Quality-by-design in pharmaceutical development: From current perspectives to practical applications

Question-based review for pharmaceutical development: An enhanced quality approach

Over the last years, the pharmaceutical industry has faced real challenges regarding quality assurance. In this context, the establishment of more holistic approaches to the pharmaceutical development has been encouraged. The emergence of the Quality by Design (QbD) paradigm as systematic, scientific and risk-based methodology introduced a new concept of pharmaceutical quality. In essence, QbD can be interpreted as a strategy to maximize time and cost savings. An in-depth understanding of the formulation and manufacturing process is demanded to optimize the safety, efficacy and quality of a drug product at all stages of development. This innovative approach streamlines the pharmaceutical Research and Development (R&D) process, provides greater manufacturing flexibility and reduces regulatory burden. To assist in QbD implementation, International Conference on Harmonisation (ICH), U.S. Food and Drug Administration (FDA) and European Medicines Agency (EMA) organized and launched QbD principles in their guidance for industry, identifying key concepts and tools to design and develop a high-quality drug product. Despite the undeniable advantages of the QbD approach, and the widespread information on QbD regulatory expectations, its full implementation in the pharmaceutical field is still limited. The present review aims to establish a crosswise overview on the current application status of QbD within the framework of the ICH guidelines (ICH Q8(R2) - Q14 and ICH Q2(R2)). Moreover, it outlines the way information gathered from the QbD methodology is being harmonized in Marketing Authorization Applications (MAAs) for European market approval. This work also highlights the challenges that hinder the deployment of the QbD strategy as a standard practice.

Machine Learning-Enabled NIR Spectroscopy. Part 3: Hyperparameter by Design (HyD) Based ANN-MLP Optimization, Model Generalizability, and Model Transferability

Data variations, library changes, and poorly tuned hyperparameters can cause failures in data-driven modelling. In such scenarios, model drift, a gradual shift in model performance, can lead to inaccurate predictions. Monitoring and mitigating drift are vital to maintain model effectiveness. USFDA and ICH regulate pharmaceutical variation with scientific risk-based approaches. In this study, the hyperparameter optimization for the Artificial Neural Network Multilayer Perceptron (ANN-MLP) was investigated using open-source data. The design of experiments (DoE) approach in combination with target drift prediction and statistical process control (SPC) was employed to achieve this objective. First, pre-screening and optimization DoEs were conducted on lab-scale data, serving as internal validation data, to identify the design space and control space. The regression performance metrics were carefully monitored to ensure the right set of hyperparameters was selected, optimizing the modelling time and storage requirements. Before extending the analysis to external validation data, a drift analysis on the target variable was performed. This aimed to determine if the external data fell within the studied range or required retraining of the model. Although a drift was observed, the external data remained well within the range of the internal validation data. Subsequently, trend analysis and process monitoring for the mean absolute error of the active content were conducted. The combined use of DoE, drift analysis, and SPC enabled trend analysis, ensuring that both current and external validation data met acceptance criteria. Out-of-specification and process control limits were determined, providing valuable insights into the model's performance and overall reliability. This comprehensive approach allowed for robust hyperparameter optimization and effective management of model lifecycle, crucial in achieving accurate and dependable predictions in various real-world applications.

Implementation of Quality by Design (QbD) for development of bilayer tablets

Bilayer tablets offer various drug release profiles for individual drugs incorporated in each layer of a bilayer tablet, which is rarely achievable by conventional tablets. These tablets also help avoid physicochemical incompatibilities between drugs and excipients. Successful manufacturing of such more complex dosage forms depends upon screening of material attributes of API and excipients as well as optimization of processing parameters of individual unit operations of the manufacturing process that must be strictly monitored and controlled to obtain an acceptable drug product quality and performance in order to achieve safety and efficacy per regulatory requirements. Optimizing formulation attributes and manufacturing processes during critical stages, such as blending, granulation, pre-compression, and main compression, can help avoid problems such as weight variation, segregation, and delamination of individual layers, which are frequently faced during the production of bilayer tablets. The main objective of this review is to establish the basis for the implementation of Quality by Design (QbD) system principles for the design and development of bilayer tablets, encompassing the preliminary and systematic risk assessment of critical material attributes (CMAs) and critical process parameters (CPPs) with respect to in-process and finished product critical quality attributes (CQAs). Moreover, the applicability of the QbD methodology based on its purpose is discussed and complemented with examples of bilayer tablet technology.

New Trends in the Quality Control of Enantiomeric Drugs: Quality by Design-Compliant Development of Chiral Capillary Electrophoresis Methods

Capillary electrophoresis (CE) is a potent method for analyzing chiral substances and is commonly used in the enantioseparation and chiral purity control of pharmaceuticals from different matrices. The adoption of Quality by Design (QbD) concepts in analytical method development, optimization and validation is a widespread trend observed in various analytical approaches including chiral CE. The application of Analytical QbD (AQbD) leads to the development of analytical methods based on sound science combined with risk management, and to a well understood process clarifying the influence of method parameters on the analytical output. The Design of Experiments (DoE) method employing chemometric tools is an essential part of QbD-based method development, allowing for the simultaneous evaluation of experimental parameters as well as their interaction. In 2022 the International Council for Harmonization (ICH) released two draft guidelines (ICH Q14 and ICH Q2(R2)) that are intended to encourage more robust analytical procedures. The ICH Q14 guideline intends to harmonize the scientific approaches for analytical procedures’ development, while the Q2(R2) document covers the validation principles for the use of analytical procedures including the recent applications that require multivariate statistical analyses. The aim of this review is to provide an overview of the new prospects for chiral CE method development applied for the enantiomeric purity control of pharmaceuticals using AQbD principles. The review also provides an overview of recent research (2012–2022) on the applicability of CE methods in chiral drug impurity profiling.

Challenges and Opportunities of Implementing Data Fusion in Process Analytical Technology—A Review

The release of the FDA’s guidance on Process Analytical Technology has motivated and supported the pharmaceutical industry to deliver consistent quality medicine by acquiring a deeper understanding of the product performance and process interplay. The technical opportunities to reach this high-level control have considerably evolved since 2004 due to the development of advanced analytical sensors and chemometric tools. However, their transfer to the highly regulated pharmaceutical sector has been limited. To this respect, data fusion strategies have been extensively applied in different sectors, such as food or chemical, to provide a more robust performance of the analytical platforms. This survey evaluates the challenges and opportunities of implementing data fusion within the PAT concept by identifying transfer opportunities from other sectors. Special attention is given to the data types available from pharmaceutical manufacturing and their compatibility with data fusion strategies. Furthermore, the integration into Pharma 4.0 is discussed.

Applicability of Image Analysis to Support QbD driven Development of Pellets

Objective The stages of preparing high drug loaded pellets were investigated using static and dynamic imaging techniques to provide a greater understanding and ease the scale up process. Significance: An example of a real case laboratory and production scale Quality by design (QbD) based development of pellets is demonstrated. Potential Process analytical technology (PAT) approaches by dynamic image analysis (DIA) are presented in various process phases. Methods: Pellets were prepared at laboratory and production scale (high shear granulation, extrusion/spheronization, drying, coating). The influence of process parameters on pellet properties (aspect ratio, yield, pellet size, and their distribution) was investigated using static and dynamic image analysis. During coating, we focused on the coating thickness and identification of potential agglomeration. Results and conclusions: The effects of kneading time, amount of water, extrusion screen plate (ESP) opening diameter and thickness on pellet properties were confirmed in accordance with literature. In terms of screw speed, spheronization speed and time, no considerable influence on pellet properties was observed in the range of studied process parameters, thereby confirming the design space. . In addition to the ESP thickness and opening diameter, quality of the ESP impacts the pellet properties. Lastly, coating thickness measurements with dynamic and static image analysis were comparable and an exemplary case of in-line agglomeration detection was presented. Real time evaluation with PATVIS APA is an effective PAT tool for the evaluation of spheronization (pellet size distribution, aspect ratio, yield) and coating (coating thickness, agglomeration detection).

Evaluation of a Combined MHE-NMPC Approach to Handle Plant-Model Mismatch in a Rotary Tablet Press

The transition from batch to continuous processes in the pharmaceutical industry has been driven by the potential improvement in process controllability, product quality homogeneity, and reduction of material inventory. A quality-by-control (QbC) approach has been implemented in a variety of pharmaceutical product manufacturing modalities to increase product quality through a three-level hierarchical control structure. In the implementation of the QbC approach it is common practice to simplify control algorithms by utilizing linearized models with constant model parameters. Nonlinear model predictive control (NMPC) can effectively deliver control functionality for highly sensitive variations and nonlinear multiple-input-multiple-output (MIMO) systems, which is essential for the highly regulated pharmaceutical manufacturing industry. This work focuses on developing and implementing NMPC in continuous manufacturing of solid dosage forms. To mitigate control degradation caused by plant-model mismatch, careful monitoring and continuous improvement strategies are studied. When moving horizon estimation (MHE) is integrated with NMPC, historical data in the past time window together with real-time data from the sensor network enable state estimation and accurate tracking of the highly sensitive model parameters. The adaptive model used in the NMPC strategy can compensate for process uncertainties, further reducing plant-model mismatch effects. The nonlinear mechanistic model used in both MHE and NMPC can predict the essential but complex powder properties and provide physical interpretation of abnormal events. The adaptive NMPC implementation and its real-time control performance analysis and practical applicability are demonstrated through a series of illustrative examples that highlight the effectiveness of the proposed approach for different scenarios of plant-model mismatch, while also incorporating glidant effects.