Drug-drug interaction prediction using PASS

Dmitriev, A.V.; Filimonov, D.A.; Rudik, A.V.; Pogodin, P.V.; Karasev, D.A.; Lagunin, A.A.; Poroikov, V.V.

doi:10.6084/m9.figshare.9767195.v1

gsar_a_1653966_sm2561.docx (33.82 kB)

Drug-drug interaction prediction using PASS

journal contribution

posted on 2019-09-04, 11:04 authored by A.V. Dmitriev, D.A. Filimonov, A.V. Rudik, P.V. Pogodin, D.A. Karasev, A.A. Lagunin, V.V. Poroikov

Simultaneous use of the drugs may lead to undesirable Drug-Drug Interactions (DDIs) in the human body. Many DDIs are associated with changes in drug metabolism that performed by Drug-Metabolizing Enzymes (DMEs). In this case, DDI manifests itself as a result of the effect of one drug on the biotransformation of other drug(s), its slowing down (in the case of inhibiting DME) or acceleration (in case of induction of DME), which leads to a change in the pharmacological effect of the drugs combination. We used OpeRational ClassificAtion (ORCA) system for categorizing DDIs. ORCA divides DDIs into five classes: contraindicated (class 1), provisionally contraindicated (class 2), conditional (class 3), minimal risk (class 4), no interaction (class 5). We collected a training set consisting of several thousands of drug pairs. Algorithm of PASS program was used for the first, second and third classes DDI prediction. Chemical descriptors called PoSMNA (Pairs of Substances Multilevel Neighbourhoods of Atoms) were developed and implemented in PASS software to describe in a machine-readable format drug substances pairs instead of the single molecules. The average accuracy of DDI class prediction is about 0.84. A freely available web resource for DDI prediction was developed (http://way2drug.com/ddi/).