Subventions et des contributions :

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Titre :
Toward the accurate prediction of adverse drug reactions and drug-drug interactions using novel MM methods and QM-derived rules
Numéro de l’entente :
CRDPJ
Valeur d'entente :
110 000,00 $
Date d'entente :
14 juin 2017 -
Organisation :
Conseil de recherches en sciences naturelles et en génie du Canada
Location :
Québec, Autre, CA
Numéro de référence :
GC-2017-Q1-00263
Type d'entente :
subvention
Type de rapport :
Subventions et des contributions
Informations supplémentaires :

Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2019-2020)

Nom légal du bénéficiaire :
Moitessier, Nicolas (Université McGill)
Programme :
Subventions de recherche et développement coopérative - projet
But du programme :

Adverse drug reactions (ADRs) and toxicity are major causes of the high attrition rates observed in drug discovery and development programs. It has been shown that reactive metabolites produced by metabolic enzymes such as cytochrome P450s (CYPs) further react with biomolecules such as proteins, DNA or glutathione, leading to hepatotoxicity or DNA mutations causing cancer. In parallel, drug-drug interaction (DDI) is often caused by CYP inhibition by one of the co-administered drugs. Computational prediction of reactive metabolites and DDIs is a promising avenue for more successful drug discovery programs. In this context, we have developed a program, IMPACTS, which predicts the site of metabolism of drugs and the binding of drugs to CYPs. IMPACTS combines ligand reactivity rules based on pre-computed activation energies of drug sites and docking using molecular mechanics (MM) routines. We propose to carry out additional QM investigations and to develop an improved molecular mechanics approach as a continuation of our previously successful CRD grant applications with AstraZeneca (2010-2012, development of IMPACTS) then chemical computing group (2013-now). Over the last 3 years we completed proof-of-principle studies and will further develop our software to include a module for DDIs prediction and an improved version for reactive metabolites prediction, as well as a generalized and more accurate force field (FF) based on chemical principles.