In silico modeling-based new alternative methods to predict drug and herb-induced liver injury: A review

Preface to the special issue of Food and Chemical Toxicology on "New approach methodologies and machine learning in food safety and chemical risk assessment: Development of reproducible, open-source, and user-friendly tools for exposure, toxicokinetic, and toxicity assessments in the 21st century"

This Special Issue contains articles on applications of various new approach methodologies (NAMs) in the field of toxicology and risk assessment. These NAMs include in vitro high-throughput screening, quantitative structure-activity relationship (QSAR) modeling, physiologically based pharmacokinetic (PBPK) modeling, network toxicology analysis, molecular docking simulation, omics, machine learning, deep learning, and "template-and-anchor" multiscale computational modeling. These in vitro and in silico approaches complement each other and can be integrated together to support different applications of toxicology, including food safety assessment, dietary exposure assessment, chemical toxicity potency screening and ranking, chemical toxicity prediction, chemical toxicokinetic simulation, and to investigate the potential mechanisms of toxicities, as introduced further in selected articles in this Special Issue.

Synthesis, molecular docking analysis, drug-likeness evaluation, and inhibition potency of new pyrazole-3,4-dicarboxamides incorporating sulfonamide moiety as carbonic anhydrase inhibitors

A series of novel pyrazole-dicarboxamides were synthesized from pyrazole-3,4-dicarboxylic acid chloride and various primary and secondary sulfonamides. The structures of the new compounds were confirmed by FT-IR, 1H-NMR, 13C-NMR, and HRMS. Then the inhibition effects of newly synthesized molecules on human erythrocyte hCA I and hCA II isoenzymes were investigated. Ki values of the compounds were in the range of 0.024-0.496 µM for hCA I and 0.006-5.441 µM for hCA II. Compounds 7a and 7i showed nanomolar level of inhibition of hCA II, and these compounds exhibited high selectivity for this isoenzyme. Molecular docking studies were performed between the most active compounds 7a, 7b, 7i, and the reference inhibitor AAZ and the hCAI and hCAII to investigate the binding mechanisms between the compounds and the isozymes. These compounds showed better interactions than the AAZ. ADMET and drug-likeness analyses for the compounds have shown that the compounds can be used pharmacologically in living organisms.

Computational models for predicting liver toxicity in the deep learning era

Drug-induced liver injury (DILI) is a severe adverse reaction caused by drugs and may result in acute liver failure and even death. Many efforts have centered on mitigating risks associated with potential DILI in humans. Among these, quantitative structure-activity relationship (QSAR) was proven to be a valuable tool for early-stage hepatotoxicity screening. Its advantages include no requirement for physical substances and rapid delivery of results. Deep learning (DL) made rapid advancements recently and has been used for developing QSAR models. This review discusses the use of DL in predicting DILI, focusing on the development of QSAR models employing extensive chemical structure datasets alongside their corresponding DILI outcomes. We undertake a comprehensive evaluation of various DL methods, comparing with those of traditional machine learning (ML) approaches, and explore the strengths and limitations of DL techniques regarding their interpretability, scalability, and generalization. Overall, our review underscores the potential of DL methodologies to enhance DILI prediction and provides insights into future avenues for developing predictive models to mitigate DILI risk in humans.