Computational prediction of drug toxicity: the case of mutagenicity and carcinogenicity

Molecular Design of Novel Inhibitor by Targeting IL-6Rα using Combined Pharmacophore and Experimentally Verified Plant Products with Scaffold-Hopping Techniques: A Dual Therapeutic Strategy for COVID-19 and Cancer

The IL-6/IL-6R/gp130 complex serves as a significant indicator of cytokine release syndrome in COVID-19 and chronic inflammation, increasing the risk of cancer. Therefore, we identified IL-6Rα as a potential target to block gp130 interaction. Notably, there has been no reception of approval for an orally available drug to serve this purpose, to date. In this study, we targeted IL-6Rα to inhibit IL-6Rα/gp130 interaction. The selection of the lead candidate L821 involved the amalgamation of three drug discovery approaches. This library was screened employing tertiary structure-based pharmacophore models followed by molecular docking models, scaffold-hopping, MM/PBSA as well as MM/GBSA analysis, and assessments of pKi and ADMET properties. After evaluating the binding interactions with key amino acids, 15 potential ligands were chosen, with the top ligand undergoing further investigation by means of molecular dynamics simulations. Considering the stability of the complexes, the strong interactions observed between ligand and residues of IL-6Rα/gp130, and the favorable binding free energy calculations, L821 emerged as the prime candidate for inhibiting IL-6Rα. Notably, L821 exhibited a docking-based binding affinity of -9.5 kcal/mol. Our study presents L821 as a promising inhibitor for future in vitro analysis, potentially combatting SARS-CoV-2-related cytokine storms and serving as an oncogenic drug therapy.

Evaluation of cytotoxicity and antiviral activity of Rhazya stricta Decne leaves extract against influenza A/PR/8/34 (H1N1)

Influenza viruses have developed resistance to the current classes of drugs, which means they could eventually become more virulent and cause more mortality and hospitalization. Our study aims to investigate the antiviral activity of Rhazya stricta Decne leaves extract in vitro and search for new promising drugs from R. stricta identified compounds in silico. The study was performed in vitro by utilizing Madin-Darby Canine Kidney cell line (MDCK) as a substrate for the influenza virus and estimating the inhibition performance of the plant leaves extract. Additionally, in silico screening was conducted to explore the antiviral activity of R. stricta phytochemicals. We investigated the cytotoxicity of R. stricta leaves extract and its antiviral activity against influenza virus (A/Puerto Rico/8/34 (H1N1)) using the MTT assay. The mode of action of the plant leaves extract during the viral life cycle was tested using time-of-addition assay. In silico analyses were performed, including molecular docking, drug-likeness analysis, and toxicity risk assessment, to state the leading compounds to be developed into an anti-influenza virus drug. The 50% cytotoxicity concentration of the leaves extract was CC50: 184.6 µg/mL, and the 50% inhibition concentration was CI50: 19.71 µg\mL. The time of addition assay revealed that R. stricta leaves extract exerted its activity in the late step of the influenza virus replication cycle. In comparison to Oseltamivir, the leading compounds showed better binding affinity and can be developed into oral drugs with low toxicity risk. Isolation and purification of the leading compounds and testing their antiviral activity in vitro and in vivo are required.

Expert Systems for Toxicity Prediction

Expert systems offer the facility to predict a toxicity endpoint, as well sometimes as additional relevant information, simply by inputting the chemical structure of a compound. There is now a number of expert systems available, mostly on a commercial basis although a few are free to use or download. This chapter discusses nineteen currently available expert systems, and their performances (if known). Published studies of consensus predictions with these expert systems indicate that these give better results than do individual expert systems.

A test set of compounds with Tetrahymena pyriformis toxicities has been run through the two expert systems known to predict these toxicities; the predictions were quite good, with standard errors of prediction of 0.395 and 0.433 log unit. A further test set of compounds with local lymph node assay skin sensitisation data has been run through seven expert systems, and it was found that consensus predictions were better than were those from any individual expert system.

Promises and pitfalls of quantitative structure-activity relationship approaches for predicting metabolism and toxicity

The description of quantitative structure-activity relationship (QSAR) models has been a topic for scientific research for more than 40 years and a topic within the regulatory framework for more than 20 years. At present, efforts on QSAR development are increasing because of their promise for supporting reduction, refinement, and/or replacement of animal toxicity experiments. However, their acceptance in risk assessment seems to require a more standardized and scientific underpinning of QSAR technology to avoid possible pitfalls. For this reason, guidelines for QSAR model development recently proposed by the Organization for Economic Cooperation and Development (OECD) [Organization for Economic Cooperation and Development (OECD) (2007) Guidance document on the validation of (quantitative) structure-activity relationships [(Q)SAR] models. OECD Environment Health and Safety Publications: Series on Testing and Assessment No. 69, Paris] are expected to help increase the acceptability of QSAR models for regulatory purposes. The guidelines recommend that QSAR models should be associated with (i) a defined end point, (ii) an unambiguous algorithm, (iii) a defined domain of applicability, (iv) appropriate measures of goodness-of-fit, robustness, and predictivity, and (v) a mechanistic interpretation, if possible [Organization for Economic Cooperation and Development (OECD) (2007) Guidance document on the validation of (quantitative) structure-activity relationships [(Q)SAR] models. The present perspective provides an overview of these guidelines for QSAR model development and their rationale, as well as the promises and pitfalls of using QSAR approaches and these guidelines for predicting metabolism and toxicity of new and existing chemicals.