A diverse stochastic search algorithm for combination therapeutics.

ABSTRACT:

Background

Design of drug combination cocktails to maximize sensitivity for individual patients presents a challenge in terms of minimizing the number of experiments to attain the desired objective. The enormous number of possible drug combinations constrains exhaustive experimentation approaches, and personal variations in genetic diseases restrict the use of prior knowledge in optimization.

Results

We present a stochastic search algorithm that consisted of a parallel experimentation phase followed by a combination of focused and diversified sequential search. We evaluated our approach on seven synthetic examples; four of them were evaluated twice with different parameters, and two biological examples of bacterial and lung cancer cell inhibition response to combination drug

SUBMITTER: Caglar MU 

PROVIDER: S-EPMC3971504 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature