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.

有益药物组合的识别是面向联合药物治疗的制药与临床研究中极具挑战性的课题。本研究中，我们基于药物效能与靶点特征谱，开发了一种可用于大规模预测有益药物组合的新型计算方法。我们基于代表药物靶向蛋白与解剖治疗化学分类系统（Anatomical Therapeutic Chemical Classification System）编码的多维度药物特征，为每一组药物对设计了信息丰富的描述符。随后，我们基于来自橙皮书（Orange Book）与KEGG药物数据库（KEGG DRUG）的已知药物组合数据，通过学习稀疏诱导分类器构建了预测模型。实验结果表明，所提方法在高置信度预测的准确率上优于现有方法，且提取的特征具备生物学意义。最后，我们针对2639种获批药物开展了新型药物组合的全面预测，共得到142988组潜在有益的全新药物对。我们还展示了针对多种疾病的成功预测药物组合实例。