In silico metabolism and toxicity prediction using a knowledge-based approach

The application of in silico approaches for predicting metabolic pathways and toxicity profiles has significantly advanced drug discovery and chemical risk evaluation. By harnessing developments in cheminformatics, machine learning, and expert-driven platforms, these computational techniques enable the early assessment of how candidate molecules may be metabolized and their possible toxic effects, often prior to laboratory synthesis or experimental testing. In this study, knowledge-based algorithms were utilized to predict the metabolism and toxicity of a previously identified dual-acting pyrrole-based compound using in silico methods. The licensed Lhasa software suite (Lhasa Limited, Leeds, UK), specifically the Meteor and Derek modules, was employed for these analyses. Toxicity assessment indicated that compound 7b has a plausible potential to cause skin irritation or corrosion in mammalian systems. However, it was computationally predicted to be inactive in bacterial mutagenicity assays (Ames test) and did not trigger any alerts across 58 other evaluated toxicity endpoints. The analysis also identified the closest metabolic analogues of compound 7b, revealing that the compound is most likely to undergo hydrolysis of its acyclic carboxylic ester, followed by hydroxylation of the tryptophan ring. These in silico findings provide valuable insights, but further validation through in vitro and in vivo studies should be carried out.