Search algorithms as a framework for the optimization of drug combinations

Authors:
  • DCDiego Calzolari
    Diego Calzolari
  • SBStefania Bruschi
    Stefania Bruschi
  • LCLaurence Coquin
    Laurence Coquin
  • JSJennifer Schofield
    Jennifer Schofield
  • JFJacob Feala
    Jacob Feala
  • JRJohn C. Reed
    John C. Reed
  • AMAndrew D. McCulloch
    Andrew D. McCulloch
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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 1/3 the number of tests as 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 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 could identify 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 potential to enhance the discovery of novel therapeutic combinations.

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