Pharmacometrics: modelling and simulation tools to improve decision makingin clinical drug development

There is broad recognition within the pharmaceutical industry that the drug development process, especially the clinical part of it, needs considerabl e improvement to cope with rapid changes in research and health care enviro nments. Modelling and simulation are mathematically founded techniques that have been used extensively and for a long time in other areas than the pha rmaceutical industry (e.g. automobile, aerospace) to design and develop pro ducts more efficiently. Both modelling and simulation rely on the use of (m athematical and statistical) models which are essentially simplified descri ptions of complex systems under investigation. It has been proposed to inte grate pharmacokinetic (PK) and pharmacodynamic (PD) principles into drug de velopment to make it more rational and efficient. There is evidence from a survey on 18 development projects that a PK/PD guided approach can contribu te to streamline the drug development process. This approach extensively re lies on PK/PD models describing the relationships among dose, concentration land more generally exposure), and responses such as surrogate markers, ef ficacy measures, adverse events. Well documented empirical and physiologica lly based PK/PD models are becoming available more and more, and there are ongoing efforts to integrate models for disease progression and patient beh avior (e.g. compliance) as well. Other types of models which are becoming increasingly important are populat ion PK/PD models which, in addition to the characterization of PK and PD, i nvolve relationships between covariates (i.e. patient characteristics such as age, body weight) and PK/PD parameters. Population models allow to asses s and to quantify potential sources of variability in exposure and response in the target population, even under sparse sampling conditions. As will b e shown for an anticancer agent, implications of significant covariate effe cts can be evaluated by computer simulations using the population PK/PD mod el. Stochastic simulation is widely used as a tool for evaluation of statistica l methodology including for example the evaluation of performance of measur es for bioequivalence assessment. Recently, it was suggested to expand the use of simulations in support of clinical drug development for predicting o utcomes of planned trials. The methodological basis for this approach is pr ovided by (population) PK/PD models together with random sampling technique s. Models for disease progression and behavioral features like compliance, drop-out rates, adverse event dependent dose reductions, etc. have to be ad ded to population PK/PD models in order to mimic the real situation. It wil l be shown that computer simulation helps to evaluate consequences of desig n features on safety and efficacy assessment of the drug, enabling identifi cation of statistically valid and practically realisable study designs. For both modelling and simulation a guidance on 'best practices' is currently worked out by a panel of experts comprising representatives from academia, regulatory bodies and industry, thereby providing a necessary condition tha t model-based analysis and simulation will further contribute to streamlini ng pharmaceutical drug development processes.