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Neural network applications, as an emerging machine learning technology, are increasingly being integrated into pharmaceutical manufacturing technologies, offering significant improvement opportunities for performance, efficiency and sustainability. This review paper utilizes a systematic methodology to establish key literature trends and themes. The state-of-the-art body of knowledge in this hot research area is analyzed in descriptive (e.g. neural network technologies studied, sustainability indicators considered, manufacturing process addressed) and thematic synthesis components. Process analysis and improvement, quality control and additive manufacturing were identified as the three focal research themes, and research lines within these themes were further studied and discussed. To guide future research, potential paths and research questions are proposed against the gaps identified. The originality of this work lies in its methodology (adoption of a systematic review approach, highly limited in the current literature), its inclusion of sustainability (as an imperative concept for manufacturing technology research) and its specific focus on neural network applications in the context of pharmaceutical manufacturing technologies (a perspective, either has been missing or addressed too widely by extant contributions). [ABSTRACT FROM AUTHOR]

This article examines the complexities and challenges associated with onshoring manufacturing through local content requirements within the context of World Trade Organization (WTO) law. Governments have intensified efforts to relocate production within their territories, leveraging local content requirements and incentives. This shift is primarily justified by the need for supply chain resilience, public health, and national security, aiming to mitigate risks associated with reliance on foreign suppliers during crises. However, these strategies may conflict with WTO obligations, particularly those concerning trade-related investment measures and subsidies. Using pharmaceuticals as a case study, this article explores the rationale, scope, and implications of WTO rules on local content policies. The analysis provides insights into how nations navigate the delicate balance between enhancing domestic capabilities and adhering to international trade laws. [ABSTRACT FROM AUTHOR]

The perpetual significance of the pharmaceutical industry in society necessitates ongoing research efforts to enhance the efficacy of its manufacturing processes. Given that drug product manufacturing typically involves powder processing, a thorough understanding of powder characterization is needed for optimal process performance. Powder rheology is commonly examined in pharmaceutical manufacturing to elucidate the relationship between powder properties and the performance of pharmaceutical processes. This paper provides a brief discussion of recent literature regarding the various powder properties and characterization techniques encompassed in powder rheology. The powder properties are categorized into particle size, particle morphology, friability, electrostatics, permeability, wettability, cohesion, bulk density, and agglomeration sections. A distinct focus is placed on the segment about powder wettability. This review informs readers about the fundamental properties of powders known to influence pharmaceutical processes. It discusses the interrelationships among these properties, powder characterization techniques, and ideal states of powder properties that lead to optimal process performance. [ABSTRACT FROM AUTHOR]

The perpetual significance of the pharmaceutical industry in society necessitates ongoing research efforts to enhance the efficacy of its manufacturing processes. Given that drug product manufacturing typically involves powder processing, a thorough understanding of powder characterization is needed for optimal process performance. Powder rheology is commonly examined in pharmaceutical manufacturing to elucidate the relationship between powder properties and the performance of pharmaceutical processes. This paper provides a brief discussion of recent literature regarding the various powder properties and characterization techniques encompassed in powder rheology. The powder properties are categorized into particle size, particle morphology, friability, electrostatics, permeability, wettability, cohesion, bulk density, and agglomeration sections. A distinct focus is placed on the segment about powder wettability. This review informs readers about the fundamental properties of powders known to influence pharmaceutical processes. It discusses the interrelationships among these properties, powder characterization techniques, and ideal states of powder properties that lead to optimal process performance.

Current treatment strategies are moving towards patient‐centricity, which emphasizes the need for new solutions allowing for medication tailored to a patient. This can be realized by precision medicine where patient diversity is considered during treatment. However, the broader use of precision medicine is restricted by the current technological solutions and rigid manufacturing of pharmaceutical products by mass production principles. Additive manufacturing of pharmaceutical products can provide a feasible solution to this challenge. In this review, a particular subtype of additive manufacturing, that is, binder jetting 3D printing, is introduced as a solution for fabricating pharmaceutical solid products that can be considered as precision medicine. Technical aspects, practical applications, unique advantages and challenges related to this technique are discussed, indicating that binder jetting 3D printing possesses the potential for fabricating already new product prototypes, where diversity in patient treatment in terms of the needs for specific drug type, dose and drug release can be accounted. To further advance this type of mass customization of pharmaceuticals, multidisciplinary research initiatives are needed not only to cover the engineering aspects but also to bridge these innovations with patient‐centric perspectives. [ABSTRACT FROM AUTHOR]

The traditional method of producing medicine using the “one-size fits all” model is becoming a major issue for pharmaceutical manufacturers due to its inability to produce customizable medicines for individuals’ needs. Three-dimensional (3D) printing is a new disruptive technology that offers many benefits to the pharmaceutical industry by revolutionizing the way pharmaceuticals are developed and manufactured. 3D printing technology enables the on-demand production of personalized medicine with tailored dosage, shape and release characteristics. Despite the lack of clear regulatory guidance, there is substantial interest in adopting 3D printing technology in the large-scale manufacturing of medicine. This review aims to evaluate the research efforts of 3D printing technology in the Middle East and North Africa (MENA) region, with a particular emphasis on pharmaceutical research and development. Our analysis indicates an upsurge in the overall research activity of 3D printing technology but there is limited progress in pharmaceuticals research and development. While the MENA region still lags, there is evidence of the regional interest in expanding the 3D printing technology applications in different sectors including pharmaceuticals. 3D printing holds great promise for pharmaceutical development within the MENA region and its advancement will require a strong collaboration between academic researchers and industry partners in parallel with drafting detailed guidelines from regulatory authorities.

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. [ABSTRACT FROM AUTHOR]

Antibiotic fermentation residue (AFR) is nutrient-rich solid waste generated from fermentative antibiotic production process. It is demonstrated that AFR contains high-concentration of remaining antibiotics, and thus may promote antibiotic resistance development in receiving environment or feeding farmed animals. However, the dominate microorganisms and antibiotic resistance genes (ARGs) in AFRs have not been adequately explored, hampering understanding on the potential antibiotic resistance risk development caused by AFRs. Herein, seven kinds of representative AFRs along their production, storage, and treatment processes were collected, and multiple methods including amplicon sequencing, metagenomic sequencing, and bioinformatic approaches were adopted to explore the biological characteristics of AFRs. As expected, antibiotic fermentation producer was found as the predominant species in raw AFRs, which were collected at the outlet of fermentation tanks. However, except for producer species, more environment-derived species persisted in stored AFRs, which were temporarily stored at a semi-open space. Lactobacillus genus, classified as Firmicutes phylum and Bacilli class, became predominant bacterial taxa in stored AFRs, which might attribute to its tolerance to high concentration of antibiotics. Results from metagenomic sequencing together with assembly and binning approaches showed that these newly-colonizing species (e.g., Lactobacillus genus) tended to carry ARGs conferring resistance to the remaining antibiotic. However, after thermal treatment, remaining antibiotic could be efficiently removed from AFRs, and microorganisms together with DNA could be strongly destroyed. In sum, the main risk from the AFRs was the remaining antibiotic, while environment-derived bacteria which tolerate extreme environment, survived in ARFs with high content antibiotics, and may carry ARGs. Thus, hydrothermal or other harmless treatment technologies are recommended to remove antibiotic content and inactivate bacteria before recycling of AFRs in pharmaceutical industry. [Display omitted] [ABSTRACT FROM AUTHOR]

Digital twins are virtual models of physical artefacts that may or may not be synchronously connected, and that can be used to simulate their behavior. They are widely used in several domains such as manufacturing and automotive to enable achieving specific quality goals. In the health domain, so-called digital patient twins have been understood as virtual models of patients generated from population data and/or patient data, including, for example, real-time feedback from wearables. Along with the growing impact of data science technologies like artificial intelligence, novel health data ecosystems centered around digital patient twins could be developed. This paves the way for improved health monitoring and facilitation of personalized therapeutics based on management, analysis, and interpretation of medical data via digital patient twins. The utility and feasibility of digital patient twins in routine medical processes are still limited, despite practical endeavors to create digital twins of physiological functions, single organs, or holistic models. Moreover, reliable simulations for the prediction of individual drug responses are still missing. However, these simulations would be one important milestone for truly personalized therapeutics. Another prerequisite for this would be individualized pharmaceutical manufacturing with subsequent obstacles, such as low automation, scalability, and therefore high costs. Additionally, regulatory challenges must be met thus calling for more digitalization in this area. Therefore, this narrative mini-review provides a discussion on the potentials and limitations of digital patient twins, focusing on their potential bridging function for personalized therapeutics and an individualized pharmaceutical manufacturing while also looking at the regulatory impacts. [ABSTRACT FROM AUTHOR]

[Display omitted] The identification of process Design Space (DS) is of high interest in highly regulated industrial sectors, such as pharmaceutical industry, where assurance of manufacturability and product quality is key for process development and decision-making. If the process can be controlled by a set of manipulated variables, the DS can be expanded in comparison to an open-loop scenario, where there are no controls in place. Determining the benefits of control strategies may be challenging, particularly when the available model is complex and computationally expensive – which is typically the case of pharmaceutical manufacturing. In this study, we exploit surrogate-based feasibility analysis to determine whether the process satisfies all process constraints by manipulating the process inputs and reduce the effect of uncertainty. The proposed approach is successfully tested on two simulated pharmaceutical case studies of increasing complexity, i.e., considering (i) a single pharmaceutical unit operation, and (ii) a pharmaceutical manufacturing line comprised of a sequence of connected unit operations. Results demonstrate that different control actions can be effectively exploited to operate the process in a wider range of inputs and mitigate uncertainty. [ABSTRACT FROM AUTHOR]

Ensuring the quality and safety of medicinal products is of paramount importance to the pharmaceutical industry. Good manufacturing practice (GMP) regulations are part of a pharmaceutical manufacturer's quality management system and ensure that medicinal products are manufactured, imported and controlled consistently to quality standards appropriate to their intended use. The aim of the present study is to analyze the non-compliant operations identified during GMP inspections carried out by national competent authorities (NCA) in the EU/EEC between 2013 and 2022. A retrospective analysis of non-compliance reports published in the EudraGMDP database between 2013 and 2022 was performed. Overall, 99 reports by 21 national competent authorities were analyzed presenting the results of inspections in 19 countries. A total of 1458 deficiencies were identified, of which 544 (37%) were classified as major and 127 (9%) as critical. The most common non-compliant operations were the manufacturing of active substances (49 deficiencies) and the preparation of non-sterile products (47 deficiencies). In 41 cases, the NCA recommended suspension or voiding of the certificate of suitability (CEP) and in 36 cases revocation of the GMP certificates. The observed deficiencies highlight the importance and need for continuous monitoring and improvement of manufacturers' production processes and quality management systems. [ABSTRACT FROM AUTHOR]

Antimicrobial resistance is considered to be one of the biggest public health problems, and airborne transmission is an important but under-appreciated pathway for the spread of antibiotic resistance genes (ARGs) in the environment. Previous research has shown pharmaceutical factories to be a major source of ARGs and antibiotic resistant bacteria (ARB) in the surrounding receiving water and soil environments. Pharmaceutical factories are hotspots of antibiotic resistance, but the atmospheric transmission and its environmental risk remain more concerns. Here, we conducted a metagenomic investigation into the airborne microbiome and resistome in three pharmaceutical factories in China. Soil (average: 38.45%) and wastewater (average: 28.53%) were major contributors of airborne resistome. ARGs (vanR/vanS, blaOXA, and CfxA) conferring resistance to critically important clinically used antibiotics were identified in the air samples. The wastewater treatment area had significantly higher relative abundances of ARGs (average: 0.64 copies/16S rRNA). Approximately 28.2% of the detected airborne ARGs were found to be associated with plasmids, and this increased to about 50% in the wastewater treatment area. We have compiled a list of high-risk airborne ARGs found in pharmaceutical factories. Moreover, A total of 1,043 viral operational taxonomic units were identified and linked to 47 family-group taxa. Different CRISPR-Cas immune systems have been identified in bacterial hosts in response to phage infection. Similarly, higher phage abundance (average: 2451.70 PPM) was found in the air of the wastewater treatment area. Our data provide insights into the antibiotic resistance gene profiles and microbiome (bacterial and non-bacterial) in pharmaceutical factories and reveal the potential role of horizontal transfer in the spread of airborne ARGs, with implications for human and animal health.

Digital twins are virtual models of physical artefacts that may or may not be synchronously connected, and that can be used to simulate their behavior. They are widely used in several domains such as manufacturing and automotive to enable achieving specific quality goals. In the health domain, so-called digital patient twins have been understood as virtual models of patients generated from population data and/or patient data, including, for example, real-time feedback from wearables. Along with the growing impact of data science technologies like artificial intelligence, novel health data ecosystems centered around digital patient twins could be developed. This paves the way for improved health monitoring and facilitation of personalized therapeutics based on management, analysis, and interpretation of medical data via digital patient twins. The utility and feasibility of digital patient twins in routine medical processes are still limited, despite practical endeavors to create digital twins of physiological functions, single organs, or holistic models. Moreover, reliable simulations for the prediction of individual drug responses are still missing. However, these simulations would be one important milestone for truly personalized therapeutics. Another prerequisite for this would be individualized pharmaceutical manufacturing with subsequent obstacles, such as low automation, scalability, and therefore high costs. Additionally, regulatory challenges must be met thus calling for more digitalization in this area. Therefore, this narrative mini-review provides a discussion on the potentials and limitations of digital patient twins, focusing on their potential bridging function for personalized therapeutics and an individualized pharmaceutical manufacturing while also looking at the regulatory impacts.

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. [ABSTRACT FROM AUTHOR]

Objective: To determine the impact of major disease epidemics on pharmaceutical manufacturing firms' Research & Development (R&D) investments and economic consequences. Methods: The sample consists of 1 582 firm-year observations from 2009 to 2022 in China, of which, 26.6% of pharmaceutical companies are involved in the diagnosis and treatment of prevalent diseases. Linear models using R&D investments, patent applications, operating performances and stock returns as dependent variables are constructed separately to examine the response of pharmaceutical companies to disease epidemics and the resulting economic consequences. Results: The prevalence of five major diseases led to a 17.5% increase in the amount of R&D investment and an 87.8% rise in the ratio of R&D investment to total assets by disease-related pharmaceutical companies, compared to unrelated pharmaceutical companies. Further evidence indicated that the patent applications for disease-related firms increased by 44.3% relative to unrelated firms after the epidemics. Though the impacts of the epidemics on firms' operating performances were insignificant in the short term, a major disease epidemic was associated with an increase in stock returns of 67.4% and 44.6%, respectively, as measured by the capital asset pricing model and Fama-French five-factor model. Additional analysis revealed that the impacts of the epidemics on R&D investments and patent applications were more pronounced for nonstate- owned enterprises than state-owned enterprises. Conclusions: This study demonstrates that disease-related pharmaceutical firms respond to the disease epidemics through increasing R&D investment. More patent applications and higher market value are the main gains from the firms' increased investments in R&D following the epidemic, rather than the improvements of short-term operating performances. [ABSTRACT FROM AUTHOR]

A strategy for combining theoretical and empirical model predictions to enhance process monitoring and disturbance detection in continuous pharmaceutical manufacturing is investigated using the first two steps of ciprofloxacin. The first-principles component is a dynamic model that reads in process parameter data and returns a concentration prediction for each species in the system using well-established equations and numerical discretization. The input data for the dynamic model comes from low-cost and reliable sensors that are already commonly deployed in manufacturing scenarios, such as flowmeters and thermocouples, making the approach amenable to potential uniform deployment across numerous manufacturing sites. The empirical component is infrared spectra collected from an inline flow cell that feeds to a partial least squares regression model for product concentration. Process parameter disturbances were introduced while continuously collecting the outlet stream infrared spectra, reagent flowrates, reactor temperature, and running the theoretical and empirical prediction models. Post-processing included the application of changepoint analysis, which is a statistical method of determining changes in the mean of a given time-series dataset. Both types of disturbances were captured as changepoints in the theoretical and empirical model predictions and could be obtained more rapidly by analyzing the residuals between the two predictions. This indicates that the deployment of theoretical models along with empirical is a robust approach for rapidly detecting deviations in the process health, reducing the time that potentially out of specification material is sent downstream. Additionally, by comparing trends in the models with the process parameter data, root-cause analysis can be rapidly carried out for a given disturbance. This places emphasis on holistic process monitoring by incorporating characterization knowledge and understanding into the process along with applying all available data sources to ensure product quality. Highlights: • First-principles and empirical concentration predictions were analyzed and compared for a multi-step continuous flow reaction. • Process disturbances were enacted to assess model responses. • And changepoint analysis was applied to observe when they were detected. • Changepoint analysis of the predictions' residuals show this single metric can be monitored to rapidly detect process deviations. [ABSTRACT FROM AUTHOR]

The pharmaceutical industry is in the midst of a transition from traditional batch processes to continuous manufacturing. However, the challenges in making this transition vary depending on the selected manufacturing process. Compared with other oral solid dosage processes, wet granulation has been challenging to move towards continuous processing since traditional equipment has been predominantly strictly batch, instead of readily adapted to material flow such as dry granulation or tablet compression, and there have been few equipment options for continuous granule drying. Recently, pilot and commercial scale equipment combining a twin-screw wet granulator and a novel horizontal vibratory fluid-bed dryer have been developed. This study describes the process space of that equipment and compares the granules produced with batch high-shear and fluid-bed wet granulation processes. The results of this evaluation demonstrate that the equipment works across a range of formulations, effectively granulates and dries, and produces granules of similar or improved quality to batch wet granulation and drying. [ABSTRACT FROM AUTHOR]

Purpose: This work focuses on developing a production planning tool for small- and medium-sized enterprises (SMEs) in pharmaceutical manufacturing to achieve service levels that can meet their needs under highly competitive market conditions and fluctuating demand with efficiently and reasonable production costs. And, the tool is developed with the requirements of the general characteristics and constraints of industries. Methods: The proposed tool is developed on a spreadsheet-based model that includes decisions to determine production and sequence orders to manage inventory levels. The production plans are simulated with actual data for 9 months. The results are then compared with the as-is method in terms of the service level and inventory level. Results: The computational experiment shows that the service level has improved to 100% as targeted without additional inventory cost and capacity. In addition, it can improve the efficiency of planners with an easy-to-implement automation tool yielding less planning time and more agility to unexpected changes. Conclusions: The model aims to improve the service level and operational efficiency of the case study. And, it is a practical approach for production planning in SMEs with limited on investment and expertise. It also has potential to be extended to high efficiency production planning systems. [ABSTRACT FROM AUTHOR]

Purpose: Little attention has generally been given to non-sterile pharmaceutical manufacturing, because these products represent less microbial contamination risks compared to parenterals. Nevertheless, an extensive risk management program is required to prevent risk to patients and incidence of complaints or product recalls. Few scientific studies discuss on non-sterile production environment, which led to poor references for unreliable statistical approaches. The aim of this study was therefore to develop a statistical approach for microbial monitoring and limit calculation to be applied on non-sterile pharmaceutical industries, assessing two manufacturing facilities environments located in intertropical zones on different hemispheres. Methods: The study evaluated a 2-year data of environmental monitoring active and passive air and surface microbiological recovery for both facilities using JMP™ as a statistical tool. Process and non-process rooms were evaluated to propose the statistical approach for monitoring and limit calculation. Results: Fit tests showed that microbiological environmental monitoring results for both facilities followed Gamma-Poisson distribution. Capability analyses performed against regulatory limits show that all data sub-groups were in control (Cpk > 1.3). Alert and action limits were proposed according to the actual distribution curve. Conclusions: The study succeeded in developing a step-by-step statistical approach for assessing the EM program, which will support most non-sterile industries. The establishment of adequate limits is essential for false alarm prevention and for cost reduction with overreactions to in-control results. This study can also be extended to other process parameter control with similar distribution, expanding the applicability. [ABSTRACT FROM AUTHOR]

This observational descriptive study that was carried out with the objective of exploring the contribution of the local pharmaceutical industry to the Saudi drug security. Using a drug formulary provided from the Saudi Food and Drug Authority, containing all registered pharmaceutical products available in Saudi Arabia, we extracted information about drug class, drug type, country and place of manufacturing, shelf-life and price. Results showed that the majority of drugs in the market are manufactured in Europe (43.86%), followed by Saudi Arabia (22.55%), China and India (20.47%), Americas (10.24%), and other nations (2.61%). Most of the manufactured drugs were prescription drugs (90.62%). In this work, the local pharmaceutical industry with the highest percentage of contribution to local drug security was Pharmaceutical Solution Industries (PSI), representing the 5% of the items available in the Saudi market. The second highest percentage was 4% by TABUK Pharmaceutical Manufacturing CO., followed by SPIMACO (3%), JAMJOOM pharmaceutical company (2%), Riyadh pharma (2%), and Jazeera pharmaceutical industries (2%). In addition, results from this study provide information about the most essential pharmaceutical products that needs to be nationally manufactured or increased in production in order to rise the contribution of local pharmaceutical industries in Saudi drug security. Unfortunately, the small contribution of the Saudi pharmaceutical industry in local drug security increases the burden on the Kingdom's annual budget due to the over-reliance on international pharmaceuticals.

Background: The global pharmaceuticals market is a trillion-dollar industry which grows more than 5% annually. However, in comparison to other manufacturing industries (e.g., oil refining, automotive), the pharmaceutical sector lags in manufacturing innovation and automation. In the production of pharmaceutical solid dosage forms, the use of energy utilization as a performance measure of production efficiency has neither been implemented extensively, nor been optimized to maximize efficiency. This study will focus on the development and implementation of a smart manufacturing platform to optimize energy productivity whilst maintaining tablet quality via the consideration of different manufacturing scenarios. Methods: This study will consider three main unit operations (wet granulation, drying and milling) which are relatively more energy intensive in pharmaceutical downstream processing, used to produce solid dosage forms, such as tablets. Four case-studies will be considered, which are 1: baseline batch, 2: baseline continuous, 3: optimized batch and 4: optimized continuous. Smart manufacturing is implemented to present optimized cases 3: and 4: Improvements in the energy and performance metrics are quantified and compared to the baseline cases. Results and conclusions: The smart manufacturing platform used in this study, integrates advanced process model development, optimization, technoeconomic analysis and data integration. The utilization of this framework contributed to a ~70% and ~80% improvement in energy utilization in the optimized batch and continuous cases, respectively, when compared to the baseline batch case. In the optimized cases, tablet quality was maintained within targeted specifications and was comparable to the baseline batch case. This smart manufacturing framework can be generalized for drug product manufacturing and other particulate-based industries such as food, agriculture, and fine chemicals.

The global pharmaceuticals market is a trillion-dollar industry which grows more than 5% annually. However, in comparison to other manufacturing industries (e.g., oil refining, automotive), the pharmaceutical sector lags in manufacturing innovation and automation. In the production of pharmaceutical solid dosage forms, the use of energy utilization as a performance measure of production efficiency has neither been implemented extensively, nor been optimized to maximize efficiency. This study will focus on the development and implementation of a smart manufacturing platform to optimize energy productivity whilst maintaining tablet quality via the consideration of different manufacturing scenarios. This study will consider three main unit operations (wet granulation, drying and milling) which are relatively more energy intensive in pharmaceutical downstream processing, used to produce solid dosage forms, such as tablets. Four case-studies will be considered, which are 1: baseline batch, 2: baseline continuous, 3: optimized batch and 4: optimized continuous. Smart manufacturing is implemented to present optimized cases 3: and 4: Improvements in the energy and performance metrics are quantified and compared to the baseline cases. The smart manufacturing platform used in this study, integrates advanced process model development, optimization, technoeconomic analysis and data integration. The utilization of this framework contributed to a ~70% and ~80% improvement in energy utilization in the optimized batch and continuous cases, respectively, when compared to the baseline batch case. In the optimized cases, tablet quality was maintained within targeted specifications and was comparable to the baseline batch case. This smart manufacturing framework can be generalized for drug product manufacturing and other particulate-based industries such as food, agriculture, and fine chemicals. [ABSTRACT FROM AUTHOR]

The crystallization of peptides offers a sustainable and inexpensive alternative to the purification process. In this study, diglycine was crystallised in porous silica, showing the porous templates' positive yet discriminating effect. The diglycine induction time was reduced by five-fold and three-fold upon crystallising in the presence of silica with pore sizes of 6 nm and 10 nm, respectively. The diglycine induction time had a direct relationship with the silica pore size. The stable form (α-form) of diglycine was crystallised in the presence of porous silica, with the diglycine crystals obtained associated with the silica particles. Further, we studied the mechanical properties of diglycine tablets for their tabletability, compactability, and compressibility. The mechanical properties of the diglycine tablets were similar to those of pure MCC, even with the presence of diglycine crystals in the tablets. The diffusion studies of the tablets using the dialysis membrane presented an extended release of diglycine through the dialysis membrane, confirming that the peptide crystal can be used for oral formulation. Hence, the crystallization of peptides preserved their mechanical and pharmacological properties. More data on different peptides can help us produce oral formulation peptides faster than usual. [ABSTRACT FROM AUTHOR]

This observational descriptive study that was carried out with the objective of exploring the contribution of the local pharmaceutical industry to the Saudi drug security. Using a drug formulary provided from the Saudi Food and Drug Authority, containing all registered pharmaceutical products available in Saudi Arabia, we extracted information about drug class, drug type, country and place of manufacturing, shelf-life and price. Results showed that the majority of drugs in the market are manufactured in Europe (43.86%), followed by Saudi Arabia (22.55%), China and India (20.47%), Americas (10.24%), and other nations (2.61%). Most of the manufactured drugs were prescription drugs (90.62%). In this work, the local pharmaceutical industry with the highest percentage of contribution to local drug security was Pharmaceutical Solution Industries (PSI), representing the 5% of the items available in the Saudi market. The second highest percentage was 4% by TABUK Pharmaceutical Manufacturing CO., followed by SPIMACO (3%), JAMJOOM pharmaceutical company (2%), Riyadh pharma (2%), and Jazeera pharmaceutical industries (2%). In addition, results from this study provide information about the most essential pharmaceutical products that needs to be nationally manufactured or increased in production in order to rise the contribution of local pharmaceutical industries in Saudi drug security. Unfortunately, the small contribution of the Saudi pharmaceutical industry in local drug security increases the burden on the Kingdom's annual budget due to the over-reliance on international pharmaceuticals. [ABSTRACT FROM AUTHOR]

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 and 3D printing (3DP) to automate and personalize the drug production processes. This study aims to build a formulation and process design (FPD) framework for a pharmaceutical 3DP platform that recommends operating (formulation and process) conditions at which consistent drop printing can be obtained. The platform used in this study is a displacement‐based drop‐on‐demand 3D printer that manufactures dosages by additively depositing the drug formulation as droplets on a substrate. The FPD framework is built in two parts: the first part involves building a machine learning model to simulate the forward problem—predicting printer operation for given operating conditions and the second part seeks to solve and experimentally validate the inverse problem—predicting operating conditions that can yield desired printer operation. [ABSTRACT FROM AUTHOR]

The pharmaceutical industry has important reasons to adopt sustainability and reduce its environmental footprint. Joining the trend, the Indian pharmaceutical industry is also embracing a cleaner and greener transition and is adopting responsible manufacturing. Adoption of data-driven approaches like Life-Cycle Analysis (LCA) to understand the environmental footprint associated with synthesis of an Active Pharmaceutical Ingredient (API) can help the manufacturers to review and improve their legacy processes. Prioritizing process optimization can result in better environmental performance along with economic, social and competitive advantages. This paper presents a data-driven practical approach for implementing an LCA based method for making the production process greener while comparing environmental impacts under different categories. Accordingly, the approach and methodology adopted and the results highlighted are expected to give an idea to pharmaceutical manufacturing companies to innovate and optimize their approaches to enhance environmental performance within a set of system boundaries. The present study demonstrates 5% improvement in yield for Celecoxib (API) synthesis and the resulting improvement in environmental performance. The framework presents an alternative method to reduce waste, energy consumption and emissions to minimize the environmental burden. Accordingly, the system boundaries cover cradle-to-synthesis stages of the API manufacturing. The effectiveness and potential of using LCA in analyzing and optimizing the pharmaceutical manufacturing process alongside improving the environmental performance with higher yield has been shown. The need to integrate LCA methodology with product and process development has thus been shown. Process optimization through greener methods of API manufacturing can be guided by such framework studies. [ABSTRACT FROM AUTHOR]

Fermentation-based antibiotic production results in abundant nutrient-rich fermentation residue with high potential for recycling, but the high antibiotic residual concentration restricts its usefulness (e.g., in land application as organic fertilizer). In this study, an industrial-scale hydrothermal facility for the treatment of erythromycin fermentation residue (EFR) was investigated, and the potential risk of the long-term soil application of treated EFR promoting environmental antibiotic resistance development was evaluated. The treatment effectively removed bacteria and their DNA, and an erythromycin removal ratio of up to approximately 98% was achieved. The treated EFR was utilized as organic fertilizer for consecutive field applications from 2018 to 2020, with dosages ranging from 3750 to 15 000 kg∙hm−2, resulting in sub-inhibitory levels of erythromycin (ranging from 0.83–76.00 μg∙kg−1) in soils. Metagenomic shotgun sequencing was then used to characterize the antibiotic resistance genes (ARGs), mobile genetic elements (MGEs), and bacterial community composition of the soils. The soil ARG abundance and diversity did not respond to the treated EFR application in the first year, but gradually changed in the second and third year of application. The highest fold change in relative abundance of macrolide–lincosamide–streptogramin (MLS) and total ARGs were 12.59 and 2.75 times, compared with the control (CK; without application), respectively. The soil MGEs and taxonomic composition showed similar temporal trends to those of the ARGs, and appeared to assist in driving increasing ARG proliferation, as revealed by correlation analysis and structural equation models (SEMs). The relative abundance of particular erm resistance genes (RNA methyltransferase genes) increased significantly in the third year of treated EFR application. The close association of erm with MGEs suggested that horizontal gene transfer played a critical role in the observed erm gene enrichment. Metagenomic binning results demonstrated that the proliferation of mac gene-carrying hosts was responsible for the increased abundance of mac genes (efflux pump genes). This study shows that sub-inhibitory levels of erythromycin in soils had a cumulative effect on soil ARGs over time and emphasizes the importance of long-term monitoring for assessing the risk of soil amendment with treated industrial waste.

Orodispersible Films (ODFs) are drug delivery systems manufactured with a wide range of methods on a big scale or for customized medicines and small-scale pharmacy. Both ODFs and their fabrication methods have certain limitations. Many pharmaceutical companies and academic research centers across the world cooperate in order to cope with these issues and also to find new formulations for a wide array of APIs what could make their work profitable for them and beneficial for patients as well. The number of pending patent applications and granted patents with their innovative approaches makes the progress in the manufacturing of ODFs unquestionable. The number of commercially available ODFs is still growing. However, some of them were discontinued and are no longer available on the markets. This review aims to summarize currently marketed ODFs and those withdrawn from sale and also provides an insight into recently published studies concerning orodispersible films, emphasizing of utilized APIs. The work also highlights the attempts of scientific communities to overcome ODF's manufacturing methods limitations. [ABSTRACT FROM AUTHOR]

Three-dimensional (3D) printing is an additive manufacturing technique that creates objects under computer control. Owing to the rapid advancement of science and technology, 3D printing technology has been widely utilized in processing and manufacturing but rarely used in the pharmaceutical field. The first commercial form of Spritam® immediate-release tablet was approved by FDA in 2015, which promoted the advancement of 3D printing technology in pharmaceutical development. Three-dimensional printing technology is able to meet individual treatment demands with customized size, shape, and release rate, which overcomes the difficulties of traditional pharmaceutical technology. This paper intends to discuss the critical process parameters of binder jet 3D printing technology, list its application in pharmaceutical manufacturing in recent years, summarize the still-open questions, and demonstrate its great potential in the pharmaceutical industry. [ABSTRACT FROM AUTHOR]

Purpose: In pharmaceutical manufacturing, understanding and quantifying how process conditions impact product quality is pivotal to guaranteeing process profitability with sustained product yield. We describe an integrated system model for a commercial-scale continuous manufacturing process of a high-value active pharmaceutical ingredient (API) and its use to optimize process conditions to maximize yield with assured product quality. Methodology: Global sensitivity analysis (GSA) was used to assess different process parameters' impacts on API yield in order to guide the selection of decision variables for yield optimization. We then formulated different scenarios for optimization within approved process parameter ranges to propose optimal process conditions to increase API yield. Results: Within the considered parameter space, varying only key initial starting material and feed reagent mass fractions within their allowed parameter ranges showed potential for + 0.2% yield improvement while varying all process parameters could allow + 0.4% yield improvement. Conclusions: The general modelling framework to guide control strategies, highlight process improvements in silico, and reduce experimental burden can be applied to multiple pharmaceutical products across different manufacturing modalities and operating modes. [ABSTRACT FROM AUTHOR]

Purpose: To propose a statistical methodology for quantitative analysis of the quality management system (QMS) of pharmaceutical manufacturing. Methods: (1) Based on the manufacturing data from two established active pharmaceutical ingredient (API) manufacturers in China from 2010 to 2019, the linear regression with Pearson correlation coefficient is used to find the correlations between the proposed QMS operation indicators and performance indicators. (2) A stepwise multiple linear regression is used to identify the independent operation indicators with the biggest impact on a given performance indicator. (3) The Akaike Information Criterion is used to predict the performance indicators based on the operation indicators. Results: (1) Correlation: the right-first-time rate correlates strongly with various changes and deviations; the customer complaints correlate with changes, deviations, and CAPAs; the deficiency rate of foreign inspections correlates with deviations and CAPAs; and the CAPA on-time completion rate correlates with changes, deviations, and the ratio of employees in quality. (2) Impact: the right-first-time rate and the customer complaints are mostly impacted by the total deviations; the deficiency rate of foreign inspections is mostly impacted by deviations in equipment and instrument, and deviations due to human error; the CAPA on-time completion rate is mainly impacted by deviations in facility and utilities. (3) Predictability: the right-first-time rate, the customer complaints, the deficiency rate of foreign inspections, and the CAPA on-time completion rate can all be predicted based on the existing data with statistical significance. Conclusions: Deviations emerge as a key leading indicator for the performance of QMS. The proposed statistical methodology provides a basis for the data-driven quality management and regulation, whose visibility and predictability are likely to progress as the data accumulates. [ABSTRACT FROM AUTHOR]

[Display omitted] Segregation refers to the separation of components in a powder mixture, resulting in potential issues related to concentration inhomogeneity. Any well-mixed blend that undergoes secondary processing is inherently susceptible to segregation which, if unmitigated, will lead to active compound concentration variance and poorer product quality. The consequences range from adverse financial impact to manufacturers with product failures to the detrimental health effects to product users. Hence, the topic of segregation is of paramount importance to the industry, requiring it to be dissected and scrutinized intensively by scientists worldwide. This review provides a well-crafted theoretical framework designed to understand the common segregation mechanisms that manufacturing facilities face, followed by the efforts to gauge the degree of segregation. To minimize segregation in blends, various approaches – mathematical modeling, empirical experiments, and empirical methods with modeling consideration – have been utilized in segregation research and are covered in this review. The past segregation studies from many fields are discussed, with particular emphasis on pharmaceuticals. The review also discusses the evolution and advances in mixing technology and storage systems implemented by the pharmaceutical industry to prevent segregation. In the conclusion, the authors articulated their perspectives on potential mitigation measures, including suggestions for improvements and future studies. [ABSTRACT FROM AUTHOR]

• The integrated capacity and production planning problem is studied. • A methodology is proposed which increases problem complexity stepwise. • Deterministic and stochastic MILP models are proposed. • Local (OAT) and global (Sobol') sensitivity analyses are performed. • The methodology is applied to industry derived case studies of multi-stage networks. This work presents a methodology for capacity planning under uncertainty in general multi-stage manufacturing networks. Multiple manufacturing lines are available and the production time on each line must be divided between a set of materials. The methodology generates mixed-integer linear programming models that represent capacity expansions with economy of scale costs functions and production planning details. A deterministic model is solved to create a baseline and the impact of uncertainty is investigated by sensitivity analysis and stochastic programming. The applicability of the methodology is exemplified through two case studies derived from industrial pharmaceutical manufacturing. The methodology identifies bottlenecks that limit supply and, where required, activates, and assigns capacity expansion projects for satisfying demand subject to uncertainty. The methodology determines the best use of existing resources and the location and size of capacity expansions thereby generating a portfolio of recommendations for decision making on integrated planning of capacity and production. [ABSTRACT FROM AUTHOR]

[Display omitted] • Application of economic and governance indicators to supply risk assessment. • Modifications of the indicators to enable comparison of risks at different levels. • Development of a method to assess potential supply risks quantitatively. • Suggestions to reduce supply risks of excipients in Japan. This work presents an indicator-based assessment of potential supply risks for pharmaceutical excipients. The assessment was conducted systematically through the steps of survey, data collection, and indicator value calculation. Japan was specified as the target, and all marketed products were analyzed. As the basis of the indicator, the Herfindahl-Hirschman index (HHI), a well-established method for measuring market concentration in economics, was adopted. Modifications were made to enable comparison of the risk levels of compounds, dosage forms, and diseases. Case studies were performed on each of these levels, and recommendations were obtained for risk mitigation. On the compound level, magnesium stearate, a commonly used lubricant, showed the highest risk, and a switch to alternative compounds was recommended. The dosage-form level evaluation showed that tablets and capsules could have higher risk levels than granules, indicating the importance of diversification. Finally, the disease-level evaluation showed that the supply of adult disease drugs might be risky because of the frequent use of tablets; less reliance on tablet forms was advised for adult diseases. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) enables the production of complex, lightweight, and customized components with superior quality. Selecting the right materials considering their thermal properties, printability, and layer adhesion is crucial in melting-based AM techniques. This study investigates Droplet Deposition Modelling (DDM), an innovative material extrusion process that utilizes thermoplastic granules. DDM is distinguished by its shorter manufacturing times and a wider range of materials, setting it apart from traditional material extrusion methods such as fused filament fabrication. We investigated the printability and part quality in DDM using two common pharmaceutical excipients: Polyvinylpyrrolidone/vinyl acetate 6:4 (PVP/VA), which is highly brittle, and Polycaprolactone (PCL), known for its low solubility and role in controlled drug release. Different ratios of PVP/VA and PCL were compounded via hot melt extrusion (HME) and used in DDM to study the impact of ingredient content on printability and part quality, employing geometrical models to assess material compatibility and printability. The study revealed that increasing PVP/VA content leads to higher viscosity, reduced flowability, and uneven deposition, with formulations of 80 % and 100 % PVP/VA showing poor processability. In contrast, formulations with 60 % and 40 % PVP/VA exhibited smooth processing and compatibility with DDM. We identified processing temperature and Drop Aspect Ratio (DAR) as key factors influencing material printability and part quality. Elevated processing temperatures and reduced DAR were found to increase interface temperatures, reduce diffusion, and potentially cause the 'elephant feet' issue. Additionally, smaller droplet sizes and material characteristics, such as higher interfacial tension in PCL, could lead to coalescence. Our findings highlight the complexities in optimizing DDM processing parameters and material blends, underscoring the need for careful formulation design to achieve high-quality 3D printed products. [Display omitted] [ABSTRACT FROM AUTHOR]