title: 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 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.
authors.affiliation: Department of Physics, Texas Tech University, P.O. Box 41051, Lubbock, TX 79409, USA ; Department of Electrical and Computer Engineering, Texas Tech University, P.O. Box 43102, Lubbock, TX 79409, USA.
authors.forename: Mehmet Umut
authors.initials: MU
authors.lastname: Caglar
authors.affiliation: Department of Electrical and Computer Engineering, Texas Tech University, P.O. Box 43102, Lubbock, TX 79409, USA.
authors.forename: Ranadip
authors.identifier: 0000-0002-9311-5901
authors.initials: R
authors.lastname: Pal
mesh_terms: D000465:Algorithms; D000971:Antineoplastic Combined Chemotherapy Protocols / Q000008:administration & dosage; D004359:Drug Therapy, Combination; D006801:Humans; D008175:Lung Neoplasms / Q000188:drug therapy*; D008962:Models, Theoretical
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