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Mol Pharm. 2016 Feb 1;13(2):545-56. doi: 10.1021/acs.molpharmaceut.5b00762. Epub 2016 Jan 5.

QSAR Modeling and Prediction of Drug-Drug Interactions.

Author information

1
Computer-Aided Drug Design Group, Chemical Biology Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, DHHS, NCI-Frederick , 376 Boyles Street, Frederick, Maryland 21702, United States.
2
Department of Molecular Structure and Cheminformatics, A.V. Bogatsky Physical Chemical Institute, National Academy of Sciences of Ukraine , Lustdorfskaya Doroga 86, Odessa 65080, Ukraine.
3
Chemical-Technological Department, Odessa National Polytechnic University , 1 Shevchenko Ave, Odessa 65000, Ukraine.
4
Institute of Biochemical Chemistry , 10/8, Pogodinskaya street, 119121 Moscow, Russia.
5
Medico-Biological Department, Pirogov Russian National Research Medical University , Ostrovitianov str. 1, Moscow 117997, Russia.
6
Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina , Beard Hall 301, CB#7568, Chapel Hill, North Carolina 27599, United States.
7
Department of Chemistry, Bioinformatics Research Center, North Carolina State University , Raleigh, North Carolina 27695, United States.

Abstract

Severe adverse drug reactions (ADRs) are the fourth leading cause of fatality in the U.S. with more than 100,000 deaths per year. As up to 30% of all ADRs are believed to be caused by drug-drug interactions (DDIs), typically mediated by cytochrome P450s, possibilities to predict DDIs from existing knowledge are important. We collected data from public sources on 1485, 2628, 4371, and 27,966 possible DDIs mediated by four cytochrome P450 isoforms 1A2, 2C9, 2D6, and 3A4 for 55, 73, 94, and 237 drugs, respectively. For each of these data sets, we developed and validated QSAR models for the prediction of DDIs. As a unique feature of our approach, the interacting drug pairs were represented as binary chemical mixtures in a 1:1 ratio. We used two types of chemical descriptors: quantitative neighborhoods of atoms (QNA) and simplex descriptors. Radial basis functions with self-consistent regression (RBF-SCR) and random forest (RF) were utilized to build QSAR models predicting the likelihood of DDIs for any pair of drug molecules. Our models showed balanced accuracy of 72-79% for the external test sets with a coverage of 81.36-100% when a conservative threshold for the model's applicability domain was applied. We generated virtually all possible binary combinations of marketed drugs and employed our models to identify drug pairs predicted to be instances of DDI. More than 4500 of these predicted DDIs that were not found in our training sets were confirmed by data from the DrugBank database.

KEYWORDS:

DDI; GUSAR; QNA; QSAR modeling; adverse drug reactions; drug−drug interactions; mixtures; simplex descriptors; toxicity

[Indexed for MEDLINE]

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