Rational formulation and industrial manufacturing of lipid-based complex injectables: Landmarks and trends

Lipid-based complex injectables are renowned for their effectiveness in delivering drugs, with many approved products. While significant strides have been made in formulating nanosystems for small molecular weight drugs, a pivotal breakthrough emerged with the recognition of lipid nanoparticles as a promising platform for delivering nucleic acids. This finding has paved the way for tackling long-standing challenges in molecular and delivery aspects (e.g., mRNA stability, intracellular delivery) that have impeded the clinical translation of gene therapy, especially in the realm of immunotherapy. Nonetheless, developing and implementing new lipid-based delivery systems pose significant challenges, as industrial manufacturing of these formulations often involves complex, multi-batch processes, giving rise to issues related to scalability, stability, sterility, and regulatory compliance. To overcome these obstacles, embracing the principles of quality-by-design (QbD) is imperative. Furthermore, adopting cutting-edge manufacturing and process analytical tools (PAT) that facilitate the transition from batch to continuous production is essential. Herein, the key milestones and insights derived from the development of currently approved lipid- nanosystems will be explored. Additionally, a comprehensive and critical overview of the latest technologies and regulatory guidelines that underpin the creation of more efficient, scalable, and flexible manufacturing processes for complex lipid-based nanoformulations will be provided.

Liquid API feeding in pharmaceutical HME: Novel options in solid dosage manufacturing

Hot melt extrusion (HME) is a common unit operation. It is broadly applicable in the pharmaceutical industry and can be implemented in a continuous manufacturing line. However, the conventional way of active pharmaceutical ingredient (API) feeding with a pre-blend consisting of a powdered API and a polymer does not allow the flexibility and agility to adjust the process parameters, which is generally an essential part of continuous manufacturing. In addition, this method of API feeding may result in the segregation of the individual powder components or agglomeration of highly cohesive materials, leading to an inhomogeneous API content in the extrudates, especially at low doses. In this study, the universal applicability of liquid side feeding in pharmaceutical HME was demonstrated using various APIs suspended or dissolved in water and fed as suspension or undersaturated, supersaturated, and highly concentrated solutions into anterior parts of the extruder. The extrudates were characterized in terms of their API content, residual moisture content, and solid-state of the API embedded in the polymer. The results show that a uniform API content without major deviations can be obtained via this method. Furthermore, the residual moisture content of the extrudates was low enough to have no significant influence on further processing of the final dosage form. In summary, this advanced way of feeding allows an accurate, flexible, and agile feeding of APIs, facilitating the production of personalized final dosage forms and a novel option to link the manufacturing of the drug substance and the drug product.

Liposome manufacturing under continuous flow conditions: towards a fully integrated set-up with in-line control of critical quality attributes

Continuous flow manufacturing (CFM) has shown remarkable advantages in the industrial-scale production of drug-loaded nanomedicines, including mRNA-based COVID-19 vaccines. Thus far, CFM research in nanomedicine has mainly focused on the initial particle formation step, while post-formation production steps are hardly ever integrated. The opportunity to implement in-line quality control of critical quality attributes merits closer investigation. Here, we designed and tested a CFM setup for the manufacturing of liposomal nanomedicines that can potentially encompass all manufacturing steps in an end-to-end system. Our main aim was to elucidate the key composition and process parameters that affect the physicochemical characteristics of the liposomes. Total flow rate, lipid concentration and residence time of the liposomes in a high ethanol environment (i.e., above 20% v/v) emerged as critical parameters to tailor liposome size between 80 and 150 nm. After liposome formation, the pressure and the surface area of the filter in the ultrafiltration unit were critical parameters in the process of clearing the dispersion from residual ethanol. As a final step, we integrated in-line measurement of liposome size and residual ethanol content. Such in-line measurements allow for real-time monitoring and in-process adjustment of key composition and process parameters.

Using residence time distribution in pharmaceutical solid dose manufacturing - A critical review

While continuous manufacturing (CM) of pharmaceutical solid-based drug products has been shown to be advantageous for improving the product quality and process efficiency in alignment with FDA's support of the quality-by-design paradigm (Lee, 2015; Ierapetritou et al., 2016; Plumb, 2005; Schaber, 2011), it is critical to enable full utilization of CM technology for robust production and commercialization (Schaber, 2011; Byrn, 2015). To do so, an important prerequisite is to obtain a detailed understanding of overall process characteristics to develop cost-effective and accurate predictive models for unit operations and process flowsheets. These models are utilized to predict product quality and maintain desired manufacturing efficiency (Ierapetritou et al., 2016). Residence time distribution (RTD) has been a widely used tool to characterize the extent of mixing in pharmaceutical unit operations (Vanhoorne, 2020; Rogers and Ierapetritou, 2015; Teżyk et al., 2015) and manufacturing lines and develop computationally cheap predictive models. These models developed using RTD have been demonstrated to be crucial for various flowsheet applications (Kruisz, 2017; Martinetz, 2018; Tian, 2021). Though extensively used in the literature (Gao et al., 2012), the implementation, execution, evaluation, and assessment of RTD studies has not been standardized by regulatory agencies and can thus lead to ambiguity regarding their accurate implementation. To address this issue and subsequently prevent unforeseen errors in RTD implementation, the presented article aims to aid in developing standardized guidelines through a detailed review and critical discussion of RTD studies in the pharmaceutical manufacturing literature. The review article is divided into two main sections - 1) determination of RTD including different steps for RTD evaluation including experimental approach, data acquisition and pre-treatment, RTD modeling, and RTD metrics and, 2) applications of RTD for solid dose manufacturing. Critical considerations, pertaining to the limitations of RTDs for solid dose manufacturing, are also examined along with a perspective discussion of future avenues of improvement.

How Technological Innovation Affect China's Pharmaceutical Smart Manufacturing Industrial Upgrading

In recent years, a new generation of information technology has provided sufficient technical support for the smart manufacturing industry. In order to promote the upgrading of China's pharmaceutical smart manufacturing industry, the direction of industrial upgrading and transformation will be discussed from the perspective of technological innovation. According to the input and output data of technological innovation in China's pharmaceutical manufacturing industry from 2007 to 2019, the DEA method is used to analyze the allocation of innovative resources in China's pharmaceutical manufacturing industry in recent years. The study found that the efficiency of technological innovation in China's pharmaceutical manufacturing industry fluctuated greatly from 2007 to 2019, with a low overall level and varying degrees of wasted resources. On this basis, an in-depth analysis of the system architecture of the pharmaceutical smart manufacturing industry under the Industry 4.0 environment was performed. Finally, four paths for the digital transformation of China's pharmaceutical manufacturing industry are proposed. Chinese pharmaceutical manufacturing companies need to use new technologies to carry out comprehensive intelligent upgrading and digital transformation to improve innovation efficiency.

Reactor design and selection for effective continuous manufacturing of pharmaceuticals

Pharmaceutical production remains one of the last industries that predominantly uses batch processes, which are inefficient and can cause drug shortages due to the long lead times or quality defects. Consequently, pharmaceutical companies are transitioning away from outdated batch lines, in large part motivated by the many advantages of continuous manufacturing (e.g., low cost, quality assurance, shortened lead time). As chemical reactions are fundamental to any drug production process, the selection of reactor and its design are critical to enhanced performance such as improved selectivity and yield. In this article, relevant theories, and models, as well as their required input data are summarized to assist the reader in these tasks, focusing on continuous reactions. Selected examples that describe the application of plug flow reactors (PFRs) and continuous-stirred tank reactors (CSTRs)-in-series within the pharmaceutical industry are provided. Process analytical technologies (PATs), which are important tools that provide real-time in-line continuous monitoring of reactions, are recommended to be considered during the reactor design process (e.g., port design for the PAT probe). Finally, other important points, such as density change caused by thermal expansion or solid precipitation, clogging/fouling, and scaling-up, are discussed.

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Pharmaceutical application of multivariate modelling techniques: a review on the manufacturing of tablets

The tablet manufacturing process is a complex system, especially in continuous manufacturing (CM). It includes multiple unit operations, such as mixing, granulation, and tableting. In tablet manufacturing, critical quality attributes are influenced by multiple factorial relationships between material properties, process variables, and interactions. Moreover, the variation in raw material attributes and manufacturing processes is an inherent characteristic and seriously affects the quality of pharmaceutical products. To deepen our understanding of the tablet manufacturing process, multivariable modeling techniques can replace univariate analysis to investigate tablet manufacturing. In this review, the roles of the most prominent multivariate modeling techniques in the tablet manufacturing process are discussed. The review mainly focuses on applying multivariate modeling techniques to process understanding, optimization, process monitoring, and process control within multiple unit operations. To minimize the errors in the process of modeling, good modeling practice (GMoP) was introduced into the pharmaceutical process. Furthermore, current progress in the continuous manufacturing of tablets and the role of multivariate modeling techniques in continuous manufacturing are introduced. In this review, information is provided to both researchers and manufacturers to improve tablet quality.

Approach to Establishment of Control Strategy for Oral Solid Dosage Forms Using Continuous Manufacturing

As a result of the research activities of the Japan Agency for Medical Research and Development (AMED), this document aims to show an approach to establishing control strategy for continuous manufacturing of oral solid dosage forms. The methods of drug development, technology transfer, process control, and quality control used in the current commercial batch manufacturing would be effective also in continuous manufacturing, while there are differences in the process development using continuous manufacturing and batch manufacturing. This document introduces an example of the way of thinking for establishing a control strategy for continuous manufacturing processes.

Development of an RTD-Based Flowsheet Modeling Framework for the Assessment of In-Process Control Strategies

Continuous manufacturing (CM) is an emerging technology which can improve pharmaceutical manufacturing and reduce drug product quality issues. One challenge that needs to be addressed when adopting CM technology is material traceability through the entire continuous process, which constitutes one key aspect of control strategy. Residence time distribution (RTD) plays an important role in material traceability as it characterizes the material spreading through the process. The propagation of upstream disturbances could be predictively tracked through the entire process by convolution of the disturbance and the RTD. The present study sets up the RTD-based modeling framework in a commonly used process modeling environment, gPROMS, and integrates it with existing modules and built-in tools (e.g., parameter estimation). Concentration calculations based on the convolution integral requires access to historical stream property information, which is not readily available in flowsheet modeling platforms. Thus, a novel approach is taken whereby a partial differential equation is used to propagate and store historical data as the simulation marches forward in time. Other stream properties not modeled by an RTD are determined in auxiliary modules. To illustrate the application of the framework, an integrated RTD-auxiliary model for a continuous direct compression manufacturing line was developed. An excellent agreement was found between the model predictions and experiments. The validated model was subsequently used to assess in-process control strategies for feeder and material traceability through the process. Our simulation results show that the employed modeling approach facilitates risk-based assessment of the continuous line by promoting our understanding on the process.

Model-assisted approaches for continuous chromatography: Current situation and challenges

Continuous bioprocessing is a promising trend in biopharmaceutical production, and multi-column continuous chromatography shows advantages of high productivity, high resin capacity utilization, small footprint, low buffer consumption and less waste. Due to the complexity and dynamic nature of continuous processing, traditional experiment-based approaches are often time-consuming and inefficient. In this review, model-assisted approaches were focused and their applications in continuous chromatography process development, validation and control were discussed. Chromatographic models are useful in describing particular process performances of continuous capture and polishing with multi-column chromatography. Model-assisted tools showed powerful ability in evaluating multiple operating parameters and identifying optimal points over the entire design space. The residence time distribution models, model-assisted process analytical technologies and model-predictive control strategies were also developed to reveal the propagation of disturbances, enhance real time monitor and achieve adaptive control to match the transient disturbances and deviations of continuous processes. Moreover, artificial neural networks and machine learning concepts were integrated into modeling approaches to improve data treatment. In general, further development in research and applications of model-assisted approaches for continuous chromatography are needed urgently to support the continuous manufacturing.

Continuous Manufacturing of a Polymer Stabilized Emulsion Monitored with Process Analytical Technology

Moving from batch to continuous manufacturing (CM) requires implementation of process analytical technology (PAT), as it is crucial to monitor and control these processes. CM of semi-solids has been demonstrated but implementation of a broader range of PAT tools with in- or on-line process interfacing at the end of the CM line has not been demonstrated. The goal of this work was to continuously manufacture creams and to investigate whether in- and on-line measurement of viscosity, changes in the concentration of active pharmaceutical ingredient (API), and pH could be used to support optimization of a model cream product. Additionally, the torque of the mixers was assessed for determination of the physical properties of the cream. Two Raman probes with different probe optics were compared for characterization of the API concentration. The API concentration, amount of neutralizer, and mixing speed of the CM line were systematically varied. Both the PhAT probe with a larger sampling volume and immersion Raman probe with a smaller sampling volume could detect the step changes in the API concentration. The torque from the mixer was compared with the viscosity measurements, but the torque signal could not be correlated with the viscosity due to the dynamic nature of the polymer conformation and the time-dependency of this property. Adjustment of pH of the cream could be monitored with the current installation. The investigated PAT tools could be implemented into a continuous line and, further, be used to support the optimization of a model cream composition and related process parameters.