Deriving time-concordant event cascades from gene expression data: A case study for Drug-Induced Liver Injury (DILI).

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.

Investigation into Small Molecule Isomeric Glucuronide Metabolite Differentiation Using In Silico and Experimental Collision Cross-Section Values.

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.

Large scale meta-analysis of preclinical toxicity data for target characterisation and hypotheses generation.

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.

Identification and Optimization of Novel Small c-Abl Kinase Activators Using Fragment and HTS Methodologies.

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.

Application of the quantum mechanical IEF/PCM-MST hydrophobic descriptors to selectivity in ligand binding.

We have recently reported the development and validation of quantum mechanical (QM)-based hydrophobic descriptors derived from the parametrized IEF/PCM-MST continuum solvation model for 3D-QSAR studies within the framework of the Hydrophobic Pharmacophore (HyPhar) method. In this study we explore the applicability of these descriptors to the analysis of selectivity fields. To this end, we have examined a series of 88 compounds with inhibitory activities against thrombin, trypsin and factor Xa, and the HyPhar results have been compared with 3D-QSAR models reported in the literature. The quantitative models obtained by combining the electrostatic and non-electrostatic components of the octanol/water partition coefficient yield results that compare well with the predictive potential of standard CoMFA and CoMSIA techniques. The results also highlight the potential of HyPhar descriptors to discriminate the selectivity of the compounds against thrombin, trypsin, and factor Xa. Moreover, the graphical representation of the hydrophobic maps provides a direct linkage with the pattern of interactions found in crystallographic structures. Overall, the results support the usefulness of the QM/MST-based hydrophobic descriptors as a complementary approach for disclosing structure-activity relationships in drug design and for gaining insight into the molecular determinants of ligand selectivity. Graphical Abstract Quantum Mechanical continuum solvation calculations performed with the IEF/PCM-MST method are used to derived atomic hydrophobic descriptors, which are then used to discriminate the selectivity of ligands against thrombin, trypsin and factor Xa. The descriptors provide complementary view to standard 3D-QSAR analysis, leading to a more comprehensive understanding of ligand recognition.

Development and validation of hydrophobic molecular fields derived from the quantum mechanical IEF/PCM-MST solvation models in 3D-QSAR.

Since the development of structure-activity relationships about 50 years ago, 3D-QSAR methods belong to the most refined ligand-based in silico techniques for prediction of biological data using physicochemical molecular fields. In this scenario, this study reports the development and validation of quantum mechanical (QM)-based hydrophobic descriptors derived from the parametrized MST continuum solvation model to be used in 3D-QSAR studies within the framework of the Hydrophobic Pharmacophore (HyPhar) method. To this end, five sets of compounds reported in the literature (dopamine D2/D4 antagonists, antifungal 2-aryl-4-chromanones, and inhibitors of GSK-3, cruzain and thermolysin) have been revisited. The results derived from the QM/MST-based hydrophobic descriptors have been compared with previous CoMFA and CoMSIA studies, and examined in light of the available X-ray crystallographic structures of the targets. The analysis reveals that the combination of electrostatic and nonelectrostatic components of the octanol/water partition coefficient yields pharmacophoric models fully comparable with the predictive potential of standard 3D-QSAR techniques. Moreover, the graphical representation of the hydrophobic maps provides a direct linkage with the pattern of interactions found in crystallographic structures. Overall, the introduction of the QM/MST-based descriptors, which could be easily adapted to other continuum solvation formalisms, paves the way to novel computational strategies for disclosing structure-activity relationships in drug design. © 2016 Wiley Periodicals, Inc.

Elucidation of Drug Metabolite Structural Isomers Using Molecular Modeling Coupled with Ion Mobility Mass Spectrometry.

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.

Evaluation of an in silico cardiac safety assay: using ion channel screening data to predict QT interval changes in the rabbit ventricular wedge.

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.

Configuration of a high-content imaging platform for hit identification and pharmacological assessment of JMJD3 demethylase enzyme inhibitors.

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.

Enabling lead discovery for histone lysine demethylases by high-throughput RapidFire mass spectrometry.

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.

Sites of metabolic substitution: investigating metabolite structures utilising ion mobility and molecular modelling.

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.

Wavelet Approximation of GRID Fields: Application to Quantitative Structure-Activity Relationships.

Molecular interaction fields such as those computed by the GRID program are widely used in applications such as virtual screening, molecular docking and 3D-QSAR modelling. They characterise molecules according to their favourable interaction sites and therefore enable predictions to be made on how molecules might interact. The fields are, however, comprised of a very large number of data points which presents difficulties for many applications. For example, there are likely to be high degrees of correlation between the variables which can lead to misleading results in 3D-QSAR. We describe the use of wavelet methods for approximating such data into a much smaller number of variables. We present a number of validation experiments, including use of the approximated GRIDs in 3D-QSAR, and demonstrate that wavelet approximation at high levels of data compression preserves the information content in GRID fields while significantly reducing computational requirements.

A hydrophobic similarity analysis of solvation effects on nucleic acid bases.

We investigate the changes in the solvation properties of the natural nucleic acid bases due to the formation of the canonical Watson-Crick hydrogen-bonded complexes. To this end, the changes in the free energy of solvation of the bases induced upon hydrogen-bonded dimerization are analyzed by means of the hydrophobic similarity index, which relies on the atomic contributions to the free energy of solvation determined by the partitioning method implemented in the framework of the MST continuum model. Such an index is also used to examine the hydrophobic similarity between the canonical nucleic acid bases and a series of highly apolar analogues, which have been designed as potential candidates to expand the genetic alphabet. The ability of these analogues to be incorporated into modified DNA duplexes can be related to the large reduction in the hydrophilicity of the natural bases upon formation of the canonical hydrogen-bonded dimers. The results illustrate the suitability of the hydrophobic similarity index to rationalize the role played by solvation in molecular recognition.

Binding of 13-amidohuprines to acetylcholinesterase: exploring the ligand-induced conformational change of the gly117-gly118 peptide bond in the oxyanion hole.

The acetylcholinesterase (AChE) inhibitory activity of a series of 13-amido derivatives of huprine Y, designed to enlarge the occupancy of the catalytic binding site by mimicking the piridone moiety present in (-)-huperzine A, has been assessed. Although both 13-formamido and 13-methanesulfonamido derivatives are more potent human AChE inhibitors than tacrine and (-)-huperzine A, none of them equals the potency of huprine Y. Molecular modeling studies show that the two derivatives effectively trigger the Gly117-Gly118 conformational flip induced upon binding of (-)-huperzine A, leading to a similar pattern of interactions as that formed by the pyridone amido group of (-)-huperzine A. The detrimental effect on the binding affinity relative to the 13-unsubstituted huprine could be ascribed to a sizable deformation cost associated with the ligand-induced peptide flip. This finding can be interpreted as a mechanism selected by evolution to ensure the preorganization of the functionally relevant oxyanion hole in the binding site of AChE, where residues Gly117 and Gly118 play a relevant role in mediating substrate recognition.

Hydrophobic molecular similarity from MST fractional contributions to the octanol/water partition coefficient.

The use of a recently proposed hydrophobic similarity index for the alignment of molecules and the prediction of their differences in biological activity is described. The hydrophobic similarity index exploits atomic contributions to the octanol/water transfer free energy, which are evaluated by means of the fractional partitioning scheme developed within the framework of the Miertus-Scrocco-Tomasi continuum model. Those contributions are used to define global and local measures of hydrophobic similarity. The suitability of this computational strategy is examined for two series of compounds (ACAT inhibitors and 5-HT3 receptor agonists), which are aligned to maximize the global hydrophobic similarity using a Monte Carlo-simulated protocol. Indeed, the concept of local hydrophobic similarity is used to explore structure-activity relationships in a series of COX-2 inhibitors. Inspection of the 3D distribution of hydrophobic/hydrophilic contributions in the aligned molecules is valuable to identify regions of very similar hydrophobicity, which can define pharmacophoric recognition patterns. Moreover, low similar regions permit to identify structural elements that modulate the differences in activity between molecules. Finally, the quantitative relationships found between the pharmacological activity and the hydrophobic similarity index points out that not only the global hydrophobicity, but its 3D distribution, is important to gain insight into the activity of molecules.

Modulation of binding strength in several classes of active site inhibitors of acetylcholinesterase studied by comparative binding energy analysis.

The comparative binding energy (COMBINE) methodology has been used to identify the key residues that modulate the inhibitory potencies of three structurally different classes of acetylcholinesterase inhibitors (tacrines, huprines, and dihydroquinazolines) targeting the catalytic active site of this enzyme. The extended set of energy descriptors and the partial least-squares methodology used by COMBINE analysis on a unique training set containing all the compounds yielded an interpretable model that was able to fit and predict the activities of the whole series of inhibitors reasonably well (r2 = 0.91 and q2 = 0.76, 4 principal components). A more robust model (q2 = 0.81 and SDEP = 0.25, 3 principal components) was obtained when the same chemometric analysis was applied to the huprines set alone, but the method was unable to provide predictive models for the other two families when they were treated separately from the rest. This finding appears to indicate that the enrichment in chemical information brought about by the inclusion of different classes of compounds into a single training set can be beneficial when an internally consistent set of pharmacological data can be derived. The COMBINE model was externally validated when it was shown to predict the activity of an additional set of compounds that were not employed in model construction. Remarkably, the differences in inhibitory potency within the whole series were found to be finely tuned by the electrostatic contribution to the desolvation of the binding site and a network of secondary interactions established between the inhibitor and several protein residues that are distinct from those directly involved in the anchoring of the ligand. This information can now be used to advantage in the design of more potent inhibitors.