Implementation of a fully integrated CM direct compression and coating process at a commercial pharmaceutical facility - Part 2: PAT and RTD results for normal operational conditions batches.

This is the second of two articles detailing the continuous manufacturing (CM) development and implementation activities for an marketed product which have been realized in novel, qualified equipment, using validated control strategy elements to enable manufacture of batches under current good manufacturing practices (cGMP) and compliant with data integrity principles. Here, the application of process analytical technologies (PAT) and automation tools on batches produced under normal operational conditions is reviewed. The results from residence time distribution (RTD) models for predicting API concentration, in-line near infrared (NIR) testing of blend uniformity (BU) and at-line NIR spectroscopy analysis of core tablet concentration and tablet identity for real-time release testing (RTRT) are discussed. The influences of process equipment and design choices on NIR and RTD model variability, as well as the use of the PAT tools for monitoring the evolving properties understanding of CM process development, such as overcoming flow instabilities, is described. Results demonstrate that the RTD and NIR models developed and validated are robust to operating conditions and are critical for assuring steady state control of the continuous manufacturing process. Finally, the NIR and RTD model lifecycle, including procedures for necessary and normal model upgrades in a cGMP production environment, are presented.

Implementation of a fully integrated continuous manufacturing line for direct compression and coating at a commercial pharmaceutical facility - Part 1: Operational considerations and control strategy.

We implement a fully integrated continuous manufacturing (CM) line for direct compression and coating of a pharmaceutical oral solid dosage form in a commercial production facility. In this first paper of a two-part series, we describe process design and operational choices made to introduce CM using infrastructure originally intended for batch operations. Consistent with lean manufacturing principles, we select equipment, facilities, and novel process analytical technologies that meet production agility goals alongside an existing batch process. Choices address process risks, are aligned with existing quality systems, yet allow exploration of CM agility benefits in commercial operations. We outline how operating procedures, control schemes, and release criteria from the historical batch process are adapted for CM with modified lot and yield definitions based on patient demand. We devise a hierarchy of complementary controls including real-time process interrogation, predictive residence time distribution models of tablet concentration, real-time product release testing using automated tablet NIR spectroscopy, active rejection and diversion, and throughput-based sampling. Results from lots produced under normal operational conditions confirm our CM process provides assurance of product quality. Qualification strategies to achieve lot size flexibility aims are also described. Finally, we consider CM extensions to formulations with differing risk profiles. Further analysis of results for lots produced under normal operational conditions is provided in part 2 (Rosas et al., 2023).

NIR spectroscopy for the in-line monitoring of a multicomponent formulation during the entire freeze-drying process.

Freeze drying is a complex, time consuming and thus expensive process, hence creating a need for understanding the material behaviour in the process environment and for process optimization. Near-infrared (NIR) spectroscopy offers the opportunity to monitor physicochemical changes of the formulation during freeze-drying. The aim of this work was to examine whether NIR spectroscopy allows in-line monitoring of all components during the entire freeze-drying process of a multi-component pharmaceutical formulation (a solution of fenofibrate and mannitol in a mixture of tertiary-butyl alcohol, and water). To extract useful information of all components in the formulation from the large multivariate data-sets obtained during in-line spectroscopic monitoring, several spectral pre-processing techniques and spectral data analysis techniques such as the mean of selected wavenumbers (Mws), the correlation coefficient (CorrCoef) and principal component analysis (PCA) have been evaluated and compared. To find out whether these chemometric techniques are also able to differentiate between changes in the process settings influencing the freeze-drying process of the formulation, freeze-drying processes were performed at four different conditions. Results demonstrated that in-line measurements using NIR spectroscopy were possible in an icy environment and that a further process understanding could be obtained. Data-analysis revealed the crystallization behaviour of each of the four components. In addition, using the three pre-processing techniques allowed observe the sublimation of the solvents. Mws and CorrCoef have proven to be adequate methods for monitoring the main physicochemical changes of product during the processes; this affirmation was confirmed by observing the outputs of PCA for entire processes.

A criterion for assessing homogeneity distribution in hyperspectral images. Part 2: application of homogeneity indices to solid pharmaceutical dosage forms.

This article is the second of a series of two articles detailing the application of mixing index to assess homogeneity distribution in oral pharmaceutical solid dosage forms by image analysis. Chemical imaging (CI) is an emerging technique integrating conventional imaging and spectroscopic techniques with a view to obtaining spatial and spectral information from a sample. Near infrared chemical imaging (NIR-CI) has proved an excellent analytical tool for extracting high-quality information from sample surfaces. The primary objective of this second part was to demonstrate that the approach developed in the first part could be successfully applied to near infrared hyperspectral images of oral pharmaceutical solid dosage forms such as coated, uncoated and effervescent tablets, as well as to powder blends. To this end, we assessed a new criterion for establishing mixing homogeneity by using four different methods based on a three-dimensional (M×N×λ) data array of hyperspectral images (spectral standard deviations and correlation coefficients) or a two-dimensional (M×N) data array (concentration maps and binary images). The four methods were used applying macropixel analysis to the Poole (M(P)) and homogeneity (H%(Poole)) indices. Both indices proved useful for assessing the degree of homogeneity of pharmaceutical samples. The results testify that the proposed approach can be effectively used in the pharmaceutical industry, in the finished products (e.g., tablets) and in mixing unit operations for example, as a process analytical technology tool for the blending monitoring (see part 1).

A criterion for assessing homogeneity distribution in hyperspectral images. Part 1: homogeneity index bases and blending processes.

The Process Analytical Technologies (PAT) initiative of the US Food and Drug Administration (US FDA) has established a framework for the development of imaging techniques to determine the real-time distribution of mixture components during the production of solid dosage forms. This study, which is the first in a series of two parts, uses existing mixing indices and a new criterion called the "percentage of homogeneity" (H%) to assess image homogeneity. Image analysis techniques use feature extraction procedures to extract information from images subjected to treatments including colour segmentation and binarization. The surface distribution of components was determined by macropixel analysis, which splits an image into non-overlapping blocks of a preset size and calculates several statistical parameters for the resulting divisional structure. Such parameters were used to compute mixing indices. In this work, we explored the potential of image processing in combination with mixing indices and H% for assessing blending end-point and component distribution on images. As a simplified test, an arrangement of binary and ternary systems of coloured particles was mixed collecting at-line multispectral (MSI) and non-invasive RGB pictures at preset intervals.

Real-time determination of critical quality attributes using near-infrared spectroscopy: a contribution for Process Analytical Technology (PAT).

Process Analytical Technology (PAT) is playing a central role in current regulations on pharmaceutical production processes. Proper understanding of all operations and variables connecting the raw materials to end products is one of the keys to ensuring quality of the products and continuous improvement in their production. Near infrared spectroscopy (NIRS) has been successfully used to develop faster and non-invasive quantitative methods for real-time predicting critical quality attributes (CQA) of pharmaceutical granulates (API content, pH, moisture, flowability, angle of repose and particle size). NIR spectra have been acquired from the bin blender after granulation process in a non-classified area without the need of sample withdrawal. The methodology used for data acquisition, calibration modelling and method application in this context is relatively inexpensive and can be easily implemented by most pharmaceutical laboratories. For this purpose, Partial Least-Squares (PLS) algorithm was used to calculate multivariate calibration models, that provided acceptable Root Mean Square Error of Predictions (RMSEP) values (RMSEP(API)=1.0 mg/g; RMSEP(pH)=0.1; RMSEP(Moisture)=0.1%; RMSEP(Flowability)=0.6 g/s; RMSEP(Angle of repose)=1.7° and RMSEP(Particle size)=2.5%) that allowed the application for routine analyses of production batches. The proposed method affords quality assessment of end products and the determination of important parameters with a view to understanding production processes used by the pharmaceutical industry. As shown here, the NIRS technique is a highly suitable tool for Process Analytical Technologies.

Quality by design approach of a pharmaceutical gel manufacturing process, part 1: determination of the design space.

This work was conducted in the framework of a quality by design project involving the production of a pharmaceutical gel. Preliminary work included the identification of the quality target product profiles (QTPPs) from historical values for previously manufactured batches, as well as the critical quality attributes for the process (viscosity and pH), which were used to construct a D-optimal experimental design. The experimental design comprised 13 gel batches, three of which were replicates at the domain center intended to assess the reproducibility of the target process. The viscosity and pH models established exhibited very high linearity and negligible lack of fit (LOF). Thus, R(2) was 0.996 for viscosity and 0.975 for pH, and LOF was 0.53 for the former parameter and 0.84 for the latter. The process proved reproducible at the domain center. Water content and temperature were the most influential factors for viscosity, and water content and acid neutralized fraction were the most influential factors for pH. A desirability function was used to find the best compromise to optimize the QTPPs. The body of information was used to identify and define the design space for the process. A model capable of combining the two response variables into a single one was constructed to facilitate monitoring of the process.

Quality by design approach of a pharmaceutical gel manufacturing process, part 2: near infrared monitoring of composition and physical parameters.

We applied the principles of quality by design to the production process of a pharmaceutical gel by using the near infrared spectroscopy (NIRS) technique in combination with multivariate chemometric tools. For this purpose, we constructed a D-optimal experimental design having normal operational condition (NOC) batches as central point. The primary aim here was to develop an expeditious NIRS method for determining the composition of a pharmaceutical gel and assess the temporal changes in major physical factors affecting the quality of the product (specifically, viscosity and pH). Gel components were quantified by using partial least squares (PLS) calibration models of the PLS1 type. The study was completed by using the batch statistical process control method to compare product batches included in the experimental design with NOC batches. Similarities and differences between the two types of batches were identified by using control charts for residuals (Q-statistic) and Hotteling's T2 (D-statistic). The ensuing models, which were subject to errors less than 5%, allowed the gel production process to be effectively monitored. As shown in this work, the NIRS technique is a highly suitable tool for process analytical technology.