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Adverse event pathogenesis is often a complex process which compromises multiple events ranging from the molecular to the phenotypic level. In toxicology, Adverse Outcome Pathways (AOPs) aim to formalize this as temporal sequences of events, in which event relationships should be supported by causal evidence according to the tailored Bradford-Hill criteria. One of the criteria is whether events are consistently observed in a certain temporal order and, in this work, we study this time concordance using the concept of "first activation" as data-driven means to generate hypotheses on potentially causal mechanisms. As a case study, we analysed liver data from repeat-dose studies in rats from the TG-GATEs database which comprises measurements across eight timepoints, ranging from 3 hours to 4 weeks post-treatment. We identified time-concordant gene expression-derived events preceding adverse histopathology, which serves as surrogate readout for Drug-Induced Liver Injury (DILI). We find known mechanisms in DILI to be time-concordant, and show further that significance, frequency and log fold change (logFC) of differential expression are metrics which can additionally prioritize events although not necessary to be mechanistically relevant. Moreover, we used the temporal order of transcription factor (TF) expression and regulon activity to identify transcriptionally regulated TFs and subsequently combined this with prior knowledge on functional interactions to derive detailed gene-regulatory mechanisms, such as reduced Hnf4a activity leading to decreased expression and activity of Cebpa. At the same time, also potentially novel events are identified such as Sox13 which is highly significantly time-concordant and shows sustained activation over time. Overall, we demonstrate how time-resolved transcriptomics can derive and support mechanistic hypotheses by quantifying time concordance and how this can be combined with prior causal knowledge, with the aim of both understanding mechanisms of toxicity, as well as potential applications to the AOP framework. We make our results available in the form of a Shiny app (https://anikaliu.shinyapps.io/dili_cascades), which allows users to query events of interest in more detail., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: AL received funding from GlaxoSmithKline and is a consultant at PharmEnable Ltd. JM was an employee of GlaxoSmithKline throughout the study and is now an employee at UCB. NH is a cofounder of KURE.ai and CardiaTec Biosciences. AB is a shareholder of Healx Ltd. and PharmEnable Ltd., and CSO at Terra Lumina.

Identifying isomeric metabolites remains a challenging and time-consuming process with both sensitivity and unambiguous structural assignment typically only achieved through the combined use of LC-MS and NMR. Ion mobility mass spectrometry (IMMS) has the potential to produce timely and accurate data using a single technique to identify drug metabolites, including isomers, without the requirement for in-depth interpretation ( cf. MS/MS data) using an automated computational pipeline by comparison of experimental collision cross-section (CCS) values with predicted CCS values. An ion mobility enabled Q-Tof mass spectrometer was used to determine the CCS values of 28 (14 isomeric pairs of) small molecule glucuronide metabolites, which were then compared to two different in silico models; a quantum mechanics (QM) and a machine learning (ML) approach to test these approaches. The difference between CCS values within isomer pairs was also assessed to evaluate if the difference was large enough for unambiguous structural identification through in silico prediction. A good correlation was found between both the QM- and ML-based models and experimentally determined CCS values. The predicted CCS values were found to be similar between ML and QM in silico methods, with the QM model more accurately describing the difference in CCS values between isomer pairs. Of the 14 isomeric pairs, only one (naringenin glucuronides) gave a sufficient difference in CCS values for the QM model to distinguish between the isomers with some level of confidence, with the ML model unable to confidently distinguish the studied isomer pairs. An evaluation of analyte structures was also undertaken to explore any trends or anomalies within the data set.

Recent technological advances in the field of big data have increased our capabilities to query large databases and combine information from different domains and disciplines. In the area of preclinical studies, initiatives like SEND (Standard for Exchange of Nonclinical Data) will also contribute to collect and present nonclinical data in a consistent manner and increase analytical possibilities. With facilitated access to preclinical data and improvements in analytical algorithms there will surely be an expectation for organisations to ensure all the historical data available to them is leveraged to build new hypotheses. These kinds of analyses may soon become as important as the animal studies themselves, in addition to being critical components to achieve objectives aligned with 3Rs. This article proposes the application of meta-analyses at large scale in corporate databases as a tool to exploit data from both preclinical studies and in vitro pharmacological activity assays to identify associations between targets and tissues that can be used as seeds for the development of causal hypotheses to characterise of targets. A total of 833 in-house preclinical toxicity studies relating to 416 compounds reported to be active (pXC50 ≥ 5.5) against a panel of 96 selected targets of interest for potential off-target non desired effects were meta-analysed, aggregating the data in tissue-target pairs. The primary outcome was the odds ratio (OR) of the number of animals with observed events (any morphology, any severity) in treated and control groups in the tissue analysed. This led to a total of 2139 meta-analyses producing a total of 364 statistically significant associations (random effects model), 121 after adjusting by multiple comparison bias. The results show the utility of the proposed approach to leverage historical corporate data and may offer a vehicle for researchers to share, aggregate and analyse their preclinical toxicological data in precompetitive environments., Competing Interests: Jordi Munoz-Muriedas is a full-time employee of GlaxoSmithKline. Jordi Munoz-Muriedas has no other competing interests in relation to other companies, organisations or persons. GlaxoSmithKline is a global healthcare company with a portfolio of medicines in respiratory, HIV, immune-inflammatory and oncology therapeutic areas in addition to vaccines and healthcare products. Jordi Munoz-Muriedas confirms his commercial affiliation does not alter his adherence to all PLOS ONE policies on sharing data and materials.

Abelson kinase (c-Abl) is a ubiquitously expressed, nonreceptor tyrosine kinase which plays a key role in cell differentiation and survival. It was hypothesized that transient activation of c-Abl kinase via displacement of the N-terminal autoinhibitory "myristoyl latch", may lead to an increased hematopoietic stem cell differentiation. This would increase the numbers of circulating neutrophils and so be an effective treatment for chemotherapy-induced neutropenia. This paper describes the discovery and optimization of a thiazole series of novel small molecule c-Abl activators, initially identified by a high throughput screening. Subsequently, a scaffold-hop, which exploited the improved physicochemical properties of a dihydropyrazole analogue, identified through fragment screening, delivered potent, soluble, cell-active c-Abl activators, which demonstrated the intracellular activation of c-Abl in vivo.

Ion mobility-mass spectrometry (IM-MS) in combination with molecular modeling offers the potential for small molecule structural isomer identification by measurement of their gas phase collision cross sections (CCSs). Successful application of this approach to drug metabolite identification would facilitate resource reduction, including animal usage, and may benefit other areas of pharmaceutical structural characterization including impurity profiling and degradation chemistry. However, the conformational behavior of drug molecules and their metabolites in the gas phase is poorly understood. Here the gas phase conformational space of drug and drug-like molecules has been investigated as well as the influence of protonation and adduct formation on the conformations of drug metabolite structural isomers. The use of CCSs, measured from IM-MS and molecular modeling information, for the structural identification of drug metabolites has also been critically assessed. Detection of structural isomers of drug metabolites using IM-MS is demonstrated and, in addition, a molecular modeling approach has been developed offering rapid conformational searching and energy assessment of candidate structures which agree with experimental CCSs. Here it is illustrated that isomers must possess markedly dissimilar CCS values for structural differentiation, the existence and extent of CCS differences being ionization state and molecule dependent. The results present that IM-MS and molecular modeling can inform on the identity of drug metabolites and highlight the limitations of this approach in differentiating structural isomers.

Introduction: Drugs that prolong the QT interval on the electrocardiogram present a major safety concern for pharmaceutical companies and regulatory agencies. Despite a range of assays performed to assess compound effects on the QT interval, QT prolongation remains a major cause of attrition during compound development. In silico assays could alleviate such problems. In this study we evaluated an in silico method of predicting the results of a rabbit left-ventricular wedge assay., Methods: Concentration-effect data were acquired from either: the high-throughput IonWorks/FLIPR; the medium-throughput PatchXpress ion channel assays; or QSAR, a statistical IC50 value prediction model, for hERG, fast sodium, L-type calcium and KCNQ1/minK channels. Drug block of channels was incorporated into a mathematical differential equation model of rabbit ventricular myocyte electrophysiology through modification of the maximal conductance of each channel by a factor dependent on the IC50 value, Hill coefficient and concentration of each compound tested. Simulations were performed and agreement with experimental results, based upon input data from the different assays, was evaluated., Results: The assay was found to be 78% accurate, 72% sensitive and 81% specific when predicting QT prolongation (>10%) using PatchXpress assay data (77 compounds). Similar levels of predictivity were demonstrated using IonWorks/FLIPR data (121 compounds) with 78% accuracy, 73% sensitivity and 80% specificity. QT shortening (<-10%) was predicted with 77% accuracy, 33% sensitivity and 90% specificity using PatchXpress data and 71% accuracy, 42% sensitivity and 81% specificity using IonWorks/FLIPR data. Strong quantitative agreement between simulation and experimental results was also evident., Discussion: The in silico action potential assay demonstrates good predictive ability, and is suitable for very high-throughput use in early drug development. Adoption of such an assay into cardiovascular safety assessment, integrating ion channel data from routine screens to infer results of animal-based tests, could provide a cost- and time-effective cardiac safety screen., (Copyright © 2013 The Authors. Published by Elsevier Inc. All rights reserved.)

A high-throughput RapidFire mass spectrometry assay is described for the JMJD2 family of Fe(2+), O(2), and α-ketoglutarate-dependent histone lysine demethylases. The assay employs a short amino acid peptide substrate, corresponding to the first 15 amino acid residues of histone H3, but mutated at two positions to increase assay sensitivity. The assay monitors the direct formation of the dimethylated-Lys9 product from the trimethylated-Lys9 peptide substrate. Monitoring the formation of the monomethylated and des-methylated peptide products is also possible. The assay was validated using known inhibitors of the histone lysine demethylases, including 2,4-pyridinedicarboxylic acid and an α-ketoglutarate analogue. With a sampling rate of 7 s per well, the RapidFire technology permitted the single-concentration screening of 101 226 compounds against JMJD2C in 10 days using two instruments, typically giving Z' values of 0.75 to 0.85. Several compounds were identified of the 8-hydroxyquinoline chemotype, a known series of inhibitors of the Lys9-specific histone demethylases. The peptide also functions as a substrate for JMJD2A, JMJD2D, and JMJD2E, thus enabling the development of assays for all 3 enzymes to monitor progress in compound selectivity. The assay represents the first report of a RapidFire mass spectrometry assay for an epigenetics target.

The biological complexity associated with the regulation of histone demethylases makes it desirable to configure a cellular mechanistic assay format that simultaneously encompasses as many of the relevant cellular processes as possible. In this report, the authors describe the configuration of a JMJD3 high-content cellular mechanistic imaging assay that uses single-cell multiparameter measurements to accurately assess cellular viability and the enzyme-dependent demethylation of the H3K27(Me)3 mark by exogenously expressed JMJD3. This approach couples robust statistical analyses with the spatial resolving power of cellular imaging. This enables segregation of expressing and nonexpressing cells into discrete subpopulations and consequently pharmacological quantification of compounds of interest in the expressing population at varying JMJD3 expression levels. Moreover, the authors demonstrate the utility of this hit identification strategy through the successful prosecution of a medium-throughput focused campaign of an 87 500-compound file, which has enabled the identification of JMJD3 cellular-active chemotypes. This study represents the first report of a demethylase high-content imaging assay with the ability to capture a repertoire of pharmacological tools, which are likely both to inform our mechanistic understanding of how JMJD3 is modulated and, more important, to contribute to the identification of novel therapeutic modalities for this demethylase enzyme.

Drug metabolism is an integral part of the drug development and drug discovery process. It is required to validate the toxicity of metabolites in support of safety testing and in particular provide information on the potential to form pharmacologically active or toxic metabolites. The current methodologies of choice for metabolite structural elucidation are liquid chromatography/tandem mass spectrometry (LC/MS/MS) and nuclear magnetic resonance (NMR) spectroscopy. There are, in certain cases, examples of metabolites whose sites of metabolism cannot be unequivocally identified by MS/MS alone. Utilising commercially available molecular dynamics packages and known quantum chemistry basis sets, an ensemble of lowest energy structures were generated for a group of aromatic hydroxylated metabolites of the model compound ondansetron. Theoretical collision cross-sections were calculated for each structure. Travelling-wave ion mobility (IMS) measurements were also performed on the compounds, thus enabling experimentally derived collision cross-sections to be calculated. A comparison of the theoretical and experimentally derived collision cross-sections were utilised for the accurate assignment of isomeric drug metabolites. The UPLC/IMS-MS method, described herein, demonstrates the ability to measure reproducibly by ion mobility, metabolite structural isomers, which differ in collision cross-section, both theoretical and experimentally derived, by less than 1 Å(2). This application has the potential to supplement and/or complement current methods of metabolite structural characterisation., (Copyright © 2010 John Wiley & Sons, Ltd.)