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1. Introduction
In an attempt to provide insight into the field, this review focuses on two distinct aspects of population pharmacokinetic validation: (i) what is validation; and (ii) how results from population models are validated or evaluated. We provide an overview of commonly applied techniques that perform these functions.
The question of what constitutes validation has been repeatedly posed in the literature and online discussion groups such as NMusers (Online UsersNet: http://www.cognigencorp.com/index.php/cognigen/resources_nonmem), but there is no consensus answer. There is debate as to which term should be used, with some favouring evaluation, qualification or validation. Other terms found in the literature include accreditation and credible model.[1] In general, the approach has been dependent upon the model, how results will be applied and the investigators.
The second question of what does validation provide for those who use the model is equally important. This can be answered from the aspect of the model itself in which validation is an attempt to quantify how accurate and reproducible the model is and perhaps under what circumstances it is applicable. For the researcher, model validation provides insight into the modelled system and its inherent limitations that will require new approaches or additional data to overcome. For the clinician who takes the time to understand the model, it provides a sense of how comfortable they should be with the predictions made by the model, prior to applying the results to their patients.
Two main guidelines that help define model validation/evaluation for population pharmacokinetic modelling are the US FDA Guidance for Industry: Population Pharmacokinetics[2] and European Medicines Agency (EMA) Guideline on Reporting the Results of Population Pharmacokinetic Analyses.[3] The FDA guideline uses the term 'model validation' and describes the objective of examining if the model is a good description of the validation dataset, with regards to behaviour of the model and the application proposed.[2] Similarly, the EMA guideline uses the term 'model evaluation' and outlines that this should demonstrate the final model is robust and a good description of the data; therefore, the objective of the analysis can be met.[3] A guide for reporting results of population pharmacokinetic analyses by Wade et al.[4] was used as the basis for the EMA guidelines.[3]
Various methods have been proposed to validate or evaluate...