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arXiv · 0803.1819

Search algorithms as a framework for the optimization of drug combinations

Diego Calzolari·Stefania Bruschi·Laurence Coquin·Jennifer Schofield·Jacob Feala·John C. Reed·Andrew D. McCulloch·Giovanni Paternostro
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Abstract

Combination therapies are often needed for effective clinical outcomes in the management of complex diseases, but presently they are generally based on empirical clinical experience. Here we suggest a novel application of search algorithms, originally developed for digital communication, modified to optimize combinations of therapeutic interventions. In biological experiments measuring the restoration of the decline with age in heart function and exercise capacity in Drosophila melanogaster, we found that search algorithms correctly identified optimal combinations of four drugs with only one third of the tests performed in a fully factorial search. In experiments identifying combinations of three doses of up to six drugs for selective killing of human cancer cells, search algorithms resulted in a highly significant enrichment of selective combinations compared with random searches. In simulations using a network model of cell death, we found that the search algorithms identified the optimal combinations of 6-9 interventions in 80-90% of tests, compared with 15-30% for an equivalent random search. These findings suggest that modified search algorithms from information theory have the potential to enhance the discovery of novel therapeutic drug combinations. This report also helps to frame a biomedical problem that will benefit from an interdisciplinary effort and suggests a general strategy for its solution.

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Diego Calzolari, Stefania Bruschi, Laurence Coquin, Jennifer Schofield, Jacob Feala, John C. Reed, Andrew D. McCulloch, Giovanni Paternostro. 2008-10-13. Search algorithms as a framework for the optimization of drug combinations. https://doi.org/10.1371/journal.pcbi.1000249

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