Improvement of a 1D Population Balance Model for Twin-Screw Wet Granulation by Using Identifiability Analysis.

Ana Alejandra Barrera Jiménez,Thomas De Beer,Michiel Peeters,Ingmar Nopens,Daan Van Hauwermeiren

doi:10.3390/pharmaceutics13050692

Ana Alejandra Barrera Jiménez, Thomas De Beer + Show 3 more

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https://doi.org/10.3390/pharmaceutics13050692

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Journal: Pharmaceutics	Publication Date: May 11, 2021
Citations: 14	License type: CC BY 4.0

Affiliation: Ghent University

Abstract

Recently, the pharmaceutical industry has undergone changes in the production of solid oral dosages from traditional inefficient and expensive batch production to continuous manufacturing. The latest advancements include increased use of continuous twin-screw wet granulation and application of advanced modeling tools such as Population Balance Models (PBMs). However, improved understanding of the physical process within the granulator and improvement of current population balance models are necessary for the continuous production process to be successful in practice. In this study, an existing compartmental one-dimensional PBM of a twin-screw granulation process was improved by altering the original aggregation kernel in the wetting zone as a result of an identifiability analysis. In addition, a strategy was successfully applied to reduce the number of model parameters to be calibrated in both the wetting zone and kneading zones. It was found that the new aggregation kernel in the wetting zone is capable of reproducing the particle size distribution that is experimentally observed at different process conditions as well as different types of formulations, varying in hydrophilicity and API concentration. Finally, it was observed that model parameters could be linked not only to the material properties but also to the liquid to solid ratio, paving the way to create a generic PBM to predict the particle size distribution of a new formulation.

Highlights

Introduction to IdentifiabilityIn the work of [28], Chapter 7 on loss functions for Population Balance Models (PBMs), it was observed that some parameters of the model described in the previous section have flat regions in the loss function that could not be attributed to the behavior of the loss function itself
In order to study the influence of the granulation parameters such as screw speed, material throughput and liquid-to-solid (L/S) ratio, in addition to the effect of the nature of the active pharmaceutical ingredient (API), on the size distribution of the resulting granules, a five-level central composite design of experiments (DoE) was performed
In the work of [28], Chapter 7 on loss functions for PBMs, it was observed that some parameters of the model described in the previous section have flat regions in the loss function that could not be attributed to the behavior of the loss function itself

Summary

Introduction

In the work of [28], Chapter 7 on loss functions for PBMs, it was observed that some parameters of the model described in the previous section have flat regions in the loss function that could not be attributed to the behavior of the loss function itself This is an issue for interpretability: if a simulated PSD can be attained by a range of parameter values, what is the physical meaning of this parameter? A model is structurally identifiable if it is possible to determine the values of its parameters from observations of its outputs and knowledge of its dynamic Equations [29]. In light of this work, we have measurements of PSDs and we know the system equations, i.e., a PBM

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