Reduction of Controls in Preclinical Clamp Studies using a Nonlinear Mixed-Effects Model

Specialeforsvar ved Emilie Prang Nielsen

Titel: Reduction of Controls in Preclinical Clamp Studies using a Nonlinear Mixed-Effects Model 

Abstract: This thesis examines how many control animals are really needed if we make use of historical information on past studies. First, a nonlinear logistic curve for the dose-response relationship is presented, and used as the foundation for the different modelling approaches. Then it is applied to two chosen historical studies with a focus on the results of the relative potency of the test drug under investigation (an insulin analogue) compared to the control drug, human insulin. Here we include data only from the study under investigation to clarify how the studies are normally analysed, the so-called common way, whereupon we continue by building a mixed-e ffects model only for human insulin, investigating thoroughly which fixed and random e ects as well as different transformations to include. Next, we find that the results do not change dramatically when incorporating the analogues in the model, and for the two chosen analogues analysed in depth, we find that the common and mixed-effects analysis yield similar results for the estimate of the relative potency. However, the standard errors for these decrease noticeably compared to the benchmark results from the common method, and these results are challenged further in a simulation experiment. This experiment suggests overall that by including historical information in the form of the mixed-e ects model proposed, we are able to remove at least 50% of the control rats in each of the studies looked closely upon to get the same level of uncertainty on the relative potency as in the common analysis. Thereafter, we find that this is in compliance with the theoretical foundation presented, from which we calculate an explicit reduction in the number of control rats of 61.8% and 52.5% respectively for two of the studies. Ultimately, how to incorporate the past information in the form of the mixed-e ects model is discussed, where both a meta approach as well as a Bayesian approach are suggested. The above conclusions are found to be similar for the two approaches, and therefore we can finally conclude that the inclusion of historical information is beneficial in regard to using fewer control rats, though, there is no clear answer to which approach to be preferred.

Vejledere: Susanne Ditlevsen, Søren Andersen, Novo
Censor:     Birger Stjernholm Madsen, Novozymes