Integrated AI approach to predict drug interactions due to drug metabolizing enzymes and transporters for the design of safer drugs

Drug-drug interactions (DDI) are key for safety treatments. Drug metabolizing enzymes (DME) and drug transporters strongly influence the drug disposition and can be involved in the DDI of a large number of drugs. We will present an AI approach integrating structural bioinformatics and machine/deep learning methodologies to predict interactions of drugs with DME and ABC transporters. We focus on the DMEs of phase I, as cytochrome P450, and phase II conjugate enzymes. Enhanced molecular dynamics simulations allowing to elucidate efflux drug mechanisms of the transporters P-glycoprotein and BCRP will be discussed. Such AI approaches can improve the prediction of DDI in clinics and drug development pipelines.

Speaker:

Dr. Maria A. Miteva is a Research Director at the French National Institute for Health and Medical Research (INSERM) and Deputy Director of the lab INSERM U1268 "Medicinal Chemistry and Translational Research" at the unit CiTCoM, CNRS - University Paris Cité, Faculty of Pharmacy in Paris. She obtained her Master's degree in biotechnology engineering and completed her PhD degree in 2000 in the Institute of Organic Chemistry at the Bulgarian Academy of Science. She has been working in the fields of AI, bioinformatics, chemoinformatics, drug discovery and toxicity prediction in Bulgaria, Sweden and France. She joined INSERM Institute in 2005. The current focus of her research is on the development of AI approaches for identification of drug candidates and prediction of drug-drug interactions mediated by drug metabolizing enzymes and ABC transporters. Dr. Miteva has published more than 100 papers in peer-reviewed journals. Currently, she is the Editor-in-Chief of Advances and Applications in Bioinformatics and Chemistry.