Algorithm quickly identifies new indications for old drugs
Computer program developed by Case Western Reserve University School of Medicine researchers helps find a new use for a long-used drug to treat ovarian cancer.
A research team at Case Western Reserve University School of Medicine’s Case Comprehensive Cancer Center has developed an algorithm that identifies new indications for old drugs, while potentially uncovering a novel approach to treating ovarian cancer.
The computer program—called DrugPredict—matches data about FDA-approved medications to diseases, and then predicts their potential effectiveness. Specifically, the algorithm found that non-steroidal anti-inflammatories (NSAIDs)—a common class of pain relievers—kill epithelial ovarian cancer cells, the most lethal gynecologic malignancy and the fifth leading cause of cancer deaths in women.
A computation-based drug-repositioning system, DrugPredict conducts both genome- and phenome-wide analysis to match diseases to medication candidates—a much quicker and less costly approach than the traditional drug discovery process.
“For any given disease, DrugPredict simultaneously performs both a target-based and phenotypic screening of over half a million chemicals, all in just a few minutes,” says Rong Xu, associate professor of biomedical informatics in the Department of Population and Quantitative Health Sciences at Case Western Reserve University School of Medicine.
“The primary advantage of drug re-positioning over traditional drug development is that it starts from compounds with well-characterized pharmacology and safety profiles. This significantly reduces the risk of adverse effects and attrition in clinical trials,” Xu adds.
Also See: Deep learning excels at analysis of breast cancer biopsy slides
Case Western researchers recently published the results of a study in the journal Oncogene. The study showed they were able to rapidly identify and validate drug-repositioning candidates for epithelial ovarian cancer.
By connecting computer-generated drug profiles with information about how a molecule may interact with human proteins in ovarian cancer, the algorithm used in the study was able to produce a prioritized list of 6,996 chemicals with potential to treat epithelial ovarian cancer cells.
In particular, NSAIDs ranked significantly higher in the study than other drug classes. Researchers then exposed patient-derived epithelial ovarian cancer cells growing in their laboratory to a specific NSAID—indomethacin—and confirmed their finding.
“For many years, NSAIDs have been shown in large epidemiological studies to prevent or decrease the incidence of ovarian cancer,” says Analisa DiFeo, co-senior author of the study and professor of ovarian cancer research in the Case Comprehensive Cancer Center at Case Western Reserve University School of Medicine. “It was never thought that these (NSAIDs) could be used as an actual adjuvant chemotherapy to kill ovarian cancer cells.”
As a result of this study, DiFeo adds that she is planning to test indomethacin’s ability to specifically target ovarian cancer stem cells in patient tumors in a phase 1 clinical trial.
“We already know the safety profiles for a lot of these drugs, and if we now see that they are effective in treating cancer, it’s a faster route to translation and to clinical trials,” she concludes. “The unique thing that came out of this was we potentially found a cancer stem cell-specific drug, which not only could help treat the tumor but also prevent it from coming back.”
The computer program—called DrugPredict—matches data about FDA-approved medications to diseases, and then predicts their potential effectiveness. Specifically, the algorithm found that non-steroidal anti-inflammatories (NSAIDs)—a common class of pain relievers—kill epithelial ovarian cancer cells, the most lethal gynecologic malignancy and the fifth leading cause of cancer deaths in women.
A computation-based drug-repositioning system, DrugPredict conducts both genome- and phenome-wide analysis to match diseases to medication candidates—a much quicker and less costly approach than the traditional drug discovery process.
“For any given disease, DrugPredict simultaneously performs both a target-based and phenotypic screening of over half a million chemicals, all in just a few minutes,” says Rong Xu, associate professor of biomedical informatics in the Department of Population and Quantitative Health Sciences at Case Western Reserve University School of Medicine.
“The primary advantage of drug re-positioning over traditional drug development is that it starts from compounds with well-characterized pharmacology and safety profiles. This significantly reduces the risk of adverse effects and attrition in clinical trials,” Xu adds.
Also See: Deep learning excels at analysis of breast cancer biopsy slidesCase Western researchers recently published the results of a study in the journal Oncogene. The study showed they were able to rapidly identify and validate drug-repositioning candidates for epithelial ovarian cancer.
By connecting computer-generated drug profiles with information about how a molecule may interact with human proteins in ovarian cancer, the algorithm used in the study was able to produce a prioritized list of 6,996 chemicals with potential to treat epithelial ovarian cancer cells.
In particular, NSAIDs ranked significantly higher in the study than other drug classes. Researchers then exposed patient-derived epithelial ovarian cancer cells growing in their laboratory to a specific NSAID—indomethacin—and confirmed their finding.
“For many years, NSAIDs have been shown in large epidemiological studies to prevent or decrease the incidence of ovarian cancer,” says Analisa DiFeo, co-senior author of the study and professor of ovarian cancer research in the Case Comprehensive Cancer Center at Case Western Reserve University School of Medicine. “It was never thought that these (NSAIDs) could be used as an actual adjuvant chemotherapy to kill ovarian cancer cells.”
As a result of this study, DiFeo adds that she is planning to test indomethacin’s ability to specifically target ovarian cancer stem cells in patient tumors in a phase 1 clinical trial.
“We already know the safety profiles for a lot of these drugs, and if we now see that they are effective in treating cancer, it’s a faster route to translation and to clinical trials,” she concludes. “The unique thing that came out of this was we potentially found a cancer stem cell-specific drug, which not only could help treat the tumor but also prevent it from coming back.”
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