Optimisation du processus de développement du médicament grâce à la modélisation PK et les simulations d’études cliniques
Drug development is complex with results often differing from those anticipated or sought after.  Despite technological advances in the many fields which are a part of drug development, there are still many drugs that fail in the late stages of clinical development.  Indeed, the success rate of drugs reaching commercialization is declining.  Improvements to the conventional drug process are therefore required in order to facilitate development and allow new medications to be provided more rapidly to patients who require them.  The aim of this Ph.D. project was to explore and propose ways to improve this inefficient drug development process with the use of advanced modeling and clinical trial simulations. For the first part of this research, new algorithms available in ADAPT 5® were tested against other available algorithms in order to determine their potential strengths and weaknesses.  The two new algorithms tested were the iterative two-stage and the maximum likelihood expectation maximization (MLEM) methods.  Our results demonstrated that the MLEM algorithm was consistently better than the iterative two-stage algorithm.  It was also comparable with the first order conditional estimate method available in NONMEM®, with significantly fewer shrinkage issues in the estimation of the variances.  Therefore, these new tools were used for the clinical trial simulations performed during the course of this Ph.D. research. In order to calculate appropriate noncompartmental pharmacokinetic parameter estimates during the drug development process, it is essential that the terminal elimination half-life be well characterized.  Properly conducted and analyzed pharmacokinetic studies are essential to any drug development plan, and even more so for generic and supergeneric (a formulation similar to the reference product, containing the same active ingredient; however differing from the original reference product it its delivery process) submission where they often are the only pivotal studies that need to be done to decide if a drug product is good or not.  Thus, the purpose of the second part of the research was to determine if the pharmacokinetic (PK) parameters obtained from a subject whose half-life is calculated from a sampling scheme duration that is considered too short could bias the bioequivalence conclusions of a study and if these parameters should be removed from statistical analyses.  Results demonstrated that subjects with a long half-life relative to the duration of the sampling scheme negatively influenced results when these were maintained in the analysis of variance.  Therefore, these subjects should be removed from the analyses and guidelines to this effect are required a priori.  Pivotal pharmacokinetic studies done within the drug development process should therefore follow this recommendation to make sure that the right decision be taken on a drug product formulation.  This information was utilized with the clinical trial simulations that were subsequently performed in this research in order to ensure the most accurate conclusions. Finally, clinical trial simulations were used to improve the development process of a nonsteroidal anti-inflammatory drug.  A supergeneric was being developed and results from a pilot study were promising.  However, some results from the pilot study required closer attention to determine if the test and reference compounds were indeed equivalent and if the test compound would meet the equivalence criteria of the different required pivotal studies.  Clinical trial simulations were therefore undertaken to address the multiple questions left unanswered by the pilot study and these suggested that the test compound would probably not meet the equivalence criteria.  In addition, these results helped determine what modifications to the test drug would be required to meet the equivalence criteria.  This research brought forward solutions to improve different aspects of the drug development process.  Notably, clinical trial simulations reduced the number of studies that would have been done for a supergeneric, decreased the number of subjects unnecessarily exposed to a drug, lowered costs and helped established new criteria for the exclusion of subjects from analyses. Research conducted during this Ph.D. provided concrete ways to improve the drug development process by evaluating some newly available tools for compartmental analyses, setting standards stipulating which estimated PK parameters should be excluded from certain PK analyses and illustrating how clinical trial simulations are useful to throughout the process.
ADAPT 5® MLEM ITS Clinical trial simulations Drug Development Half-life Iterative two-stage