Hasil untuk "Pharmaceutical industry"

Stephen A Kelly, Stefan Pohle, S. Wharry et al.

Payel Dhar, B. Jose Ravindra Raj, Amayappanallur Kannan Dasarathy et al.

ABSTRACT The rapid growth of agri‐food industries has led to an alarming increase in waste generation, posing environmental, economic, and sustainability challenges. This review explores recent advancements in the valorization of agri‐food by‐products into value‐added products through green extraction and biorefinery technologies. It emphasizes the recovery of bioactive compounds such as polyphenols, flavonoids, carotenoids, and dietary fibers from fruit, vegetable, dairy, meat, and seafood wastes, highlighting their potential applications in the food, pharmaceutical, cosmetic, and bioenergy sectors. Emerging eco‐friendly extraction techniques—including supercritical and subcritical fluid extraction, enzyme‐assisted extraction, microwave‐ and ultrasound‐assisted methods, and pulsed electric field processing—offer improved yield, purity, and energy efficiency while reducing ecological impact. Despite technological progress, large‐scale adoption remains constrained by high costs, lack of standardization, and limited industrial integration. Key research gaps include the need for techno‐economic assessments, solvent recovery strategies, and life‐cycle evaluations to ensure process scalability and sustainability. Future research should focus on developing hybrid extraction systems, AI‐driven process optimization, and pilot‐scale biorefineries supported by robust policy frameworks and industry–academia collaboration. Overall, agri‐food waste valorization presents a viable pathway toward achieving environmental sustainability and circular bioeconomy goals, enabling a transition from waste‐intensive practices to resource‐efficient and climate‐resilient production systems.

C. Nirmala, M. S. Bisht, Harjit Kaur Bajwa et al.

Abstract Background: Bamboo is a multipurpose plant known mostly for its industrial uses but is now being recognized as a potential source of bioactive compounds and natural antioxidants. All the parts of the bamboo plant such as rhizome, culm shaving, leaves, roots, shoots and seeds have clinical applications. Studies have revealed that bamboo is a rich source of antioxidants and regular consumption of bamboo-based products may reduce the risk of age-related chronic diseases including cardiovascular diseases, Alzheimer's disease, Parkinson's disease, cancer and diabetes. Scope and approach: This review article reports a comprehensive insight concerning antioxidants and antioxidant properties of bamboo shoots and leaves and their prospects for utilization in the development of functional foods and nutraceuticals. Antioxidants are vital constituents in the food and pharmaceutical industry as they scavenge free radicals that cause deterioration of products during processing and storage. They also promote human health by neutralizing cell damage caused by free radicals. Key findings and conclusion: Antioxidants are known to confer health benefits such as prevention of cancer and degenerative diseases, slowing down the aging process and promotion of cardiovascular health. The main antioxidants in bamboo leaves and shoots are phenols, vitamin C & E and mineral elements such as selenium, copper, zinc, iron and manganese. At present, natural antioxidants are in great demand as synthetic antioxidants being used in food and pharmaceuticals may be deleterious to health. Hence, bamboo a fast growing plant with huge biomass can serve as an alternative for the production of natural antioxidants.

Vívian Terra de Azevedo Decúpero, Caroline Damascena Cardoso, Sarah Santos Gomes et al.

Introdução: As universidades públicas desempenham um papel fundamental na produção de conhecimento científico, e por meio da extensão universitária, conectam ensino e pesquisa às necessidades sociais. Nesse contexto, o curso de Farmácia do CCENS-UFES, em parceria com a escola EEEFM Sirena Rezende Fonseca, localizada no distrito de Celina (Alegre-ES), desenvolveu, com o apoio da FAPES, um projeto voltado para a capacitação e conscientização dos alunos do ensino médio sobre Infecções Sexualmente Transmissíveis (IST). A metodologia adotada integrou abordagens teóricas e práticas, com foco em ensaios imunodiagnósticos, permitindo aos estudantes uma aplicação real dos conhecimentos adquiridos. Um dos casos abordados durante o projeto envolveu a história fictícia de Gabriel, aluno do programa de iniciação científica Jr. Gabriel, ao aprender sobre as IST, foi capaz de reconhecer os sinais de uma possível infecção em seu irmão Henrique, que trabalha na roça. Henrique, ao notar uma lesão genital, procurou Gabriel em busca de ajuda. A partir do aprendizado sobre as IST, Gabriel suspeitou da infecção e sugeriu que seu irmão realizasse a bateria de testes rápidos no Centro de Testagem e Aconselhamento de Alegre. O resultado positivo para sífilis evidenciou como a disseminação do conhecimento no âmbito escolar pode ter um impacto direto na saúde e bem-estar da comunidade. Além dessa aplicação prática, observou-se que, ao longo da experiência, muitos alunos demonstraram desconhecimento sobre IST, incluindo sinais, sintomas, modos de transmissão e formas de tratamento. No entanto, houve grande receptividade ao aprendizado, refletida na participação ativa nas atividades laboratoriais e discussões. A evolução na compreensão e na aplicação dos conceitos foi uma das conquistas mais significativas do projeto. Apesar do entusiasmo gerado nas atividades práticas, um dos desafios foi manter o interesse dos alunos durante as exposições teóricas. Para lidar com isso, foram inseridos casos cotidianos, como o mencionado acima, estruturados com narrativas interativas, nas quais os alunos contribuíam com suas próprias soluções para as situações apresentadas. Essa abordagem contribuiu para uma maior imersão no tema e facilitou a assimilação do conteúdo. O alcance do projeto foi limitado pelo número reduzido de alunos atendidos, o que comprometeu sua abrangência. Diante dos resultados, é clara a necessidade de investimentos públicos para ampliar iniciativas como o PICJr, possibilitando a inclusão de mais estudantes e a exploração de outras questões de saúde. A integração entre escolas, universidades e serviços de saúde é fundamental para fortalecer a educação em saúde e incentivar o ingresso no ensino superior, promovendo impactos positivos na formação dos alunos e na comunidade.

Zhiyu Xiong, Yuan Yuan, Bopeng Qiu et al.

Abstract Background Sleep quality decreased can result in a major health issue in older people with age. While not all sleep changes are pathological in older people’s life, severe disturbances may lead to depression, cognitive impairments, deterioration of quality of life, significant stresses for careers and increased healthcare costs. Despite the known benefits of exercise for improving sleep quality, it is necessary to identify the optimal exercise type and dose. Objective This systematic review and network meta-analysis (NMA) combined to examine evaluated the existing evidence on the effectiveness of different exercises, and to examine the dose and response relationship between overall and specific types with improving sleep quality in older people. Methods PubMed, Cochrane Central, Web of Science, and Embase were systematically searched for this review, including studies up to April 2025. Only randomized controlled trials were included. Studies involved at least one type of exercise intervention and reported changes in sleep quality assessments. To address the limitations of relying solely on statistical significance, we also calculated the minimal clinically important difference (MCID) to determine the smallest meaningful improvement in sleep quality among older people, both overall and across different exercise doses. Data analysis and visualization were conducted using the “meta”, “netmeta”, “MBNMA”, and “ggplot2” packages in the R environment. Results A total of 62 RCTs involving 5005 older adults were included. Overall, exercise significantly improved sleep quality, with clinically meaningful improvements achieved from as early as 5 weeks of intervention. The optimal exercise type was combined aerobic and resistance training, followed by aerobic exercise, resistance training, walking, and yoga. The estimated optimal exercise dose was around 660 to 990 METs*min/week, with longer durations at 15 weeks producing the greatest benefits. Improvements were more pronounced among participants with poorer baseline sleep quality. Conclusion If older people receive the most appropriate exercise intervention, they can obtain clinically meaningful benefits of improving sleep in the elderly within the WHO guidelines for exercise doses. The results support the WHO recommendation that combine aerobic exercise and resistance training should be an important part of interventions for the older people. Protocol registration PROSPERO registration number: CRD42024566751. Graphical Abstract

Sahar Marefat, Ali Shayanfar, Farnaz Monajjemzadeh

The evaluation of color in pharmaceutical materials and products serves as an essential method for assessing physical appearance, stability, and overall quality control. Any recognizable color change in pharmaceuticals may lead to failure in meeting quality objectives. Traditional visual examinations, while commonly used, are subjective and prone to inconsistency, making them inadequate for precise assessment. As a result, advanced instrumental techniques have gained prominence, with CIE (Commission Internationale de l'Eclairage) Lab color space being widely recognized for its accuracy and applicability. Developed by the International Commission on Illumination, the CIELab system characterizes color using three components: L (lightness), a⁎ (red-green axis), and b⁎ (yellow-blue axis), providing a quantitative, standardized approach for color measurement. This method has been extensively utilized in pharmaceutical research and industry for diverse applications, such as quality control, stability studies, and batch-to-batch consistency evaluations. The present review aims to discuss studies that have employed this method in pharmaceutical color assessment, and related quality control issues.

Chaozheng Wang, Zezhou Yang, Shuzheng Gao et al.

Code completion, a crucial practice in industrial settings, helps developers improve programming efficiency by automatically suggesting code snippets during development. With the emergence of Large Code Models (LCMs), this field has witnessed significant advancements. Due to the natural differences between open-source and industrial codebases, such as coding patterns and unique internal dependencies, it is a common practice for developers to conduct domain adaptation when adopting LCMs in industry. There exist multiple adaptation approaches, among which retrieval-augmented generation (RAG) and fine-tuning are the two most popular paradigms. However, no prior research has explored the trade-off of the two approaches in industrial scenarios. To mitigate the gap, we comprehensively compare the two paradigms including Retrieval-Augmented Generation (RAG) and Fine-tuning (FT), for industrial code completion in this paper. In collaboration with Tencent's WXG department, we collect over 160,000 internal C++ files as our codebase. We then compare the two types of adaptation approaches from three dimensions that are concerned by industrial practitioners, including effectiveness, efficiency, and parameter sensitivity, using six LCMs. Our findings reveal that RAG, when implemented with appropriate embedding models that map code snippets into dense vector representations, can achieve higher accuracy than fine-tuning alone. Specifically, BM25 presents superior retrieval effectiveness and efficiency among studied RAG methods. Moreover, RAG and fine-tuning are orthogonal and their combination leads to further improvement. We also observe that RAG demonstrates better scalability than FT, showing more sustained performance gains with larger scales of codebase.

Anastasia Zhukova, Jonas Lührs, Christian E. Lobmüller et al.

Recent trends in NLP utilize knowledge graphs (KGs) to enhance pretrained language models by incorporating additional knowledge from the graph structures to learn domain-specific terminology or relationships between documents that might otherwise be overlooked. This paper explores how SciNCL, a graph-aware neighborhood contrastive learning methodology originally designed for scientific publications, can be applied to the process industry domain, where text logs contain crucial information about daily operations and are often structured as sparse KGs. Our experiments demonstrate that language models fine-tuned with triplets derived from graph embeddings (GE) outperform a state-of-the-art mE5-large text encoder by 9.8-14.3% (5.45-7.96p) on the proprietary process industry text embedding benchmark (PITEB) while having 3 times fewer parameters.

Marcos Lima Romero, Ricardo Suyama

The recent development of Agentic AI systems, empowered by autonomous large language models (LLMs) agents with planning and tool-usage capabilities, enables new possibilities for the evolution of industrial automation and reduces the complexity introduced by Industry 4.0. This work proposes a conceptual framework that integrates Agentic AI with the intent-based paradigm, originally developed in network research, to simplify human-machine interaction (HMI) and better align automation systems with the human-centric, sustainable, and resilient principles of Industry 5.0. Based on the intent-based processing, the framework allows human operators to express high-level business or operational goals in natural language, which are decomposed into actionable components. These intents are broken into expectations, conditions, targets, context, and information that guide sub-agents equipped with specialized tools to execute domain-specific tasks. A proof of concept was implemented using the CMAPSS dataset and Google Agent Developer Kit (ADK), demonstrating the feasibility of intent decomposition, agent orchestration, and autonomous decision-making in predictive maintenance scenarios. The results confirm the potential of this approach to reduce technical barriers and enable scalable, intent-driven automation, despite data quality and explainability concerns.

Suman Kumar Das

India's growing population and economy have significantly increased the demand and consumption of natural resources. As a result, the potential benefits of transitioning to a circular economic model have been extensively discussed and debated among various Indian stakeholders, including policymakers, industry leaders, and environmental advocates. Despite the numerous initiatives, policies, and transnational strategic partnerships of the Indian government, most small and medium enterprises in India face significant challenges in implementing circular economy practices. This is due to the lack of a clear pathway to measure the current state of the circular economy in Indian industries and the absence of a framework to address these challenges. This paper examines the circularity of the 93-textile industry in India using the C-Readiness Tool. The analysis comprehensively identified 9 categories with 34 barriers to adopting circular economy principles in the textile sector through a narrative literature review. The identified barriers were further compared against the findings from a C-readiness tool assessment, which revealed prominent challenges related to supply chain coordination, consumer engagement, and regulatory compliance within the industry's circularity efforts. In response to these challenges, the article proposes a strategic roadmap that leverages digital technologies to drive the textile industry towards a more sustainable and resilient industrial model.

Mari Ashiga, Vardan Voskanyan, Fateme Dinmohammadi et al.

Recent advancements in Large Language Models (LLMs) for code optimization have enabled industrial platforms to automate software performance engineering at unprecedented scale and speed. Yet, organizations in regulated industries face strict constraints on which LLMs they can use - many cannot utilize commercial models due to data privacy regulations and compliance requirements, creating a significant challenge for achieving high-quality code optimization while maintaining cost-effectiveness. We address this by implementing a Mixture-of-Agents (MoA) approach that directly synthesizes code from multiple specialized LLMs, comparing it against TurinTech AI's vanilla Genetic Algorithm (GA)-based ensemble system and individual LLM optimizers using real-world industrial codebases. Our key contributions include: (1) First MoA application to industrial code optimization using real-world codebases; (2) Empirical evidence that MoA excels with open-source models, achieving 14.3% to 22.2% cost savings and 28.6% to 32.2% faster optimization times for regulated environments; (3) Deployment guidelines demonstrating GA's advantage with commercial models while both ensembles outperform individual LLMs; and (4) Real-world validation across 50 code snippets and seven LLM combinations, generating over 8,700 variants, addresses gaps in industrial LLM ensemble evaluation. This provides actionable guidance for organizations balancing regulatory compliance with optimization performance in production environments.

Jingzhi Gong, Rafail Giavrimis, Paul Brookes et al.

There is a growing interest in leveraging multiple large language models (LLMs) for automated code optimization. However, industrial platforms deploying multiple LLMs face a critical challenge: prompts optimized for one LLM often fail with others, requiring expensive model-specific prompt engineering. This cross-model prompt engineering bottleneck severely limits the practical deployment of multi-LLM systems in production environments. We introduce Meta-Prompted Code Optimization (MPCO), a framework that automatically generates high-quality, task-specific prompts across diverse LLMs while maintaining industrial efficiency requirements. MPCO leverages metaprompting to dynamically synthesize context-aware optimization prompts by integrating project metadata, task requirements, and LLM-specific contexts. It is an essential part of the ARTEMIS code optimization platform for automated validation and scaling. Our comprehensive evaluation on five real-world codebases with 366 hours of runtime benchmarking demonstrates MPCO's effectiveness: it achieves overall performance improvements up to 19.06% with the best statistical rank across all systems compared to baseline methods. Analysis shows that 96% of the top-performing optimizations stem from meaningful edits. Through systematic ablation studies and meta-prompter sensitivity analysis, we identify that comprehensive context integration is essential for effective meta-prompting and that major LLMs can serve effectively as meta-prompters, providing actionable insights for industrial practitioners.

Qinwen Chen, Wenbiao Tao, Zhiwei Zhu et al.

Community Question Answering (CQA) platforms can be deemed as important knowledge bases in community, but effectively leveraging historical interactions and domain knowledge in real-time remains a challenge. Existing methods often underutilize external knowledge, fail to incorporate dynamic historical QA context, or lack memory mechanisms suited for industrial deployment. We propose ComRAG, a retrieval-augmented generation framework for real-time industrial CQA that integrates static knowledge with dynamic historical QA pairs via a centroid-based memory mechanism designed for retrieval, generation, and efficient storage. Evaluated on three industrial CQA datasets, ComRAG consistently outperforms all baselines--achieving up to 25.9% improvement in vector similarity, reducing latency by 8.7% to 23.3%, and lowering chunk growth from 20.23% to 2.06% over iterations.

Saeid Ghasemshirazi, Ghazaleh Shirvani, Marziye Ranjbar Tavakoli et al.

The pharmaceutical supply chain faces escalating cybersecurity challenges threatening patient safety and operational continuity. This paper examines the transformative potential of zero trust architecture for enhancing security and resilience within this critical ecosystem. We explore the challenges posed by data breaches, counterfeiting, and disruptions and introduce the principles of continuous verification, least-privilege access, and data-centric security inherent in zero trust. Real-world case studies illustrate successful implementations. Benefits include heightened security, data protection, and adaptable resilience. As recognized by researchers and industrialists, a reliable drug tracing system is crucial for ensuring drug safety throughout the pharmaceutical production process. One of the most pivotal domains within the pharmaceutical industry and its associated supply chains where zero trust can be effectively implemented is in the management of narcotics, high-health-risk drugs, and abusable substances. By embracing zero trust, the pharmaceutical industry fortifies its supply chain against constantly changing cyber threats, ensuring the trustworthiness of critical medical operations.

Panna Vass, E. Szabó, A. Domokos et al.

Recently, electrospinning (ES) of fibers has been shown to be an attractive strategy for drug delivery. One of the main features of ES is that a wide variety of drugs can be loaded into the fibers to improve their bioavailability, to enhance dissolution, or to achieve controlled release. Besides, ES is a continuous technology with low energy consumption, which can make it a very economic production alternative to the widely used freeze drying and spray drying. However, the low production rate of laboratory-scaled ES has limited the industrial application of the technology so far. This article covers the various ES technologies developed for scaled-up fiber production with an emphasis on pharmaceutically relevant examples. The methods used for increasing the productivity are complied, which is followed by a review of specific examples from literature where these technologies are utilized to produce oral drug delivery systems. The different technologies are compared in terms of their basic principles, advantages, and limitations. Finally, the different downstream processing options to prepare tablets or capsules containing the electrospun drug are covered as well. This article is categorized under: Therapeutic Approaches and Drug Discovery > Emerging Technologies.

J. Hayler, David K. Leahy, E. Simmons

As companies grow ever more mindful of the sustainability aspects of their products and supply chains, an increasing focus on the environmental impact of pharmaceutical manufacture spurs innovation from chemists who support this industry. Metal catalysis has the potential to greatly enhance the sustainability of pharmaceutical products, leading to shorter and more efficient synthetic routes and more direct access to single stereoisomeric products. This perspective article seeks to highlight a number of important considerations for the design of new and improved sustainable metal-catalyzed transformations in order to facilitate rapid adoption by the pharmaceutical industry.

Aaron P. Mitchell, Niti U. Trivedi, R. Gennarelli et al.

BACKGROUND Financial payments from the drug industry to U.S. physicians are common. Payments may influence physicians' clinical decision making and drug prescribing. PURPOSE To evaluate whether receipt of payments from the drug industry is associated with physician prescribing practices. DATA SOURCES MEDLINE (Ovid), Embase, the Cochrane Library, Web of Science, and EconLit were searched without language restrictions. The search had no limiting start date and concluded on 16 September 2020. STUDY SELECTION Studies that estimated the association between receipt of industry payments (exposure) and prescribing (outcome). DATA EXTRACTION Pairs of reviewers extracted the primary analysis or analyses from each study and evaluated risk of bias (ROB). DATA SYNTHESIS Thirty-six studies comprising 101 analyses were included. Most studies (n = 30) identified a positive association between payments and prescribing in all analyses; the remainder (n = 6) had a mix of positive and null findings. No study had only null findings. Of 101 individual analyses, 89 identified a positive association. Payments were associated with increased prescribing of the paying company's drug, increased prescribing costs, and increased prescribing of branded drugs. Nine studies assessed and found evidence of a temporal association; 25 assessed and found evidence of a dose-response relationship. LIMITATION The design was observational, 21 of 36 studies had serious ROB, and publication bias was possible. CONCLUSION The association between industry payments and physician prescribing was consistent across all studies that have evaluated this association. Findings regarding a temporal association and dose-response suggest a causal relationship. PRIMARY FUNDING SOURCE National Cancer Institute.

A. Bogdan, A. Dombrowski

The field of flow chemistry has garnered considerable attention over the past 2 decades. This Perspective highlights many recent advances in the field of flow chemistry and discusses applications to the pharmaceutical industry, from discovery to manufacturing. From a synthetic perspective, a number of new enabling technologies are providing more rationale to run reactions in flow over batch techniques. Additionally, highly automated flow synthesis platforms have been developed with broad applicability across the pharmaceutical industry, ranging from advancing medicinal chemistry programs to self-optimizing synthetic routes. A combination of simplified and automated systems is discussed, demonstrating how flow chemistry solutions can be tailored to fit the specific needs of a project.

Maknunah Hilyatul, Wonorahardjo Surjani

Sensors play a crucial role in various fields by enabling the detection and analysis of a wide range of substances, including hazardous substance detection, environmental and food safety monitoring, pharmaceutical industry, gas analysis, and others. Research continues to identify and develop sensor matrix materials that can increase the sensitivity, selectivity and responsiveness of sensors. Silica, an oxide mineral is a potential matrix material for sensor applications because of its unique characteristics. It has a large pore structure and modifiable pore size distribution. Silica’s stable chemical properties, high-temperature resistance and corrosion resistance make it an ideal matrix material for a wide range of sensor applications. In recent years, silica cellulose also become a potential material for sensor applications. Silica cellulose is produced by combining silica with cellulose components from natural materials, such as rice husk ash, bamboo leaf ash, rice straw ash, and other plant fibers. This article provides a comprehensive exploration of various methods of synthesis and characterization of silica and silica cellulose materials. The methods include sol-gel, acid leaching, alkaline extraction, and other techniques for extracting cellulose from natural sources. In addition, sensor applications that have been tested using this material are also discussed, including its use in detecting molecular compounds, food and environmental applications. The development of silica and silica cellulose materials based on natural materials is considered because of their sustainability. By continuing to explore the potential of these materials, it is hoped that it can make a significant contribution in the development of sensor technology that is more innovative, environmentally friendly and sustainable.

Érica Mendes dos Santos, Lucas Malvezzi de Macedo, Janaína Artem Ataide et al.

Abstract Consumer demand for natural, chemical-free products has grown. Food industry residues, like coffee pulp, rich in caffeine, chlorogenic acid and phenolic compounds, offer potential for pharmaceutical and cosmetic applications due to their antioxidant, anti-inflammatory, and antibacterial properties. Therefore, the objective of this work was to develop a phytocosmetic only with natural products containing coffee pulp extract as active pharmaceutical ingredient with antioxidant, antimicrobial and healing activity. Eight samples from Coffea arabica and Coffea canephora Pierre were analyzed for caffeine, chlorogenic acid, phenolic compounds, tannins, flavonoids, cytotoxicity, antibacterial activity, and healing potential. The Robusta IAC—extract had the greatest prominence with 192.92 μg/mL of chlorogenic acid, 58.98 ± 2.88 mg GAE/g sample in the FRAP test, 79.53 ± 5.61 mg GAE/g sample in the test of total phenolics, was not cytotoxic, and MIC 3 mg/mL against Staphylococcus aureus. This extract was incorporated into a stable formulation and preferred by 88% of volunteers. At last, a scratch assay exhibited the formulation promoted cell migration after 24 h, therefore, increased scratch retraction. In this way, it was possible to develop a phytocosmetic with the coffee pulp that showed desirable antioxidant, antimicrobial and healing properties.