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Published on: August 16, 2020
Matching anticancer compounds and tumor cell lines by neural networks with ranking loss
Abstract:
Computational drug sensitivity models have the potential to improve therapeutic outcomes by identifying targeted drug components that are likely to achieve the highest efficacy for a cancer cell line at hand at a therapeutic dose. State of the art drug sensitivity models use regression techniques to predict the inhibitory concentration of a drug for a tumor cell line. This regression objective is not directly aligned with either of these principal goals of drug sensitivity models: We argue that drug sensitivity modeling should be seen as a ranking problem with an optimization criterion that quantifies a drug's inhibitory capacity for the cancer cell line at hand relative to its toxicity for healthy cells. We derive an extension to the well-established drug sensitivity regression model PaccMann that employs a ranking loss and focuses on the ratio of inhibitory concentration and therapeutic dosage range. We find that the ranking extension significantly enhances the model's capability to identify the most effective anticancer drugs for unseen tumor cell profiles based in on in-vitro data.
Insights
This study redefines computational drug sensitivity modeling as a ranking problem, not just regression. The enhanced PaccMann model effectively identifies potent anticancer drugs by considering efficacy relative to toxicity.
Area of Science:
- Computational biology
- Pharmacology
- Bioinformatics
Background:
- Current drug sensitivity models use regression to predict drug inhibitory concentration.
- This regression objective does not fully align with optimizing therapeutic outcomes by balancing efficacy and toxicity.
- Identifying effective anticancer drugs requires considering both tumor inhibition and healthy cell toxicity.
Purpose of the Study:
- To reformulate drug sensitivity modeling as a ranking problem.
- To develop an extension of the PaccMann model using a ranking loss function.
- To optimize the selection of anticancer drugs based on efficacy and therapeutic dosage range.
Main Methods:
- Derived an extension to the PaccMann regression model.
- Implemented a ranking loss function.
- Focused on the ratio of inhibitory concentration to therapeutic dosage range.
Main Results:
- The ranking extension significantly improved the model's ability to identify effective anticancer drugs.
- The enhanced model demonstrated superior performance on unseen tumor cell profiles.
- Results were validated using in-vitro data.
Conclusions:
- Drug sensitivity modeling should be approached as a ranking problem for improved therapeutic outcomes.
- The extended PaccMann model with a ranking loss enhances the identification of optimal anticancer drugs.
- This approach offers a more effective strategy for personalized cancer therapy selection.
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