Prof. Maria A. Miteva: “AI Approach to Predict Inhibitors of Drug Metabolizing Enzymes”

Abstract

Artificial Intelligence (AI) and Machine Learning (ML) are more than just buzz words being used in the pharmaceutical and biotechnology industry. There is now a steady stream of publications and evidence outlining what these terms really mean, how they can be applied in a drug discovery and development setting, and how much value they add in terms of saving time, effort and costs. AI and ML can be used for target identification, drug design and optimization, predicting drug toxicity and adverse events. We will present in silico study integrating structural bioinformatics and machine learning approaches to predict inhibition of drug-metabolizing enzyme. Drug metabolizing enzymes (DME) play a key role in the metabolism, elimination and detoxification of xenobiotics, drugs and endogenous molecules. While their principal role is to detoxify organisms by modifying compounds, such as pollutants or drugs in some cases they render their substrates more toxic thereby inducing adverse drug reactions, or their inhibition can lead to drug-drug interactions. Predicting potential inhibition of DME is important in early-stage drug discovery. We focus on Cytochrome P450 (CYP) responsible for the metabolism of 90 % drugs and on sulfotransferases (SULT), phase II conjugate drug metabolizing enzymes, acting on a large number of drugs, hormones and natural compounds. We performed modeling using two learning algorithms, Support Vector Machine (SVM) and RandomForest combining chemicals, protein-ligand interactions and protein structure and dynamics information. Our inhibition models predict CYP and SULT inhibition for three isoforms with an accuracy of >80 % and are implemented in the new software DrugME.

Curriculum Vitae

Maria Miteva is a Research Director at INSERM. She has been working in Bulgaria (Bulgarian Academy of Sciences), Sweden (Karolinska Institutet), and France (CNRS, Inserm). She joined INSERM in 2002 and is currently co-directing the INSERM Unit ERL U1268 “Medicinal Chemistry and Translational Research”, at the Faculty of Pharmacy, University of Paris. She has strong expertise in medicinal chemistry, biophysics, drug-drug interactions, bioinformatics, chemoinformatics and AI for drug discovery and toxicity prediction. She has 4 patents and more than 100 peer-reviewed publications (ID ORCID:N-2419-2018). She edited the book “In silico lead discovery“ (Bentham Sci 2011). She is an editorial board member for several reputed journals in the field and an Associated Editor for BMC Pharmacology and Toxicology.