Researchers at the Case Comprehensive Cancer Center at Case Western Reserve University School of Medicine have developed a computer program to find new indications for old drugs. The computer program, called DrugPredict, 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—like aspirin—can kill patient-derived epithelial ovarian cancer cells.
In the new study, DrugPredict suggested non-steroidal anti-inflammatory drugs, also known as NSAIDs, could have applications for epithelial ovarian cancer. The researchers exposed patient-derived epithelial ovarian cancer cells growing in their laboratory to a specific NSAID, indomethacin, and confirmed the DrugPredict finding. Indomethacin killed both drug-resistant and drug-sensitive epithelial ovarian cancer cells. Interestingly, cisplatin-resistant epithelial ovarian cancer cells were most sensitive to indomethacin. When the researchers added chemotherapy drugs to the experiments, the cancer cells died even faster. The findings could represent the first step toward a new therapy regimen for epithelial ovarian cancer.
Epithelial ovarian cancer is the fifth leading cause of cancer deaths in women, killing approximately 14,000 women annually in the United States. Available therapies are only moderately successful, with more than 70% of women dying within five years of diagnosis. According to the authors, part of the challenge in developing new ovarian cancer drugs lies in escalating clinical trial costs and lengthy drug development timelines. Programs like DrugPredict could “reposition” FDA-approved medications for new indications—a more efficient strategy.
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.
