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.

The aim of this paper was to develop a convolution-based 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 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 Long Acting Injectable (LAI) products and for deploying an effective model-informed drug development process. 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 vivo drug 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.

Modeling Complex Pharmacokinetics of Long-Acting Injectable Products Using Convolution-Based Models With Nonparametric Input Functions.

The interest in the development and the therapeutic use of long-acting injectable (LAI) products for chronic or long-term treatments has grown exponentially. The complexity and the multiphase drug release process represent serious issues for an effective modeling of the PK properties of LAI products. The objective of this article is to show how convolution-based models with piecewise-linear approximation of the nonlinear drug release function can provide an enhanced modeling tool for (1) characterizing the complex PK profiles of LAI formulations with completely different drug release properties, and (2) addressing key questions supporting the optimal development of LAI products by simulating the PK time course resulting from different dosing strategies. Convolution-based modeling and simulation were implemented in NONMEM, and 3 case studies were presented to assess the performances of this new modeling approach using PK data of LAI products developed using different technologies and administered using different routes: microsphere technology and aqueous nanosuspension intramuscularly administered and biodegradable polymer subcutaneously administered. The performance of the convolution-based modeling approach was compared with the performance of conventional parametric models using a reference data set on theophylline. The results of the comparison indicated that the nonparametric input function provided a more accurate description of the data either in terms of global measure of goodness of fit (ie, Akaike information criterion and Bayesian information criterion) or in terms of performance of the fitted model (ie, the percent prediction error on Cmax and AUC0-t ).

Deconvolution Analysis by Non-linear Regression Using a Convolution-Based Model: Comparison of Nonparametric and Parametric Approaches.

The convolution-based modeling approach has been shown to be flexible and easy to implement for performing a deconvolution analysis and for assessing in vitro/in vivo correlation using non-linear regression and a pre-specified model describing the in vivo drug absorption. A generalization of this method has been developed using a nonparametric description of the in vivo drug absorption process in replacement of a model-based definition. A comparison of the parametric and nonparametric deconvolution and convolution analyses was conducted on the pharmacokinetic (PK) data observed in four published studies after the administration of an extended-release formulation of methylphenidate at the dose of 18 mg. All the analyses were conducted using a conventional non-linear regression software (NONMEM). The results of the deconvolution analysis indicated that the parametric and nonparametric approaches performed similarly. The parametric approach described the input function using a double Weibull equation (6 parameters) while the nonparametric approach described the input function using a piecewise approximation (12-13 parameters). The validation of the results of the deconvolution analysis was conducted by comparing observed and predicted PK concentrations by the convolution analysis. The performance of the parametric and nonparametric approaches for assessing deconvolution was evaluated using the Akaike and the Bayesian information criteria. These criteria indicated that, despite the similar results obtained with the two approaches, the nonparametric approach provided better results. In conclusion, these results indicated that the nonparametric approach should be considered as the preferred approach for conducting a deconvolution analysis.