Prediction of drug cocktail effects when the number of measurements is limited

PLoS Biology
Anat ZimmerUri Alon

Abstract

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 high-order 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 6-drug cocktails in the present data, outperforming commonly used B...Continue Reading

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Citations

May 6, 2019·Pharmaceutics·Vahideh Vakil, Wade Trappe
May 23, 2019·PLoS Computational Biology·Avichai TendlerUri Alon
Jan 31, 2019·PLoS Computational Biology·Itay KatzirUri Alon
May 15, 2020·PloS One·Hope MurphyHana M Dobrovolny
Apr 8, 2020·Nanoscale Horizons : the Home for Rapid Reports of Exceptional Significance in Nanoscience and Nanotechnolgy·Dean HoTheodore Kee
Dec 29, 2020·PLoS Computational Biology·Otto I PulkkinenTero Aittokallio
Jan 26, 2020·Trends in Biotechnology·Dean HoAli Zarrinpar

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Methods Mentioned

BETA
ELISA

Software Mentioned

Matlab

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