Prediction of drug cocktail effects when the number of measurements is limited
Anat Zimmer, Avichai Tendler, Itay Katzir, Avi Mayo, Uri S. Alon · PLoS Biology · 2017
Cocktails of drugs can be more effective than single drugs, because they can potentially work at lower doses and avoid resistance.However, it is impossible to test all drug cocktails drawn from a large set of drugs because of the huge number of combinations.To overcome this combinatorial explosion problem, one can sample a relatively small number of combinations and use a model to predict the rest.Recently, Zimmer and Katzir et al. presented a model that accurately predicted the effects of cocktails at all doses based on measuring pairs of drugs.This model requires measuring each pair at several different doses and uses interpolation to reduce experimental noise.However, often, it is not possible to measure each pair at multiple doses (for example, in scarce patient-derived tumor material or in large screens).Here, we ask whether measurements at only a single dose can also predict highorder drug cocktails.To address this, we present a fully factorial experimental dataset on all drug cocktails built of 6 chemotherapy drugs on 2 cancer cell lines.We develop a formula that uses only pair measurements at a single dose to predict much of the variation up to 6drug cocktails in the present data, outperforming commonly used Bliss independence and regression approaches.This model, called the pairs model, is an extension of the Bliss independence model to pairs: For M drugs, it equals the product of all pair effects to the power 1/ (M-1).The pairs model also shows good agreement with previously published data on antibiotic triplets and quadruplets.The present model can only predict combinations at the same doses in which the pairs were measured and is not able to predict effects at other doses.This study indicates that pair-based approaches might be able to usefully predict and prioritize high-order combinations, even in large screens or when material for testing is limited. Author summaryDrug cocktails are a promising strategy for diseases such as cancer and infections, because cocktails can be more effective than individual drugs and can overcome problems of drug resistance.However, finding the best cocktail comprising a given set of drugs is challenging because the number of experiments needed is huge and grows exponentially with the number of drugs.This problem is exacerbated when the experiments are expensive and the material for testing is rare.Here, we present a way to address this challenge using a