  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.
