Open Access
Efficient experimental design and nonparametric modeling of drug interaction
Hong-Bin Fang1,Tinghui Yu1,Ming Tan1
Division of Biostatistics, Department of Epidemiology and Preventive Medicine, University of Maryland Greenebaum Cancer Center, 10 South Pine Street, MSTF Suite 261, Baltimore, MD 21201, USA
DOI: 10.2741/E88 Volume 2 Issue 1, pp.258-265
Published: 01 January 2010

The design and analysis of drug combination studies continue to be an area requiring further methodological developments. Faessel et al. (1998) studied the joint effects of the combinations of trimetrexate (TMQ) and the GARFT inhibitor AG2034 to inhibit the growth of HCT-8 human ileocecal adenocarcinoma cells. Their experiments provide a rich data resource to validate the performance of new experimental design and analysis methods for future experiments. In this paper, we first re-analyze the same data with a nonparametric model and briefly review the experimental design used in the original paper. By comparing the analysis results, we found that the fixed ratio design and the usage of the parametric model for estimating the interaction index are based on an assumption not supported by the data. We then show how the efficiency of the experiments would be improved had the maximal power experimental design based on uniform measures been used. The usage of the proposed maximal power experimental design is further supported by simulation studies.

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Hong-Bin Fang, Tinghui Yu, Ming Tan. Efficient experimental design and nonparametric modeling of drug interaction. Frontiers in Bioscience-Elite. 2010. 2(1); 258-265.