Pharmaceutical Manufacturing Processes Research Articles

Theoretical Approaches to Process Validation in Pharmaceutical Manufacturing Process

Under the more general statutory CGMP provisions of section 501(a)(2)(B) of the Federal Food, Drug and Cosmetic Act, validation of manufacturing processes is deemed a legal part of current good manufacturing practice for active pharmaceutical ingredients (APIs). Validation of manufacturing processes is required by the Current Good Manufacturing Practice (CGMP) regulations for finished pharmaceuticals (21 CFR 211.100 and 211.110). A medication should be created that is suitable for its intended purpose, according to the fundamental tenet of quality assurance. Procedure Validation is the collection and estimation of data that proves a process can reliably produce high-quality goods, starting with process design and continuing through commercial production. Three stages of validation are suggested by the 2011 USFDA process validation guideline - Process design, Process qualification, Continued Process qualification. An introduction and broad review of process validation in the pharmaceutical manufacturing process, specifically in the tablet manufacturing process, are provided in this article. Process validation ensures that a process will consistently create a product that meets its predefined quality features and qualities. It is a crucial step in the design, prototyping, and manufacturing processes. Since quality is always a necessary precondition for any product, pharmaceuticals must be produced to the greatest standards of quality. Furthermore, end-product testing does not ensure the product's quality on its own; rather, quality assurance methods need to be applied to incorporate the product's quality throughout the whole process, rather than only testing it at the conclusion.

Isolation and purification of high molecular weight adiponectin from human plasma fraction

Adiponectin, a crucial protein hormone originating from adipose tissue, regulates key metabolic processes, including lipid metabolism, mitochondrial activity, and insulin sensitivity. These pleiotropic roles of adiponectin, along with its inverse correlation with metabolic disorders such as obesity, type II diabetes, and atherosclerosis, establish this protein as a potential therapeutic target. However, due to this complexity, challenges have arisen in its production with a natural conformation in bacterial or mammalian expression systems, hindering clinical translation. Furthermore, while inducers for adiponectin secretion or chemical agonists targeting adiponectin receptors have shown promise in laboratory settings, clinical studies with these agents have not yet been conducted. This study proposes a method for isolating and purifying natural high molecular weight (HMW) adiponectin from discarded plasma fractions during the conventional pharmaceutical protein manufacturing process. The process involved Cohn-Oncley fractionation, initial chromatography using reduced cellufine formyl, and subsequent purification via DEAE Sepharose chromatography. Characterization involved gel electrophoresis and biological assays on a hepatocyte cell-line. The purification process effectively captured adiponectin from the I + III paste, demonstrating that this fraction contained a significant portion of total plasma adiponectin. The two-step chromatography led to highly purified HMW adiponectin, confirmed by native-PAGE showing a 780 kDa multimeric complex. Biological assessments demonstrated normal downstream signaling, with HMW adiponectin inducing AMPK phosphorylation. This study demonstrates the feasibility of obtaining purified HMW adiponectin by repurposing plasma fractionation processes. It offers a promising avenue for the HMW adiponectin production, tapping into HMW adiponectin’s therapeutic potential against metabolic disorders while optimizing plasma resource utilization in healthcare.

The feasibility of Raman spectroscopy for accurate assessment of essential criteria in pharmaceutical industry by investigation of Metformin hydrochloride tableting process

AbstractAnalytical and real‐time technology in pharmaceutical manufacturing process is an important need to ensure that manufactured drugs are safe and effective. Raman spectroscopy is an emerging technique that is able to perform quantitative analysis nondestructively due to the molecular structure of many drugs. Monitoring the content uniformity and quantification of active pharmaceutical ingredient (API) in the tablet preparation process, without the assistance of solvent, is one of the key concerns in the formulation design in order to provide stable, pure, and homogenous finished products. In this study, we investigated the possibility of using Raman data as an analytical method to quantify API, the intra‐ and inter‐sample uniformity of content in the tableting process of Metformin hydrochloride tablets (C4H11N5.HCl). Analysis of all standard samples for prediction of API uniformity represents an acceptable accuracy and precision with a relative standard deviation of 2.55%. Further investigation of tablets regarding to relative Raman intensity of some characteristic peaks demonstrates the amount of API content with an accuracy of ≥96%. These values have a good adaption with pharmacopeia monograph. Findings reveal that the Raman method can be routinely utilized for quantifying API, controlling the content uniformity and the stability of drugs in different stages of manufacturing.

Bottom-up production of injectable itraconazole suspensions using membrane technology

Bottom-up production of active pharmaceutical ingredient (API) crystal suspensions offers advantages in surface property control and operational ease over top-down methods. However, downstream separation and concentration pose challenges. This proof-of-concept study explores membrane diafiltration as a comprehensive solution for downstream processing of API crystal suspensions produced via anti-solvent crystallization. It involves switching the residual solvent (N-methyl-2-pyrrolidone, NMP) with water, adjusting the excipient (d-α-Tocopherol polyethylene glycol 1000 succinate, TPGS) quantity, and enhancing API loading (solid concentration) in itraconazole crystal suspensions. NMP concentration was decreased from 9 wt% to below 0.05 wt% (in compliance with European Medicine Agency guidelines), while the TPGS concentration was decreased from 0.475 wt% to 0.07 wt%. This reduced the TPGS-to-itraconazole ratio from 1:2 to less than 1:50 and raised the itraconazole loading from 1 wt% to 35.6 wt%. Importantly, these changes did not adversely affect the itraconazole crystal stability in suspension. This study presents membrane diafiltration as a one-step solution to address downstream challenges in bottom-up API crystal suspension production. These findings contribute to optimizing pharmaceutical manufacturing processes and hold promise for advancing the development of long-acting API crystal suspensions via bottom-up production techniques at a commercial scale.

What they forgot to tell you about machine learning with an application to pharmaceutical manufacturing.

Predictive models (a.k.a. machine learning models) are ubiquitous in all stages of drug research, safety, development, manufacturing, and marketing. The results of these models are used inside and outside of pharmaceutical companies for the purpose of understanding scientific processes and for predicting characteristics of new samples or patients. While there are many resources that describe such models, there are few that explain how to develop a robust model that extracts the highest possible performance from the available data, especially in support of pharmaceutical applications. This tutorial will describe pitfalls and best practices for developing and validating predictive models with a specific application to a monitoring a pharmaceutical manufacturing process. The pitfalls and best practices will be highlighted to call attention to specific points that are not generally discussed in other resources.

Optimizing Energy Efficiency of a Twin-Screw Granulation Process in Real-Time Using a Long Short-Term Memory (LSTM) Network.

Traditional pharmaceutical manufacturing processes for solid oral dosage forms can be inefficient and have been known to produce a large amount of undesired product. With the progressing trend of achieving carbon neutrality, there is an impetus to increase the energy efficiency of these manufacturing processes while maintaining the critical quality attributes of the product. One of the important steps in downstream pharmaceutical manufacturing is wet granulation, and within that, twin screw granulation (TSG) is a popular continuous manufacturing technique. In this study, the energy efficiency of the TSG process was maximized by combining a long-term memory (LSTM) model with an optimization algorithm. The LSTM model was trained on time-series process data obtained from the TSG experimental runs. The optimization process, with the objective of maximizing energy efficiency, was performed using a stochastic optimization algorithm, and constraints were enforced on the process parameter design space. Experimental runs at the optimal process parameters were conducted on the TSG equipment with updates occurring at predefined intervals depending on the optimization scenarios. The purpose of these experimental runs was to validate the capability of increasing the overall process energy efficiency when operating at the optimized process parameters. A maximum increase of 27% was obtained between two tested optimization scenarios while maintaining the yield of the granules at the end of the twin-screw granulation process.

Continuous biomanufacturing in upstream and downstream processing

Abstract Continuous bioprocesses have become a significant technological change in regulated industries, with process analytical technology (PAT) and quality-by-design (QbD) being essential for enabling continuous biomanufacturing. PAT and QbD are associated with process automation and control, providing real-time key process information. Continuous manufacturing eliminates hold times and reduces processing times, providing benefits such as improved product quality, reduced waste, lower costs, and increased manufacturing flexibility and agility. Over the past decade, advancements in science and engineering, along with the adoption of QbD and the advancement of PAT, have progressed the scientific and regulatory readiness for continuous manufacturing. Regulatory authorities support the implementation of continuous manufacturing using science- and risk-based approaches, providing a great deal of potential to address issues of agility, flexibility, cost, and robustness in the development of pharmaceutical manufacturing processes.

Comprehensive modeling of cell culture profile using Raman spectroscopy and machine learning

Chinese hamster ovary (CHO) cells are widely utilized in the production of antibody drugs. To ensure the production of large quantities of antibodies that meet the required specifications, it is crucial to monitor and control the levels of metabolites comprehensively during CHO cell culture. In recent years, continuous analysis methods employing on-line/in-line techniques using Raman spectroscopy have attracted attention. While these analytical methods can nondestructively monitor culture data, constructing a highly accurate measurement model for numerous components is time-consuming, making it challenging to implement in the rapid research and development of pharmaceutical manufacturing processes. In this study, we developed a comprehensive, simple, and automated method for constructing a Raman model of various components measured by LC–MS and other techniques using machine learning with Python. Preprocessing and spectral-range optimization of data for model construction (partial least square (PLS) regression) were automated and accelerated using Bayes optimization. Subsequently, models were constructed for each component using various model construction techniques, including linear regression, ridge regression, XGBoost, and neural network. This enabled the model accuracy to be improved compared with PLS regression. This automated approach allows continuous monitoring of various parameters for over 100 components, facilitating process optimization and process monitoring of CHO cells.

Performance Characteristics of Mass Spectrometry-Based Analytical Procedures for Quantitation of Nitrosamines in Pharmaceuticals: Insights from an Inter-laboratory Study

With the discovery of carcinogenic nitrosamine impurities in pharmaceuticals in 2018 and subsequent regulatory requirements for risk assessment for nitrosamine formation during pharmaceutical manufacturing processes, storage or from contaminated supply chains, effective testing of nitrosamines has become essential to ensure the quality of drug substances and products. Mass spectrometry has been widely applied to detect and quantify trace amounts of nitrosamines in pharmaceuticals. As part of an effort by regulatory authorities to assess the measurement variation in the determination of nitrosamines, an inter-laboratory study was performed by the laboratories from six regulatory agencies with each of the participants using their own analytical procedures to determine the amounts of nitrosamines in a set of identical samples. The results demonstrated that accurate and precise quantitation of trace level nitrosamines can be achieved across multiple analytical procedures and provided insight into the performance characteristics of mass spectrometry-based analytical procedures in terms of accuracy, repeatability and reproducibility.

Using PharmaPy with Jupyter Notebook to teach digital design in pharmaceutical manufacturing.

The use of digital tools in pharmaceutical manufacturing has gained traction over the past two decades. Whether supporting regulatory filings or attempting to modernize manufacturing processes to adopt new and quickly evolving Industry 4.0 standards, engineers entering the workforce must exhibit proficiency in modeling, simulation, optimization, data processing, and other digital analysis techniques. In this work, a course that addresses digital tools in pharmaceutical manufacturing for chemical engineers was adjusted to utilize a new tool, PharmaPy, instead of traditional chemical engineering simulation tools. Jupyter Notebook was utilized as an instructional and interactive environment to teach students to use PharmaPy, a new, open-source pharmaceutical manufacturing process simulator. Students were then surveyed to see if PharmaPy was able to meet the learning objectives of the course. During the semester, PharmaPy's model library was used to simulate both individual unit operations as well as multiunit pharmaceutical processes. Through the initial survey results, students indicated that: (i) through Jupyter Notebook, learning Python and PharmaPy was approachable from varied coding experience backgrounds and (ii) PharmaPy strengthened their understanding of pharmaceutical manufacturing through active pharmaceutical ingredient process design and development.

Data-Knowledge-Driven Modeling and Operational Adjustment for the Pharmaceutical Tablet Manufacturing Process via Wet Granulation.

In the context of Pharma 4.0, pharmaceutical quality control (PQC) is beset by issues such as uncertainties from ever-changing critical material attributes and strong coupling between variables in the multi-unit pharmaceutical tablet manufacturing process (PTMP), and how to timely adjust the operational variables to deal with such challenges has become a key problem in PQC. In this study, we propose a novel data-knowledge-driven modeling and operational adjustment framework for PTMP by integrating Bayesian network (BN) and case-based reasoning (CBR). At the modeling level, first, a distributed concept is introduced, i.e., the BN model for each subunit of PTMP is established in accordance with the operation process sequence, and the transition variables are given by the BN model established first and retrieved as the new query for the next unit. Once the BN models of all subunits are built, they are integrated into a global BN model. At the operational adjustment level, by taking the expected critical quality attributes (CQAs) and related prior information as evidence, the operational adjustment is achieved through global BN reasoning. Finally, the case study in a sprayed fluidized-bed granulation-based PTMP demonstrates the feasibility and effectiveness in improving the terminal CQAs of the proposed method, which is also compared with other methods to showcase its efficacy and merits.

Greedy design space construction based on regression and latent space extraction for pharmaceutical development

Implementation of the design space (DS) is a scientific concept for ensuring quality to be submitted as a part of the regulatory filing of a drug product for approval to market. An empirical approach is constructing the DS based on the regression model whose inputs are process parameters and material attributes over the different unit operations, i.e., a high-dimensional statistical model. While the high-dimensional model assures quality and process flexibility through a comprehensive process understanding, it has difficulty visualizing the feasible range of input parameters, i.e., DS. Therefore, this study proposes a greedy approach to constructing the extensive and flexible low-dimensional DS based on the high-dimensional statistical model and the observed internal representations that satisfies both comprehensive process understanding and the DS visualization capability. Introducing the observed correlation structure enabled the dimensionality reduction of the DS. The non-critical controllable parameters were fixed to the target values in visualizing the low-dimensional DS as a function of critical parameters. The expected variation of non-critical non-controllable parameters was considered the source of variation in prediction. The case study demonstrated the proposed approach’s usefulness for developing the pharmaceutical manufacturing process.

Digital Twin Implementation for Manufacturing of Adjuvants

Pharmaceutical manufacturing processes are moving towards automation and real-time process monitoring with the help of process analytical technologies (PATs) and predictive process models representing the real system. In this paper, we present a digital twin developed for an adjuvant manufacturing process involving a microfluidic formation of lipid particles. The twin uses a hybrid model for estimating the current state of the process and predicting system behavior in real time. The twin is used to control the adjuvant particle size, a critical quality attribute, by varying process parameters such as the temperature and inlet flow rates. We describe steps in the design and implementation of the twin, starting from the conception of the mechanistic model, up to the generation of its surrogate model used as state estimator, PATs and the setup of the information technology—Operational technology architecture. We demonstrate the performance of the twin by introducing different disturbances in the process and comparing the effect on the product critical quality attributes with and without the control of the digital twin. Finally, we showcase the digital twin implementation for the process in good manufacturing practice, through an engineering run, which demonstrated the robustness of the process when controlled by the digital twin.

An integrated data management and informatics framework for continuous drug product manufacturing processes: A case study on two pilot plants

The pharmaceutical industry continuously looks for ways to improve its development and manufacturing efficiency. In recent years, such efforts have been driven by the transition from batch to continuous manufacturing and digitalization in process development. To facilitate this transition, integrated data management and informatics tools need to be developed and implemented within the framework of Industry 4.0 technology. In this regard, the work aims to guide the data integration development of continuous pharmaceutical manufacturing processes under the Industry 4.0 framework, improving digital maturity and enabling the development of digital twins. This paper demonstrates two instances where a data integration framework has been successfully employed in academic continuous pharmaceutical manufacturing pilot plants. Details of the integration structure and information flows are comprehensively showcased. Approaches to mitigate concerns in incorporating complex data streams, including integrating multiple process analytical technology tools and legacy equipment, connecting cloud data and simulation models, and safeguarding cyber-physical security, are discussed. Critical challenges and opportunities for practical considerations are highlighted.

3D printing of pharmaceuticals for disease treatment.

Three-dimensional (3D) printing or Additive manufacturing has paved the way for developing and manufacturing pharmaceuticals in a personalized manner for patients with high volume and rare diseases. The traditional pharmaceutical manufacturing process involves the utilization of various excipients to facilitate the stages of blending, mixing, pressing, releasing, and packaging. In some cases, these excipients cause serious side effects to the patients. The 3D printing of pharmaceutical manufacturing avoids the need for excessive excipients. The two major components of a 3D printed tablet or dosage form are polymer matrix and drug component alone. Hence the usage of the 3D printed dosage forms for disease treatment will avoid unwanted side effects and provide higher therapeutic efficacy. With respect to the benefits of the 3D printed pharmaceuticals, the present review was constructed by discussing the role of 3D printing in producing formulations of various dosage forms such as fast and slow releasing, buccal delivery, and localized delivery. The dosage forms are polymeric tablets, nanoparticles, scaffolds, and films employed for treating different diseases.

Design, Development, and Validation of a Culture-Independent Nucleic Acid Diagnostics Method for the Rapid Detection and Quantification of the Burkholderia cepacia Complex in Water with an Equivalence to ISO/TS 12869:2019.

In the wake of a series of outbreaks of finished pharmaceutical product-related Burkholderia cepacia complex (Bcc) human infections worldwide, the United States Food and Drug Administration (FDA) in 2017, and subsequently in 2021, issued advisory notifications to the pharmaceutical industry for stringent Bcc testing requirements for pharmaceutical manufacturing processes and for finished pharmaceutical products prior to release to the marketplace. The advisory notifications highlight non-sterile aqueous finished pharmaceutical products as being a major culprit associated with many of these human infection events. As such, there has been a significant number of Bcc-contaminated finished product recalls resulting in company revenue losses, delayed finished product release, finished product shortages for patients, and manufacturing plant shutdowns coupled with company reputational damage. With many of the finished product recall events, pharmaceutical grade water and/or manufacturing facility water distribution systems were identified as the primary origin source of Bcc contamination. Testing and monitoring regimes currently employed to identify Bcc contamination of water associated with pharmaceutical manufacturing are often limited by costly, laborious, lengthy, and nonspecific traditional microbial culture-based methodologies. Presently FDA approved, European Conformity (CE) marked, and International Organization for Standardization (ISO) standard microbial culture-independent rapid, quantitative, specific, and sensitive nucleic acid diagnostics (NAD) methodologies are now gaining greater widespread acceptance in their routine usage in testing laboratories. Here we present the development of a rapid (<4 hours from sample in to result out) single test culture-independent Bcc NAD method, incorporating a quantitative real-time polymerase chain reaction (qPCR) assay. This method can be used for the detection and simultaneous identification of all 24 Bcc species currently assigned, directly from water samples. This culture-independent Bcc NAD method is validated to the testing method equivalent of the ISO/TS 12869:2019 standard, which is a widely used rapid culture-independent NAD method for detecting Gram-negative Legionella species in water.

Optimization of key energy and performance metrics for drug product manufacturing

During the development of pharmaceutical manufacturing processes, detailed systems-based analysis and optimization are required to control and regulate critical quality attributes within specific ranges, to maintain product performance. As discussions on carbon footprint, sustainability, and energy efficiency are gaining prominence, the development and utilization of these concepts in pharmaceutical manufacturing are seldom reported, which limits the potential of pharmaceutical industry in maximizing key energy and performance metrics. Based on an integrated modeling and techno-economic analysis framework previously developed by the authors (Sampat et al., 2022), this study presents the development of a combined sensitivity analysis and optimization approach to minimize energy consumption while maintaining product quality and meeting operational constraints in a pharmaceutical process. The optimal input process conditions identified were validated against experiments and good agreement resulted between simulated and experimental data. The results also allowed for a comparison of the capital and operational costs for batch and continuous manufacturing schemes under nominal and optimized conditions. Using the nominal batch operations as a basis, the optimized batch operation results in a 71.7% reduction of energy consumption, whereas the optimized continuous case results in an energy saving of 83.3%.

Simulation-optimization framework for the digital design of pharmaceutical processes using Pyomo and PharmaPy.

The problem of performing model-based process design and optimization in the pharmaceutical industry is an important and challenging one both computationally and in choice of solution implementation. In this work, a framework is presented to directly utilize a process simulator via callbacks during derivative-based optimization. The framework allows users with little experience in translating mechanistic ODEs and PDEs to robust, fully discretized algebraic formulations, required for executing simultaneous equation-oriented optimization, to obtain mathematically guaranteed optima at a competitive solution time when compared with existing derivative-free and derivative-based frameworks. The effectiveness of the framework in accuracy of optimal solution as well as computational efficiency is analyzed on on two case studies: (i) an integrated 2-unit reaction synthesis train used for the synthesis of an anti-cancer active pharmaceutical ingredient, and (ii) a more complex flowsheet representing a common synthesis-purification-isolation train of a pharmaceutical manufacturing processes.

Enzyme-inspired dry-powder polymeric catalyst for green and fast pharmaceutical manufacturing processes

Catalysis in pharma manufacturing processes is typically homogeneous, expensive and with hard catalyst recovery/regeneration. Herein an enzyme-inspired dry-powder molecularly imprinted polymeric (MIP) system was designed for fast, selective oxidation of a cholesterol derivative and easy catalyst regeneration. The strategy involved the synthesis of a template-monomer (T:M) complex followed by the crosslinked polymerization in supercritical carbon dioxide (scCO2). A 2,2,6,6-tetramethyl-1-piperidinyloxy (TEMPO)-MIP catalyst is obtained after the template cleavage from the matrix, and the oxidation of the NH groups turns available TEMPO moieties within the MIP. The oxidation of benzyl alcohol, 5α-cholestan-3β-ol and cholic acid was fast, in high yield and with selective oxidation capacity.

Investigating Tm Method Specificity Using Oligonucleotide Sequence Variants.

All starting materials and the active pharmaceutical ingredient (API) of a drug product must be subjected to analytical identity (ID) testing as part of the release prior to their introduction into the pharmaceutical manufacturing process. Generally, it is preferable for Quality Control (QC) laboratories to perform ID tests using a simple and fast to perform, yet highly specific, analytical method. This preference also applies to oligonucleotides, an emerging class of APIs, where a combined ID testing strategy should be applied, including intact mass determination and a sequence-specific method. Within this work, we investigated whether ultravioloet (UV)-spectrometric determination of the melting temperature (Tm) of oligonucleotides is a suitable sequence-specific ID test for these substances in the pharmaceutical routine QC. Therefore, this method was evaluated for its specificity toward deviating oligonucleotide sequences. For this, model oligonucleotide sequences and variants thereof were designed, synthesized, and analyzed, resulting in precise and specific data. Even single base exchanges or single nucleotide deletions and insertions in the sequences led to significant changes in the measured Tm of the corresponding oligonucleotide duplexes. These results indicate a generally high specificity of the method toward subtle changes in oligonucleotide sequences and confirm the applicability of the analytical method as part of the ID testing strategy for oligonucleotides in the pharmaceutical QC environment.