Convolution-Based Approach for modeling the Paliperidone Extended Release and Long-Acting Injectable (LAI) PK of Once-, and Three-Monthly Products Administration and for Optimizing the Development of New LAI products

Abstract The aim of this paper was to develop a new modeling approach for describing the paliperidone PK resulting from the administration of extended-release once-a-day oral dose, and once- and three monthly long-acting injectable products and to compare the performances of this new approach to the traditional modeling strategy. The results of the analyses indicated that the traditional and convolution-based models showed comparable performances in the characterization of the paliperidone PK. However, the convolution-based approach showed several appealing features that justify the choice of this modeling as a preferred tool for modeling LAI products and for deploying an effective model-informed drug development process (MIDD). In particular, the convolution-based modeling can a) facilitate the development of in-vitro/in-vivo correlation, b) be used to identify formulations with optimal in vivo release properties, and c) be used for optimizing the clinical benefit of a treatment by supporting the implementation of integrated models connecting in-vitro and in-vivo drug release, in-vivo drug release to PK, and PK to PD and biomarker endpoints. A case study was presented to illustrate the benefits and the flexibility of the convolution-based modeling outcomes. The model was used to evaluate the in-vivodrug release properties associated with a hypothetical once-a-year administration of a LAI product with the assumption that the expected paliperidone exposure during a 3-year treatment overlays the exposure expected after repeated administrations of a 3-month LAI product.