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BioMed Research International
Volume 2014 (2014), Article ID 873436, 9 pages
http://dx.doi.org/10.1155/2014/873436
Research Article

A Diverse Stochastic Search Algorithm for Combination Therapeutics

1Department of Physics, Texas Tech University, P.O. Box 41051, Lubbock, TX 79409, USA
2Department of Electrical and Computer Engineering, Texas Tech University, P.O. Box 43102, Lubbock, TX 79409, USA

Received 17 November 2013; Revised 20 January 2014; Accepted 6 February 2014; Published 12 March 2014

Academic Editor: Hua Xu

Copyright © 2014 Mehmet Umut Caglar and Ranadip Pal. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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 drugs. The performance of our approach as compared to recently proposed adaptive reference update approach was superior for all the examples considered, achieving an average of 45% reduction in the number of experimental iterations. Conclusions. As the results illustrate, the proposed diverse stochastic search algorithm can produce optimized combinations in relatively smaller number of iterative steps. This approach can be combined with available knowledge on the genetic makeup of the patient to design optimal selection of drug cocktails.