Experimental designs for detecting synergy and antagonism between two drugs in a pre‐clinical study. (21st March 2015)

• Record Type: Journal Article
• Authors: Sperrin, Matthew; Thygesen, Helene; Su, Ting‐Li; Harbron, Chris; Whitehead, Anne
• Is Part Of: Pharmaceutical statistics. Volume 14:Number 3(2015:May/Jun.)
• Page Start: 216
• Page End: 225
• Publication Date: 2015-03-21
• Subjects: Pharmacy -- Statistical methods -- Periodicals; Pharmacy -- Statistics -- Periodicals; 615.10727
• DOI: 10.1002/pst.1676
• Languages: English
• ISSNs: 1539-1604

Abstract

<abstract abstract-type="main" id="pst1676-abs-0001"> <title> <x xml:space="preserve">Abstract</x> </title> <p id="pst1676-para-0001">The identification of synergistic interactions between combinations of drugs is an important area within drug discovery and development. Pre‐clinically, large numbers of screening studies to identify synergistic pairs of compounds can often be ran, necessitating efficient and robust experimental designs. We consider experimental designs for detecting interaction between two drugs in a pre‐clinical <italic>in vitro</italic> assay in the presence of uncertainty of the monotherapy response. The monotherapies are assumed to follow the Hill equation with common lower and upper asymptotes, and a common variance. The optimality criterion used is the variance of the interaction parameter. We focus on ray designs and investigate two algorithms for selecting the optimum set of dose combinations. The first is a forward algorithm in which design points are added sequentially. This is found to give useful solutions in simple cases but can lack robustness when knowledge about the monotherapy parameters is insufficient. The second algorithm is a more pragmatic approach where the design points are constrained to be distributed log‐normally along the rays and monotherapy doses. We find that the pragmatic algorithm is more stable than the forward algorithm, and even when the forward algorithm has converged, the pragmatic algorithm can still out‐perform it. Practically, we find that good designs for detecting an interaction have equal numbers of points on monotherapies and combination therapies, with those points typically placed in positions where a 50% response is expected. More uncertainty in monotherapy parameters leads to an optimal design with design points that are more spread out. Copyright © 2015 John Wiley & Sons, Ltd.</p> </abstract>