member companies reviewed their nonclinical approach to progress small molecules safely to FIH trials. As a common practice, safety evaluation begins with target selection and continues through iterative in silico and in vitro screening to identify molecules with increased probability of acceptable in vivo safety profiles. High attrition routinely occurs during this phase. In vivo exploratory and pivotal FIH-enabling toxicity studies are then conducted to identify molecules with a favorable benefit-risk profile for humans. The recent serious incident has reemphasized the importance of nonclinical testing plans that are customized to the target, the molecule, and the intended clinical plan. Despite the challenges and inherent risks of transitioning from nonclinical to clinical testing, Phase 1 studies have a remarkably good safety record. Given the rapid scientific evolution of safety evaluation, testing paradigms and regulatory guidance must evolve with emerging science. The authors posit that the practices described herein, together with science-based risk assessment and management, support safe FIH trials while advancing development of important new medicines.

A New Structure-Activity Relationship (SAR) Model for Predicting Drug-Induced Liver Injury, Based on Statistical and Expert-Based Structural Alerts

The prompt identification of chemical molecules with potential effects on liver may help in drug discovery and in raising the levels of protection for human health. Besides in vitro approaches, computational methods in toxicology are drawing attention. We built a structure-activity relationship (SAR) model for evaluating hepatotoxicity. After compiling a data set of 950 compounds using data from the literature, we randomly split it into training (80%) and test sets (20%). We also compiled an external validation set (101 compounds) for evaluating the performance of the model. To extract structural alerts (SAs) related to hepatotoxicity and non-hepatotoxicity we used SARpy, a statistical application that automatically identifies and extracts chemical fragments related to a specific activity. We also applied the chemical grouping approach for manually identifying other SAs. We calculated accuracy, specificity, sensitivity and Matthews correlation coefficient (MCC) on the training, test and external validation sets. Considering the complexity of the endpoint, the model performed well. In the training, test and external validation sets the accuracy was respectively 81, 63, and 68%, specificity 89, 33, and 33%, sensitivity 93, 88, and 80% and MCC 0.63, 0.27, and 0.13. Since it is preferable to overestimate hepatotoxicity rather than not to recognize unsafe compounds, the model's architecture followed a conservative approach. As it was built using human data, it might be applied without any need for extrapolation from other species. This model will be freely available in the VEGA platform.

Predicting drug-induced liver injury in human with Naïve Bayes classifier approach

Drug-induced liver injury (DILI) is one of the major safety concerns in drug development. Although various toxicological studies assessing DILI risk have been developed, these methods were not sufficient in predicting DILI in humans. Thus, developing new tools and approaches to better predict DILI risk in humans has become an important and urgent task. In this study, we aimed to develop a computational model for assessment of the DILI risk with using a larger scale human dataset and Naïve Bayes classifier. The established Naïve Bayes prediction model was evaluated by 5-fold cross validation and an external test set. For the training set, the overall prediction accuracy of the 5-fold cross validation was 94.0 %. The sensitivity, specificity, positive predictive value and negative predictive value were 97.1, 89.2, 93.5 and 95.1 %, respectively. The test set with the concordance of 72.6 %, sensitivity of 72.5 %, specificity of 72.7 %, positive predictive value of 80.4 %, negative predictive value of 63.2 %. Furthermore, some important molecular descriptors related to DILI risk and some toxic/non-toxic fragments were identified. Thus, we hope the prediction model established here would be employed for the assessment of human DILI risk, and the obtained molecular descriptors and substructures should be taken into consideration in the design of new candidate compounds to help medicinal chemists rationally select the chemicals with the best prospects to be effective and safe.

Alarms about structural alerts

Structural alerts are widely accepted in chemical toxicology and regulatory decision support as a simple and transparent means to flag potential chemical hazards or group compounds into categories for read-across. However, there has been a growing concern that alerts disproportionally flag too many chemicals as toxic, which questions their reliability as toxicity markers. Conversely, the rigorously developed and properly validated statistical QSAR models can accurately and reliably predict the toxicity of a chemical; however, their use in regulatory toxicology has been hampered by the lack of transparency and interpretability. We demonstrate that contrary to the common perception of QSAR models as "black boxes" they can be used to identify statistically significant chemical substructures (QSAR-based alerts) that influence toxicity. We show through several case studies, however, that the mere presence of structural alerts in a chemical, irrespective of the derivation method (expert-based or QSAR-based), should be perceived only as hypotheses of possible toxicological effect. We propose a new approach that synergistically integrates structural alerts and rigorously validated QSAR models for a more transparent and accurate safety assessment of new chemicals.

In silico toxicology: computational methods for the prediction of chemical toxicity

Determining the toxicity of chemicals is necessary to identify their harmful effects on humans, animals, plants, or the environment. It is also one of the main steps in drug design. Animal models have been used for a long time for toxicity testing. However, in vivo animal tests are constrained by time, ethical considerations, and financial burden. Therefore, computational methods for estimating the toxicity of chemicals are considered useful. In silico toxicology is one type of toxicity assessment that uses computational methods to analyze, simulate, visualize, or predict the toxicity of chemicals. In silico toxicology aims to complement existing toxicity tests to predict toxicity, prioritize chemicals, guide toxicity tests, and minimize late-stage failures in drugs design. There are various methods for generating models to predict toxicity endpoints. We provide a comprehensive overview, explain, and compare the strengths and weaknesses of the existing modeling methods and algorithms for toxicity prediction with a particular (but not exclusive) emphasis on computational tools that can implement these methods and refer to expert systems that deploy the prediction models. Finally, we briefly review a number of new research directions in in silico toxicology and provide recommendations for designing in silico models. WIREs Comput Mol Sci 2016, 6:147-172. doi: 10.1002/wcms.1240 For further resources related to this article, please visit the WIREs website.

In silico Prediction of Drug Induced Liver Toxicity Using Substructure Pattern Recognition Method

Drug-induced liver injury (DILI) is a leading cause of acute liver failure in the US and less severe liver injury worldwide. It is also one of the major reasons of drug withdrawal from the market. Thus, DILI has become one of the most important concerns of drugs, and should be predicted in very early stage of drug discovery process. In this study, a comprehensive data set containing 1317 diverse compounds was collected from publications. Then, high accuracy classification models were built using five machine learning methods based on MACCS and FP4 fingerprints after evaluating by substructure pattern recognition method. The best model was built using SVM method together with FP4 fingerprint at the IG value threshold of 0.0005. Its overall predictive accuracies were 79.7 % and 64.5 % for the training and test sets, separately, which yielded overall accuracy of 75.0 % for the external validation dataset, consisting of 88 compounds collected from a benchmark DILI database - the Liver Toxicity Knowledge Base. This model could be used for drug-induced liver toxicity prediction. Moreover, some key substructure patterns correlated with drug-induced liver toxicity were also identified as structural alerts.

Current and Future Perspectives on the Development, Evaluation, and Application of in Silico Approaches for Predicting Toxicity

Exploiting non-testing approaches to predict toxicity early in the drug discovery development cycle is a helpful component in minimizing expensive drug failures due to toxicity being identified in late development or even during clinical trials. Changes in regulations in the industrial chemicals and cosmetics sectors in recent years have prompted a significant number of advances in the development, application, and assessment of non-testing approaches, such as (Q)SARs. Many efforts have also been undertaken to establish guiding principles for performing read-across within category and analogue approaches. This review offers a perspective, as taken from these sectors, of the current status of non-testing approaches, their evolution in light of the advances in high-throughput approaches and constructs such as adverse outcome pathways, and their potential relevance for drug discovery. It also proposes a workflow for how non-testing approaches could be practically integrated within testing and assessment strategies.

In Silico Models for Hepatotoxicity

In this chapter we review the challenges of predicting human hepatotoxicity. Principally, this is our partial understanding of a very complex biochemical system and our ability to emulate that in a predictive capacity. We give an overview of the published modeling approaches in this area to date and discuss their design, strengths, and weaknesses. It is interesting to note the shift during the period of this review in the direction of evidenced-based approaches including structural alerts and pharmacophore models. Proposals on how best to utilize the data emerging from modeling studies are also discussed.

Ado-Trastuzumab Emtansine Targets Hepatocytes Via Human Epidermal Growth Factor Receptor 2 to Induce Hepatotoxicity

Ado-trastuzumab emtansine (T-DM1) is an antibody-drug conjugate (ADC) approved for the treatment of HER2-positive metastatic breast cancer. It consists of trastuzumab, a humanized mAb directed against HER2, and a microtubule inhibitor, DM1, conjugated to trastuzumab via a thioether linker. Hepatotoxicity is one of the serious adverse events associated with T-DM1 therapy. Mechanisms underlying T-DM1-induced hepatotoxicity remain elusive. Here, we use hepatocytes and mouse models to investigate the mechanisms of T-DM1-induced hepatotoxicity. We show that T-DM1 is internalized upon binding to cell surface HER2 and is colocalized with LAMP1, resulting in DM1-associated cytotoxicity, including disorganized microtubules, nuclear fragmentation/multiple nuclei, and cell growth inhibition. We further demonstrate that T-DM1 treatment significantly increases the serum levels of aspartate aminotransferase, alanine aminotransferase, and lactate dehydrogenase in mice and induces inflammation and necrosis in liver tissues, and that T-DM1-induced hepatotoxicity is dose dependent. Moreover, the gene expression of TNFα in liver tissues is significantly increased in mice treated with T-DM1 as compared with those treated with trastuzumab or vehicle. We propose that T-DM1-induced upregulation of TNFα enhances the liver injury that may be initially caused by DM1-mediated intracellular damage. Our proposal is underscored by the fact that T-DM1 induces the outer mitochondrial membrane rupture, a typical morphologic change in the mitochondrial-dependent apoptosis, and mitochondrial membrane potential dysfunction. Our work provides mechanistic insights into T-DM1-induced hepatotoxicity, which may yield novel strategies to manage liver injury induced by T-DM1 or other ADCs.

Metabolic activation and drug-induced liver injury: in vitro approaches for the safety risk assessment of new drugs

Drug-induced liver injury (DILI) is a significant leading cause of hepatic dysfunction, drug failure during clinical trials and post-market withdrawal of approved drugs. Many cases of DILI are unexpected reactions of an idiosyncratic nature that occur in a small group of susceptible individuals. Intensive research efforts have been made to understand better the idiosyncratic DILI and to identify potential risk factors. Metabolic bioactivation of drugs to form reactive metabolites is considered an initiation mechanism for idiosyncratic DILI. Reactive species may interact irreversibly with cell macromolecules (covalent binding, oxidative damage), and alter their structure and activity. This review focuses on proposed in vitro screening strategies to predict and reduce idiosyncratic hepatotoxicity associated with drug bioactivation. Compound incubation with metabolically competent biological systems (liver-derived cells, subcellular fractions), in combination with methods to reveal the formation of reactive intermediates (e.g., formation of adducts with liver proteins, metabolite trapping or enzyme inhibition assays), are approaches commonly used to screen the reactivity of new molecules in early drug development. Several cell-based assays have also been proposed for the safety risk assessment of bioactivable compounds. Copyright © 2015 John Wiley & Sons, Ltd.

Toward a unifying strategy for the structure-based prediction of toxicological endpoints

Most computational methods used for the prediction of toxicity endpoints are based on the assumption that similar compounds have similar biological properties. This principle can be exploited using computational methods like read across or quantitative structure-activity relationships. However, there is no general agreement about which method is the most appropriate for quantifying compound similarity neither for exploiting the similarity principle in order to obtain reliable estimations of the compound properties. Moreover, optimal similarity metrics and modeling methods might depend on the characteristics of the endpoints and training series used in each case. This study describes a comparative analysis of the predictive performance of diverse similarity metrics and modeling methods in toxicological applications. A collection of two quantitative (n = 660, n = 1114) and three qualitative (n = 447, n = 905, n = 1220) datasets representing very different endpoints of interest in drug safety evaluation and rigorous methods were used to estimate the external predictive ability in each case. The results confirm that no single approach produces the best results in all instances, and the best predictions were obtained using different tools in different situations. The trends observed in this study were exploited to propose a unifying strategy allowing the use of the most suitable method for every compound. A comparison of the quality of the predictions obtained by the unifying strategy with those obtained by standard prediction methods confirmed the usefulness of the proposed approach.