Model predictive in vitro dissolution testing in pharmaceutical Continuous Manufacturing: an equivalence study

Abstract

A predictive mathematical model for tablet dissolution was developed and implemented in an end-to-end integrated continuous manufacturing pilot plant. The tablets were produced for immediate release with a proprietary Extrusion-Molding-Coating (EMC) unit operation. The model development considered the unique dissolution mechanism of the tablets, which was mainly controlled by the swelling and erosion of the excipient matrix. The mechanistic model consisted of the dissolution, diffusion, and population balance of API particles in the swollen matrix, as well as the mass balance of API solute in the buffer solution.
API particle size distribution by laser diffraction and tablet dosage strength by near infrared spectroscopy were imported in real time as model inputs. An equivalence study was demonstrated by comparing the model prediction to the experiments that were conducted according to USP42-NF37 General Chapter <711> Dissolution. Concluding remarks and industrial perspectives on model predictive in vitro dissolution testing are provided.

Introduction

Pharmaceutical tablets are the most common solid oral dosage form. They typically contain several types of excipients in the formulation to achieve the desired fill weight of a dosage, to improve the processability, and to affect the drug release behavior in the body. With different excipient formulations and/or operating conditions, important mechanisms, such as disintegration and dissolution in physiological fluids, may be affected. During drug product development and manufacturing, in vitro dissolution testing plays an important role to characterize drug release profile (Shrivas, et al., 2020). For example, in vitro dissolution testing is used routinely for batch release testing to detect target product profiles (TPPs) or critical quality attributes (CQAs) changes that affect in vivo release of the drug product. However, unlike batch manufacturing which has a clear definition of batch size and adopts a statistical sampling strategy to detect the quality variations within each batch, an end-to-end Continuous Manufacturing process maintains a state of control to produce drug substance and drug product seamlessly, indicating the potential quality variations in real time and requiring a real-time release testing (RTRT) strategy by nature (Hu, et al., 2020a; Rogers, et al., 2020). According to ICH Q8(R2) Pharmaceutical Development, RTRT is defined as “the ability to evaluate and ensure the quality of in-process and/or final product based on process data, which typically include a valid combination of measured material attributes and process controls.” Apart from assuring product quality, the RTRT approach is also aimed at reducing labor and cost-intensive off-line product release testing (Markl, et al., 2020).
In light of these points, it is of practical significance to develop a surrogate to in vitro dissolution testing for Continuous Manufacturing processes based on process measurements of critical process parameters (CPPs) and CQAs obtained from process analytical technology (PAT) tools. With this approach, in vitro drug dissolution performance can be evaluated to support an RTRT strategy.
The real-time release testing for identity, assay, and content uniformity using spectroscopy PAT tools has been broadly investigated. Conversely, tablet dissolution requires a more complicated strategy. Normally, a dissolution test requires ~30 minutes for immediate release tablets, and up to multiple hours for extended-release tablets (Nanjwade, Mhase, & Manvi, 2011). A real-time surrogate dissolution test is only possible by developing a mathematical model that can predict dissolution properties (e.g., drug release profiles) using the quality attributes of formulated ingredients and the manufacturing process parameters.
Several research efforts have been reported to predict dissolution characteristics using near infrared spectroscopy (n-IR) with multivariate models (Hernandez, et al., 2016; Otsuka, Tanabe, Osaki, Otsuka, & Ozaki, 2007; Pawar, et al., 2016), or a combination of n-IR and Raman spectroscopic measurements using artificial neural networks (Nagy, et al., 2019). Notably, AstraZeneca deployed a real-time release strategy for an immediate-release tablet manufactured by wet granulation and tablet compression in a batch processing line, wherein the dissolution properties were predicted by a multivariate regression model, based on a design of experiments (DoE) with variations in two material attributes. This was the first achieved regulatory approval of RTRT in Europe (Herkert & Zahn, 2008).
However, these early empirical dissolution models provided an incomplete process and product understanding (e.g., lacking the elucidation of the underlying mass transport mechanisms, limited generalizability when the formulation or process changes). Therefore, first-principles model-based predictive dissolution testing has been emerging rapidly over the past decade (Zaborenko, et al., 2019). By overcoming the drawbacks of empirical models, first-principles models can be generalized to certain types of tablet formulations (e.g., immediate release, extended release), or to specific manufacturing techniques, such as the Extrusion-Molding-Coating (EMC) process described in this study. With this technique, the rate-limiting step or mechanism of dissolution can be understood and characterized.
It further facilitates the development of model-based predictive in vitro dissolution testing based on chemical engineering first principles. This can support release testing by dissolution using a rational risk- and science-based manufacturing technology, quantitatively describing the relationship between formulation and dissolution performance (Su, et al., 2019a; Lee, et al., 2020). More importantly, the development of RTRT based on PAT tools, product knowledge, and process understanding provides an excellent opportunity to reduce the burden of off-line end-product testing, some of which is destructive, such as in vitro dissolution testing. A high frequency of sampling and prediction by RTRT also improves manufacturing efficiency by optimizing process operations based on real-time quality insights, such as fine-tuning process parameters based on the models underlying RTRT, and diversion of non-conforming material to waste (Sacher, et al., 2020; Huang, et al., 2020).
It is challenging to develop mathematical models that accurately describe the physics of the drug release process and reliably predict tablet disintegration, dissolution, and ultimately the drug release profile. For example, API powder is often embedded in a polymeric matrix of excipients, which swell and dissolve at a different rate than the API. Polymer swelling, erosion, and dissolution need to be considered to fully understand drug release kinetics. The performance of the excipients largely depends on their physical and chemical properties (e.g., particle size distribution, molecular weight distribution, solubility in water, hydrophilicity, permeability). However, it is not common for excipients to be as tightly controlled compared to the API. Since all models supporting real-time release are categorized as high impact models by the regulatory agencies, they may require rigorous verification and validation (ICH, 2011; Huang, et al., 2020).
In this study, a predictive mathematical model for tablet dissolution was developed and implemented in an end-to-end Integrated Continuous Manufacturing (ICM) pilot plant at CONTINUUS. The tablets were produced for immediate release with a proprietary EMC unit operation by which the API (a water-soluble hydrochloride salt) particles were dispersed in melted polymeric excipients.
The model development considered the unique dissolution mechanism of the EMC tablets, which was mainly controlled by the swelling and erosion of the polymeric matrix due to liquid penetration. Major governing equations of the mechanistic model consisted of the dissolution, diffusion, and population balance of API particles in the swollen polymeric matrix, as well as the mass balance of API solute in the buffer solution.
During in-line implementation, laser diffraction for API particle size distribution and n-IR spectroscopy for tablet dosage strength were imported as model inputs. An equivalence between the predictive in vitro dissolution and off-line testing was demonstrated by comparing the model prediction to the experimental dissolution profiles that were conducted according to USP42-NF37 General Chapter <711> Dissolution. A consistent equivalence was demonstrated during pilot-scale engineering runs of the EMC unit at CONTINUUS.
The manuscript is organized as follows. The ICM pilot plant for end-to-end Continuous Manufacturing is briefly introduced in Section 2. The development of the model-based predictive in vitro dissolution testing is described in Section 3, covering the dissolution test method, dissolution mechanism, and model verification and validation. This is followed by Section 4, Results and Discussion, which demonstrate the equivalence of the proposed model in pilot-scale engineering runs. Conclusions and perspectives are provided in the final section.

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