Translational pharmacological modelling supports drug development by selecting the most promising anti-infective drug candidates, and translating drug activity, resistance emergence, and toxicodynamics from in vitro to animal infection models and ultimately humans. This quantitative modelling approach utilises insights on receptor binding and specific resistance mechanisms, thereby providing an integrated understanding of anti-infective pharmacology.
The science of pharmacometrics aims to provide a quantitative description of the time-course of drug concentrations and effects. These mathematical models establish predictive relationships for pharmacokinetic (PK), pharmacodynamic (PD), toxicodynamic (TD), pharmacogenomic (PG) and other data.
Population PK/PD/TD models describe and predict the true biological variability between patients and the impact of specific characteristics (such as body weight and renal function) on concentrations, effects and safety. These models enable the optimisation of dosage regimens to achieve a desired outcome in a particular patient or patient group.
Outcomes in chronic progressive diseases may be predicted by incorporating disease progression and placebo effects. Development of optimised sampling designs for clinical and other studies maximises the amount of knowledge that can be derived. This is particularly important when only sparse sampling designs can be applied, such as in children or critically ill patients.
D4's innovative translational models can be utilised for in vitro, animal and human studies, thereby enabling an efficient translation of research insights to improve patient outcomes. D4 researchers have expertise in a range of disease areas and have created an extensive set of tools to facilitate pharmacometric projects. Our analyses have had an impact on pre-clinical and clinical drug development, regulatory decision making by the US Food and Drug Administration, and patient treatment.