Prediction and identification of synergistic compound combinations against pancreatic cancer cells
Yasaman KalantarMotamedi1, Ran Joo Choi1, Siang-Boon Koh2
1Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK.
Abstract:
Resistance to current therapies is common for pancreatic cancer and hence novel treatment options are urgently needed. In this work, we developed and validated a computational method to select synergistic compound combinations based on transcriptomic profiles from both the disease and compound side, combined with a pathway scoring system, which was then validated prospectively by testing 30 compounds (and their combinations) on PANC-1 cells. Some compounds selected as single agents showed lower GI50 values than the standard of care, gemcitabine. Compounds suggested as combination agents with standard therapy gemcitabine based on the best performing scoring system showed on average 2.82-5.18 times higher synergies compared to compounds that were predicted to be active as single agents. Examples of highly synergistic in vitro validated compound pairs include gemcitabine combined with Entinostat, thioridazine, loperamide, scriptaid and Saracatinib. Hence, the computational approach presented here was able to identify synergistic compound combinations against pancreatic cancer cells.
Insights
A new computational method identifies effective drug combinations for pancreatic cancer, offering novel treatment strategies. This approach found synergistic pairings, including gemcitabine with other agents, outperforming single-drug therapies.
Area of Science:
- Computational biology
- Oncology
- Pharmacology
Background:
- Pancreatic cancer exhibits significant resistance to existing therapies, necessitating the development of novel treatment strategies.
- Identifying effective drug combinations is crucial for improving patient outcomes in pancreatic cancer treatment.
Purpose of the Study:
- To develop and validate a computational method for predicting synergistic compound combinations against pancreatic cancer.
- To identify novel therapeutic combinations that overcome resistance to current treatments.
Main Methods:
- Development of a computational method integrating transcriptomic profiles (disease and compound) and a pathway scoring system.
- Prospective validation of 30 predicted compounds and their combinations on PANC-1 pancreatic cancer cells.
- Assessment of drug synergy and comparison with standard-of-care gemcitabine.
Main Results:
- The computational method successfully predicted synergistic compound combinations.
- Some single agents showed improved efficacy (lower GI50) compared to gemcitabine.
- Combinations predicted by the top-scoring system demonstrated 2.82-5.18 times higher synergy than single agents.
- Validated synergistic pairs include gemcitabine with Entinostat, thioridazine, loperamide, scriptaid, and Saracatinib.
Conclusions:
- The computational approach effectively identifies synergistic drug combinations for pancreatic cancer.
- This method offers a promising strategy for discovering novel, effective treatments for pancreatic cancer.
- Validated combinations provide a basis for further preclinical and clinical investigation.
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