H-RACS: a handy tool to rank anti-cancer synergistic drugs
Xinmiao Yan1, Yiyan Yang1, Zikun Chen1
1Department of Gastroenterology, Shanghai Tenth People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China.
Abstract:
Though promising, identifying synergistic combinations from a large pool of candidate drugs remains challenging for cancer treatment. Due to unclear mechanism and limited confirmed cases, only a few computational algorithms are able to predict drug synergy. Yet they normally require the drug-cell treatment results as an essential input, thus exclude the possibility to pre-screen those unexplored drugs without cell treatment profiling. Based on the largest dataset of 33,574 combinational scenarios, we proposed a handy webserver, H-RACS, to overcome the above problems. Being loaded with chemical structures and target information, H-RACS can recommend potential synergistic pairs between candidate drugs on 928 cell lines of 24 prevalent cancer types. A high model performance was achieved with AUC of 0.89 on independent combinational scenarios. On the second independent validation of DREAM dataset, H-RACS obtained precision of 67% among its top 5% ranking list. When being tested on new combinations and new cell lines, H-RACS showed strong extendibility with AUC of 0.84 and 0.81 respectively. As the first online server freely accessible at http://www.badd-cao.net/h-racs, H-RACS may promote the pre-screening of synergistic combinations for new chemical drugs on unexplored cancers.
Insights
A new webserver, H-RACS, predicts synergistic drug combinations for cancer treatment using chemical structures. It enables pre-screening of unexplored drugs and novel combinations, aiding cancer therapy development.
Area of Science:
- Computational Biology
- Drug Discovery
- Oncology
Background:
- Identifying synergistic drug combinations for cancer treatment is challenging due to the vast number of potential drug pairs and limited predictive models.
- Existing computational methods often require extensive drug-cell treatment data, hindering the exploration of novel or uncharacterized compounds.
Purpose of the Study:
- To develop a novel computational tool, H-RACS (Web Server for Predicting Synergistic Drug Combinations), to facilitate the pre-screening of synergistic drug combinations.
- To enable prediction of drug synergy using chemical structures and target information, even for drugs lacking prior cell treatment profiling.
Main Methods:
- Utilized the largest dataset of 33,574 combinational scenarios to train a predictive model.
- Developed H-RACS, a web server that accepts chemical structures and target information to recommend synergistic drug pairs.
- Validated the model on independent datasets, including the DREAM dataset, and assessed its performance on new combinations and cell lines.
Main Results:
- Achieved high model performance with an Area Under the Curve (AUC) of 0.89 on independent combinational scenarios.
- Demonstrated strong predictive capability with 67% precision in the top 5% ranking list on the DREAM dataset.
- Showcased significant extendibility with AUCs of 0.84 and 0.81 when tested on new combinations and cell lines, respectively.
Conclusions:
- H-RACS is the first freely accessible online server for predicting synergistic drug combinations, promoting the pre-screening of novel chemical drugs.
- The tool supports the identification of potential synergistic pairs across 928 cell lines and 24 cancer types, advancing cancer therapy research.
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