Design and Statistical Analysis of Multidrug Combinations in Preclinical Studies and Phase I Clinical Trials
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.
KeywordsClinical trial design Biological networks Drug combinations Experimental design Maximal Power design Statistical modeling Synergy analysis
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.
- Berenbaum, M. C. What is Synergy? Pharmcological Reviews 41: 93–141, 1989.Google Scholar
- Fang, H.B. Huang, H. Clarke, R. and Tan, M. Predicting multi-drug inhibition interactions based on signaling networks and single drug dose–response information. Journal of Computational Systems Biology, 2, 2016. Google Scholar
- Gojo, I. Tan, M. Fang, H.B. Sadowska, M. Lapidus, R. Baer, M.R. Carrier, F. Beumer, J.H. Anyang, B.N. Srivastava, R.K. Espinoza-Delgado, I. and Ross, D.D. Translational phase I trial of Vorinostat (suberoylanilid.e hydroxamic acid) combined with cytarabine and etoposide in patients with relapsed, refractory, or high-risk acute myeloid leukemia, Clinical Cancer Research 19:1838–1851, 2013.CrossRefGoogle Scholar
- Greco, W.R. Bravo, G. and Parsons, J. C. The Search for Synergy: A Critical Review from a Response Surface Perspective. Pharmcological Reviews 47: 331–385, 1995.Google Scholar
- Loewe, S. Isobols of Dose-Effect Relations in the Combination of Pentylenetetrazole and Phenobarbital. Journal of Pharmacology and Experimental Therapeutics 114: 185–191, 1955.Google Scholar
- Pei, X.Y. Dai, Y. and Grant, S. The small-molecule Bcl-2 inhibitor HA14-1 interacts synergistically with flavopiridol to induce mitochondrial injury and apoptosis in human myeloma cells through a free radical-dependent and Jun NH2-terminal kinase-dependent mechanism. Molecular Cancer Therapeutics 3: 1513–1524, 2004.Google Scholar
- Shiozawa, K., Nakanishi, T., Tan, M., Fang, H.B., Wang, W.C., Edelman, M.J., et al. Preclinical studies of Vorinostat (suberoylanilide hydroxamic acid) combined with cytosine arabinoside and etoposide for treatment of acute leukemias. Clinical Cancer Research 15:1698–1707, 2009.CrossRefGoogle Scholar
- Weiss, J.N. The hill equation revised: uses and misuses. FASEB journal 11: 835--841, 1997.Google Scholar
- Yang, Y., Fang, H.B., Roy, A. and Tan, M. An adaptive Bayesian dose finding approach for drug combinations with drug-drug interaction. Statistics and Its Interface (in review), 2016.Google Scholar
- Yin, G. and Yuan, Y. Bayesian dose finding in oncology for drug combinations by copula regression. Journal of the Royal Statistical Society: Series C (Applied Statistics) 58(2): 211–224, 2009b.Google Scholar