Generation and application of avatars in pharmacometric modelling

Abstract

AbstractSimulations from population models have critical applications in drug discovery and development. Avatars or digital twins, defined as individual simulations matching clinical criteria of interest compared to observations from a real subject within a predefined margin of accuracy, may be a better option for simulations performed to inform future drug development stages in cases where an adequate model is not achievable. The aim of this work was to (1) investigate methods for generating avatars with pharmacometric models, and (2) explore the properties of the generated avatars to assess the impact of the different selection settings on the number of avatars per subject, their closeness to the individual observations, and the properties of the selected samples subset from the theoretical model parameters probability density function. Avatars were generated using different combinations of nature and number of clinical criteria, accuracy of agreement, and/or number of simulations for two examples models previously published (hemato-toxicity and integrated glucose-insulin model). The avatar distribution could be used to assess the appropriateness of the models assumed parameter distribution. Similarly it could be used to assess the models ability to properly describe the trajectories of the observations. Avatars can give nuanced information regarding the ability of a model to simulate data similar to the observations both at the population and at the individual level. Further potential applications for avatars may be as a diagnostic tool, an alternative to simulations with insurance to replicate key clinical features, and as an individual measure of model fit.