Experimental designs for drug combination studies

Alex Donev (University of Manchester)

The interest in drug combinations is growing rapidly due to the opportunities they create to increase the therapeutic effect and to reduce the frequency or magnitude of undesirable side effects when single drugs fail to deliver satisfactory results. Considerable effort in studying benefits of the joint action of drugs has been matched by the development of relevant statistical methods and tools for statistical analysis of the data obtained in such studies that allow important statistical assumptions to be taken into account, i.e. the appropriate statistical model and the distribution of the response of interest (e.g. Gaussian, Binomial, Poisson).

Less attention has been given to the choice of suitable experimental designs for such studies, while only high quality data can ensure that the objectives of the studies will be fulfilled. Two approaches will be presented. One of them is using the D-optimality criterion to search for suitable designs to estimate nonlinear models for the joint drug action for each studied drug combination. The other approach aims at collecting evidence whether the joint drug action is additive, synergistic or antagonistic at chosen inhibition levels, using simple statistical analysis. Useful features of both approaches will be illustrated.

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