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Search algorithms as a framework for the optimization of drug combinations | | Diego Calzolari
; Stefania Bruschi
; Laurence Coquin
; Jennifer Schofield
; John C. Reed
; Andrew D. McCulloch
; Giovanni Paternostro
; | | Date: |
12 Mar 2008 | | 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. | | Source: | arXiv, 0803.1819 | | Services: | Forum | Review | PDF | Favorites | |
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