Network-based estimation of therapeutic efficacy and adverse reaction potential for prioritisation of anti-cancer
Arindam Ghosh1, Vittorio Fortino1
1Institute of Biomedicine, University of Eastern Finland, 70210 Kuopio, Finland.
Abstract:
Drug combinations, although a key therapeutic agent against cancer, are yet to reach their full applicability potential due to the challenges involved in the identification of effective and safe drug pairs. In vitro or in vivo screening would have been the optimal approach if combinatorial explosion was not an issue. In silico methods, on the other hand, can enable rapid screening of drug pairs to prioritise for experimental validation. Here we present a novel network medicine approach that systematically models the proximity of drug targets to disease-associated genes and adverse effect-associated genes, through the combination of network propagation algorithm and gene set enrichment analysis. The proposed approach is applied in the context of identifying effective drug combinations for cancer treatment starting from a training set of drug combinations curated from DrugComb and DrugBank databases. We observed that effective drug combinations usually enrich disease-related gene sets while adverse drug combinations enrich adverse-effect gene sets. We use this observation to systematically train classifiers distinguishing drug combinations with higher therapeutic effects and no known adverse reaction from combinations with lower therapeutic effects and potential adverse reactions in six cancer types. The approach is tested and validated using drug combinations curated from in vitro screening data and clinical reports. Trained classification models are also used to identify novel potential anti-cancer drug combinations for experimental validation. We believe our framework would be a key addition to the anti-cancer drug combination identification pipeline by enabling rapid yet robust estimation of therapeutic efficacy or adverse reaction potential.
Insights
This study introduces a novel network medicine approach for identifying effective and safe cancer drug combinations. The method uses computational analysis to predict therapeutic efficacy and reduce adverse reactions, aiding drug discovery.
Area of Science:
- Computational biology
- Network medicine
- Pharmacogenomics
Background:
- Drug combinations are crucial for cancer therapy but identifying effective and safe pairs is challenging.
- Combinatorial explosion limits in vitro/in vivo screening, necessitating in silico approaches.
- Current methods struggle with systematic identification of optimal drug combinations for cancer treatment.
Purpose of the Study:
- To develop a novel network medicine approach for systematic in silico screening of anti-cancer drug combinations.
- To model the proximity of drug targets to disease and adverse effect genes.
- To identify effective and safe drug combinations for cancer therapy, prioritizing experimental validation.
Main Methods:
- A network medicine approach combining network propagation and gene set enrichment analysis.
- Modeling proximity of drug targets to disease-associated and adverse effect-associated genes.
- Training classifiers to distinguish effective/safe from ineffective/adverse drug combinations using curated databases (DrugComb, DrugBank).
Main Results:
- Effective drug combinations were found to enrich disease-related gene sets, while adverse combinations enriched adverse-effect gene sets.
- Classifiers successfully distinguished between drug combinations with high therapeutic effects and no known adverse reactions versus those with lower effects and potential adverse reactions across six cancer types.
- The approach was validated using in vitro screening data and clinical reports, demonstrating its robustness.
Conclusions:
- The developed network medicine framework enables rapid and robust estimation of therapeutic efficacy and adverse reaction potential for anti-cancer drug combinations.
- This approach can significantly enhance the anti-cancer drug combination identification pipeline.
- The study successfully identifies novel potential anti-cancer drug combinations for future experimental validation.
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