Background: The development of antiinfective drugs for neglected tropical diseases requires new integrated, methods to ensure that suitable molecules and combination regimens are selected for evaluation in early clinical trials. In fact, the identification of companion drugs for combination therapy is a key factor for combating multi-drug resistance. Although new agents are emerging, the path to registration of such regimens remains uncertain, with the lack of a strong dose rationale being one of the main hurdles. Evolving understanding of growth dynamics has provided an opportunity to characterise drug efficacy using pharmacokinetic-pharmacodynamic principles. In this project, pharmacokinetic-pharmacodynamic and disease modelling will be used as a tool to predict the dose rationale in humans and optimise clinical study design. It is anticipated that this approach will ensure accurate selection of dosing regimens and innovative study protocols.
Objectives: To develop and refine available bacterial growth dynamics models describing antibiotic effect across different nonclinical experimental protocols and evaluate the translational value of microbiological data for the prediction of treatment response in Phase II and III studies.
Methods: Data mining techniques will be used to retrieve data from available clinical trials as well as summary data from the published literature. Subsequently, population pharmacokinetic and pharmacokinetic-pharmacodynamic modelling will be applied to establish the impact of disease and other concurrent factors (covariates) on drug disposition and response.
Skills and competencies: Basic understanding of clinical pharmacokinetics, pharmacodynamics and affinity with biostatistical methods, including working knowledge of computer programming.