Understanding Conditional Associations between ToxCast in Vitro Readouts and the Hepatotoxicity of Compounds Using Rule-Based Methods

Current in vitro models for hepatotoxicity commonly suffer from low detection rates due to incomplete coverage of bioactivity space. Additionally, in vivo exposure measures such as Cmax are used for hepatotoxicity screening which are unavailable early on. Here we propose a novel rule-based framework to extract interpretable and biologically meaningful multi-conditional associations to prioritize in vitro endpoints for hepatotoxicity and understand the associated physicochemical conditions. The data used in this study was derived for 673 compounds from 361 ToxCast bioactivity measurements and 29 calculated physicochemical properties against two lowest effective levels (LEL) of rodent hepatotoxicity from ToxRefDB, namely 15mg/kg/day and 500mg/kg/day. In order to achieve 80% coverage of toxic compounds, 35 rules with accuracies ranging from 96% to 73% using 39 unique ToxCast assays are needed at a threshold level of 500mg/kg/day, whereas to describe the same coverage at a threshold of 15mg/kg/day 20 rules with accuracies of between 98% and 81% were needed, comprising 24 unique assays. Despite the 33-fold difference in dose levels, we found relative consistency in the key mechanistic groups in rule clusters, namely i) activities against Cytochrome P, ii) immunological responses, and iii) nuclear receptor activities. Less specific effects, such as oxidative stress and cell cycle arrest, were used more by rules to describe toxicity at the level of 500mg/kg/day. Although the endocrine disruption through nuclear receptor activity formulated an essential cluster of rules, this bioactivity is not covered in four commercial assay setups for hepatotoxicity. Using an external set of 29 drugs with drug-induced liver injury (DILI) labels, we found promiscuity over important assays discriminates between compounds with different levels of liver injury. In vitro-in vivo associations were also improved by incorporating physicochemical properties especially for the potent, 15mg/kg/day toxicity level, as well for assays describing nuclear receptor activity and phenotypic changes. The most frequently used physicochemical properties, predictive for hepatotoxicity in combination with assay activities, are linked to bioavailability, which were the number of rotatable bonds (less than 7) at a of level of 15mg/kg/day, and the number of rings (of less than 3) at level of 500mg/kg/day. In summary, hepatotoxicity cannot very well be captured by single assay endpoints, but better by a combination of bioactivities in relevant assays, with the likelihood of hepatotoxicity increasing with assay promiscuity. Together these findings can be used to prioritize assay combinations which are appropriate to assess potential hepatotoxicity

Research data supporting "A fragment profiling approach to inhibitors of the orphan M. tuberculosis P450 CYP144A1"

Fragment-based approaches to targeting CYP144 from Mycobacterium tuberculosi

Information-Derived Mechanistic Hypotheses for Structural Cardiotoxicity

Adverse events resulting from drug therapy can be a cause of drug withdrawal, reduced and or restricted clinical use, as well as a major economic burden for society. To increase the safety of new drugs, there is a need to better understand the mechanisms causing the adverse events. One way to derive new mechanistic hypotheses is by linking data on drug adverse events with the drugs’ biological targets. In this study, we have used data mining techniques and mutual information statistical approaches to find associations between reported adverse events collected from the FDA Adverse Event Reporting System and assay outcomes from ToxCast, with the aim to generate mechanistic hypotheses related to structural cardiotoxicity (morphological damage to cardiomyocytes and/or loss of viability). Our workflow identified 22 adverse event-assay outcome associations. From these associations, 10 implicated targets could be substantiated with evidence from previous studies reported in the literature. For two of the identified targets, we also describe a more detailed mechanism, forming putative adverse outcome pathways associated with structural cardiotoxicity. Our study also highlights the difficulties deriving these type of associations from the very limited amount of data available

Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen

The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca’s large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.Peer reviewe

eMolTox: prediction of molecular toxicity with confidence

In this work, we present eMolTox, a web server for the prediction of potential toxicity associated with a given molecule. A total of 174 toxicology-related in vitro/vivo experimental datasets were used for model construction and Mondrian conformal prediction was used to estimate the confidence of the resulting predictions. Toxic substructure analysis is also implemented in eMolTox. eMolTox predicts and displays a wealth of information of potential molecular toxicities for safety analysis in drug development

A fragment profiling approach to inhibitors of the orphan M. tuberculosis\textit{M. tuberculosis} P450 CYP144A1

Similarity in the ligand binding profile of two enzymes may provide insight for functional characterization and be of greater relevance to inhibitor development than sequence similarity or structural homology. Fragment screening is an efficient approach to characterizing the ligand profile of an enzyme and has been applied here to study the family of cytochrome P450 enzymes (P450s) expressed by Mycobacterium tuberculosis (Mtb). The Mtb P450s have important roles in bacterial virulence, survival and pathogenicity. Comparing the fragment profiles of seven of these enzymes revealed that P450s which share a similar biological function have significantly similar fragment profiles, while functionally unrelated or orphan P450s exhibit distinct ligand binding properties, despite overall high structural homology. Chemical structures that exhibit promiscuous binding between enzymes have been identified, as have selective fragments that could provide leads for inhibitor development. The similarity in the fragment binding profile of the orphan enzyme CYP144A1 to CYP121A1, an enzyme important for Mtb viability, provides a case study illustrating the subsequent identification of novel CYP144A1 ligands. The different binding modes of these compounds to CYP144A1 provide insight into structural and dynamic aspects of the enzyme, suggest a hypothesis into biological function and provide opportunity for inhibitor development. Expanding this fragment profiling approach to include a greater number of functionally characterized and orphan proteins may provide a valuable resource for understanding enzyme-ligand interactions.M.E.K. was supported by a Commonwealth (University of Cambridge) Scholarship awarded in conjunction with the Cambridge Commonwealth Trust and Cambridge Overseas Trust. J.C. was supported by funding from the William D Ford program from the US Department of Education. K.J.M. and A.G.C. was supported by grants from the BBSRC (Grant No: BB/K001884/1 and BB/I019227/1). A.Z. was funded by the European Research Commission (ERC Starting Grant 2012 MIXTURE)

Toward Understanding the Cold, Hot, and Neutral Nature of Chinese Medicines Using in Silico Mode-of-Action Analysis

One important, however, poorly understood, concept of Traditional Chinese Medicine (TCM) is that of hot, cold, and neutral nature of its bioactive principles. To advance the field, in this study, we analyzed compound-nature pairs from TCM on a large scale (>23 000 structures) via chemical space visualizations to understand its physicochemical domain and in silico target prediction to understand differences related to their modes-of-action (MoA) against proteins. We found that overall TCM natures spread into different subclusters with specific molecular patterns, as opposed to forming coherent global groups. Compounds associated with cold nature had a lower clogP and contain more aliphatic rings than the other groups and were found to control detoxification, heat-clearing, heart development processes, and have sedative function, associated with "Mental and behavioural disorders" diseases. While compounds associated with hot nature were on average of lower molecular weight, have more aromatic ring systems than other groups, frequently seemed to control body temperature, have cardio-protection function, improve fertility and sexual function, and represent excitatory or activating effects, associated with "endocrine, nutritional and metabolic diseases" and "diseases of the circulatory system". Compounds associated with neutral nature had a higher polar surface area and contain more cyclohexene moieties than other groups and seem to be related to memory function, suggesting that their nature may be a useful guide for their utility in neural degenerative diseases. We were hence able to elucidate the difference between different nature classes in TCM on the molecular level, and on a large data set, for the first time, thereby helping a better understanding of TCM nature theory and bridging the gap between traditional medicine and our current understanding of the human body.Xianjun Fu, Xuebo Li, Zhenguo Wang received funding from the National Natural Science Foundation of China (Grant No.81473369), Xianjun Fu received funding from scholarship of Shandong Provincial Education Association for International Exchanges. LHM received funding from the BBSRC and AstraZeneca