Search Results (136)

The resilience of the pharmaceutical supply chain (PSC) is crucial to ensuring the availability of medical products. However, increasing complexity and logistical bottlenecks have exposed weaknesses within PSC frameworks. These challenges underscore the urgent need for more resilient and intelligent supply chain solutions. Recently, Artificial Intelligence and machine learning (AI/ML) have emerged as transformative technologies to enhance PSC resilience. This study presents a systematic review evaluating the role of AI/ML in advancing PSC resilience and their applications across PSC functions. A comprehensive search of five academic databases (Scopus, the Web of Science, IEEE Xplore, PubMed, and EMBASE) identified 89 peer-reviewed studies published between 2019 and 2025. PRISMA 2020 guidelines were implemented, resulting in a final dataset of 32 studies. In addition to analyzing applications, this study identifies the AI/ML grouped into five main categories, providing a clearer understanding of their impact on PSC resilience. The findings reveal that despite AI/ML’s promise, significant research gaps persist. Particularly, AI/ML-driven regulatory compliance and real-time supplier collaboration remain underexplored. Over 59.3% of studies fail to address regulatory frameworks and ethical considerations. In addition, major challenges emerge such as the limited real-world deployment of AI/ML-driven solutions and the lack of managerial impacts on PSC resilience. This study emphasizes the need for stronger regulatory frameworks, broader empirical validation, and AI/ML-driven predictive modeling. This study proposes recommendations for future research to foster more efficient, transparent and ethical PSCs capable of navigating the complexities of global healthcare. Full article

Facing growing sustainability challenges and the critical priority of digital transformation, this study explores, through the lens of the dynamic capability view, the links between big data, sustainable performance, and green supply chain in a circular economy logic, filling a notable gap in emerging markets, particularly the pharmaceutical sector. Our study proposes an original conceptual model linking big data analytics to the circular economy, tested with 275 employees from the Saudi pharmaceutical sector. The results, obtained through state-of-the-art PLS-SEM modeling, indicate a significant positive impact of big data analytics on sustainable performance and green supply chain management within the circular economy framework. The study also reveals the crucial mediating role of sustainable performance and green supply chain management in the relationship between big data analytics and the circular economy. Our study proposes an integrated framework for understanding how digital technologies support the circular economy in emerging markets, with practical implications for pharmaceutical sector actors and policymakers, in line with Saudi Arabia’s Vision 2030. Full article

In the past two decades, particularly through the pandemic, the demand for real-time logistics has significantly increased. Cold chain logistics ensures specific temperature conditions for perishable goods such as food and pharmaceuticals, which is crucial for maintaining product quality, safety, and regulatory compliance. The integration of the Internet of Things (IoT) into cold chain logistics has transformed supply chain operations. The COVID-19 pandemic and the global urgency for vaccine distribution accelerated the adoption of cold chain technologies, emphasizing their role in preserving perishable goods’ integrity. IoT enables real-time monitoring, remote control, predictive analytics, and data-driven decision-making, all of which are essential for modern logistics. We conducted a bibliometric analysis of 50 publications from 1997 to 2024 to examine IoT’s role in cold chain management. Through co-occurrence and co-citation network analysis, core themes, influential works, and major contributors were identified. Thematic mapping highlighted the importance of temperature monitoring, logistics optimization, and risk management. Additionally, the transition from conventional logistics practices to IoT-driven methodologies was investigated in cold chain operations. The findings of this study provide a basis for understanding the structural properties of co-citation and co-occurrence networks in cold chain logistics and the evolving landscape of cold chain technology, and its impact on logistics, emphasizing the importance of intelligent, reliable, and sustainable cold chain systems to meet the growing demands in global supply chains. Full article

Softwood bark and twigs represent by-products of forest supply chains rich in extractable bioactive compounds. This study aimed at evaluating the bioactive molecules of hydrodynamic cavitation (HC) based extracts of bark and twigs from different conifer plants and exploring their antioxidant capacity. Samples of Picea abies twigs (RAR) and bark (CAR) and Abies alba twigs (SFT) underwent extraction using a pilot-scale Venturi reactor HC device. The freeze-dried extracts were characterized for the antioxidant capacity, through both in vitro and ex vivo assays, the antimicrobial activity, and the content of phenolics and free amino acids by UHPLC-ESI-MS/MS. HC-based aqueous extracts were obtained quickly and with low energy consumption. We found 10 phenolic acids, nine flavonols, three flavan-3-ols, five flavanones, three procyanidins, two stilbenoids, and 10 other phenolic compounds. Moreover, eight essential and seven dispensable amino acids were found. The principal component analysis showed clear discrimination among the three extracts. The CAR extract showed antimicrobial activity. The SFT extract showed the higher anthocyanins content and antioxidant activity, both through in vitro and ex vivo methods. These preliminary results confirm that by-products of Picea abies and Abies alba are rich in bioactive compounds and antioxidant activities, suggesting potential applications in the nutraceutical and pharmaceutical fields. Full article

Microbial contamination and biofilm formation on food contact materials (FCMs) represent critical challenges within the food supply chain, compromising food safety and quality while increasing the risk of foodborne illnesses. Traditional materials often lack sufficient microbial resistance to contamination, creating a high demand for innovative antimicrobial surfaces. This study assessed the effectiveness of a nanosized deposited SiO_xC_yH_z coating approved for food contact on 3D-printed polyamide 12 (PA12) disk substrates, aiming at providing antimicrobial and anti-biofilm functionality to mechanical components and packaging material in the food supply chain. The coating was applied using plasma-enhanced chemical vapor deposition (PECVD) and characterized through Fourier-transform infrared spectroscopy (FTIR), Raman spectroscopy, thermogravimetric analysis (TGA), scanning electron microscopy (SEM), and contact angle measurements. Coated PA12 samples exhibited significantly enhanced hydrophobicity, with an average water contact angle of 112.9°, thus improving antibacterial performance by markedly reducing bacterial adhesion. Microbiological assays revealed a significant (p < 0.001) bactericidal activity (up to 4 logarithms after 4 h, ≥99.99%) against Gram-positive and Gram-negative bacteria, including notable foodborne pathogens such as L. monocytogenes, S. aureus, E. coli, and S. typhimurium. SiO_xC_yH_z-coated PA12 surfaces exhibited strong antibacterial activity, representing a promising approach for coating additive-manufactured components and equipment for packaging production in the food and pharmaceutical supply chain able to enhance safety, extend product shelf life, and reduce reliance on chemical sanitizers. Full article

The Amazon region’s rich biodiversity supports a bioindustry model that utilizes various biological assets from different plant species, and where it will add value to existing production chains, starting to supply bio industrialized products and not just primary products. Guarana (Paullinia cupana) is rich in bioactive compounds that interest the food and pharmaceutical industries. Thus, the main objective of this review is to present ways to add value to the guarana production chain by developing bioproducts using the residues generated in its processing. During processing, various residues are generated, as follows: peel (corresponding to 30% of the total mass of the fruit), and pulp (aryl), shell, and spent seeds, which have potential for application according to their characteristics. These residues were used to obtain bioactive compounds (catechins, theobromine, and caffeine) through different types of extraction (conventional, enzymatic, and pressurized liquid), and, subsequently, encapsulation. They were also applied in biodegradable and active packaging. Due to the high hemicellulose concentration, residual guarana seeds’ characteristics could potentially produce xylooligosaccharides (XOS). Therefore, the concept of biorefinery applied within the guarana production chain provides products that can be studied in the future to determine which processes are viable for expanding and valuing the productive chain of this fruit, in addition to strengthening sustainable development in the Amazon. Full article

The application of Artificial Intelligence and Machine Learning in the supply chain fields is significantly changing the way businesses manage their operations, forecast their demand, manage their inventory, optimize their logistics, and increase their level of resilience. This research explores, through a bibliometric literature review, how the integration of these technologies can support the implementation of a demand-driven supply chain approach in the global healthcare and pharmaceutical supply chains, which are facing remarkable challenges in ensuring demand-driven operations, especially in light of sudden disruptions such as the COVID-19 pandemic. Full article

AI data governance is a crucial framework for ensuring that data are utilized in the lifecycle of large language model (LLM) activity, from the development process to the end-to-end testing process, model validation, secure deployment, and operations. This requires the data to be managed responsibly, confidentially, securely, and ethically. The main objective of data governance is to implement a robust and intelligent data governance framework for LLMs, which tends to impact data quality management, the fine-tuning of model performance, biases, data privacy laws, security protocols, ethical AI practices, and regulatory compliance processes in LLMs. Effective data governance steps are important for minimizing data breach activity, enhancing data security, ensuring compliance and regulations, mitigating bias, and establishing clear policies and guidelines. This paper covers the foundation of AI data governance, key components, types of data governance, best practices, case studies, challenges, and future directions of data governance in LLMs. Additionally, we conduct a comprehensive detailed analysis of data governance and how efficient the integration of AI data governance must be for LLMs to gain a trustable approach for the end user. Finally, we provide deeper insights into the comprehensive exploration of the relevance of the data governance framework to the current landscape of LLMs in the healthcare, pharmaceutical, finance, supply chain management, and cybersecurity sectors and address the essential roles to take advantage of the approach of data governance frameworks and their effectiveness and limitations. Full article

Green supply chain integration (GSCI) has emerged as a significant technique for improving sustainable performance by promoting collaboration with supply chain partners and breaking down organizational barriers to utilize complementary resources. This study investigates the relationships among GSCI, supply chain agility (SCA), digital orientation (DO), and sustainable performance, grounded in the Natural Resource-Based View (NRBV) and Contingency Theory (CT), based on survey data from 288 Chinese pharmaceutical manufacturing enterprises. Using mediation, moderation, and moderated mediation analyses, the findings indicate that SCA serves as a mediator between GSCI and sustainable performance. Significantly, DO strengthens both the direct effect of SCA on sustainable performance and the overall mediating pathway; nevertheless, it does not substantially boost the association between GSCI and SCA. This study’s innovation lies in elucidating the significance of GSCI as a resource for sustainable performance within the pharmaceutical enterprises, while further delineating the pathways and contingent elements for achieving sustainable performance in a digital context. This study offers valuable implications for both academic research and managerial practice. Full article

Identification of chemical properties in the inner tissues of fresh produce would enable us to identify major issues plaguing the agriculture supply chain, like off-flavors and core rot since these are caused by or accompanied by known chemical elements. We show the development of transmission Raman spectroscopy system for identifying these elements by addressing several issues: we located an optimal spectral window by conducting optical attenuation measurements and calculated the required LASER power in that range. For apple tissues, this optical window was found in the 700–950 nm range, and the required LASER power range was calculated to be in the 40–700 mW range. We also calculated that the optimal shifted-excitation Raman difference spectroscopy wavelengths should be separated by 0.7 nm in order to optimally produce narrow and high-intensity Raman peak features and eliminate the competing fluorescence signal. Finally, we provide a complete optical system design with exact optimal parameters. In contrast to other fields like pharmaceuticals and medicine, transmission Raman spectroscopy has not been applied extensively in agriculture. Therefore, this study fills a gap in that field’s applicability. Full article

Traditional herbal medicine, ethnopharmacology, and evidence-based phytotherapy inspire the development of botanical active ingredients for cosmetics. Ensuring their authenticity and quality is essential in guaranteeing the safety and efficacy of cosmetic formulations. However, the industry faces challenges related to adulteration and inconsistent verification practices. Adulteration can occur at both the crude raw material stage and during processing, involving misidentification, contamination, or the addition of unauthorized substances. This review emphasizes the need for robust authentication methods, including botanical identification, genetic testing, and phytochemical/metabolomic profiling. Analytical tools such as UV/VIS spectroscopy, HPTLC, GC-MS, HPLC/UHPLC, and isotope analysis provide complementary data for detecting and addressing adulteration. Adulteration jeopardizes product safety, efficacy, regulatory compliance, and consumer trust, while dilutions or substitutions erode the intended health benefits. A standardized, comprehensive approach across the supply chain—from raw material sourcing to extract manufacturing—is critical for maintaining the integrity of botanical ingredients. Cosmetovigilance and nutrivigilance are crucial aspects of ensuring product safety and compliance. This review presents a novel perspective by highlighting that, while the pharmaceutical and nutraceutical industries have long recognized the risks of botanical adulteration, awareness in the cosmetics industry remains limited. It further integrates recent advancements in metabolomic profiling, global regulatory challenges, and the economic implications of botanical adulteration in cosmetics. Future developments in AI-driven authentication technologies may represent a promising solution for addressing evolving challenges in product safety and traceability. Full article

A two-stage cold supply chain manages the transportation, storage, and distribution of temperature-sensitive products like frozen food, fresh/green products, and pharmaceuticals, which makes it costly. It consists of three key elements: a supplier, a warehouse, and multiple customers. Procurement planning can be conducted for various products, and this study assumes the transport of a fresh/green product with gradually decreasing quality due to its perishable nature. In a two-stage cold supply chain, multiple objective functions can be defined, including cost minimization, product quality optimization, and transportation/storage condition optimization. We developed a mathematical model to optimize these objectives, incorporating two specific functions, cost minimization and product age reduction, to ensure efficient supply chain performance. Traditional solution methods often struggle with multi-objective mathematical models due to their complexity. Therefore, the Non-Dominated Sorting Genetic Algorithm II (NSGA-II), a Genetic Algorithm-based approach, was applied to solve the model efficiently. NSGA-II optimized planning for a 7-day period under specific demand conditions, ensuring better resource allocation. The results showed that NSGA-II was better than traditional methods at making decisions and routing efficiently in the two-stage cold supply chain. This led to much better outcomes, with lower costs, less waste, and better product quality throughout the process. Full article

Background: China’s pharmaceutical regulatory framework is undergoing a pivotal shift from a traditional “command-control” model to a “lifecycle regulation” approach, aiming to balance drug safety, innovation, and accessibility. This study systematically examines the evolution, achievements, and challenges of China’s regulatory reforms, offering insights for global pharmaceutical governance. Methods: Using a mixed-methods approach integrating historical analysis, policy text mining, and case studies, we reviewed the pharmaceutical laws and regulations enacted since 1949, supplemented by case studies (e.g., COVID-19 vaccine emergency approvals) and a comparative analysis with international models (e.g., U.S. FDA and EU EMA frameworks). The data were sourced from authoritative platforms such as the PKULAW database, criminal law amendments, and international regulatory texts. Results: China’s regulatory evolution is categorized into four phases: Emergence (1949–1984), Foundational (1985–2000), Deepening Reform (2001–2018), and Lifecycle Regulation (2019–present). The revised Drug Administration Law (2019) institutionalized risk management, dynamic GMP inspections, and post-market surveillance, marking a transition to holistic lifecycle oversight. Key milestones include the introduction of the Vaccine Management Law (2019) and stricter penalties under the Criminal Law Amendment (XI) (2020). Conclusions: China’s lifecycle regulation model demonstrates potential to harmonize safety and innovation, evidenced by improved API export compliance (e.g., 15% increase in international certifications by 2023) and accelerated approvals for breakthrough therapies (e.g., domestically developed PD-1 inhibitors). However, challenges persist, including uneven enforcement capacities, tensions between conditional approvals and risk mitigation, and reliance on global supply chains. These findings provide critical lessons for developing countries navigating similar regulatory dilemmas. Full article

In-space manufacturing represents a transformative frontier in space exploration and industrial production, offering the potential to revolutionize how goods are produced and resources are utilized beyond Earth. This paper explores the multifaceted aspects of in-space manufacturing, including its evolution, technologies, challenges, and future prospects, while also addressing ethical and legal dimensions critical to its development. Beginning with an overview of its significance and historical context, this paper underscores key concepts such as resource optimization and the reduction of launch costs. It examines terrestrial and space-based manufacturing processes, emphasizing additive manufacturing, advanced materials processing, autonomous robotic systems, and biomanufacturing for pharmaceuticals. Unique challenges posed by the space environment, such as microgravity, vacuum conditions, and radiation exposure, are analyzed alongside issues related to supply chains, quality assurance, and energy management. Drawing from case studies, including missions aboard the International Space Station, this paper evaluates the lessons learned over six decades of innovation in in-space manufacturing. It further explores the potential for large-scale production to support deep-space missions and assesses the commercial and economic feasibility of these technologies. This paper also delves into the policy, legal, and ethical considerations to address as space-based manufacturing becomes integral to future space exploration and the global space economy. Ultimately, this work provides a comprehensive roadmap for advancing in-space manufacturing technologies and integrating them into humanity’s pursuit of sustainable and scalable space exploration. Full article

Background/Objectives: The integration of Artificial Intelligence (AI) and Machine Learning (ML) in pharmaceutical research and development is transforming the industry by improving efficiency and effectiveness across drug discovery, development, and healthcare delivery. This review explores the diverse applications of AI and ML, emphasizing their role in predictive modeling, drug repurposing, lead optimization, and clinical trials. Additionally, the review highlights AI’s contributions to regulatory compliance, pharmacovigilance, and personalized medicine while addressing ethical and regulatory considerations. Methods: A comprehensive literature review was conducted to assess the impact of AI and ML in various pharmaceutical domains. Research articles, case studies, and industry reports were analyzed to examine AI-driven advancements in predictive modeling, computational chemistry, clinical trials, drug safety, and supply chain management. Results: AI and ML have demonstrated significant advancements in pharmaceutical research, including improved target identification, accelerated drug discovery through generative models, and enhanced structure-based drug design via molecular docking and QSAR modeling. In clinical trials, AI streamlines patient recruitment, predicts trial outcomes, and enables real-time monitoring. AI-driven predictive maintenance, process optimization, and inventory management have enhanced efficiency in pharmaceutical manufacturing and supply chains. Furthermore, AI has revolutionized personalized medicine by enabling precise treatment strategies through genomic data analysis, biomarker discovery, and AI-driven diagnostics. Conclusions: AI and ML are reshaping pharmaceutical research, offering innovative solutions across drug discovery, regulatory compliance, and patient care. The integration of AI enhances treatment outcomes and operational efficiencies while raising ethical and regulatory challenges that require transparent, accountable applications. Future advancements in AI will rely on collaborative efforts to ensure its responsible implementation, ultimately driving the continued transformation of the pharmaceutical sector. Full article