Design and analysis of pre-clinical experiments using a method combining multiple comparisons and modeling techniques for dose-response studies
Abstract. Identifying and estimating the dose-response relationship between a compound and a pharmacological endpoint of interest is one of the most important and difficult goals in the preclinical stage of pharmaceutical drug development. We conduct pharmacodynamic studies to investigate the dose-response profile and then different studies to find doses that lead to desired efficacy or acceptable safety in the endpoint(s). The aim of this thesis is to provide an overview of existing techniques and design of experiments which are appropriate for addressing the goals of these studies simultaneously. We have used a method combining multiple comparisons and modeling techniques (MCPMod) in designing the experiments and found that we can reduce the required total sample size by using an optimal design. We have analysed the simulated data using MCPMod and observed that this method can be used to identify the dose-response relationship and estimate dose at a required effect. We have compared the two approaches of estimating dose and discovered that using a weighted average of all fitted models gives a similar result as compared with using the best fitted model. Finally we have investigated the possibility of identifying the presence of toxicity in response of a few or many samples at higher doses and found that we can detect toxicity if there are many samples with toxic response at a higher dose. This combined strategy is both financially and ethically rewarding as it reduces the time and cost of study and also reduces the number of animals used in pre-clinical trials.