[Frontiers in Bioscience E2, 258-265, January 1, 2010]

Efficient experiment design and nonparametric modeling of drug interaction

Hong-Bin Fang1, Tinghui Yu1,2, Ming Tan1

1Division of Biostatistics, University of Maryland Greenebaum Cancer Center and Department of Epidemiology and Preventive Medicine, 10 South Pine Street, MSTF Suite 261, Baltimore, MD 21201, 2Department of Mathematics, University of Maryland, College Park, MD 20742

TABLE OF CONTENTS

1. Abstract
2. Introduction
2. Introduction
3. Nonparametric Estimation of the Interaction Index Surface
4. Performance of the Maximal Power Design
5. Discussion
6. Acknowledgement
7. References

1. ABSTRACT

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.