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Published on: June 21, 2018
Drug combinations screening using a Bayesian ranking approach based on dose-response models
Luana Boumendil1, Morgane Fontaine2, Vincent Lévy1,3
1Université Paris Cité, INSERM U1153, Team ECSTRRA, Paris, France.
Abstract:
Drug combinations have been of increasing interest in recent years for the treatment of complex diseases such as cancer, as they could reduce the risk of drug resistance. Moreover, in oncology, combining drugs may allow tackling tumor heterogeneity. Identifying potent combinations can be an arduous task since exploring the full dose-response matrix of candidate combinations over a large number of drugs is costly and sometimes unfeasible, as the quantity of available biological material is limited and may vary across patients. Our objective was to develop a rank-based screening approach for drug combinations in the setting of limited biological resources. A hierarchical Bayesian 4-parameter log-logistic (4PLL) model was used to estimate dose-response curves of dose-candidate combinations based on a parsimonious experimental design. We computed various activity ranking metrics, such as the area under the dose-response curve and Bliss synergy score, and we used the posterior distributions of ranks and the surface under the cumulative ranking curve to obtain a comprehensive final ranking of combinations. Based on simulations, our proposed method achieved good operating characteristics to identifying the most promising treatments in various scenarios with limited sample sizes and interpatient variability. We illustrate the proposed approach on real data from a combination screening experiment in acute myeloid leukemia.
Insights
This study introduces a novel rank-based screening method for identifying effective drug combinations, even with limited biological resources. The approach efficiently ranks potential cancer treatments, addressing challenges in dose-response analysis.
Area of Science:
- Pharmacology
- Biostatistics
- Oncology
Background:
- Drug combinations are crucial for treating complex diseases like cancer, potentially reducing drug resistance and addressing tumor heterogeneity.
- Identifying optimal drug combinations is challenging due to high costs, limited biological material, and patient variability.
- Existing methods struggle with resource constraints in screening numerous drug combinations.
Purpose of the Study:
- To develop a rank-based screening approach for identifying potent drug combinations under limited biological resource conditions.
- To establish a robust method for ranking drug combinations using a hierarchical Bayesian model and activity metrics.
- To address the challenges of cost and sample limitations in drug combination screening.
Main Methods:
- Utilized a hierarchical Bayesian 4-parameter log-logistic (4PLL) model to estimate dose-response curves.
- Employed a parsimonious experimental design suitable for limited biological samples.
- Computed activity ranking metrics including area under the dose-response curve and Bliss synergy score.
- Incorporated posterior rank distributions and surface under the cumulative ranking curve for comprehensive ranking.
Main Results:
- The proposed rank-based screening method demonstrated good operating characteristics in simulations.
- The approach effectively identified promising treatments across various scenarios, including limited sample sizes and interpatient variability.
- The method was successfully illustrated using real data from an acute myeloid leukemia combination screening experiment.
Conclusions:
- The developed rank-based approach provides an efficient strategy for drug combination screening with limited resources.
- This method offers a reliable way to rank potential drug combinations, aiding in the discovery of novel cancer therapies.
- The approach is applicable to various settings with constraints on biological material and patient data, as shown in acute myeloid leukemia research.
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