Pharmacometrics for Regulatory Decision Making

As a methodology to rationalize and inform drug development, pharmacometrics (modelling and simulation) is widely appreciated by industry, academia and regulators in general. However, the integration of pharmacometric analyses into regulatory decision making is not formally established and has been the subject of discussion in many scientific and regulatory fora. The article by Lee et al. published in this issue of the journal provides instructive case studies of how pharmacometrics can be used by regulators to help decision making, interaction with companies, reviewing and labelling. Their case studies indicate that pharmacometrics is not a tool reserved only for companies for internal decision making, but also is a powerful platform that regulators may use to compile and analyse data in order to support approval and labelling. This is considered to have beneficial effects for both industry, in terms of resource optimization, and for prescribers and patients, who obtain more precise labelling instructions and optimal therapeutic interventions, respectively. The increasing impact of pharmacometrics on US FDA approval and labelling (see table I in the article by Lee et al.) indicates the success of the methodology and the need to make best use of all available data during regulatory decisionmaking, especially in controversial cases or when data are scarce (e.g. in orphan diseases or paediatrics). In Europe, modelling and simulation was identified by the European Medicines Agency (EMA) Think-Tank Group on Innovative Drug Development and Committee for Medicinal Products for Human Use (CHMP) as one of the key methodologies to overcome bottlenecks in drug development. There is no EMA guideline that generally defines how pharmacometrics should be used in regulatory decision making. Indeed, it is difficult to discuss this methodology outside a specific context (e.g. the clinical condition, feasibility of trials, availability of good biomarkers for safety and efficacy, availability of clinical efficacy and safety data from other groups, and stage of development). In general, the hurdle for regulatory acceptance ofmodelling and simulation seems lower in the exploratory phases than in the confirmatory phases of medicine development. On the basis of information compiled from various clinical efficacy/safety and methodological EMA guidelines, and discussions by the EMA Scientific Advice Working Party (SAWP) and Paediatric Committee (PDCO), we have identified the following cases to exemplify the spectrum of current thinking in the European regulatory setting. Examples where the use of modelling and simulation is well appreciated are: hypothesis generation and learning throughout drug development; use of models to minimize the burden of pharmacokinetic/ pharmacodynamic evaluations in current studies and to optimize future experiments; use of models for selection of doses to be further tested in clinical trials. Examples where the use of modelling and simulation could be accepted if properly justified are: use of models for final recommendation of intermediate doses that were not specifically tested in phase II/III trials; population pharmacokinetic analysis in phase II/III to support regulatory claims (e.g. the absence of suspected drugdrug interactions and the effect of pharmacogenetics on exposure); modelling and simulation to bridge efficacy data. Examples where the use of modelling and simulation is generally seen as controversial are: model-based inference as the ‘sole’ evidence of efficacy/ safety, notwithstanding exceptional scenarios; approval based on simulated data for efficacy and safety. An important criterion that regulators check when assessing the weight of modelling and simulation in a given submission is the quality of the exercise. The principles of transparency, traceability, parsimony, external validity and internal validity, COMMENTARY Clin Pharmacokinet 2011; 50 (10): 625-626 0312-5963/11/0010-0625/$49.95/0