Large-Scale Prediction of Beneficial Drug Combinations Using Drug Efficacy and Target Profiles

The
identification of beneficial drug combinations is a challenging
issue in pharmaceutical and clinical research toward combinatorial
drug therapy. In the present study, we developed a novel computational
method for large-scale prediction of beneficial drug combinations
using drug efficacy and target profiles. We designed an informative
descriptor for each drug–drug pair based on multiple drug profiles
representing drug-targeted proteins and Anatomical Therapeutic Chemical
Classification System codes. Then, we constructed a predictive model
by learning a sparsity-induced classifier based on known drug combinations
from the Orange Book and KEGG DRUG databases. Our results show that
the proposed method outperforms the previous methods in terms of the
accuracy of high-confidence predictions, and the extracted features
are biologically meaningful. Finally, we performed a comprehensive
prediction of novel drug combinations for 2,639 approved drugs, which
predicted 142,988 new potentially beneficial drug–drug pairs.
We showed several examples of successfully predicted drug combinations
for a variety of diseases.