Literature DB >> 20183430

Design and sample size for evaluating combinations of drugs of linear and loglinear dose-response curves.

Hong-Bin Fang1, Guo-Liang Tian, Wei Li, Ming Tan.   

Abstract

The study of drug combinations has become important in drug development due to its potential for efficacy at lower, less toxic doses and the need to move new therapies rapidly into clinical trials. The goal is to identify which combinations are additive, synergistic, or antagonistic. Although there exists statistical framework for finding doses and sample sizes needed to detect departure from additivity, e.g., the power maximized F-test, different classes of drugs of different does-response shapes require different derivation for calculating sample size and finding doses. Motivated by two anticancer combination studies that we are involved with, this article proposes dose-finding and sample size method for detecting departures from additivity of two drugs with linear and log-linear single dose-response curves. The first study involves combination of two drugs, where one single drug dose-response curve is linear and the other is log-linear. The second study involves combinations of drugs whose single drug dose-response curves are linear. The experiment had been planned with the common fixed ratio design before we were consulted, but the resulting data missed the synergistic combinations. However, the experiment based on the proposed design was able to identify the synergistic combinations as anticipated. Thus we shall summarize the analysis of the data collected according to the proposed design and discuss why the commonly used fixed ratio method failed and the implications of the proposed method for other combination studies.

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Year:  2009        PMID: 20183430     DOI: 10.1080/10543400902964019

Source DB:  PubMed          Journal:  J Biopharm Stat        ISSN: 1054-3406            Impact factor:   1.051


  1 in total

1.  Nonlinear response surface in the study of interaction analysis of three combination drugs.

Authors:  Wen Wan; Xin-Yan Pei; Steven Grant; Jeffrey B Birch; Jessica Felthousen; Yun Dai; Hong-Bin Fang; Ming Tan; Shumei Sun
Journal:  Biom J       Date:  2016-05-17       Impact factor: 2.207

  1 in total

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