In-Silico ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity), Prediction and Optimization of Lead compounds for Oral Anticancer Therapy

This research presents an integrated in-silico approach to the ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) prediction and optimization of lead compounds tailored for oral anticancer therapy. The study evaluates a diverse chemical library, including quinoline and 7-ethyl-10-hydroxycamptothecin scaffolds, alongside indole-thiosemicarbazone and 5-fluorouracil (5-FU) derivatives. Utilizing advanced computational tools such as molecular docking and molecular dynamics simulations, the candidates were assessed against three pivotal cancer-related targets: topoisomerase I, bromodomain-containing protein 4 (BRD4), and the efflux transporter ABCG2.Results revealed exceptional binding affinities, with docking scores ranging from -9.0 to -10.3 kcal/mol for topoisomerase I, -6.6 to -8.0 kcal/mol for BRD4, and -8.0 to -10.0 kcal/mol for ABCG2. These scores indicate robust molecular recognition and stable target engagement. Parallel ADMET modelling predicted favourable pharmacokinetic profiles, specifically highlighting high oral bioavailability, moderate-to-good aqueous solubility, and significantly low toxicity risks. Experimental validation further supported the computational findings. The indole-thiosemicarbazone compounds demonstrated moderate antioxidant properties and high selectivity, exhibiting low cytotoxicity toward normal cells while maintaining potent antitumor activity. Furthermore, the optimized 5-fluorouracil derivatives showed superior efficacy and safety in lung cancer models compared to the conventional 5-FU drug. Overall, this study demonstrates that in-silico optimization effectively bridges the gap between theoretical drug design and successful in vitro application, providing a high-speed pathway for developing safer, orally bioavailable chemotherapeutic agents.