Implementation of Quality by Design (QbD) Principles in Regulatory Dossiers of Medicinal Products in the European Union (EU) Between 2014 and 2019

A roadmap for model-based bioprocess development

The bioprocessing industry is undergoing a significant transformation in its approach to quality assurance, shifting from the traditional Quality by Testing (QbT) to Quality by Design (QbD). QbD, a systematic approach to quality in process development, integrates quality into process design and control, guided by regulatory frameworks. This paradigm shift enables increased operational efficiencies, reduced market time, and ensures product consistency. The implementation of QbD is framed around key elements such as defining the Quality Target Product Profile (QTPPs), identifying Critical Quality Attributes (CQAs), developing Design Spaces (DS), establishing Control Strategies (CS), and maintaining continual improvement. The present critical analysis delves into the intricacies of each element, emphasizing their role in ensuring consistent product quality and regulatory compliance. The integration of Industry 4.0 and 5.0 technologies, including Artificial Intelligence (AI), Machine Learning (ML), Internet of Things (IoT), and Digital Twins (DTs), is significantly transforming the bioprocessing industry. These innovations enable real-time data analysis, predictive modelling, and process optimization, which are crucial elements in QbD implementation. Among these, the concept of DTs is notable for its ability to facilitate bi-directional data communication and enable real-time adjustments and therefore optimize processes. DTs, however, face implementation challenges such as system integration, data security, and hardware-software compatibility. These challenges are being addressed through advancements in AI, Virtual Reality/ Augmented Reality (VR/AR), and improved communication technologies. Central to the functioning of DTs is the development and application of various models of differing types - mechanistic, empirical, and hybrid. These models serve as the intellectual backbone of DTs, providing a framework for interpreting and predicting the behaviour of their physical counterparts. The choice and development of these models are vital for the accuracy and efficacy of DTs, enabling them to mirror and predict the real-time dynamics of bioprocessing systems. Complementing these models, advancements in data collection technologies, such as free-floating wireless sensors and spectroscopic sensors, enhance the monitoring and control capabilities of DTs, providing a more comprehensive and nuanced understanding of the bioprocessing environment. This review offers a critical analysis of the prevailing trends in model-based bioprocessing development within the sector.

Design and Optimization of Solid Lipid Nanoparticles Loaded with Triamcinolone Acetonide

Principles of quality by design and design of experiments are acquiring more importance in the discovery and application of new drug carriers, such as solid lipid nanoparticles. In this work, an optimized synthesis of solid lipid nanoparticles loaded with Triamcinolone Acetonide is presented using an approach that involves Stearic Acid as a lipid, soy PC as an ionic surfactant, and Tween 80 as a nonionic surfactant. The constructed circumscribed Central Composite Design considers the lipid and nonionic surfactant quantities and the sonication amplitude in order to optimize particle size and Zeta potential, both measured by means of Dynamic Light Scattering, while the separation of unentrapped drug from the optimized Triamcinolone Acetonide-loaded solid lipid nanoparticles formulation is performed by Size Exclusion Chromatography and, subsequently, the encapsulation efficiency is determined by HPLC-DAD. The proposed optimized formulation-with the goal of maximizing Zeta potential and minimizing particle size-has shown good accordance with predicted values of Zeta potential and dimensions, as well as a high value of encapsulated Triamcinolone Acetonide. Experimental values obtained from the optimized synthesis reports a dimension of 683 ± 5 nm, which differs by 3% from the predicted value, and a Zeta potential of -38.0 ± 7.6 mV (12% difference from the predicted value).

A multi-step machine learning approach for accelerating QbD-based process development of protein spray drying

This study proposes a new material-efficient multi-step machine learning (ML) approach for the development of a design space (DS) for spray drying proteins. Typically, a DS is developed by performing a design of experiments (DoE) with the spray dryer and the protein of interest, followed by deriving the DoE models via multi-variate regression. This approach was followed as a benchmark to the ML approach. The more complex the process and required accuracy of the final model is, the more experiments are necessary. However, most biologics are expensive and thus experiments should be kept to a minimum. Therefore, the suitability of using a surrogate material and ML for the development of a DS was investigated. To this end, a DoE was performed with the surrogate and the data used for training the ML approach. The ML and DoE model predictions were compared to measurements of three protein-based validation runs. The suitability of using lactose as surrogate was investigated and advantages of the proposed approach were demonstrated. Limitations were identified at protein concentrations >35mg/ml and particle sizes of x₅₀>6µm. Within the investigated DS protein secondary structure was preserved, and most process settings, resulted in yields >75% and residual moisture <10wt.%.

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.

Quality-by-design-based engineered liposomal nanomedicines to treat cancer: an in-depth analysis

Engineered nano-sized liposomes have attained the highest success rate in commercialization among the reported nanomedicines. However, developing industrially acceptable nanoliposomes is still challenging because the process, formulation factors and even their properties may critically influence the desired attributes of the final nanoliposomal product. Implementation of quality-by-design (QbD) in nanoliposomal fabrication has led to revolutionary advancement int better analysis of the interacting factors (drug and lipid ratio, hydration, sonication, etc), which, in turn, leads to better product performance with predefined attributes (entrapment efficiency percentage, drug release time and pattern, vesicles size, polydispersity index, surface charge and surface morphology). This review provides a summary of decade of research and an in-depth analysis of QbD-based nanoliposomes developed to address different cancers. The review aims to provide complete details of QbD-inspired nanoliposomal development from process to application.

Quality by Design (QbD) Approach in Marketing Authorization Procedures of Non-Biological Complex Drugs: A Critical Evaluation

The emergence of innovator-driven complex drug products, such as Non-Biological Complex Drugs (NBCDs), has provided disruptive advances in the Nanotechnology and Biotechnology fields. However, the design and development of NBCDs can be particularly challenging due to some unresolved scientific and regulatory challenges associated with the pharmaceutical quality assessment. The application of a more holistic, systematic, integrated science and risk-based approach, such as Quality by Design (QbD), is essential to address key scientific, technological, and regulatory constraints in the research and development of the NBCDs. The deeper product and process understanding derived from the implementation of the QbD approach ensures consistent, reliable, and high-quality pharmaceutical products. Furthermore, this approach promotes innovation and continuous improvement in the entire product lifecycle. Regulatory authorities highly recommend QbD-based submissions to successfully translate NBCDs from laboratory-scale research to the pharmaceutical market with the required quality, safety, and efficacy standards. The main aim of this article is to obtain a comprehensive and in-depth investigation into the state of implementation of the QbD approach in the pharmaceutical development and marketing authorization of NBCDs in Europe and the United States, through the analysis of the available data from their regulatory dossiers. In addition, it aims to understand and discuss how the QbD approach is used and implemented for complex drug products in the pharmaceutical industry, highlighting the gaps and challenges involved with its implementation. An analysis is held regarding QbD's advantages in terms of knowledge growth, regulatory flexibility, and the speed of development based on big data science, along with the reduction of regulatory failures and market withdrawals.

Optimization of a mAb production process with regard to robustness and product quality using quality by design principles

Quality by Design principles are well described and widely used in biopharmaceutical industry. The characterization of a monoclonal antibody (mAb) production process is crucial for novel process development and control. Yet, the application throughout the entire upstream process was rarely demonstrated. Following previously published research, this study marks the second step toward a complete process characterization and is focused on the effect of critical process parameters on the antibody production efficiency and quality of the process. In order to conduct the complex Design of Experiments approach with optimal control and comparability, the ambr®15 micro bioreactor platform was used. Investigated parameters included the pH and dissolved oxygen set points, the initial viable cell density (iVCD) as well as the N‐1 duration. Various quality attributes (e.g., growth rate, viability, mAb titer, and peak proportion) were monitored and analyzed using multivariate data analysis to evaluate the parameter effects. The pH set point and the initial VCD were identified as key process parameters with strong influence on the cell growth as well as the mAb production and its proportion to the total protein concentration. For optimization and improvement in robustness of these quality attributes the pH must be increased to 7.2, while the iVCD must be lowered to 0.2 × 10⁶ cells/mL. Based on the defined design space, additional experiments verified the results and confirmed the intact bioactivity of the antibody. Thereby, process control strategies could be tuned toward high cell maintenance and mAb production, which enable optimal downstream processing.

A review on the modernization of pharmaceutical development and manufacturing - Trends, perspectives, and the role of mathematical modeling

Recently, the pharmaceutical industry has been facing several challenges associated to the use of outdated development and manufacturing technologies. The return on investment on research and development has been shrinking, and, at the same time, an alarming number of shortages and recalls for quality concerns has been registered. The pharmaceutical industry has been responding to these issues through a technological modernization of development and manufacturing, under the support of initiatives and activities such as quality-by-design (QbD), process analytical technology, and pharmaceutical emerging technology. In this review, we analyze this modernization trend, with emphasis on the role that mathematical modeling plays within it. We begin by outlining the main socio-economic trends of the pharmaceutical industry, and by highlighting the life-cycle stages of a pharmaceutical product in which technological modernization can help both achieve consistently high product quality and increase return on investment. Then, we review the historical evolution of the pharmaceutical regulatory framework, and we discuss the current state of implementation and future trends of QbD. The pharmaceutical emerging technology is reviewed afterwards, and a discussion on the evolution of QbD into the more effective quality-by-control (QbC) paradigm is presented. Further, we illustrate how mathematical modeling can support the implementation of QbD and QbC across all stages of the pharmaceutical life-cycle. In this respect, we review academic and industrial applications demonstrating the impact of mathematical modeling on three key activities within pharmaceutical development and manufacturing, namely design space description, process monitoring, and active process control. Finally, we discuss some future research opportunities on the use of mathematical modeling in industrial pharmaceutical environments.

Lipid-Based Nanocarriers for Ophthalmic Administration: Towards Experimental Design Implementation

Nanotherapeutics based on biocompatible lipid matrices allow for enhanced solubility of poorly soluble compounds in the treatment of ophthalmic diseases, overcoming the anatomical and physiological barriers present in the eye, which, despite the ease of access, remains strongly protected. Micro-/nanoemulsions, solid lipid nanoparticles (SLN) or nanostructured lipid carriers (NLC) combine liquid and/or solid lipids with surfactants, improving drug stability and ocular bioavailability. Current research and development approaches based on try-and-error methodologies are unable to easily fine-tune nanoparticle populations in order to overcome the numerous constraints of ocular administration routes, which is believed to hamper easy approval from regulatory agencies for these systems. The predictable quality and specifications of the product can be achieved through quality-by-design (QbD) implementation in both research and industrial environments, in contrast to the current quality-by-testing (QbT) framework. Mathematical modelling of the expected final nanoparticle characteristics by variation of operator-controllable variables of the process can be achieved through adequate statistical design-of-experiments (DoE) application. This multivariate approach allows for optimisation of drug delivery platforms, reducing research costs and time, while maximising the understanding of the production process. This review aims to highlight the latest efforts in implementing the design of experiments to produce optimised lipid-based nanocarriers intended for ophthalmic administration. A useful background and an overview of the different possible approaches are presented, serving as a starting point to introduce the design of experiments in current nanoparticle research.