QSAR Modeling and Prediction of Drug–Drug Interactions

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.