Machine Learning-Guided Screening and Molecular Docking for Proposing Naturally Derived Drug Candidates Against MERS-CoV 3CL Protease

In this study, we utilized machine learning techniques to identify potential inhibitors of the MERS-CoV 3CL protease. Among the models evaluated, the Random Forest (RF) algorithm exhibited the highest predictive performance, achieving an accuracy of 0.97, an ROC-AUC score of 0.98, and an F1-score of 0.98. Following model validation, we applied it to a dataset of 14,194 naturally occurring compounds from PubChem. The top-ranked compounds were subsequently subjected to molecular docking, which identified Perenniporide B, Phellifuropyranone A, and Terrestrol G as the most promising candidates, with binding energies of -9.17, -9.08, and -8.71 kcal/mol, respectively. These compounds formed strong interactions with key catalytic residues, suggesting significant inhibitory potential against the viral protease. Furthermore, molecular dynamics simulations confirmed their stability within the active site, reinforcing their viability as antiviral agents. This study demonstrates the effectiveness of integrating machine learning with molecular modeling to accelerate the discovery of therapeutic candidates against emerging viral threats.

The Impact of Novel Assessment Methodologies in Toxicology on Green Chemistry and Chemical Alternatives

The field of experimental toxicology is rapidly advancing by incorporating novel techniques and methods that provide a much more granular view into the mechanisms of potential adverse effects of chemical exposures on human health. The data from various in vitro assays and computational models are useful not only for increasing confidence in hazard and risk decisions, but also are enabling better, faster and cheaper assessment of a greater number of compounds, mixtures, and complex products. This is of special value to the field of green chemistry where design of new materials or alternative uses of existing ones is driven, at least in part, by considerations of safety. This article reviews the state of the science and decision-making in scenarios when little to no data may be available to draw conclusions about which choice in green chemistry is "safer." It is clear that there is no "one size fits all" solution and multiple data streams need to be weighed in making a decision. Moreover, the overall level of familiarity of the decision-makers and scientists alike with new assessment methodologies, their validity, value and limitations is evolving. Thus, while the "impact" of the new developments in toxicology on the field of green chemistry is great already, it is premature to conclude that the data from new assessment methodologies have been widely accepted yet.

The Identification of Pivotal Transcriptional Factors Mediating Cell Responses to Drugs With Drug-Induced Liver Injury Liabilities

Drug-induced liver injury (DILI) is a leading cause of drug attrition during drug development and a common reason for drug withdrawal from the market. The poor predictability of conventional animal-based approaches necessitates the development of alternative testing approaches. A body of evidence associates DILI with the induction of stress-response genes in liver cells. Here, we set out to identify signal transduction pathways predominantly involved in the regulation of gene transcription by DILI drugs. To this end, we employed ATTAGENE's cell-based multiplexed reporter assay, the FACTORIAL transcription factor (TF), that enables quantitative assessment of the activity of multiple stress-responsive TFs in a single well of cells. Homogeneous reporter system enables quantitative functional assessment of multiple transcription factors. Nat. Methods 5, 253-260). Using this assay, we assessed TF responses of the human hepatoma cell line HepG2 to a panel of 64 drug candidates, including 23 preclinical DILI and 11 clinical DILI compounds and 30 nonhepatotoxic compounds from a diverse physicochemical property space. We have identified 16 TF families that specifically responded to DILI drugs, including nuclear factor (erythroid-derived 2)-like 2 antioxidant response element, octamer, hypoxia inducible factor 1 alpha, farnesoid-X receptor, TCF/beta-catenin, aryl hydrocarbon receptor, activator protein-1, E2F, early growth response-1, metal-response transcription factor 1, sterol regulatory element-binding protein, paired box protein, peroxisome proliferator-activated receptor, liver X receptor, interferone regulating factor, and P53, and 2 promoters that responded to multiple TFs (cytomegalovirus and direct repeat 3/vitamin D receptor). Some of TFs identified here also have previously defined role in pathogenesis of liver diseases. These data demonstrate the utility of cost-effective, animal-free, TF profiling assay for detecting DILI potential of drug candidates at early stages of drug development.

Mechanisms of Skin Toxicity Associated with Metabotropic Glutamate Receptor 5 Negative Allosteric Modulators

Cutaneous reactions represent one of the most common adverse drug effects observed in clinical trials leading to substantial compound attrition. Three negative allosteric modulators (NAMs) of metabotropic glutamate receptors (mGluRs), which represent an important target for neurological diseases, developed by Pfizer, were recently failed in preclinical development due to delayed type IV skin hypersensitivity observed in non-human primates (NHPs). Here we employed large-scale phenotypic profiling in standardized panels of human primary cell/co-culture systems to characterize the skin toxicity mechanism(s) of mGluR5 NAMs from two different series. Investigation of a database of chemicals tested in these systems and transcriptional profiling suggested that the mechanism of toxicity may involve modulation of nuclear receptor targets RAR/RXR, and/or VDR with AhR antagonism. The studies reported here demonstrate how phenotypic profiling of preclinical drug candidates using human primary cells can provide insights into the mechanisms of toxicity and inform early drug discovery and development campaigns.

Detection of statin cytotoxicity is increased in cells expressing the OATP1B1 transporter

Cytotoxicity of a compound is determined by the intracellular concentration mediated both by passive permeability and active uptake through drug transporters. However, the major liver uptake transporters were either absent or expressed at significantly lower levels in human liver cell lines than in human liver. When comparing cytotoxicity of five statins, the organic anion transporting polypeptide 1B1 expressing HEK cells showed a significantly higher sensitivity than the wild-type HEK cells. The IC50 shifts ranged from 9- to >100-fold, and the potency shifts collapsed in the presence of rifampicin, the inhibitor for OATPs. The extent of the IC50 shift correlated with the permeability of the statins with high permeable compounds having smaller shifts and low permeable compounds having larger shifts. The changes in statin potency in transporter-transfected cells reflect the active uptake of statins into the cells, and the increased intracellular drug concentration lead to increased toxicity. The data suggested that uptake transporters have a significant impact on the outcomes of a cell-based assay and should be considered during the early stages of compound toxicity screening in drug discovery. For compounds with low permeability that are likely to undergo transporter-mediated uptake, it is important to test them in transporter-competent cell models.