- Future Health,
A conference with Dr. Maria Miteva: combining artifical intelligence and health for better medicines
Dr Maria Miteva (CiTCoM Cnrs/Inserm, Paris) will give a conference at Centrale Nantes for bioinformatics students and biologists on structural bioinformatics and drug metabolism. Objective: Harnessing structural bioinformatics to predict drug /metabolizing enzyme interactions.
Centrale Nantes
On October 17, 2019 from 18:00 To 19:00
Free entry
6 to 7 pm
Centrale Nantes - 1 rue de la Noë, Nantes
Lecture Theatre S
More information: arnaud.nicot@inserm.fr
Conference in French / slides in English
6 to 7 pm
Centrale Nantes - 1 rue de la Noë, Nantes
Lecture Theatre S
More information: arnaud.nicot@inserm.fr
Conference in French / slides in English
Artificial Intelligence (AI) and Machine Learning (ML) are not just buzzwords used in the pharmaceutical and biotechnology industry. There is now a steady stream of publications and evidence outlining what these terms actually 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 drug-metabolizing enzyme interactions. 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 will present an original in silico approach for the prediction of CYP2C9 and SULT1A1 inhibition combining the knowledge of the protein structure and its dynamic behavior in response to the binding of various ligands and machine learning modeling.
Making better medicines
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 drug-metabolizing enzyme interactions. 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 will present an original in silico approach for the prediction of CYP2C9 and SULT1A1 inhibition combining the knowledge of the protein structure and its dynamic behavior in response to the binding of various ligands and machine learning modeling. 
Dr Maria Miteva
- Co-director of Unit U1268 "Medicinal Chemistry and Translational Research" of INSERM, of the CiTCoM laboratory (UMR8038 CNRS) - Univ. Paris, Faculty of Pharmacy of Paris
- Completed her PhD in 2000 in Bulgaria
- Has worked in several university laboratories in Bulgaria, Sweden and France
- Joined INSERM in 2005
- Over 20 years of experience in structural bioinformatics, in silico screening and machine learning for drug discovery.
- 2 patents and over 90 publications
- Member of the editorial board of several reputable journals and associate editor for BMC Pharmacology and Toxicology
