In Silico identification of taxane analogs as potentially-safer, chemoresistance-evading candidates for breast cancer

Breast cancer (BC) remains a leading cause of cancer-related death in women, with challenges like chemoresistance and treatment-induced toxicity limiting the efficacy of current therapies. The development of new drugs is a costly and lengthy process. This study employed a multi-step computational pipeline to identify novel drug candidates for BC with potentially improved efficacy and safety. Starting with FDA-approved BC drugs, a similarity search was performed in the ZINC database to identify structural analogs. These compounds were then screened using eight validated Quantitative Structure-Activity Relationship (QSAR) models, which demonstrated high predictive accuracy (ROC AUC > 0.86) for inhibiting BC cell growth. Top-scoring candidates from the QSAR screen were subjected to molecular docking against the known targets of their parent drugs. The analogs demonstrated potent binding affinities comparable to their parent drugs, with several matching or exceeding the affinity of Paclitaxel against the TUBG1 isotype. The most significant findings emerged from subsequent ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicology) analysis, which revealed that several candidates possessed improved pharmacokinetic profiles, including a reduced potential for P-glycoprotein-mediated efflux, a key mechanism in multidrug resistance. The derivatives also maintained favorable safety profiles with low predicted carcinogenicity and organ toxicity. In conclusion, this in silico approach successfully identified novel compounds that, by retaining potent binding while exhibiting promising drug-like properties, may help overcome chemoresistance, warranting their further experimental investigation as potential BC therapeutics.