Hasil untuk "Pharmaceutical industry"

C. Gadipelly, Antía Pérez-González, Ganapati D. Yadav et al.

Ashley D. Wazana

C. DeJong, Thomas U Aguilar, Chien-Wen Tseng et al.

Hannah M. Jones, Yuan Chen, Christopher R. Gibson et al.

Baoyang Ding

Abstract The exploitation of the emerging technologies of Pharma Industry 4.0 facilitates sustainable value creation, leads to more agile, smart and personalised pharma industry, and thereby, in the long-run, enables pharma companies to obtain competitive advantages. A more sustainable pharmaceutical supply chain (PSC) should be implemented to match future operations and management of the pharmaceutical products across the entire life cycle. The main purpose of this study is to identify the potential sustainability barriers of PSC and to investigate how Industry 4.0 can be applied in the sustainable PSC paradigms. This paper systematically reviews 33 relevant articles concerning sustainable PSC and Industry 4.0, taken from peer-reviewed academic journals over a decade (2008–2018). Based on content analysis, we find that the major challenges that inhibit inclusion of sustainability in the PSCs are: high costs and time consumption, little expertise and training, enforcement of regulations, the paucity of business incentives, ineffective collaborations and coordination across the PSC, lack of objective benchmarks, and poor end-customer awareness. The technologies and innovations based on Industry 4.0 can solve these barriers with regards to four aspects: enhancing the flexibility of the PSC for patient-centric drug supplies; improving the effectiveness of coordination and communication across different entities within the PSC; mitigating waste and pollution at different stages; and enabling a more autonomous decision-making process for supply chain managers. Our analysis reveals that future research interest should focus on: cross-linking coordination and cooperation, eco-friendly end-of-life products disposal, proactive product recall management, new benchmarks and measurement of sustainable performance, new regulation system design, and effects of incentives for sustainable activities.

Stig Johan Wiklund, Magnus Ytterstad, Frank Miller

Many pharmaceutical companies face concerns with the maintenance of desired revenue levels. Sales forecasts for the current portfolio of products and projects may indicate a decline in revenue as the marketed products approach patent expiry. To counteract the potential downturn in revenue, and to establish revenue growth, an in-flow of new projects into the development phases is required. In this article, we devise an approach with which the in-flow of new projects could be optimized, while adhering to the objectives and constraints set on revenue targets, budget limitations and strategic considerations on the composition of the company's portfolio.

Lujun Li, Lama Sleem, Niccolo' Gentile et al.

With the emergence of ChatGPT, Transformer models have significantly advanced text classification and related tasks. Decoder-only models such as Llama exhibit strong performance and flexibility, yet they suffer from inefficiency on inference due to token-by-token generation, and their effectiveness in text classification tasks heavily depends on prompt quality. Moreover, their substantial GPU resource requirements often limit widespread adoption. Thus, the question of whether smaller language models are capable of effectively handling text classification tasks emerges as a topic of significant interest. However, the selection of appropriate models and methodologies remains largely underexplored. In this paper, we conduct a comprehensive evaluation of prompt engineering and supervised fine-tuning methods for transformer-based text classification. Specifically, we focus on practical industrial scenarios, including email classification, legal document categorization, and the classification of extremely long academic texts. We examine the strengths and limitations of smaller models, with particular attention to both their performance and their efficiency in Video Random-Access Memory (VRAM) utilization, thereby providing valuable insights for the local deployment and application of compact models in industrial settings.

Youssef Abdalla, Marrisa Taub, Eleanor Hilton et al.

Data scarcity in pharmaceutical research has led to reliance on labour-intensive trial-and-error approaches for development rather than data-driven methods. While Machine Learning offers a solution, existing datasets are often small and noisy, limiting their utility. To address this, we developed a Variationally Encoded Conditional Tabular Generative Adversarial Network (VECT-GAN), a novel generative model specifically designed for augmenting small, noisy datasets. We introduce a pipeline where data is augmented before regression model development and demonstrate that this consistently and significantly improves performance over other state-of-the-art tabular generative models. We apply this pipeline across six pharmaceutical datasets, and highlight its real-world applicability by developing novel polymers with medically desirable mucoadhesive properties, which we made and experimentally characterised. Additionally, we pre-train the model on the ChEMBL database of drug-like molecules, leveraging knowledge distillation to enhance its generalisability, making it readily available for use on pharmaceutical datasets containing small molecules, an extremely common pharmaceutical task. We demonstrate the power of synthetic data for regularising small tabular datasets, highlighting its potential to become standard practice in pharmaceutical model development, and make our method, including VECT-GAN pre-trained on ChEMBL available as a pip package.

Shinnosuke Ono, Issey Sukeda, Takuro Fujii et al.

We present a Japanese domain-specific language model for the pharmaceutical field, developed through continual pretraining on 2 billion Japanese pharmaceutical tokens and 8 billion English biomedical tokens. To enable rigorous evaluation, we introduce three new benchmarks: YakugakuQA, based on national pharmacist licensing exams; NayoseQA, which tests cross-lingual synonym and terminology normalization; and SogoCheck, a novel task designed to assess consistency reasoning between paired statements. We evaluate our model against both open-source medical LLMs and commercial models, including GPT-4o. Results show that our domain-specific model outperforms existing open models and achieves competitive performance with commercial ones, particularly on terminology-heavy and knowledge-based tasks. Interestingly, even GPT-4o performs poorly on SogoCheck, suggesting that cross-sentence consistency reasoning remains an open challenge. Our benchmark suite offers a broader diagnostic lens for pharmaceutical NLP, covering factual recall, lexical variation, and logical consistency. This work demonstrates the feasibility of building practical, secure, and cost-effective language models for Japanese domain-specific applications, and provides reusable evaluation resources for future research in pharmaceutical and healthcare NLP. Our model, codes, and datasets are released at https://github.com/EQUES-Inc/pharma-LLM-eval.

Koushik Mondal, Balagopal G Menon, Sunil Sahadev

The survival of unorganized pharmacies is increasingly challenging in the face of growing competition from organized and e-pharmaceutical retail channels in emerging economies. A theoretical model is developed to capture the triple-channel interactions among unorganized, organized and e-retailing in emerging markets, taking into account the essential features of the pharmaceutical retail landscape, consumers, retailers and pharmaceutical products. Given the retailer and customer-specific factors, the price-setting game between the triple-channel retailers yielded the optimal prices for these retailers. The analysis found that the product category level demand has no influence on optimal pricing strategies of the retailers. The analysis also reveals counterintuitive results, for instance, (i) an increase in customer acceptance of unorganized retailers will result in a decrease in profits of both unorganized and organized retailers; (ii) as the distance and transportation cost to unorganized retailers increases for the consumers, the profit of the unorganized retailer increases; and (iii) consumers marginal utility of money has no influence on the optimal price, but have an influence on the profit of the three retail channels. Our research findings offer valuable insights for policymakers facing challenges in achieving a balanced growth among the organized, unorganized, and e-pharmaceutical retail sectors in emerging economies. Keywords: Unorganized, Organized, and Online E-Retail; Nanostores; Emerging Markets; Game Theory.

Carlos Henrique Vieira-Vieira, Sarang Sanjay Kulkarni, Adam Zalewski et al.

The pharmaceutical industry faces pressure to improve the drug development process while reducing costs in an evolving regulatory landscape. This paper presents the Preclinical Information Center (PRINCE), a cloud-hosted data integration platform developed by Bayer AG in collaboration with Thoughtworks. PRINCE integrates decades of structured and unstructured safety study reports, leveraging a multi-agent architecture based on Large Language Models (LLMs) and advanced data retrieval methodologies, such as Retrieval-Augmented Generation and Text-to-SQL. In this paper, we describe the three-step evolution of PRINCE from a data search tool based on keyword matching to a resourceful research assistant capable of answering complex questions and drafting regulatory-critical documents. We highlight the iterative development process, guided by user feedback, that ensures alignment with evolving research needs and maximizes utility. Finally, we discuss the importance of building trust-based solutions and how transparency and explainability have been integrated into PRINCE. In particular, the integration of a human-in-the-loop approach enhances the accuracy and retains human accountability. We believe that the development and deployment of the PRINCE chatbot demonstrate the transformative potential of AI in the pharmaceutical industry, significantly improving data accessibility and research efficiency, while prioritizing data governance and compliance.

Fatma Kurul, Beyzanur Doruk, Seda Nur Topkaya

Abstract Green chemistry is an interdisciplinary field that focuses on minimizing hazardous substances and promoting sustainable alternatives in chemical processes to conventional chemical processes and products. This review provides a comprehensive analysis of the fundamental principles, historical development, and practical applications of green chemistry with a particular emphasis on its role in advancing sustainable chemical synthesis, analytical methodologies, and industrial practices. Originating from the environmental activism of the 1960 s inspired by Rachel Carson's"Silent Spring,"green chemistry was formally established in the 1990 s through the 12 principles set by Paul Anastas and John C. Warner. These principles emphasize waste prevention, atomic economy, reducing hazardous chemicals, and using renewable raw materials. Green chemistry significantly impacts sectors such as pharmaceuticals, cosmetics, and education. In the pharmaceutical industry, it fosters environmentally safer analytical methods. The cosmetics sector benefits from biodegradable materials, while educational institutions implement sustainable waste management and laboratory practices. International conferences and academic publications have advanced global awareness of green chemistry, promoting sustainability goals like reducing environmental impacts, optimizing resource use, and minimizing waste. A key focus of this study is the green synthesis of nanoparticles which has emerged as a sustainable alternative to traditional synthesis methods that often rely on toxic reagents Plant-derived biomolecules serve as reducing and stabilizing agents in the synthesis of silver nanoparticles (AgNPs). These eco-friendly approaches eliminate the hazardous chemicals while yielding biocompatible nanoparticles with enhanced antimicrobial and catalytic properties, demonstrating their potential in nanotechnology and biomedical applications. Additionally, green analytical chemistry has revolutionized chemical monitoring by implementing solvent-free methodologies, real-time pollution tracking, and waste minimization techniques. The integration of green chemistry into academic and industrial settings has played a critical role in addressing global challenges such as environmental pollution, climate change, and resource depletion. This review highlights the necessity of widespread adoption of green chemistry principles to ensure economic sustainability, regulatory compliance, and scientific innovation. Future research should focus on optimizing green synthetic techniques, addressing scalability challenges, and fostering interdisciplinary collaboration to accelerate the transition toward a more sustainable future. Graphical abstract

R. Khanna, Isin Guler, Atul Nerkar

Mahak Sharma, Rajat Sehrawat, S. Luthra et al.

Emergence of industry 5.0 facilitates real-time synchronisation of production processes, which helps with the production of customized products. Although industry 5.0 can revolutionize the landscape of goods production, still its adoption is in the infancy stage. Hence, the purpose of this article is to examine barriers that impede the adoption of industry 5.0, and to propose solution initiatives (SIs). After an in-depth literature review and interviews with German industry experts in pharmaceutical sector, barriers and SIs are ranked using an integrated Analytical hierarchy process-elimination and choice expressing reality-decision-making trial and evaluation laboratory (AHP-ELECTRE-DEMATEL) approach. “Linking virtual reality and reality” is found to be the most critical deterrent to the adoption of industry 5.0, and falls into a causal group, which signifies their influence on other deterrents. “Measures for better connectivity with patients” is of utmost importance for German firms to ensure secure communication and safeguard patient data. The findings also highlight the problems with the adoption of high-tech innovations due to lack of standardization and fair benchmarking policies on industry 5.0. This article concludes by proposing an industry 5.0 framework. The inhibitors to adopting industry 5.0 were investigated in this article, the results of which will assist practitioners and decision makers to understand the issues with industry 5.0. The results of this article will help potential adopters of this technology to look for the SIs. This will therefore pave the path for effective adoption of industry 5.0 across the pharmaceutical manufacturing sector.

Ingrid Carla Reinhardt, Jorge C. Oliveira, Denis T. Ring

Abstract Industry 4.0 is a concept that represents the adoption by industry of techniques and processes allowed by digitisation, cloud computing, the internet of things and big data to gain competitive advantages in domestic and global markets. The research is conducted in Ireland with the resulting data examined through a global lens, yielding information relevant to the effective adoption and integration of 4.0 concepts. Key outcomes are the perspectives of the pharmaceutical and biopharmaceutical industries with regards to the adoption of 4.0, the current level of implementation of 4.0 technologies in manufacturing facilities and planned 4.0 projects to be executed. Statistically relevant relationships evident in the responses are also investigated. This research provides novel and highly relevant information concerning the state of industry preparedness for the adoption of 4.0. Across all survey respondents only 42% indicated any knowledge of 4.0. The majority of respondents who indicated a knowledge of 4.0 identified with either the Automation or Engineering department. Among established employees with greater than 8 years of experience 82% identified with having knowledge of 4.0. Those surveyed with Vice-President or Director roles, had a 98% certainty of 4.0 awareness. A noteworthy finding of this work is the identification of a substantial disconnect in knowledge of 4.0 based on seniority, function and industry. Thus while the implementation of 4.0 is playing an increasingly significant role in the modernisation of the Pharmaceutical and Biopharmaceutical industries, challenges remain with respect to the holistic fusion of 4.0 into the culture of organisations.

Jaewoong Kim, Moohong Min

Regulatory compliance in the pharmaceutical industry entails navigating through complex and voluminous guidelines, often requiring significant human resources. To address these challenges, our study introduces a chatbot model that utilizes generative AI and the Retrieval Augmented Generation (RAG) method. This chatbot is designed to search for guideline documents relevant to the user inquiries and provide answers based on the retrieved guidelines. Recognizing the inherent need for high reliability in this domain, we propose the Question and Answer Retrieval Augmented Generation (QA-RAG) model. In comparative experiments, the QA-RAG model demonstrated a significant improvement in accuracy, outperforming all other baselines including conventional RAG methods. This paper details QA-RAG's structure and performance evaluation, emphasizing its potential for the regulatory compliance domain in the pharmaceutical industry and beyond. We have made our work publicly available for further research and development.

Xin Wang, Yifan Zhang, Xiaojing Zhang et al.

Pharmaceutical patents play a vital role in biochemical industries, especially in drug discovery, providing researchers with unique early access to data, experimental results, and research insights. With the advancement of machine learning, patent analysis has evolved from manual labor to tasks assisted by automatic tools. However, there still lacks an unified agent that assists every aspect of patent analysis, from patent reading to core chemical identification. Leveraging the capabilities of Large Language Models (LLMs) to understand requests and follow instructions, we introduce the $\textbf{first}$ intelligent agent in this domain, $\texttt{PatentAgent}$, poised to advance and potentially revolutionize the landscape of pharmaceutical research. $\texttt{PatentAgent}$ comprises three key end-to-end modules -- $\textit{PA-QA}$, $\textit{PA-Img2Mol}$, and $\textit{PA-CoreId}$ -- that respectively perform (1) patent question-answering, (2) image-to-molecular-structure conversion, and (3) core chemical structure identification, addressing the essential needs of scientists and practitioners in pharmaceutical patent analysis. Each module of $\texttt{PatentAgent}$ demonstrates significant effectiveness with the updated algorithm and the synergistic design of $\texttt{PatentAgent}$ framework. $\textit{PA-Img2Mol}$ outperforms existing methods across CLEF, JPO, UOB, and USPTO patent benchmarks with an accuracy gain between 2.46% and 8.37% while $\textit{PA-CoreId}$ realizes accuracy improvement ranging from 7.15% to 7.62% on PatentNetML benchmark. Our code and dataset will be publicly available.

Oscar Blanco-Novoa, Tiago M Fernandez-Carames, Paula Fraga-Lamas et al.

The principles of the Industry 4.0 are guiding manufacturing companies towards more automated and computerized factories. Such principles are also applied in shipbuilding, which usually involves numerous complex processes whose automation will improve its efficiency and performance. Navantia, a company that has been building ships for 300 years, is modernizing its shipyards according to the Industry 4.0 principles with the help of the latest technologies. Augmented Reality (AR), which when utilized in an industrial environment is called Industrial AR (IAR), is one of such technologies, since it can be applied in numerous situations in order to provide useful and attractive interfaces that allow shipyard operators to obtain information on their tasks and to interact with certain elements that surround them. This article first reviews the state of the art on IAR applications for shipbuilding and smart manufacturing. Then, the most relevant IAR hardware and software tools are detailed, as well as the main use cases for the application of IAR in a shipyard. Next, it is described Navantia's IAR system, which is based on a fog-computing architecture. Such a system is evaluated when making use of three IAR devices (a smartphone, a tablet and a pair of smart glasses), two AR SDKs (ARToolKit and Vuforia) and multiple IAR markers, with the objective of determining their performance in a shipyard workshop and inside a ship under construction. The results obtained show remarkable performance differences among the different IAR tools and the impact of factors like lighting, pointing out the best combinations of markers, hardware and software to be used depending on the characteristics of the shipyard scenario.

Pablo Cecilio, Antônio Perreira, Juliana Santos Rosa Viegas et al.

Pharmaceutical patents play an important role by protecting the innovation from copies but also drive researchers to innovate, create new products, and promote disruptive innovations focusing on collective health. The study of patent management usually refers to an exhaustive manual search. This happens, because patent documents are complex with a lot of details regarding the claims and methodology/results explanation of the invention. To mitigate the manual search, we proposed PATopics, a framework specially designed to extract relevant information for Pharmaceutical patents. PATopics is composed of four building blocks that extract textual information from the patents, build relevant topics that are capable of summarizing the patents, correlate these topics with useful patent characteristics and then, summarize the information in a friendly web interface to final users. The general contributions of PATopics are its ability to centralize patents and to manage patents into groups based on their similarities. We extensively analyzed the framework using 4,832 pharmaceutical patents concerning 809 molecules patented by 478 companies. In our analysis, we evaluate the use of the framework considering the demands of three user profiles -- researchers, chemists, and companies. We also designed four real-world use cases to evaluate the framework's applicability. Our analysis showed how practical and helpful PATopics are in the pharmaceutical scenario.

Julius Lange, Leonid Komissarov, Rene Lang et al.

In materials and pharmaceutical development, rapidly and accurately determining the similarity between X-ray powder diffraction (XRPD) measurements is crucial for efficient solid form screening and analysis. We present SMolNet, a classifier based on a Siamese network architecture, designed to automate the comparison of XRPD patterns. Our results show that training SMolNet on loss functions from the self-supervised learning domain yields a substantial boost in performance with respect to class separability and precision, specifically when classifying phases of previously unseen compounds. The application of SMolNet demonstrates significant improvements in screening efficiency across multiple active pharmaceutical ingredients, providing a powerful tool for scientists to discover and categorize measurements with reliable accuracy.