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Combination therapies are often needed for effective clinical outcomes in themanagement of complex diseases, but presently they are generally based onempirical clinical experience. Here we suggest a novel application of searchalgorithms, originally developed for digital communication, modified tooptimize combinations of therapeutic interventions. In biological experimentsmeasuring the restoration of the decline with age in heart function andexercise capacity in Drosophila melanogaster, we found that search algorithmscorrectly identified optimal combinations of four drugs with only one third ofthe tests performed in a fully factorial search. In experiments identifyingcombinations of three doses of up to six drugs for selective killing of humancancer cells, search algorithms resulted in a highly significant enrichment ofselective combinations compared with random searches. In simulations using anetwork model of cell death, we found that the search algorithms identified theoptimal combinations of 6-9 interventions in 80-90% of tests, compared with15-30% for an equivalent random search. These findings suggest that modifiedsearch algorithms from information theory have the potential to enhance thediscovery of novel therapeutic drug combinations. This report also helps toframe a biomedical problem that will benefit from an interdisciplinary effortand suggests a general strategy for its solution.

Search algorithms as a framework for the optimization of drug combinations