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Drug Combination Studies, Uniform Experimental Design and Extensions

In: Contemporary Experimental Design, Multivariate Analysis and Data Mining

Author

Listed:
  • Ming T. Tan

    (Georgetown University Medical Center, Department of Biostatistics, Bioinformatics and Biomathematics)

  • Hong-Bin Fang

    (Georgetown University Medical Center, Department of Biostatistics, Bioinformatics and Biomathematics)

Abstract

Drug combination has been an important therapeutic development approach for cancer, viral or microbial infections, and other diseases involving complex biological networks. Synergistic drug combinations, which are more effective than predicted from summing effects of individual drugs, often achieve improved therapeutic index. Because drug-effect is dose-dependent, multiple doses of an individual drug need to be evaluated, giving rapidly escalating number of combinations and a challenging high dimensional statistical modeling problem. The lack of proper design and analysis methods for multi-drug combination studies have resulted in many missed therapeutic opportunities. It is known that, in the presence of model uncertainties, uniform measures that scatter the design points (the dose levels) uniformly in the experiment domain is the best strategy to yield maximum information on the dose response relation. This chapter will review some efficient experimental designs for drug combination studies especially those related to uniform measures and extensions using maximum entropy design.

Suggested Citation

  • Ming T. Tan & Hong-Bin Fang, 2020. "Drug Combination Studies, Uniform Experimental Design and Extensions," Springer Books, in: Jianqing Fan & Jianxin Pan (ed.), Contemporary Experimental Design, Multivariate Analysis and Data Mining, chapter 0, pages 127-144, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-46161-4_8
    DOI: 10.1007/978-3-030-46161-4_8
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