Industry 4.0 for pharmaceutical manufacturing: Preparing for the smart factories of the future

An examination of process models and model risk frameworks for pharmaceutical manufacturing

Process models are a growing tool for pharmaceutical manufacturing process design and control. The Industry 4.0 paradigm promises to increase the amount of data available to understand manufacturing processes. Tools such as Artificial Intelligence (AI) might accelerate process development and allow better predictions of process trajectories. Several examples of process improvements realized through the application of process models have been shown in lyophilization, chromatography, fluid bed drying, bioreactor control, continuous direct compression, and wet granulation. An important consideration of implementing a process model is determining the impact of the model on the quality of the product and the risks associated with model maintenance over the product lifecycle. Several regulatory documents address risk-based considerations for process models. This work discusses existing risk-based frameworks for model validation and lifecycle maintenance that could aid the adoption of process models in pharmaceutical manufacturing. Hypothetical case studies illustrate the implications of applying a model risk framework to facilitate model validation and lifecycle maintenance in the manufacture of pharmaceuticals and biological products.

Artificial intelligence-driven pharmaceutical industry: A paradigm shift in drug discovery, formulation development, manufacturing, quality control, and post-market surveillance

The advent of artificial intelligence (AI) has catalyzed a profound transformation in the pharmaceutical industry, ushering in a paradigm shift across various domains, including drug discovery, formulation development, manufacturing, quality control, and post-market surveillance. This comprehensive review examines the multifaceted impact of AI-driven technologies on all stages of the pharmaceutical life cycle. It discusses the application of machine learning algorithms, data analytics, and predictive modeling to accelerate drug discovery processes, optimize formulation development, enhance manufacturing efficiency, ensure stringent quality control measures, and revolutionize post-market surveillance methodologies. By describing the advancements, challenges, and future prospects of harnessing AI in the pharmaceutical landscape, this review offers valuable insights into the evolving dynamics of drug development and regulatory practices in the era of AI-driven innovation.

A qualitative exploration of participants' perspectives and experiences of novel digital health infrastructure to enhance patient care in remote communities within the Home Health Project

The Home Health Project, set on Clare Island, five kilometres off the Irish Atlantic coast, is a pilot exploration of ways in which various forms of technology can be utilised to improve healthcare for individuals living in isolated communities. The integration of digital health technologies presents enormous potential to revolutionise the accessibility of healthcare systems for those living in remote communities, allowing patient care to function outside of traditional healthcare settings. This study aims to explore the personal experiences and perspectives of participants who are using digital technologies in the delivery of their healthcare as part of the Home Health Project. Individual semi-structured interviews were conducted with nine members of the Clare Island community participating in the Home Health Project. Interviews took place in-person, in June 2023. Interviews were audio-recorded and transcribed verbatim. The data were analysed inductively using reflexive thematic analysis. To identify determinants of engagement with the Home Health Project, the data was then deductively coded to the Theoretical Domains Framework (TDF) and organised into themes. Seven of the possible 14 TDF domains were supported by the interview data as influences on engagement with the Project: Knowledge, Beliefs about capabilities, Optimism, Intentions, Environmental context and resources, Social influences and Emotion. Overall, participants evaluated the Home Health Project as being of high quality which contributed to self-reported increases in health literacy, autonomy, and feeling well supported in having their health concerns addressed. There was some apprehension related to data protection, coupled with a desire for extended training to address aspects of digital illiteracy. Future iterations can capitalise on the findings of this study by refining the technologies to reflect tailored health information, personalised to the individual user.

Automated particle inspection of continuously freeze-dried products using computer vision

The pharmaceutical industry is progressing towards more continuous manufacturing techniques. To dry biopharmaceuticals, continuous freeze drying has several advantages on manufacturing and process analytical control compared to batch freeze-drying, including better visual inspection potential. Visual inspection of every freeze-dried product is a key quality assessment after the lyophilization process to ensure that freeze-dried products are free from foreign particles and defects. This quality assessment is labor-intensive for operators who need to assess thousands of samples for an extensive amount of time leading to certain drawbacks. Applying Artificial Intelligence, specifically computer vision, on high-resolution images from every freeze-dried product can quantitatively and qualitatively outperform human visual inspection. For this study, continuously freeze-dried samples were prepared based on a real-world pharmaceutical product using manually induced particles of different sizes and subsequently imaged using a tailor-made setup to develop an image dataset (with particle sizes from 50μm to 1 mm) used to train multiple object detection models. You Only Look Once version 7 (YOLOv7) outperforms human inspection by a large margin, obtaining particle detection precision of up to 88.9% while controlling the recall at 81.2%, thus detecting most of the object present in the images, with an inference time of less than 1 s per vial.

A translational framework to DELIVER nanomedicines to the clinic

Nanomedicines have created a paradigm shift in healthcare. Yet fundamental barriers still exist that prevent or delay the clinical translation of nanomedicines. Critical hurdles inhibiting clinical success include poor understanding of nanomedicines' physicochemical properties, limited exposure in the cell or tissue of interest, poor reproducibility of preclinical outcomes in clinical trials, and biocompatibility concerns. Barriers that delay translation include industrial scale-up or scale-down and good manufacturing practices, funding and navigating the regulatory environment. Here we propose the DELIVER framework comprising the core principles to be realized during preclinical development to promote clinical investigation of nanomedicines. The proposed framework comes with design, experimental, manufacturing, preclinical, clinical, regulatory and business considerations, which we recommend investigators to carefully review during early-stage nanomedicine design and development to mitigate risk and enable timely clinical success. By reducing development time and clinical trial failure, it is envisaged that this framework will help accelerate the clinical translation and maximize the impact of nanomedicines.

Explainable deep recurrent neural networks for the batch analysis of a pharmaceutical tableting process in the spirit of Pharma 4.0

Due to the continuously increasing Cost of Goods Sold, the pharmaceutical industry has faced several challenges, and the Right First-Time principle with data-driven decision-making has become more pressing to sustain competitiveness. Thus, in this work, three different types of artificial neural network (ANN) models were developed, compared, and interpreted by analyzing an open-access dataset from a real pharmaceutical tableting production process. First, the multilayer perceptron (MLP) model was used to describe the total waste based on 20 raw material properties and 25 statistical descriptors of the time series data collected throughout the tableting (e.g., tableting speed and compression force). Then using 10 process time series data in addition to the raw material properties, the cumulative waste, during manufacturing was also predicted by long short-term memory (LSTM) and bidirectional LSTM (biLSTM) recurrent neural networks (RNN). The LSTM network was used to forecast the waste production profile to allow preventive actions. The results showed that RNNs were able to predict the waste trajectory, the best model resulting in 1096 and 2174 tablets training and testing root mean squared errors, respectively. For a better understanding of the process, and the models and to help the decision-support systems and control strategies, interpretation methods were implemented for all ANNs, which increased the process understanding by identifying the most influential material attributes and process parameters. The presented methodology is applicable to various critical quality attributes in several fields of pharmaceutics and therefore is a useful tool for realizing the Pharma 4.0 concept.

Development of a robotic cluster for automated and scalable cell therapy manufacturing

BACKGROUND AIMS

The production of commercial autologous cell therapies such as chimeric antigen receptor T cells requires complex manual manufacturing processes. Skilled labor costs and challenges in manufacturing scale-out have contributed to high prices for these products.

METHODS

We present a robotic system that uses industry-standard cell therapy manufacturing equipment to automate the steps involved in cell therapy manufacturing. The robotic cluster consists of a robotic arm and customized modules, allowing the robot to manipulate a variety of standard cell therapy instruments and materials such as incubators, bioreactors, and reagent bags. This system enables existing manual manufacturing processes to be rapidly adapted to robotic manufacturing, without having to adopt a completely new technology platform. Proof-of-concept for the robotic cluster's expansion module was demonstrated by expanding human CD8+ T cells.

RESULTS

The robotic cultures showed comparable cell yields, viability, and identity to those manually performed. In addition, the robotic system was able to maintain culture sterility.

CONCLUSIONS

Such modular robotic solutions may support scale-up and scale-out of cell therapies that are developed using classical manual methods in academic laboratories and biotechnology companies. This approach offers a pathway for overcoming manufacturing challenges associated with manual processes, ultimately contributing to the broader accessibility and affordability for personalized immunotherapies.

The Artificial Intelligence-Powered New Era in Pharmaceutical Research and Development: A Review

Currently, artificial intelligence (AI), machine learning (ML), and deep learning (DL) are gaining increased interest in many fields, particularly in pharmaceutical research and development, where they assist in decision-making in complex situations. Numerous research studies and advancements have demonstrated how these computational technologies are used in various pharmaceutical research and development aspects, including drug discovery, personalized medicine, drug formulation, optimization, predictions, drug interactions, pharmacokinetics/ pharmacodynamics, quality control/quality assurance, and manufacturing processes. Using advanced modeling techniques, these computational technologies can enhance efficiency and accuracy, handle complex data, and facilitate novel discoveries within minutes. Furthermore, these technologies offer several advantages over conventional statistics. They allow for pattern recognition from complex datasets, and the models, typically developed from data-driven algorithms, can predict a given outcome (model output) from a set of features (model inputs). Additionally, this review discusses emerging trends and provides perspectives on the application of AI with quality by design (QbD) and the future role of AI in this field. Ethical and regulatory considerations associated with integrating AI into pharmaceutical technology were also examined. This review aims to offer insights to researchers, professionals, and others on the current state of AI applications in pharmaceutical research and development and their potential role in the future of research and the era of pharmaceutical Industry 4.0 and 5.0.

Real-time visual intelligence for defect detection in pharmaceutical packaging

Defect detection in pharmaceutical blister packages is the most challenging task to get an accurate result in detecting defects that arise in tablets while manufacturing. Conventional defect detection methods include human intervention to check the quality of tablets within the blister packages, which is inefficient, time-consuming, and increases labor costs. To mitigate this issue, the YOLO family is primarily used in many industries for real-time defect detection in continuous production. To enhance the feature extraction capability and reduce the computational overhead in a real-time environment, the CBS-YOLOv8 is proposed by enhancing the YOLOv8 model. In the proposed CBS-YOLOv8, coordinate attention is introduced to improve the feature extraction capability by capturing the spatial and cross-channel information and also maintaining the long-range dependencies. The BiFPN (weighted bi-directional feature pyramid network) is also introduced in YOLOv8 to enhance the feature fusion at each convolution layer to avoid more precise information loss. The model's efficiency is enhanced through the implementation of SimSPPF (simple spatial pyramid pooling fast), which reduces computational demands and model complexity, resulting in improved speed. A custom dataset containing defective tablet images is used to train the proposed model. The performance of the CBS-YOLOv8 model is then evaluated by comparing it with various other models. Experimental results on the custom dataset reveal that the CBS-YOLOv8 model achieves a mAP of 97.4% and an inference speed of 79.25 FPS, outperforming other models. The proposed model is also evaluated on SESOVERA-ST saline bottle fill level monitoring dataset achieved the mAP50 of 99.3%. This demonstrates that CBS-YOLOv8 provides an optimized inspection process, enabling prompt detection and correction of defects, thus bolstering quality assurance practices in manufacturing settings.

A review on recent advancements in pharmaceutical technology transfer of tablets from an Indian perspective

OBJECTIVE

The healthcare sector is a paramount and rapidly expanding industry in India. The pharmaceutical field in India has experienced substantial growth and transformation in recent times, making significant contributions to the global healthcare market. This comprehensive review delves into the most recent innovations in pharmaceutical technology transfer (TT), particularly in the context of tablet formulations from an Indian standpoint.

SIGNIFICANCE

The pharmaceutical sector has grappled with various challenging issues, including the escalating costs of medications and the demand for patient-friendly products.

METHODS

In this technological progress era, various cutting-edge pharmaceutical technologies, such as artificial intelligence (AI), and 3D and 4D printing, play pivotal roles in drug development. Tablets, the most promising and widely utilized dosage form worldwide, require a sophisticated approach to TT. Achieving a successful TT necessitates a dedicated team with well-defined objectives, improved documentation, and effective communication.

RESULTS

The Indian Pharmaceutical Industry (IPI) possesses the potential to make significant contributions to the global healthcare sector. Moreover, we delve into the various phases of TT, highlighting the pivotal role of formulation development and process optimization in ensuring product quality, efficiency, and cost-effectiveness along with different models of TT. Additionally, we examine the challenges associated with TT and potential solutions, as well as the initiatives of the Indian government to bolster the Indian pharmaceutical sector's position as the "Pharmacy of the World".

CONCLUSION

It is concluded that there is a need to contextualize and institutionalize the tech transfer policies for successful implementation for the benefit of the global population.

Advances in artificial intelligence for drug delivery and development: A comprehensive review

Artificial intelligence (AI) has emerged as a powerful tool to revolutionize the healthcare sector, including drug delivery and development. This review explores the current and future applications of AI in the pharmaceutical industry, focusing on drug delivery and development. It covers various aspects such as smart drug delivery networks, sensors, drug repurposing, statistical modeling, and simulation of biotechnological and biological systems. The integration of AI with nanotechnologies and nanomedicines is also examined. AI offers significant advancements in drug discovery by efficiently identifying compounds, validating drug targets, streamlining drug structures, and prioritizing response templates. Techniques like data mining, multitask learning, and high-throughput screening contribute to better drug discovery and development innovations. The review discusses AI applications in drug formulation and delivery, clinical trials, drug safety, and pharmacovigilance. It addresses regulatory considerations and challenges associated with AI in pharmaceuticals, including privacy, data security, and interpretability of AI models. The review concludes with future perspectives, highlighting emerging trends, addressing limitations and biases in AI models, and emphasizing the importance of collaboration and knowledge sharing. It provides a comprehensive overview of AI's potential to transform the pharmaceutical industry and improve patient care while identifying further research and development areas.

Exploring the uses of digital health in palliative care in Southeast Asia

BACKGROUND

This integrative review explores the use of digital health technologies in palliative care within Southeast Asia. Despite extensive documentation of digital health in palliative care in Western nations, its application in Southeast Asia remains underdeveloped.

METHOD

The review includes a total of four papers meeting the eligibility criteria.

FINDINGS

The findings reveal limited studies of digital health adoption in palliative care. Key technologies include mobile health applications, electronic health records and telemedicine platforms. Challenges, such as health inequities, data security and the need for technology validation were identified. The review underscores the necessity for region-specific research to address these challenges and improve the integration of digital health in palliative care.

CONCLUSION

This study highlights the potential of digital health to enhance palliative care delivery and patient outcomes in Southeast Asia, advocating for increased adoption and tailored implementation strategies.

Critical review on the role of excipient properties in pharmaceutical powder-to-tablet continuous manufacturing

INTRODUCTION

The pharmaceutical industry is gradually changing batch-wise manufacturing processes to continuous manufacturing processes, due to the advantages it has to offer. The final product quality and process efficiency of continuous manufacturing processes is among others impacted by the properties of the raw materials. Existing knowledge on the role of raw material properties in batch processing is however not directly transferable to continuous processes, due to the inherent differences between batch and continuous processes.

AREAS COVERED

A review is performed to evaluate the role of excipient properties for different unit operations used in continuous manufacturing processes. Unit operations that will be discussed include feeding, blending, granulation, final blending, and compression.

EXPERT OPINION

Although the potency of continuous manufacturing is widely recognized, full utilization still requires a number of challenges to be addressed effectively. An expert opinion will be provided that discusses those challenges and potential solutions to overcome those challenges. The provided overview can serve as a framework for the pharmaceutical industry to push ahead process optimization and formulation development for continuous manufacturing processes.

Dynamic real-time forecasting technique for reclaimed water volumes in urban river environmental management

In recent years, the utilization of wastewater recycling as an alternative water source has gained significant traction in addressing urban water shortages. Accurate prediction of wastewater quantity is paramount for effective urban river water resource management. There is an urgent need to develop advanced forecasting technologies to further enhance the accuracy and efficiency of water quantity predictions. Decomposition ensemble models have shown excellent predictive capabilities but are challenged by boundary effects when decomposing the original data sequence. To address this, a rolling forecast decomposition ensemble scheme was developed. It involves using a machine learning (ML) model for prediction and progressively integrating prediction outcomes into the original sequence using complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN). Long short-term memory (LSTM) is then applied for sub-signal prediction and ensemble. The ML-CEEMDAN-LSTM model was introduced for wastewater quantity prediction, compared with non-decomposed ML models, CEEMDAN-based LSTM models, and ML-CEEMDAN-based LSTM models. Three ML algorithms-linear regression (LR), gradient boosting regression (GBR), and LSTM-were examined, using real-time prediction data and historical monitoring data, with historical data selected using the decision tree method. The study used daily water volumes data from two reclaimed water plants, CH and WQ, in Beijing. The results indicate that (1) ML models varied in their selection of real-time factors, with LR performing best among ML models during testing; (2) the ML-CEEMDAN-LSTM model consistently outperformed ML models; (3) the ML-CEEMDAN-LSTM hybrid model performed better than the CEEMDAN-LSTM model across different seasons. This study offers a reliable and accurate approach for reclaimed water volumes forecasting, critical for effective water environment management.

Accelerating attribute-focused process and product development through the development and deployment of autonomous process analytical technology platform system

Enabling real-time monitoring and control of the biomanufacturing processes through product quality insights continues to be an area of focus in the biopharmaceutical industry. The goal is to manufacture products with the desired quality attributes. To realize this rigorous attribute-focused Quality by Design approach, it is critical to support the development of processes that consistently deliver high-quality products and facilitate product commercialization. Time delays associated with offline analytical testing can limit the speed of process development. Thus, developing and deploying analytical technology is necessary to accelerate process development. In this study, we have developed the micro sequential injection process analyzer and the automatic assay preparation platform system. These innovations address the unmet need for an automatic, online, real-time sample acquisition and preparation platform system for in-process monitoring, control, and release of biopharmaceuticals. These systems can also be deployed in laboratory areas as an offline analytical system and on the manufacturing floor to enable rapid testing and release of products manufactured in a good manufacturing practice environment.

Development of automated metabolite control using mid-infrared probe for bioprocesses and vaccine manufacturing

Automation of metabolite control in fermenters is fundamental to develop vaccine manufacturing processes more quickly and robustly. We created an end-to-end process analytical technology and quality by design-focused process by replacing manual control of metabolites during the development of fed-batch bioprocesses with a system that is highly adaptable and automation-enabled. Mid-infrared spectroscopy with an attenuated total reflectance probe in-line, and simple linear regression using the Beer-Lambert Law, were developed to quantitate key metabolites (glucose and glutamate) from spectral data that measured complex media during fermentation. This data was digitally connected to a process information management system, to enable continuous control of feed pumps with proportional-integral-derivative controllers that maintained nutrient levels throughout fed-batch stirred-tank fermenter processes. Continuous metabolite data from mid-infrared spectra of cultures in stirred-tank reactors enabled feedback loops and control of the feed pumps in pharmaceutical development laboratories. This improved process control of nutrient levels by 20-fold and the drug substance yield by an order of magnitude. Furthermore, the method is adaptable to other systems and enables soft sensing, such as the consumption rate of metabolites. The ability to develop quantitative metabolite templates quickly and simply for changing bioprocesses was instrumental for project acceleration and heightened process control and automation.

ONE-SENTENCE SUMMARY

Intelligent digital control systems using continuous in-line metabolite data enabled end-to-end automation of fed-batch processes in stirred-tank reactors.

A Review on the Recent Advancements and Artificial Intelligence in Tablet Technology

BACKGROUND

Tablet formulation could be revolutionized by the integration of modern technology and established pharmaceutical sciences. The pharmaceutical sector can develop tablet formulations that are not only more efficient and stable but also patient-friendly by utilizing artificial intelligence (AI), machine learning (ML), and materials science.

OBJECTIVES

The primary objective of this review is to explore the advancements in tablet technology, focusing on the integration of modern technologies like artificial intelligence (AI), machine learning (ML), and materials science to enhance the efficiency, cost-effectiveness, and quality of tablet formulation processes.

METHODS

This review delves into the utilization of AI and ML techniques within pharmaceutical research and development. The review also discusses various ML methodologies employed, including artificial neural networks, an ensemble of regression trees, support vector machines, and multivariate data analysis techniques.

RESULTS

Recent studies showcased in this review demonstrate the feasibility and effectiveness of ML approaches in pharmaceutical research. The application of AI and ML in pharmaceutical research has shown promising results, offering a potential avenue for significant improvements in the product development process.

CONCLUSION

The integration of nanotechnology, AI, ML, and materials science with traditional pharmaceutical sciences presents a remarkable opportunity for enhancing tablet formulation processes. This review collectively underscores the transformative role that AI and ML can play in advancing pharmaceutical research and development, ultimately leading to more efficient, reliable and patient-centric tablet formulations.

FormulationAI: a novel web-based platform for drug formulation design driven by artificial intelligence

Today, pharmaceutical industry faces great pressure to employ more efficient and systematic ways in drug discovery and development process. However, conventional formulation studies still strongly rely on personal experiences by trial-and-error experiments, resulting in a labor-consuming, tedious and costly pipeline. Thus, it is highly required to develop intelligent and efficient methods for formulation development to keep pace with the progress of the pharmaceutical industry. Here, we developed a comprehensive web-based platform (FormulationAI) for in silico formulation design. First, the most comprehensive datasets of six widely used drug formulation systems in the pharmaceutical industry were collected over 10 years, including cyclodextrin formulation, solid dispersion, phospholipid complex, nanocrystals, self-emulsifying and liposome systems. Then, intelligent prediction and evaluation of 16 important properties from the six systems were investigated and implemented by systematic study and comparison of different AI algorithms and molecular representations. Finally, an efficient prediction platform was established and validated, which enables the formulation design just by inputting basic information of drugs and excipients. FormulationAI is the first freely available comprehensive web-based platform, which provides a powerful solution to assist the formulation design in pharmaceutical industry. It is available at https://formulationai.computpharm.org/.

Selection of Mexican Medicinal Plants by Identification of Potential Phytochemicals with Anti-Aging, Anti-Inflammatory, and Anti-Oxidant Properties through Network Analysis and Chemoinformatic Screening

Many natural products have been acquired from plants for their helpful properties. Medicinal plants are used for treating a variety of pathologies or symptoms. The axes of many pathological processes are inflammation, oxidative stress, and senescence. This work is focused on identifying Mexican medicinal plants with potential anti-oxidant, anti-inflammatory, anti-aging, and anti-senescence effects through network analysis and chemoinformatic screening of their phytochemicals. We used computational methods to analyze drug-like phytochemicals in Mexican medicinal plants, multi-target compounds, and signaling pathways related to anti-oxidant, anti-inflammatory, anti-aging, and anti-senescence mechanisms. A total of 1373 phytochemicals are found in 1025 Mexican medicinal plants, and 148 compounds showed no harmful functionalities. These compounds displayed comparable structures with reference molecules. Based on their capacity to interact with pharmacological targets, three clusters of Mexican medicinal plants have been established. Curatella americana, Ximenia americana, Malvastrum coromandelianum, and Manilkara zapota all have anti-oxidant, anti-inflammatory, anti-aging, and anti-senescence effects. Plumeria rubra, Lonchocarpus yucatanensis, and Salvia polystachya contained phytochemicals with anti-oxidant, anti-inflammatory, anti-aging, and anti-senescence reported activity. Lonchocarpus guatemalensis, Vallesia glabra, Erythrina oaxacana, and Erythrina sousae have drug-like phytochemicals with potential anti-oxidant, anti-inflammatory, anti-aging, and anti-senescence effects. Between the drug-like phytochemicals, lonchocarpin, vallesine, and erysotrine exhibit potential anti-oxidant, anti-inflammatory, anti-aging, and anti-senescence effects. For the first time, we conducted an initial virtual screening of selected Mexican medicinal plants, which was subsequently confirmed in vivo, evaluating the anti-inflammatory activity of Lonchocarpus guatemalensis Benth in mice.

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.

Impact of GAMP 5, data integrity and QbD on quality assurance in the pharmaceutical industry: How obvious is it?

In the pharmaceutical industry, it is essential to ensure the safety and efficacy of medicinal products. Therefore a robust quality assurance framework is needed. This manuscript examines the impact of GAMP 5 and data integrity (DI) on quality assurance, while also highlighting the role of quality by design (QbD) principles. GAMP 5 is a widely used framework for validating automated systems that establishes quality assurance practices. DI guarantees the reliability of data collected throughout various stages of drug development. The integration of QbD principles promotes a systematic approach to development that emphasizes a deep understanding of critical quality attributes, risk management, and continuous improvement. With their implementation, organizations are able to meet regulatory requirements and provide safe medications to patients worldwide.

Exploiting Pharma 4.0 Technologies in the Non-Biological Complex Drugs Manufacturing: Innovations and Implications

The pharmaceutical industry has entered an era of transformation with the emergence of Pharma 4.0, which leverages cutting-edge technologies in manufacturing processes. These hold tremendous potential for enhancing the overall efficiency, safety, and quality of non-biological complex drugs (NBCDs), a category of pharmaceutical products that pose unique challenges due to their intricate composition and complex manufacturing requirements. This review attempts to provide insight into the application of select Pharma 4.0 technologies, namely machine learning, in silico modeling, and 3D printing, in the manufacturing process of NBCDs. Specifically, it reviews the impact of these tools on NBCDs such as liposomes, polymeric micelles, glatiramer acetate, iron carbohydrate complexes, and nanocrystals. It also addresses regulatory challenges associated with the implementation of these technologies and presents potential future perspectives, highlighting the incorporation of digital twins in this field of research as it seems to be a very promising approach, namely for the optimization of NBCDs manufacturing processes.

Improving protein therapeutic development through cloud-based data integration

Pharma 4.0 is a digital evolution of the pharmaceutical industry that automates scientists' traditional workflows with the implementation of modern technologies like cloud pipelines, artificial intelligence, robotic platforms, and augmented reality. Lab data capture (LDC) is an essential strategy for initiating Pharma 4.0 that aggregates and harmonizes siloed lab data from analytical instruments, reporting systems, and operational platforms. This publication describes the execution of LDC within a quantitative PCR (qPCR) workflow using the Tetra Data Platform (TDP). We selected this workflow because the qPCR instrument, the ViiA7, generates discrete file-based data that documents execution of individual assays for quantifying residual DNA throughout biologics process development and product profiling. TDP executes LDC through the deployment of file scanning software agents, scanning and ingestion processes, and a cloud-based parsing pipeline that harmonizes source data. Web applications were developed to query, visualize, and interpret harmonized qPCR data for automated experiment data processing and process control charting from the TDP platform. Our implementation of LDC enables analytical researchers to harness FAIR (Findable, Accessible, Interoperable, Reproducible) data practices across the organization and establishes a "compliance-by-code" culture in development labs.

The Coming of Age of AI/ML in Drug Discovery, Development, Clinical Testing, and Manufacturing: The FDA Perspectives

Artificial intelligence (AI) and machine learning (ML) represent significant advancements in computing, building on technologies that humanity has developed over millions of years-from the abacus to quantum computers. These tools have reached a pivotal moment in their development. In 2021 alone, the U.S. Food and Drug Administration (FDA) received over 100 product registration submissions that heavily relied on AI/ML for applications such as monitoring and improving human performance in compiling dossiers. To ensure the safe and effective use of AI/ML in drug discovery and manufacturing, the FDA and numerous other U.S. federal agencies have issued continuously updated, stringent guidelines. Intriguingly, these guidelines are often generated or updated with the aid of AI/ML tools themselves. The overarching goal is to expedite drug discovery, enhance the safety profiles of existing drugs, introduce novel treatment modalities, and improve manufacturing compliance and robustness. Recent FDA publications offer an encouraging outlook on the potential of these tools, emphasizing the need for their careful deployment. This has expanded market opportunities for retraining personnel handling these technologies and enabled innovative applications in emerging therapies such as gene editing, CRISPR-Cas9, CAR-T cells, mRNA-based treatments, and personalized medicine. In summary, the maturation of AI/ML technologies is a testament to human ingenuity. Far from being autonomous entities, these are tools created by and for humans designed to solve complex problems now and in the future. This paper aims to present the status of these technologies, along with examples of their present and future applications.

Mechanically promoted lipid-based filaments via composition tuning for extrusion-based 3D-printing

Lipid excipients are favorable materials in pharmaceutical formulations owing to their natural, biodegradable, low-toxic and solubility/permeability enhancing properties. The application of these materials with advanced manufacturing platforms, particularly filament-based 3D-printing, is attractive for personalized manufacturing of thermolabile drugs. However, the filament's weak mechanical properties limit their full potential. In this study, highly flexible filaments were extruded using PG6-C16P, a lipid-based excipient belonging to the group of polyglycerol esters of fatty acids (PGFAs), based on tuning the ratio between its major and minor composition fractions. Increasing the percentage of the minor fractions in the system was found to enhance the relevant mechanical filament properties by 50-fold, guaranteeing a flawless 3D-printability. Applying a novel liquid feeding approach further improved the mechanical filament properties at lower percentage of minor fractions, whilst circumventing the issues associated with the standard extrusion approach such as low throughput. Upon drug incorporation, the filaments retained high mechanical properties with a controlled drug release pattern. This work demonstrates PG6-C16 P as an advanced lipid-based material and a competitive printing excipient that can empower filament-based 3D-printing.

A comprehensive study on Industry 4.0 in the pharmaceutical industry for sustainable development

The four evolutionary stages have brought us to Industry 4.0. Industry 4.0 is nothing but the 4th Industrial Revolution which will change the production processes. The implementation of Industry 4.0 in the pharmaceutical sector will make the manufacturing of complex drugs easier. The arrival of Industry 4.0 and its advanced technologies such as artificial intelligence (AI), robotics, and the Internet of Things (IoT) makes the processes flexible. Industry 4.0 was introduced to reduce the human workforce and make the complicated processes unchallenging. It is used in all aspects of pharmaceutical sector like analysis, diagnosis, manufacturing, and packaging. The main aim of this paper is to comprehensively elucidate how Industry 4.0 has played a significant role in sustainable development (SD). Industry 4.0 in sustainability decreases the research efforts and examines the research sector's opportunities. This paper also discusses the impact of Industry 4.0 on sustainable development. Industry 4.0 constructs a bridge between industry and sustainability leading to sustainable development. Sustainability can be achieved by adopting innovative techniques of Industry 4.0 in manufacturing. Moreover, Industry 4.0 provides potential benefits for enhancing pharmaceutical production concerning flexibility, expenses, standards, and safety. It is noticed that Industry 4.0 has a beneficial impact on sustainable development by implementing advanced technologies leading to flexible manufacturing processes.

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.

Smart manufacturing inspired approach to research, development, and scale-up of electrified chemical manufacturing systems

As renewable electricity becomes cost competitive with fossil fuel energy sources and environmental concerns increase, the transition to electrified chemical and fuel synthesis pathways becomes increasingly desirable. However, electrochemical systems have traditionally taken many decades to reach commercial scales. Difficulty in scaling up electrochemical synthesis processes comes primarily from difficulty in decoupling and controlling simultaneously the effects of intrinsic kinetics and charge, heat, and mass transport within electrochemical reactors. Tackling this issue efficiently requires a shift in research from an approach based on small datasets, to one where digitalization enables rapid collection and interpretation of large, well-parameterized datasets, using artificial intelligence (AI) and multi-scale modeling. In this perspective, we present an emerging research approach that is inspired by smart manufacturing (SM), to accelerate research, development, and scale-up of electrified chemical manufacturing processes. The value of this approach is demonstrated by its application toward the development of CO₂ electrolyzers.

Assessing the critical success factors for implementing industry 4.0 in the pharmaceutical industry: Implications for supply chain sustainability in emerging economies

The emerging technologies of Industry 4.0 (I4.0) are crucial to incorporating agility, sustainability, smartness, and competitiveness in the business model, enabling long-term sustainability practices in the pharmaceutical supply chain (PSC). By leveraging the latest technologies of I4.0, pharmaceutical companies can gain real-time visibility into their supply chain (SC) operations, allowing them to make data-driven decisions that improve SC performance, efficiency, resilience, and sustainability. However, to date, no research has examined the critical success factors (CSFs) that enable the pharmaceutical industry to adopt I4.0 successfully to enhance overall SC sustainability. This study, therefore, analyzed the potential CSFs for adopting I4.0 to increase all facets of sustainability in the PSC, especially from the perspective of an emerging economy like Bangladesh. Initially, sixteen CSFs were identified through a comprehensive literature review and expert validation. Later, the finalized CSFs were clustered into three relevant groups and analyzed using a Bayesian best-worst method (BWM)-based multi-criteria decision-making (MCDM) framework. The study findings revealed that "sufficient investment for technological advancement", "digitalized product monitoring and traceability", and "dedicated and robust research and development (R&D) team" are the top three CSFs to adopt I4.0 in the PSC. The study's findings can aid industrial practitioners, managers, and policymakers in creating effective action plans for efficiently adopting I4.0 in PSC to avail of its competitive benefits and ensure a sustainable future for the pharmaceutical industry.

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.

Learnings from Industry 4.0 for transitioning towards Industry 4.0+: challenges and solutions for Indian pharmaceutical sector

Industry 4.0 (I4.0) is helping production units to become smarter using cyber-physical systems and cognitive intelligence. The advanced diagnostics with I4.0 technologies (I4.0t) help in making the process highly flexible, resilient and autonomous. Still, the adoption of I4.0t especially in emerging economies like India is at a very slow pace. The present research has used an integrated approach i.e., Analytical Hierarchy Process-Combinative Distance-Based Assessment-Decision-Making Trial and Evaluation Laboratory to propose a barrier solution framework using data from pharmaceutical manufacturing sector. The findings reveal that "Costly venture" is found to be the most critical deterrent while "Customer awareness and satisfaction" is one of the potential solutions for I4.0t adoption. Further, lack of standardisation and fair benchmarking policies especially in the context of developing economies needs immediate attention. This article concludes by proposing a framework which will help to move from I4.0 towards Industry 4.0 + (I4.0+) which emphasises on role of collaboration between man and machine. And leads to sustainable supply chain management.

From lab to mass production: a policy for enabling the licensing of mRNA vaccines

Using the South African vaccine technology transfer hub supported by the WHO as an example, we show that the know-how needed to move mRNA vaccines from prototype to mass-production acts as an invisible barrier to market entry of mRNA vaccines. Overcoming this barrier relies on scarce human capital. In view of this scarcity and in preparation for the next pandemic, we propose broadening the scope of an existing WHO program, the WHO Academy, so that it coordinates knowledge diffusion initiatives by forming a systematized repository of know-how and a register of experts. As we explain, this proposal has an advantage in overcoming barriers to entry over current approaches of know-how acquisition.

Developing a machine learning enabled integrated formulation and process design framework for a pharmaceutical dropwise additive manufacturing printer

The pharmaceutical manufacturing sector needs to rapidly evolve to absorb the next wave of disruptive industrial innovations - Industry 4.0. This involves incorporating technologies like artificial intelligence, smart factories and 3D printing to automate, miniaturize and personalize the production processes. The goal of this study is to build a formulation and process design (FPD) framework for a pharmaceutical 3D printing technique called drop-on-demand (DoD) printing. FPD can automate the determination of formulation properties and printing conditions (input conditions) for DoD operation that can guarantee production of drug products with desired functional attributes. This study proposes to build the FPD framework in two parts: the first part involves building a machine learning model to simulate the forward problem - predicting DoD operation based on input conditions and the second part seeks to solve and experimentally validate the inverse problem - predicting input conditions that can yield desired DoD operation.

Advances in numerical simulation of unit operations for tablet preparation

Recently, there has been an increase in the use of numerical simulation technology in pharmaceutical preparation processes. Numerical simulation can contribute to a better understanding of processes, reduce experimental costs, optimize preparation processes, and improve product quality. The intermediate material of most dosage forms is powder or granules, especially in the case of solid preparations. The macroscopic behavior of particle materials is controlled by the interactions of individual particles with each other and surrounding fluids. Therefore, it is very important to analyze and control the microscopic details of the preparation process for solid preparations. Since tablets are one of the most widely used oral solid preparations, and the preparation process is relatively complex and involves numerous units of operation, it is especially important to analyze and control the tablet production process. The present paper discusses recent advances in numerical simulation technology for the preparation of tablets, including drying, mixing, granulation, tableting, and coating. It covers computational fluid dynamics (CFD), discrete element method (DEM), population balance model (PBM), finite element method (FEM), Lattice-Boltzmann model (LBM), and Monte Carlo model (MC). The application and deficiencies of these models in tablet preparation unit operations are discussed. Furthermore, the paper provides a systematic reference for the control and analysis of the tablet preparation process and provides insight into the future direction of numerical simulation technology in the pharmaceutical industry.

Interpretable artificial neural networks for retrospective QbD of pharmaceutical tablet manufacturing based on a pilot-scale developmental dataset

As the pharmaceutical industry increasingly adopts the Pharma 4.0. concept, there is a growing need to effectively predict the product quality based on manufacturing or in-process data. Although artificial neural networks (ANNs) have emerged as powerful tools in data-rich environments, their implementation in pharmaceutical manufacturing is hindered by their black-box nature. In this work, ANNs were developed and interpreted to demonstrate their applicability to increase process understanding by retrospective analysis of developmental or manufacturing data. The in vitro dissolution and hardness of extended-release, directly compressed tablets were predicted from manufacturing and spectroscopic data of pilot-scale development. The ANNs using material attributes and operational parameters provided better results than using NIR or Raman spectra as predictors. ANNs were interpreted by sensitivity analysis, helping to identify the root cause of the batch-to-batch variability, e.g., the variability in particle size, grade, or substitution of the hydroxypropyl methylcellulose excipient. An ANN-based control strategy was also successfully utilized to mitigate the batch-to-batch variability by flexibly operating the tableting process. The presented methodology can be adapted to arbitrary data-rich manufacturing steps from active substance synthesis to formulation to predict the quality from manufacturing or development data and gain process understanding and consistent product quality.

Product Quality Research for Developing and Assessing Regulatory Submissions for Generic Cyclosporine Ophthalmic Emulsions

Approval of the first generic 0.05% cyclosporine ophthalmic emulsion (COE) in the U.S. represents a milestone achievement of the science and research program in the U.S. Food and Drug Administration's Center for Drug Evaluation and Research (CDER). COE is a locally acting complex drug product indicated to increase tear production in patients whose production is presumed to be suppressed due to ocular inflammation associated with keratoconjunctivitis sicca. The path to approval required overcoming numerous scientific challenges to determining therapeutic equivalence to the reference listed drug. Researchers in CDER's Office of Pharmaceutical Quality and Office of Generic Drugs developed a quality by design approach to understand the effects of process and formulation variables on the product's critical quality attributes, including globule size distribution (GSD), turbidity, viscosity, zeta potential, surface tension, and osmolality. CDER researchers explored multiple techniques to perform physicochemical characterization and analyze the GSD including laser diffraction, nanoparticle tracking analysis, cryogenic transmission electron microscopy, dynamic light scattering, asymmetric field flow fractionation, and two-dimensional diffusion ordered spectroscopy nuclear magnetic resonance. Biphasic models to study drug transfer kinetics demonstrated that COEs with qualitative and quantitative sameness and comparable GSDs, analyzed using earth mover's distance, can be therapeutic equivalents. This body of research facilitated the review and approval of the first U.S. generic COE. In addition, the methods and fundamental understanding developed from this research may support the development and assessment of other complex generics. The approval of a generic COE should improve the availability of this complex drug product to U.S. patients.

Lessons from CDER's Quality Management Maturity Pilot Programs

Between October 2020 and March 2022, FDA's Center for Drug Evaluation and Research (CDER) completed two pilot programs to assess the quality management maturity (QMM) of drug manufacturing establishments. Mature quality systems promote proactive detection of vulnerabilities, prevent problems before they occur, and foster a culture that rewards process and system improvements. A CDER QMM program may help to advance supply chain resiliency and robustness and mitigate drug shortages. One pilot program evaluated seven establishments located within the U.S. that produce finished dosage form products marketed in the U.S. A second pilot program evaluated eight establishments located outside the U.S. that produce active pharmaceutical ingredients used in drug products marketed in the U.S. The execution of these pilot programs afforded FDA the opportunity to learn important lessons about the establishment QMM assessment process, scoring approach, assessor behaviors, and perceptions of the assessment questions, reports, and ratings. Many of the participating establishments reported that the QMM pilot assessments helped to identify their strengths, weaknesses, and new areas for improvement which they had not previously identified through internal audits or CGMP inspections. There has been a great deal of interest in the outcomes of CDER's QMM pilot programs and this paper describes, for the first time, the lessons CDER learned and will continue to heed in the development of a QMM program.

3D Printable Drug Delivery Systems: Next-generation Healthcare Technology and Regulatory Aspects

A revolutionary shift in healthcare has been sparked by the development of 3D printing, propelling us into an era replete with boundless opportunities for personalized DDS (Drug Delivery Systems). Precise control of the kinetics of drug release can be achieved through 3D printing, improving treatment efficacy and patient compliance. Additionally, 3D printing facilitates the co-administration of multiple drugs, simplifying treatment regimens. The technology offers rapid prototyping and manufacturing capabilities, reducing development timelines and costs. The seamless integration of advanced algorithms and artificial neural networks (ANN) augments the precision and efficacy of 3D printing, propelling us toward the forefront of personalized medicine. This comprehensive review delves into the regulatory frontiers governing 3D printable drug delivery systems, with an emphasis on adhering to rigorous safety protocols to ensure the well-being of patients by leveraging the latest advancements in 3D printing technologies powered by artificial intelligence. The paradigm promises superior therapeutic outcomes and optimized medication experiences and sets the stage for an immersive future within the Metaverse, wherein healthcare seamlessly converges with virtual environments to unlock unparalleled possibilities for personalized treatments.

To infinity and beyond: Strategies for fabricating medicines in outer space

Recent advancements in next generation spacecrafts have reignited public excitement over life beyond Earth. However, to safeguard the health and safety of humans in the hostile environment of space, innovation in pharmaceutical manufacturing and drug delivery deserves urgent attention. In this review/commentary, the current state of medicines provision in space is explored, accompanied by a forward look on the future of pharmaceutical manufacturing in outer space. The hazards associated with spaceflight, and their corresponding medical problems, are first briefly discussed. Subsequently, the infeasibility of present-day medicines provision systems for supporting deep space exploration is examined. The existing knowledge gaps on the altered clinical effects of medicines in space are evaluated, and suggestions are provided on how clinical trials in space might be conducted. An envisioned model of on-site production and delivery of medicines in space is proposed, referencing emerging technologies (e.g. Chemputing, synthetic biology, and 3D printing) being developed on Earth that may be adapted for extra-terrestrial use. This review concludes with a critical analysis on the regulatory considerations necessary to facilitate the adoption of these technologies and proposes a framework by which these may be enforced. In doing so, this commentary aims to instigate discussions on the pharmaceutical needs of deep space exploration, and strategies on how these may be met.

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Space is a hostile environment that threatens human health and drug stability.

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Data on the behaviour of medicines in space is critical but lacking.

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Novel drug manufacturing and delivery strategies are needed to safeguard crewmembers’ safety.

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Chemputing, synthetic biology, and 3D printing are examples of such emerging technologies.

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A regulatory framework for space medicines must be implemented to assure quality.

Exploring the potential of machine learning for more efficient development and production of biopharmaceuticals

Principles of Industry 4.0 direct us to predict how pharmaceutical operations and regulations may exist with automation, digitization, artificial intelligence (AI), and real time data acquisition. Machine learning (ML), a sub-discipline of AI, involves the use of statistical tools to extract the desired information either through understanding the underlying patterns in the information or by development of mathematical relationships among the critical process parameters (CPPs) and critical quality attributes (CQAs) of biopharmaceuticals. ML is still in its infancy for directly supporting the quality-by-design based development and manufacturing of biopharmaceuticals. However, adoption of ML-based models in place of conventional multi-variate-data-analysis (MVDA) is increasing with the accumulation of large-scale data. This has been majorly contributed by the real-time monitoring of process variables and quality attributes of products through the implementation of process analytical technology in biopharmaceutical manufacturing. All aspects of healthcare, from drug design to product distribution, are complex and multidimensional. Thus, ML-based approaches are being applied to achieve sophistication, accuracy, flexibility and agility in all these areas. This review discusses the potential of ML for addressing the complex issues in diverse areas of biopharmaceutical development, such as biopharmaceuticals design and assessment of early stage development, upstream and downstream process development, analysis, characterization and prediction of post translational modifications (PTMs), formulation and stability studies. Moreover, the challenges in acquisition, cleaning and structuring the bioprocess data, which is one of the major hurdles in implementation of ML in biopharma industry, have also been discussed. Regulatory perspectives on implementation of AI/ML in the biopharma sector have also been briefly discussed. This article is a bird's eye view on the recent developments and applications of ML in overcoming the challenges for adopting "Industry - 4.0" in the biopharma industry.

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.

Digitalization of Calibration Data Management in Pharmaceutical Industry Using a Multitenant Platform

The global quality infrastructure (QI) has been established and is maintained to ensure the safety of products and services for their users. One of the cornerstones of the QI is metrology, i.e., the science of measurement, as the quality management systems commonly rely on measurements for evaluating quality. For this reason, the calibration procedures and the management of the data related to them are of the utmost importance for the quality management in the process industry and given a high priority by the regulatory authorities. To overcome the relatively low level of digitalization in metrology, machine-interpretable data formats such as digital calibration certificates (DCC) are being developed. In this paper, we analyze the current calibration processes in the pharmaceutical industry, and the requirements defined for them in the relevant standards and regulations. For digitalizing the calibration-related data exchange, a multitenant cloud platform-based method is presented. To test and validate the approach, a proof of concept (POC) implementation of the platform is developed with a focus on ease and cost-efficiency of deployment and use while ensuring the preservation of traceability and data integrity. The POC is based on two industrial use cases involving organizations with different roles in the metrology infrastructure. In the testing, the presented approach proves to be an efficient method for organizing the calibration data exchange in industrial use.

Technical Considerations for the Conformation of Specific Competences in Mechatronic Engineers in the Context of Industry 4.0 and 5.0

The incursion of disruptive technologies, such as the Internet of Things, information technologies, cloud computing, digitalization and artificial intelligence, into current production processes has led to a new global industrial revolution called Industry 4.0 or Manufacturing 4.0. This new revolution proposes digitization from one end of the value chain to the other by integrating physical assets into systems and networks linked to a series of technologies to create value. Industry 4.0 has far-reaching implications for production systems and engineering education, especially in the training of mechatronic engineers. In order to face the new challenges of the transition from manufacturing 3.0 to Industry 4.0 and 5.0, it is necessary to implement innovative educational models that allow the systematic training of engineers. The competency-based education model has ideal characteristics to help mechatronic engineers, especially in the development of specific competencies. This article proposes 15 technical considerations related to generic industrial needs and disruptive technologies that serve to determine those specific competencies required by mechatronic engineers to meet the challenges of Industry 4.0 and 5.0.

Application of Artificial Neural Networks in the Process Analytical Technology of Pharmaceutical Manufacturing-a Review

Industry 4.0 has started to transform the manufacturing industries by embracing digitalization, automation, and big data, aiming for interconnected systems, autonomous decisions, and smart factories. Machine learning techniques, such as artificial neural networks (ANN), have emerged as potent tools to address the related computational tasks. These advancements have also reached the pharmaceutical industry, where the Process Analytical Technology (PAT) initiative has already paved the way for the real-time analysis of the processes and the science- and risk-based flexible production. This paper aims to assess the potential of ANNs within the PAT concept to aid the modernization of pharmaceutical manufacturing. The current state of ANNs is systematically reviewed for the most common manufacturing steps of solid pharmaceutical products, and possible research gaps and future directions are identified. In this way, this review could aid the further development of machine learning techniques for pharmaceutical production and eventually contribute to the implementation of intelligent manufacturing lines with automated quality assurance.

The Advent of a New Era in Digital Healthcare: A Role for 3D Printing Technologies in Drug Manufacturing?

The technological revolution has physically affected all manufacturing domains, at the gateway of the fourth industrial revolution. Three-dimensional (3D) printing has already shown its potential in this new reality, exhibiting remarkable applications in the production of drug delivery systems. As part of this concept, personalization of the dosage form by means of individualized drug dose or improved formulation functionalities has concentrated global research efforts. Beyond the manufacturing level, significant parameters must be considered to promote the real-time manufacturing of pharmaceutical products in distributed areas. The majority of current research activities is focused on formulating 3D-printed drug delivery systems while showcasing different scenarios of installing 3D printers in patients' houses, hospitals, and community pharmacies, as well as in pharmaceutical industries. Such research presents an array of parameters that must be considered to integrate 3D printing in a future healthcare system, with special focus on regulatory issues, drug shortages, quality assurance of the product, and acceptability of these scenarios by healthcare professionals and public parties. The objective of this review is to critically present the spectrum of possible scenarios of 3D printing implementation in future healthcare and to discuss the inevitable issues that must be addressed.