Advanced Technology and Analytical Methods for Assessing the Impact of Anticancer Drug Metabolites on Drug Efficacy and Toxicity

Abstract

This study focuses on advancing the analysis of anticancer drug metabolites by integrating cutting-edge analytical and computational techniques. To improve the separation and identification of metabolites, we employ advanced chromatographic methods, including Ultra-Performance Liquid Chromatography (UPLC) coupled with high-resolution mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy. These techniques provide enhanced resolution and accuracy in metabolite profiling. Computational approaches, such as molecular dynamics (MD) simulations and quantum mechanical (QM) calculations, are utilized to predict metabolic pathways and identify novel metabolites, while quantitative structure-activity relationship (QSAR) models assess biological activity and potential toxicity. The study reveals that Metabolite A exhibits high binding affinity and favorable reaction energy, suggesting its significant role in drug efficacy, whereas Metabolite B, despite lower binding affinity, shows higher potency and may contribute substantially to therapeutic effects. In contrast, Metabolite C, with the lowest binding affinity and less favorable reaction energy, presents potential safety concerns. This integrated methodology highlights the importance of combining advanced analytical techniques with computational models to optimize drug development and personalized medicine. The findings underscore the potential for improved therapeutic efficacy and safety in oncology through detailed metabolite analysis.