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Design and Statistical Analysis of Multidrug Combinations in Preclinical Studies and Phase I Clinical Trials

  • Ming T. TanEmail author
  • Hong-Bin Fang
  • Hengzhen Huang
  • Yang Yang
Conference paper
Part of the ICSA Book Series in Statistics book series (ICSABSS)

Abstract

Multidrug combination is an important therapeutic approach for cancer, viral or microbial infections, hypertension and other diseases involving complex biological networks. Synergistic drug combinations, which are more effective than predicted from summing effects of individual drugs, often achieve increased therapeutic index. Because drug-effect is dose-dependent, multiple doses of an individual drug need to be examined, yielding rapidly increasing 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. Although systems biology holds the promise to unveil complex interactions within biological systems, the knowledge on network remains predominantly topological until very recently. This article summarizes recent work on efficient maximal power experimental designs on multi-drug combinations, and statistical modeling of the resulting data. The design and analysis of vorinostat and cytarabine combination study is presented to illustrate the approach. We then introduce a model based adaptive Bayesian phase I trial design for drug combinations utilizing the modeling concept. To tackle the challenging problem of combinations of more than three drugs, we present a novel two-stage procedure starting with an initial selection by utilizing an in silico model built upon experimental data of single drugs and current systems biology information to obtain maximum likelihood estimate.

Keywords

Clinical trial design Biological networks Drug combinations Experimental design Maximal Power design Statistical modeling Synergy analysis 

Notes

Acknowledgements

The research of Drs. Fang, Huang and Tan is partially supported by the National Cancer Institute (NCI) grant R01CA164717. Dr Yang’s research was completed and partially supported by University of Maryland while she was a PhD student there.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Ming T. Tan
    • 1
    Email author
  • Hong-Bin Fang
    • 1
  • Hengzhen Huang
    • 1
  • Yang Yang
    • 2
  1. 1.Department of Biostatistics, Bioinformatics and BiomathematicsGeorgetown University Medical CenterWashington, DCUSA
  2. 2.Division of Biometrics 1, Office of BiostatisticsCenter for Drug Evaluation and Research, US Food and Drug AdministrationSilver SpringUSA

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