2026-04-13T00:12:56Zhttps://recercat.cat/oai/request
oai:recercat.cat:2445/1920692025-12-04T21:11:26Zcom_2072_1057col_2072_478781col_2072_478908col_2072_478917
RECERCAT author Suriñach, Aristarc author Hospital Gasch, Adam author Westermaier, Yvonne author Jordà Bordoy, Luis author Orozco Ruiz, Sergi author Beltrán, Daniel author Colizzi, Francesco author Andrio, Pau author Soliva, Robert author Municoy, Martí author Gelpi Buchaca, Josep Lluís author Orozco López, Modesto 2023-01-11T13:45:35Z2023-12-28T06:10:21Z2022-12-282023-01-10T15:57:20Z Mutations in the kinase domain of the epidermal growth factor receptor (EGFR) can be drivers of cancer and also trigger drug resistance in patients receiving chemotherapy treatment based on kinase inhibitors. A priori knowledge of the impact of EGFR variants on drug sensitivity would help to optimize chemotherapy and design new drugs that are effective against resistant variants before they emerge in clinical trials. To this end, we explored a variety of in silico methods, from sequence-based to "state-of-the-art" atomistic simulations. We did not find any sequence signal that can provide clues on when a drug-related mutation appears or the impact of such mutations on drug activity. Low-level simulation methods provide limited qualitative information on regions where mutations are likely to cause alterations in drug activity, and they can predict around 70% of the impact of mutations on drug efficiency. High-level simulations based on nonequilibrium alchemical free energy calculations show predictive power. The integration of these "state-of-the-art" methods into a workflow implementing an interface for parallel distribution of the calculations allows its automatic and high-throughput use, even for researchers with moderate experience in molecular simulations. (c) American Chemical Society, 2022 info:eu-repo/semantics/openAccess Resistència als medicaments Càncer Quimioteràpia Genètica Drug resistance Cancer Chemotherapy Genetics High-Throughput Prediction of the Impact of Genetic Variability on Drug Sensitivity and Resistance Patterns for Clinically Relevant Epidermal Growth Factor Receptor Mutations from Atomistic Simulations info:eu-repo/semantics/article info:eu-repo/semantics/acceptedVersion