Researchers at Case Western Reserve University School of Medicine have developed a computer program called DrugPredict to discover new indications for old drugs. This program matches existing data about FDA-approved drugs to diseases, and predicts potential drug efficacy.
In a recent study published in Oncogene, the researchers successfully translated DrugPredict results into the laboratory, and showed common pain medications non-steroidal anti-inflammatory drugs, also known as NSAIDs could have applications for epithelial ovarian cancer.
DrugPredict was developed by co-first author QuanQiu Wang of ThinTek, LLC, and co-senior author Rong Xu, PhD, associate professor of biomedical informatics in the department of population and quantitative health sciences at Case Western Reserve University School of Medicine. The program works by connecting computer-generated drug profiles including mechanisms of action, clinical efficacy, and side effects with information about how a molecule may interact with human proteins in specific diseases, such as ovarian cancer.
DrugPredict searches databases of FDA-approved drugs, chemicals, and other naturally occurring compounds. It finds compounds with characteristics related to a disease-fighting mechanism. These include observable characteristics (phenotypes) and genetic factors that may influence drug efficacy. Researchers can collaborate with Xu to input a disease into DrugPredict and receive an output list of drugs or potential drugs with molecular features that correlate with strategies to fight the disease.
In the Oncogene study, DrugPredict produced a prioritized list of 6,996 chemicals with potential to treat epithelial ovarian cancer. At the top of the list were 15 drugs already FDA-approved to treat the cancer, helping to validate the DrugPredict approach. Of other FDA-approved medications on the list, NSAIDs ranked significantly higher than other drug classes. The researchers combined the DrugPredict results with anecdotal evidence about NSAIDs and cancer before confirming DrugPredict results in their laboratory experiments.
Citation: Nagaraj, A. B., Q. Q. Wang, P. Joseph, C. Zheng, Y. Chen, O. Kovalenko, S. Singh, A. Armstrong, K. Resnick, K. Zanotti, S. Waggoner, R. Xu, and A. Difeo. “Using a novel computational drug-repositioning approach (DrugPredict) to rapidly identify potent drug candidates for cancer treatment.” Oncogene, 2017.
DOI: 10.1038/onc.2017.328
Funding: Gynecological Cancer Translation Research Program, Case Comprehensive Cancer Center, The Mary Kay Foundation, NIH/Eunice Kennedy Shriver National Institute Of Child Health & Human Development.
Adapted from press release by Case Western Reserve University.