Abstract
Combination therapy is increasingly central to modern medicine. Yet reliable analysis of combination studies remains an open challenge. Previous work suggests that common methods of combination analysis are too susceptible to noise to support robust scientific conclusions. In this paper, we use simulated and real-world combination datasets to demonstrate that traditional index methods are unstable and biased by pharmacological and experimental conditions, whereas response-surface approaches such as the BRAID method are more consistent and unbiased. Using a publicly-available data set, we show that BRAID more accurately captures variations in compound mechanism of action, and is therefore better able to discriminate between synergistic, antagonistic, and additive interactions. Finally, we applied BRAID analysis to identify a clear pattern of consistently enhanced AKT sensitivity in a subset of cancer cell lines, and a far richer array of PARP inhibitor combination therapies for BRCA1-deficient cancers than would be identified by traditional synergy analysis.
Highlights
Combination therapy is increasingly central to modern medicine
Using the index of achievable efficacy (IAE), we identify a subset of cancer cell lines which are selectively sensitive to AKT inhibition in a manner that is independent of sensitivity to inhibition of the PI3K/
We show that combinations involving Niraparib exhibit a range of changes in synergy in response to a loss of BRCA1 function; more importantly, these changes in synergy are largely uncorrelated with changes in combined efficacy as measured by IAE, revealing several combinations that are selectively more potent in a BRCA1 deficient cell-line despite a relative lack of synergy
Summary
Combination therapy is increasingly central to modern medicine. Yet reliable analysis of combination studies remains an open challenge. Quantitative evaluation of combined action is notoriously challenging, both experimentally and analytically, requiring significantly more measurements than single-agent dose-response evaluation, and with no widely accepted general model analogous to the Hill equation for single agents. The high demand for such methods explain why the two papers describing the two most popular combination evaluation methods – the Combination Index[14] (‘CI’) and Bliss independence15 – have over 5100 citations between them as of this writing (CI appears under other names, including the interaction index or sum of FICs16) These methods are extraordinarily common, being used in hundreds of papers each year; yet we have been unable to find a systematic evaluation of these methods under a range of simulated and real-world experimental and pharmacological conditions. Using a combination of simulated response surfaces and real-world large-scale
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