Search Algorithms as a Framework for the Optimization of Drug Combinations

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 using 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.

Optimization of drug combinations using Feedback System Control

“…In a clinical setting, drug combinations are still mainly selected on the basis of empirical approaches, which are largely guided by clinical experience with individually administered drugs 31,32 . However, many optimization methods have been and are being evaluated preclinically 29,33,34 . They can be classified as follows:…”

“…(iv) A biological search algorithm. In this case, model-based predictions of effective perturbations are combined with a closed-loop iterative experimental search 34 .…”

In-Silico Methodologies for Cancer Multidrug Optimization

“…In Calzolari et al [106], the space of drug combinations is represented by a tree. Single drugs are at the bottom of the tree and the combinations are at the top.…”

Delivering an Automated and Integrated Approach to Combination Screening Using Acoustic-Droplet Technology

“…For example, a study of 50 different drugs, each tested pairwise with three possible doses, requires a minimum of 11,175 experiments. Researchers have aimed to address this challenge using in silico approaches, [10][11][12] closed-loop optimization studies, [13][14][15] and medium-throughput screening. [16][17][18][19][20][21] Challenges exist in the efficient implementation of such a capability within early drug discovery, where there is a need to translate complex drug-combination study designs into the required contents for assay plates and in some cases for medium-throughput screens.…”