Researchers repurpose painkillers to kill cancer cells

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 on FDA-approved drugs to diseases and predicts the drugs’ potential effectiveness. In a recent study published in Oncogeneresearchers successfully translated DrugPredict results into the lab and showed that common painkillers – like aspirin – can kill patient-derived epithelial ovarian cancer cells.

In the new study, DrugPredict suggested that nonsteroidal anti-inflammatory drugs, also known as NSAIDs, may have applications for epithelial ovarian cancer. The researchers exposed patient-derived epithelial cancer cells growing in their lab to a specific NSAID, indomethacin, and confirmed DrugPredict’s finding. Indomethacin killed both drug-resistant and drug-sensitive epithelial ovarian cancer cells. Interestingly, cisplatin-resistant ovarian epithelial cancer cells were the most sensitive to indomethacin. When the researchers added chemotherapy drugs to the experiments, the cancer cells died even faster. The results could represent the first step towards a new treatment regimen for epithelial ovarian cancer.

Epithelial ovarian cancer is the fifth leading cause of cancer death in women, killing approximately 14,000 women each year in the United States. Available therapies have only moderate success, with more than 70% of women dying within five years of diagnosis. According to the authors, part of the challenge in developing new drugs for ovarian cancer is escalating clinical trial costs and long drug development times. Programs like DrugPredict could “reposition” FDA-approved drugs for new indications – a more effective strategy.

“The traditional drug discovery process takes an average of 14 years and billions of dollars of investment for a lead cancer drug to transition from the lab to the clinic,” said study first author Anil Belur. Nagaraj, PhD, research associate at Case Western Reserve. University School of Medicine. “Drug repositioning significantly shortens the long latency phase in drug discovery and also reduces associated costs.”

DrugPredict was developed by co-first author QuanQiu Wang of ThinTek, LLC, and co-lead 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 natural compounds. He finds compounds with characteristics linked to a disease-fighting mechanism. These include observable characteristics – phenotypes – and genetic factors that can influence the effectiveness of drugs. Researchers can collaborate with Xu to enter a disease into DrugPredict and receive an output list of drugs – or potential drugs – with molecular characteristics that correlate with disease control strategies.

“For a given disease, DrugPredict simultaneously performs target-based and phenotypic screening of more than half a million chemicals, all in just minutes,” Xu said.

In the Oncogene study, DrugPredict produced a priority list of 6,996 chemicals that may treat epithelial ovarian cancer. Topping the list were 15 drugs already approved by the FDA to treat cancer, helping to validate the DrugPredict approach. Of the other FDA-approved drugs on the list, NSAIDs rank significantly higher than other drug classes. The researchers combined the DrugPredict results with anecdotal evidence about NSAIDs and cancer before confirming the DrugPredict results in their lab experiments.

The program could help identify safe alternatives for diseases – like epithelial ovarian cancer – that desperately need new treatment options. “The main advantage of drug repositioning over traditional drug development is that it starts from compounds with well-characterized pharmacological and safety profiles. This greatly reduces the risk of adverse effects and attrition in clinical trials,” Xu said.

“By combining my lab’s expertise in ovarian cancer biology and Dr. Xu’s expertise in bioinformatics, we were able to uncover a potentially novel drug approach to treat ovarian cancer,” said the co. -lead author Analisa DiFeo, PhD, Norma C. and Albert I. Geller Designated Professor of Ovarian Cancer Research and Assistant Professor at the Case Comprehensive Cancer Center at Case Western Reserve University School of Medicine. Said Nagaraj, “Currently, no drugs targeting cancer stem cells are being evaluated in clinical trials for ovarian cancer. Our results provide rationale for testing NSAIDs like indomethacin as a new drug in trials ovarian cancer clinics.”

DiFeo plans to test the ability of indomethacin to specifically target ovarian cancer stem cells in patient tumors in a Phase 1 clinical trial. She will conduct the trial in collaboration with Steven Waggoner, MD, chief of the division of gynecologic oncology at University Hospitals Seidman Cancer Center and professor of obstetrics and gynecology at Case Western Reserve University School of Medicine.

This study was supported by Norma C. and Albert I. Geller through the Case Comprehensive Cancer Center’s Gynecologic Cancer Translation Research Program and grants from the Mary Kay Foundation (to AD and RX), National Eunice Institute Kennedy Shriver of Child Health. & Human Development of the National Institutes of Health under NIH Director’s New Innovator Award number DP2HD084068 (to RX), National Cancer Institute number R011CA197780-01A1 (to AD), and the Young Scientist Foundation (AD). This research was also supported by the Athymic Animal and Xenograft Core Facility and the Cytometry & Imaging Microscopy Core Facility at the Case Comprehensive Cancer Center (P30CA043703).

Gordon K. Morehouse