Using response surface models to analyze drug combinations

•Index methods for identifying synergy produce structured patterns of bias.•Response surface methods (RSMs) more reliably identify synergy and antagonism.•RSMs can quantify two-drug therapeutic windows.•Discrete and probabilistic endpoints can be evaluated using RSMs.•RSMs can be extended to triplet...

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Bibliographic Details
Published in:Drug discovery today 2021-08, Vol.26 (8), p.2014-2024
Main Authors: Twarog, Nathaniel R., Martinez, Nancy E., Gartrell, Jessica, Xie, Jia, Tinkle, Christopher L., Shelat, Anang A.
Format: Article
Language:English
Subjects:
Bias
Combination therapy
Drug Development - methods
Drug Interactions
Drug Synergism
Drug Therapy, Combination
Humans
Models, Theoretical
Response surface models
Synergy
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Summary:•Index methods for identifying synergy produce structured patterns of bias.•Response surface methods (RSMs) more reliably identify synergy and antagonism.•RSMs can quantify two-drug therapeutic windows.•Discrete and probabilistic endpoints can be evaluated using RSMs.•RSMs can be extended to triplet combinations and atypical drug combination responses. Quantitative evaluation of how drugs combine to elicit a biological response is crucial for drug development. Evaluations of drug combinations are often performed using index-based methods, which are known to be biased and unstable. We examine how these methods can produce misleadingly structured patterns of bias, leading to erroneous judgments of synergy or antagonism. By contrast, response surface models are less prone to these defects and can be applied to a wide range of data that have appeared in recent literature, including the measurement of combination therapeutic windows and the analysis of discrete experimental measures, three-way drug combinations, and atypical response behaviors.
ISSN:1359-6446
1878-5832
DOI:10.1016/j.drudis.2021.06.002