Big data analysis for drug discovery: from molecular simulations to lipidomics

This work highlights the growing significance of computational tools in drug discovery, from atomistic simulations that uncover molecular mechanisms behind therapeutic actions to multivariate analysis for pattern recognition in large datasets. In Chapter 2, two metadynamics-based protocols were set-up and applied to classify M3 receptor antagonists by residence time, contributing to structure-kinetic relationship (SKR) analysis and demonstrating how chemical modifications influence the (un)binding kinetics. Chapter 3 introduces a multiscale computational protocol based on enhanced sampling and QM/MM simulations to characterize the mechanism of action of known azole urea inhibitors of MGL. This approach was successfully applied to drive the synthesis of a new compound, endowed with nanomolar activity. In Chapter 4 a similar computational protocol was applied to investigate the mechanism of action of new tyrosine kinase inhibitors (TKIs) targeting the catalytic lysine of EGFR, designed to overcome the resistance to osimertinib, the front-line treatment for non-small cell lung cancer (NSCLC). Chapter 5 explores alternative strategies to prevent the insurgence of osimertinib resistance in NSCLC investigating the role played by lipid metabolism in cancer cells. Multivariate analysis of lipidomic data obtained from LC-MS experiments on osimertinib-sensitive and -resistant cells revealed that the combination of osimertinib with a glucosylceramide synthase inhibitor (e.g., venglustat) is a promising strategy to delay the onset of resistance, potentially extending the progression-free survival of NSCLC patients.