Computer-aided design of new drugs against breast cancer

Abstract

Computational medicinal chemistry, if used properly and in accordance with the available experimental data, may provide significant support to rational drug design. Herein, an overview of the computational approaches that have been applied to an estrogen receptor α (ERα) and endowed in the rational design of pM ERα antagonists with profound anti-breast cancer activity either in vitro or in vivo, will be presented. ERα is a 17β-estradiol inducible transcriptional regulator that initiates the RNA polymerase II-dependent transcriptional machinery, pointed for breast cancer (BC) development via either genomic direct or genomic indirect (i.e., tethered) pathway. To develop innovative ligands, structure-based (SB) 3-D QSAR, ComBinE, and 3-D Pharmacophore studies have been undertaken from experimentally resolved partial agonists, SERMs, and SERDs within either wild-type or mutated ERα receptors. SB and ligand-based (LB) alignments gave rules to align the untested compounds. The protocols led to the development of 3DQs, CBEs, and 3DPQs compounds, further synthesized and submitted to either in vitro or in vivo assessments, upon which new leads were revealed as candidates for clinical trials.