Hasil untuk "Pharmaceutical industry"

Yanqing Xie, Wenjing Zhang

IntroductionThe Consistency Evaluation Policy of Generic Drugs is a major quality-oriented regulatory reform in China’s pharmaceutical manufacturing industry. Whether and how this policy facilitates the integration of the innovation chain and the industrial chain at the enterprise level remains insufficiently examined. This study evaluates the policy effect and investigates potential mechanisms.MethodsThis study used panel data on A-share listed pharmaceutical enterprises from 2013 to 2023. Enterprises were treated as the micro-level carriers of both the innovation chain and the industrial chain, and a enterprise-level index was constructed to measure their integration. A difference-in-differences (DID) design was employed to estimate the impact of the Consistency Evaluation Policy of Generic Drugs. Mechanism analyses focused on government subsidies and market concentration, and heterogeneity was assessed by market demand and total factor productivity (TFP).ResultsThe Consistency Evaluation Policy of Generic Drugs significantly promoted the integration of the innovation chain and the industrial chain. Mechanism tests suggested that the effect operated through two channels: increased government subsidies and higher market concentration. The positive effect was stronger among enterprises facing larger market demand. Moreover, the effect was significant for enterprises with higher TFP, while it was not statistically significant for enterprises with lower TFP.DiscussionThese findings suggest that policy implementation can be strengthened by (1) improving the depth and precision of the Consistency Evaluation Policy of Generic Drugs, (2) enhancing the targeting of government subsidies and supporting an appropriate degree of industry concentration where warranted, and (3) adopting differentiated guidance to stimulate enterprise vitality through multiple measures.

Suyash Mishra, Qiang Li, Srikanth Patil et al.

Vision Language Models (VLMs) have shown strong performance on multimodal reasoning tasks, yet most evaluations focus on short videos and assume unconstrained computational resources. In industrial settings such as pharmaceutical content understanding, practitioners must process long-form videos under strict GPU, latency, and cost constraints, where many existing approaches fail to scale. In this work, we present an industrial GenAI framework that processes over 200,000 PDFs, 25,326 videos across eight formats (e.g., MP4, M4V, etc.), and 888 multilingual audio files in more than 20 languages. Our study makes three contributions: (i) an industrial large-scale architecture for multimodal reasoning in pharmaceutical domains; (ii) empirical analysis of over 40 VLMs on two leading benchmarks (Video-MME and MMBench) and proprietary dataset of 25,326 videos across 14 disease areas; and (iii) four findings relevant to long-form video reasoning: the role of multimodality, attention mechanism trade-offs, temporal reasoning limits, and challenges of video splitting under GPU constraints. Results show 3-8 times efficiency gains with SDPA attention on commodity GPUs, multimodality improving up to 8/12 task domains (especially length-dependent tasks), and clear bottlenecks in temporal alignment and keyframe detection across open- and closed-source VLMs. Rather than proposing a new "A+B" model, this paper characterizes practical limits, trade-offs, and failure patterns of current VLMs under realistic deployment constraints, and provide actionable guidance for both researchers and practitioners designing scalable multimodal systems for long-form video understanding in industrial domains.

Jenny Devenport, Tobias Mielke, Mouna Akacha et al.

Research and Development is the largest budget position in the pharmaceutical industry, with clinical trials being a critical, yet costly and time-consuming component to inform decisions. Beyond drug efficacy, the probability of success and efficiency of research and development are highly dependent on the approaches used for designing, analyzing, and interpreting clinical trials. Deep understanding of statistical methodology and quantitative approaches is therefore essential. Consequently, dedicated methodology groups have emerged in mid-size and large pharmaceutical companies and CROs. Their remit is to lead the conception and implementation of innovative quantitative methodologies in order to improve drug development, often by addressing complexities or offering more efficient designs. To achieve this, they collaborate internally and externally (e.g., with academics, regulators) to identify common challenges and tear down silos in order to invest in methods with the highest impact on efficiency and value to the portfolio. Given the immense financial stakes of drug development -- where delays carry massive implications -- these groups represent a critical strategic investment. However, to realize this business impact, statistical innovations must be rigorously validated and seamlessly integrated. This manuscript explores the setup, remit, and value of dedicated methodology groups, alongside the critical organizational considerations and success factors required to maximize their impact on the speed, efficiency, and probability of success.

Sharon Paoli Bias Ramos, Gabriel Rodrigues Martins de Freitas, Silvana Teresa Lacerda Jales

Objetivo: analisar o perfil pós-consumo dos medicamentos descartados pela comunidade universitária no coletor disponibilizado no Centro de Ciências da Saúde da Universidade Federal da Paraíba. Métodos: O projeto de extensão Descarta CIM instalou um coletor no Centro de Informações sobre Medicamentos - CIM do Departamento de Ciências Farmacêuticas e promoveu campanhas educativas em outros centros de ensino do Campus I. A partir disso, foi feita a pesagem e catalogação dos medicamentos descartados durante um período de 6 meses, seguida de análise detalhada do tipo de medicamento, categoria regulatória, classificação ATC (Anatomical Therapeutic Chemical), forma farmacêutica, tipo de embalagem e prazo de validade. Resultados: Os dados encontrados indicam que os medicamentos genéricos representam 42,5% do volume descartado, seguidos por medicamentos de referência (35,7%) e similares (21,7%). A análise da classificação ATC revela uma prevalência de medicamentos relacionados ao sistema digestivo e metabolismo, seguidos por sistema cardiovascular, sistema nervoso e músculo-esquelético. Observa-se uma alta porcentagem de medicamentos fora do prazo de validade (72,8%), levantando questões sobre a prática de automedicação e a necessidade de conscientização sobre o uso racional de medicamentos. Conclusão: O estudo demonstra algumas limitações, sobretudo quando não se pode determinar com exatidão se todos os medicamentos descartados foram efetivamente utilizados pela comunidade universitária. Contudo, a análise demonstrou que a promoção de campanhas educativas e a presença de coletores incentivam o descarte adequado de medicamentos e estimulam uma mudança de comportamento e a prática de ações de proteção ambiental.  

Ajantha Vijayakumar, Joseph Abraham Sundar Koilraj, Muthaiah Rajappa

Abstract Oral dosage forms are the most widely employed method of drug delivery in therapeutic treatments. However, the presence of visual defects in blister packages can adversely affect the drug's bioavailability and therapeutic efficacy, potentially compromising treatment outcomes. Consequently, detecting tablet defects post-blister packaging in real-time represents a critical challenge in the pharmaceutical industry. Additionally, factors such as blister reflections and limited dataset size hinder the deep learning model's ability to identify defects accurately. To address these challenges, the PharmaNet deep model is developed utilizing a convolutional neural network (CNN) architecture, incorporating defect-guided dynamic feature fusion (DGDFF) in which the fusion process is dynamically guided by potential defect regions, allowing the model to focus on relevant features (defect areas) more efficiently, adaptive deep chain (ADC) which includes occlusion pattern generator (OPG) and residual recursive feature reconstructor (R2FR). The OPG creates multiple views of potential defect regions by systematically dividing features into blocks and creating layered occlusions. At the same time, the R2FR uses gates with ELU activation and residual connections to reconstruct detailed features from these occluded sequences, ultimately enhancing the model's ability to detect subtle defects. The model culminates in an uncertainty-aware detection head that enhances defect prediction reliability by incorporating uncertainty estimates alongside traditional class probabilities and bounding box predictions. This provides a more informed and interpretable decision-making process for pharmaceutical quality control in real-time. Empirical evaluation on the proposed model demonstrates state-of-the-art performance with 99.4% mAP on the PharmaBlister dataset and 97.2% mAP on MVTech AD, with minimal predictive uncertainty, validating its efficacy in pharmaceutical quality control applications.

Xiaochen Zheng, Alvaro Serra, Ilya Schneider Chernov et al.

Pharmaceutical research and development has accumulated vast and heterogeneous archives of data. Much of this knowledge stems from discontinued programs, and reusing these archives is invaluable for reverse translation. However, in practice, such reuse is often infeasible. In this work, we introduce DiscoVerse, a multi-agent co-scientist designed to support pharmaceutical research and development at Roche. Designed as a human-in-the-loop assistant, DiscoVerse enables domain-specific queries by delivering evidence-based answers: it retrieves relevant data, links across documents, summarises key findings and preserves institutional memory. We assess DiscoVerse through expert evaluation of source-linked outputs. Our evaluation spans a selected subset of 180 molecules from Roche's research and development repositories, encompassing over 0.87 billion BPE tokens and more than four decades of research. To our knowledge, this represents the first agentic framework to be systematically assessed on real pharmaceutical data for reverse translation, enabled by authorized access to confidential archives covering the full lifecycle of drug development. Our contributions include: role-specialized agent designs aligned with scientist workflows; human-in-the-loop support for reverse translation; expert evaluation; and a large-scale demonstration showing promising decision-making insights. In brief, across seven benchmark queries, DiscoVerse achieved near-perfect recall ($\geq 0.99$) with moderate precision ($0.71-0.91$). Qualitative assessments and three real-world pharmaceutical use cases further showed faithful, source-linked synthesis across preclinical and clinical evidence.

Georg Rottenwalter, Marcel Tilly, Victor Owolabi

Machine learning is an essential tool for optimizing industrial quality control processes. However, the complexity of machine learning models often limits their practical applicability due to a lack of interpretability. Additionally, many industrial machines lack comprehensive sensor technology, making data acquisition incomplete and challenging. Explainable Artificial Intelligence offers a solution by providing insights into model decision-making and identifying the most relevant features for classification. In this paper, we investigate the impact of feature reduction using XAI techniques on the quality classification of injection-molded parts. We apply SHAP, Grad-CAM, and LIME to analyze feature importance in a Long Short-Term Memory model trained on real production data. By reducing the original 19 input features to 9 and 6, we evaluate the trade-off between model accuracy, inference speed, and interpretability. Our results show that reducing features can improve generalization while maintaining high classification performance, with an small increase in inference speed. This approach enhances the feasibility of AI-driven quality control, particularly for industrial settings with limited sensor capabilities, and paves the way for more efficient and interpretable machine learning applications in manufacturing.

Gary R. Mirams

Many drugs have been withdrawn from the market worldwide, at a cost of billions of dollars, because of patient fatalities due to them unexpectedly disturbing heart rhythm. Even drugs for ailments as mild as hay fever have been withdrawn due to an unacceptable increase in risk of these heart rhythm disturbances. Consequently, the whole pharmaceutical industry expends a huge effort in checking all new drugs for any unwanted side effects on the heart. The predominant root cause has been identified as drug molecules blocking ionic current flows in the heart. Block of individual types of ionic currents can now be measured experimentally at an early stage of drug development, and this is the standard screening approach for a number of ion currents in many large pharmaceutical companies. However, clinical risk is a complex function of the degree of block of many different types of cardiac ion currents, and this is difficult to understand by looking at results of these screens independently. By using ordinary differential equation models for the electrical activity of heart cells (electrophysiology models) we can integrate information from different types of currents, to predict the effect on whole heart cells and subsequent risk of side effects. The resulting simulations can provide a more accurate summary of the risk of a drug earlier in development and hence more cheaply than the pre-existing approaches.

Ningyu He, Shangtong Cao, Haoyu Wang et al.

As JavaScript has been criticized for performance and security issues in web applications, WebAssembly (Wasm) was proposed in 2017 and is regarded as the complementation for JavaScript. Due to its advantages like compact-size, native-like speed, and portability, Wasm binaries are gradually used as the compilation target for industrial projects in other high-level programming languages and are responsible for computation-intensive tasks in browsers, e.g., 3D graphic rendering and video decoding. Intuitively, characterizing in-the-wild adopted Wasm binaries from different perspectives, like their metadata, relation with source programming language, existence of security threats, and practical purpose, is the prerequisite before delving deeper into the Wasm ecosystem and beneficial to its roadmap selection. However, currently, there is no work that conducts a large-scale measurement study on in-the-wild adopted Wasm binaries. To fill this gap, we collect the largest-ever dataset to the best of our knowledge, and characterize the status quo of them from industry perspectives. According to the different roles of people engaging in the community, i.e., web developers, Wasm maintainers, and researchers, we reorganized our findings to suggestions and best practices for them accordingly. We believe this work can shed light on the future direction of the web and Wasm.

Soraya Araujo, Andrea Bento, Carolina Cohen

Introdução Este estudo analisou a participação social nas consultas públicas promovidas pela CONITEC (Comissão Nacional de Incorporação de Tecnologias no Sistema Único de Saúde) com o objetivo de promover a equidade no acesso a tecnologias em saúde. A pesquisa, realizada por meio de um formulário eletrônico durante junho e julho de 2023, buscou compreender a percepção da sociedade sobre as consultas públicas da CONITEC e propor melhorias na metodologia de Avaliação de Tecnologias em Saúde (ATS). Métodos Foram coletadas respostas de 650 participantes por meio de um questionário eletrônico. Os dados incluíram informações demográficas, níveis de conhecimento sobre a CONITEC e ATS, percepções sobre o impacto das ATS na qualidade da assistência no SUS, viabilidade da participação pública, confiança nas decisões governamentais e a importância da participação da população na formulação de políticas de saúde. Resultados A pesquisa revelou que a maioria dos respondentes estava na faixa etária de 26 a 45 anos, com predomínio de mulheres (69,2%). Cerca de 47,7% dos participantes não trabalhavam na área de saúde, enquanto 52,3% estavam envolvidos de alguma forma, com destaque para aqueles que trabalhavam em estabelecimentos de saúde (26,2%). A maioria dos entrevistados (64,6%) conhecia a CONITEC e seu papel na saúde pública, mas apenas 58,5% entendiam o que era ATS. A maioria acreditava que as ATS impactavam positivamente a qualidade da assistência no SUS (75,5%) e que sua participação nas consultas públicas era viável (80%). No entanto, uma parcela significativa (63,1%) não tinha confiança de que o governo utilizava as informações coletadas nas consultas para incorporar novas tecnologias. A participação da população nas políticas de saúde foi considerada importante por 76,9% dos entrevistados, embora apenas 60% tivessem participado de consultas públicas online, possivelmente devido à falta de conhecimento (76,9%) e confiança (15,4%) em sua capacidade de contribuir efetivamente.  Discussão e conclusões Os resultados destacam a necessidade de melhorias nas consultas públicas da CONITEC para aumentar a confiança da população e garantir que suas opiniões sejam consideradas. Uma sugestão apoiada por 67,7% dos participantes foi a criação de formulários específicos para diferentes doenças ou tecnologias. Essas conclusões ressaltam a importância de reformular a metodologia de ATS e promover uma participação efetiva da sociedade na tomada de decisões em saúde.

Yaschilal Muche Belayneh, Getnet Mengistu, Kidan Hailay

Background: Liver disease is any disease that negatively affects the normal function of the liver, and it is a major health problem that challenges not only healthcare professionals, but also the pharmaceutical industry and drug regulatory agencies. Similarly, diarrhea is the second leading cause of death among children under five globally next to pneumonia. The available synthetic drugs for the treatment of liver disorders and diarrhoea have limited safety and efficacy. Objective: To evaluate the in vivo hepatoprotective and antidiarrheal activities of hydroalcoholic leaf and fruit extracts of Schinus molle L. (Anacardiaceae) in mice. Methods: Hepatoprotective activity of the extracts was evaluated by using CCl4 induced hepatotoxicity in mice model. In this model, mice were divided into groups and treated as follows. The normal control and toxicant control groups were treated with the vehicle used for reconstitution, the positive control was treated with the standard drug (silymarin), and the test groups were treated with different doses of plant extracts daily in the morning for seven days. Additionally, all groups except the normal control were treated with CCl4 (2 mg/kg, IP) on the 4th day of treatment, 30 min post-dose. On the 7th day, blood was collected from each mouse via a cardiac puncture. The collected blood was centrifuged, and serum levels of ALT, AST, and ALP were determined using an automated chemistry analyser. Data were analysed using one-way analysis of variance (ANOVA) followed by Tukey's post-hoc test.The antidiarrheal activity of the extract was investigated using castor oil-induced diarrhoea, enteropooling, and small intestine transit. The test groups received various doses (100, 200, and 400 mg/kg) of the extract, whereas the positive control received loperamide (3 mg/kg), and the negative control received the vehicle (distilled water, 10 ml/kg). Result: Hepatoprotective activity: The leaf and fruit crude extracts showed significant improvement in the body weight and liver weight of mice compared to the untreated toxicant control. Additionally, treatment with hydromethanol leaf and fruit extracts caused a significant (P < 0.05) improvement in liver biomarkers compared to the toxicant control. Similarly, the n-butanol and chloroform fractions of the fruit extract caused a significant reduction (P < 0.01) in serum AST, ALT, ALP and Bilirubin levels and a significant (P < 0.001) increase in total protein compared to the toxicant control. However, none of the three solvent fractions (n-butanol, chloroform, and aqueous) of the fruit extract significantly affected (P > 0.05) the level of albumin compared with the toxicant control.Antidiarrheal activity: In the castor oil-induced diarrheal model, the 80 % methanol extract delayed the onset of defaecation and significantly reduced the number and weight of faeces at all tested doses compared to the negative control. In the enteropooling test, 80 ME significantly (P < 0.001) reduced the weight and volume of intestinal fluid at all tested doses compared with the negative control. Results from the charcoal meal test revealed that the extracts produced a significant anti-motility effect at all tested doses compared with the negative control. Conclusion: This study confirmed the hepatoprotective and antidiarrheal activities of hydroalcoholic extracts. The highest test dose produced the maximum hepatoprotective and antidiarrheal activities in all models.

I. M. Derkach, S. S. Derkach, Y. V. Zhuk et al.

One of the most relevant zoocides are rodenticides used to control harmful rodents. Carrying out effective deratization measures on the territory of Ukraine is especially important nowadays during the Russian-Ukrainian war. According to the reports of Ukrainian servicemen and civilians in the de-occupied territories, the population of rodents is extremely large, and not all modern rodenticide drugs lead to the death of harmful animals of these species. However, the toxicological characteristics of rodenticides take into account the fact that they can poison non-target animals. The purpose of our study was to analyze the pharmaco-toxicological characteristics of rodenticides and the main trends in the pharmaceutical market of drugs of this group registered in Ukraine as of January 1, 2024. It has been established that bromadiolone (77 %) and brodifacoum (23 %) are the main active substances in modern deratization agents. They belong to second-generation anticoagulants with a chronic mechanism of action. All registered rodenticides are produced in Ukraine and are available in various dosage forms. The low effectiveness of rodenticides can be due to falsification of starting substances for the synthesis of rodenticides, inconsistency of the required content of active substances, development of resistance of rodents to poisonous substances, etc. It is expected that the results highlighted in the article will indicate further directions in the development of new rodenticides and/or increase the effectiveness of the generally accepted scheme of deratization. In Ukraine, in the conditions of the Russian-Ukrainian war and in the post-war period, these questions are among the most significant, which scientists, pharmacologists and toxicologists, pharmacists and industry manufacturers should work on.

Yu Han, Jingwen Tao

This document offers a critical overview of the emerging trends and significant advancements in artificial intelligence (AI) within the pharmaceutical industry. Detailing its application across key operational areas, including research and development, animal testing, clinical trials, hospital clinical stages, production, regulatory affairs, quality control and other supporting areas, the paper categorically examines AI's role in each sector. Special emphasis is placed on cutting-edge AI technologies like machine learning algorithms and their contributions to various aspects of pharmaceutical operations. Through this comprehensive analysis, the paper highlights the transformative potential of AI in reshaping the pharmaceutical industry's future.

Nitol Saha, Md Masruk Aulia, Dibakar Das et al.

A stability chamber is essential for pharmaceutical facilities to test the stability and quality of products over time by exposing them to different environmental conditions. This paper introduces an IoT-enabled stability chamber designed for the pharmaceutical industry. We constructed four stability chambers by leveraging the existing infrastructure within a manufacturing facility. Each chamber is controlled using a state-of-the-art Proportional Integral Derivative (PID) system based on the Siemens S7-1200 PLC. The Siemens WinCC Runtime Advanced platform, compliant with FDA 21 CFR Part 11, was used for visualizing chamber data. Additionally, an Internet of Things (IoT) application was developed to remotely monitor sensor data through any client application. This research aims to enhance the performance of traditional stability chambers by integrating IoT functionalities, making them more cost-effective and user-friendly.

Eric Guiffo Kaigom

As a digital environment of interconnected virtual ecosystems driven by measured and synthesized data, the Metaverse has so far been mostly considered from its gaming perspective that closely aligns with online edutainment. Although it is still in its infancy and more research as well as standardization efforts remain to be done, the Metaverse could provide considerable advantages for smart robotized applications in the industry.Workflow efficiency, collective decision enrichment even for executives, as well as a natural, resilient, and sustainable robotized assistance for the workforce are potential advantages. Hence, the Metaverse could consolidate the connection between Industry 4.0 and Industry 5.0. This paper identifies and puts forward potential advantages of the Metaverse for robotized applications and highlights how these advantages support goals pursued by the Industry 4.0 and Industry 5.0 visions. Keywords: Robotics, Metaverse, Digital Twin, VR/AR, AI/ML, Foundation Model;

Davide Frizzo, Francesco Borsatti, Alessio Arcudi et al.

Anomaly Detection (AD) is crucial in industrial settings to streamline operations by detecting underlying issues. Conventional methods merely label observations as normal or anomalous, lacking crucial insights. In Industry 5.0, interpretable outcomes become desirable to enable users to understand the rational under model decisions. This paper presents the first industrial application of ExIFFI, a recent approach for fast, efficient explanations for the Extended Isolation Forest (EIF) AD method. ExIFFI is tested on four industrial datasets, demonstrating superior explanation effectiveness, computational efficiency and improved raw anomaly detection performances. ExIFFI reaches over then 90\% of average precision on all the benchmarks considered in the study and overperforms state-of-the-art Explainable Artificial Intelligence (XAI) approaches in terms of the feature selection proxy task metric which was specifically introduced to quantitatively evaluate model explanations.

Bo Han, Xin-Yan Peng, Yan-Qing Gong et al.

Abstract Chidamide is the first oral subtype-selective histone deacetylase inhibitor approved in China for the treatment of relapsed and refractory peripheral T cell lymphoma. Due to the existence of isomers, many articles or patents have mistaken its structure. Herein we explored the synthesis of the key intermediate (E)-4-((3-(pyridin-3-yl)acrylamido)methyl)benzoic acid (A-3) and chidamide, using the condensing agent HBTU, instead of the unstable N,N'-carbonyldiimidazole. The single crystal of chidamide was determined by X-ray diffraction study. The optimized preparation process was easy to operate, and the purity of the final product can be up to 99.76%. Moreover, the structure of chidamide was established to be (E)-N-(2-amino-4-fluorophenyl)-4-((3-(pyridin-3-yl)acrylamido)methyl)benzamide.

Lawal Abubakar, Nor Azah Yusof, Abdul Halim Abdullah et al.

Due to the release of hazardous heavy metals from various industries, water pollution has become one of the biggest challenges for environmental scientists today. Mercury Hg(II) is regarded as one of the most toxic heavy metals due to its ability to cause cancer and other health issues. In this study, a tailor-made modern eco-friendly molecularly imprinted polymer (MIP)/nanoporous carbon (NC) nanocomposite was synthesized and examined for the uptake of Hg(II) using an aqueous solution. The fabrication of the MIP/NC nanocomposite occurred via bulk polymerization involving the complexation of the template, followed by polymerization and, finally, template removal. Thus, the formed nanocomposite underwent characterizations that included morphological, thermal degradation, functional, and surface area analyses. The MIP/NC nanocomposite, with a high specific surface area of 884.9 m²/g, was evaluated for its efficacy towards the adsorptive elimination of Hg(II) against the pH solution changes, the dosage of adsorbent, initial concentration, and interaction time. The analysis showed that a maximum Hg(II) adsorption effectiveness of 116 mg/g was attained at pH 4, while the Freundlich model fitted the equilibrium sorption result and was aligned with pseudo-second-order kinetics. Likewise, thermodynamic parameters like enthalpy, entropy, and Gibbs free energy indicated that the adsorption was consistent with spontaneous, favorable, and endothermic reactions. Furthermore, the adsorption efficiency of MIP/NC was also evaluated against a real sample of condensate from the oil and gas industry, showing an 87.4% recovery of Hg(II). Finally, the synthesized MIP/NC showed promise as a selective adsorbent of Hg(II) in polluted environments, suggesting that a variety of combined absorbents of different precursors is recommended to evaluate heavy metal and pharmaceutical removals.

Juliana Botelho Moreira, Thaisa Duarte Santos, Camila Gonzales Cruz et al.

The use of natural polymers has increased due to concern about environmental pollution caused by plastics and emerging pollutants from fossil fuels. In this context, polysaccharides from macroalgae and microalgae arise as natural and abundant resources for various biological, biomedical, and food applications. Different nanomaterials are produced from these polysaccharides to act as effective carriers in the food and pharmaceutical industry: drug and nutrient carriers, active compound encapsulation, and delivery of therapeutic agents to tumor tissues. Polysaccharides-based nanomaterials applied as functional ingredients incorporated into foods can improve texture properties and decrease the caloric density of food products. These nanostructures also present the potential for developing food packaging with antioxidant and antimicrobial properties. In addition, polysaccharides-based nanomaterials are biocompatible, biodegradable, and safe for medical practices to prevent and manage various chronic diseases, such as diabetes, obesity, and cardiovascular disease. In this sense, this review article addresses the use of algal polysaccharides for manufacturing nanomaterials and their potential applications in food and biomedical areas. In addition, the paper discusses the general aspects of algae as a source of polysaccharides, the nanomaterials produced from these polymers, as well as recent studies and the potential use of algal polysaccharides for industries.

Per Fors, Thomas Taro Lennerfors, Jonathan Woodward

This paper aims to outline an approach for case-based chemistry and chemical engineering education for sustainability. Education for Sustainability is assumed to offer a holistic approach to equip students with the knowledge, skills, values, and attitudes needed to contribute to a more sustainable society in their future careers. While Case-Based Education traditionally focuses on disciplinary learning in simulated settings, it can also effectively teach essential sustainability-related skills like integrated problem-solving, critical thinking, and systems thinking. The approach we propose is “case hacking”, which should be understood as utilizing existing business cases while incorporating supplementary resources to align the assignment with intended learning objectives. This expansion of the cases involves, among other things, introducing additional questions and assignments, perspectives from stakeholders previously unexplored in the original case, and the integration of recent research articles from relevant fields. We advocate for the use of case hacking when educators want to harness the educational benefits of Case-Based Education while emphasizing the complexity of sustainability-related challenges faced by industrial companies today. As an illustrative example, we demonstrate the process of hacking a case related to Green Chemistry in the pharmaceutical industry, highlighting specific challenges for chemistry and chemical engineering education. We hope this example will inspire educators in these disciplinary contexts to engage with the case hacking approach as they navigate the complex terrain of sustainability.