Hasil untuk "Pharmaceutical industry"

A. Atanasov, S. Zotchev, V. Dirsch et al.

Natural products and their structural analogues have historically made a major contribution to pharmacotherapy, especially for cancer and infectious diseases. Nevertheless, natural products also present challenges for drug discovery, such as technical barriers to screening, isolation, characterization and optimization, which contributed to a decline in their pursuit by the pharmaceutical industry from the 1990s onwards. In recent years, several technological and scientific developments — including improved analytical tools, genome mining and engineering strategies, and microbial culturing advances — are addressing such challenges and opening up new opportunities. Consequently, interest in natural products as drug leads is being revitalized, particularly for tackling antimicrobial resistance. Here, we summarize recent technological developments that are enabling natural product-based drug discovery, highlight selected applications and discuss key opportunities. Natural products have historically made a major contribution to pharmacotherapy, but also present challenges for drug discovery, such as technical barriers to screening, isolation, characterization and optimization. This Review discusses recent technological developments — including improved analytical tools, genome mining and engineering strategies, and microbial culturing advances — that are enabling a revitalization of natural product-based drug discovery.

T. Sander, Joël Freyss, M. V. Korff et al.

P. Murthy, M. Naidu

D. Constable, P. Dunn, J. Hayler et al.

Y. Roggo, P. Chalus, Lene Maurer et al.

L. Illum

O. Almarsson, M. Zaworotko

D. Wild, A. Grove, Mona Martin et al.

P. Costa, J. M. Lobo

A. Hopkins, C. Groom

C. E. Garrett, K. Prasad

T. Uhlig, Themis-Dimitrios Kyprianou, F. Martinelli et al.

This minireview touches upon the challenges and opportunities peptides experience on the track to become an approved pharmaceutical. Peptide attributes originally considered troublesome with respect to drug development may now turn out to be more convenient rather than unfavourable. Besides characteristic high target affinity, biological peptides often exhibit higher than expected stability. Clearly natural selective pressure has optimised these biomolecules beyond what can be anticipated solely on the basis of their chemical nature. This concept is gradually finding its way into the pharma and biotech industry, as illustrated by a rise in medicinal peptide patent applications and developmental work.

Niccolò Ferrari, Nicola Zanarini, Michele Fraccaroli et al.

Industrial visual inspection in pharmaceutical production requires high accuracy under strict constraints on cycle time, hardware footprint, and operational cost. Manual inline inspection is still common, but it is affected by operator variability and limited throughput. Classical rule-based computer vision pipelines are often rigid and difficult to scale to highly variable production scenarios. To address these limitations, we present a semi-supervised anomaly detection framework based on a generative adversarial architecture with a residual autoencoder and a dense bottleneck, specifically designed for online deployment on a high-speed Blow-Fill-Seal (BFS) line. The model is trained only on nominal samples and detects anomalies through reconstruction residuals, providing both classification and spatial localization via heatmaps. The training set contains 2,815,200 grayscale patches. Experiments on a real industrial test kit show high detection performance while satisfying timing constraints compatible with a 500 ms acquisition slot.

S. Nabavi, Arianna Di Lorenzo, M. Izadi et al.

Herbs and spices have been used since ancient times, because of their antimicrobial properties increasing the safety and shelf life of food products by acting against foodborne pathogens and spoilage bacteria. Plants have historically been used in traditional medicine as sources of natural antimicrobial substances for the treatment of infectious disease. Therefore, much attention has been paid to medicinal plants as a source of alternative antimicrobial strategies. Moreover, due to the growing demand for preservative-free cosmetics, herbal extracts with antimicrobial activity have recently been used in the cosmetic industry to reduce the risk of allergies connected to the presence of methylparabens. Some species belonging to the genus Cinnamomum, commonly used as spices, contain many antibacterial compounds. This paper reviews the literature published over the last five years regarding the antibacterial effects of cinnamon. In addition, a brief summary of the history, traditional uses, phytochemical constituents, and clinical impact of cinnamon is provided.

Jocasta da Costa, Ney Cristian Amaral Boa Sorte, Pablo de Moura Santos

Objetivo: avaliar a efetividade clínica de polimixina B no tratamento de infecções por bactérias resistentes a múltiplas drogas e a segurança através dos critérios de nefrotoxicidade estabelecidos por Kidney Disease: Improving Global Outcomes. Metodologia: foi realizada uma coorte retrospectiva, com avaliação dos prontuários de pacientes que utilizaram o medicamento para tratar infecções por bactérias resistentes a múltiplas drogas (no período de janeiro de 2018 a dezembro de 2020, em unidades de terapia intensiva de um hospital pediátrico de Salvador – Ba. Resultados: um total de 39 pacientes usaram polimixina B para infecções por bactérias resistentes a múltiplas drogas, sendo que o sucesso do tratamento foi obtido em 13 (33,3%) pacientes com a cura clínica e/ou microbiológica, enquanto a mortalidade foi observada em 26 (66,6%) pacientes, dentre esses, 19 (73%) foram descritos em relatório médico como óbito relacionado à infecção e 7 (27%) associados às complicações das comorbidades. A ocorrência de lesão renal foi relatada em 21 pacientes, dos quais 6 foram descartados por apresentarem lesão renal prévia ao uso da polimixina B, 15 (38,4%) com taxa de nefrotoxidade classificados em estágios 1, 2 e 3, segundo os critérios de Kidney Disease: Improving Global Outcomes. Conclusão: Aproximadamente um terço dos pacientes tiveram cura clínica e microbiológica. Faz-se necessária a avaliação cautelosa do tratamento com polimixina B, priorizando as infecções multirresistentes, o uso racional e atenção para monitoramento da função renal.

Ye Huang, Wang Li, Yuke Zhou et al.

<i>p53</i> is a tumor suppressor gene and is regarded as one of the most crucial genes in protecting humans against cancer. The protein Mdm2 and its homolog MdmX serve as negative regulators of p53. In nearly half of cancer cells, there is an overexpression of Mdm2 and MdmX, which inhibit p53 activity. Furthermore, Mdm2’s E3 ubiquitin ligase activity promotes the ubiquitination and degradation of p53. Therefore, blocking the interaction between p53 and Mdm2/MdmX to prevent the degradation of wild-type p53 is an effective strategy for inhibiting tumor growth. This paper primarily discusses the regulatory relationship between p53, MdmX and Mdm2, and provides a review of the current status of p53-Mdm2/MdmX inhibitors. It aims to offer a theoretical foundation and research direction for the future discovery and design of targeted inhibitors against the p53-Mdm2/MdmX interaction.

Muhammad Anwar, Daniel Lau, Mishca de Costa et al.

The nuclear industry possesses a wealth of valuable information locked away in unstructured text data. This data, however, is not readily usable for advanced Large Language Model (LLM) applications that require clean, structured question-answer pairs for tasks like model training, fine-tuning, and evaluation. This paper explores how synthetic data generation can bridge this gap, enabling the development of robust LLMs for the nuclear domain. We discuss the challenges of data scarcity and privacy concerns inherent in the nuclear industry and how synthetic data provides a solution by transforming existing text data into usable Q&A pairs. This approach leverages LLMs to analyze text, extract key information, generate relevant questions, and evaluate the quality of the resulting synthetic dataset. By unlocking the potential of LLMs in the nuclear industry, synthetic data can pave the way for improved information retrieval, enhanced knowledge sharing, and more informed decision-making in this critical sector.

Brian Lu, Dennis Pham, Ti-Chiun Chang et al.

We report the development of a knowledge representation and reasoning (KRR) system built on hybrid SHACL-SKOS ontologies for globally harmonized system (GHS) material Safety Data Sheets (SDS) to enhance chemical safety communication and regulatory compliance. SDS are comprehensive documents containing safety and handling information for chemical substances. Thus, they are an essential part of workplace safety and risk management. However, the vast number of Safety Data Sheets from multiple organizations, manufacturers, and suppliers that produce and distribute chemicals makes it challenging to centralize and access SDS documents through a single repository. To accomplish the underlying issues of data exchange related to chemical shipping and handling, we construct SDS related controlled vocabulary and conditions validated by SHACL, and knowledge systems of similar domains linked via SKOS. The resulting hybrid ontologies aim to provide standardized yet adaptable representations of SDS information, facilitating better data sharing, retrieval, and integration across various platforms. This paper outlines our SHACL-SKOS system architectural design and showcases our implementation for an industrial application streamlining the generation of a composite shipping cover sheet.

Andrew Crawley, Adam Daigneault, Jonathan Gendron

From 2000 to 2017, 64% of Maine's pulp and paper processing mills shut down; these closures resulted in harmful effects to communities in Maine and beyond. One question this research asks is how will key macroeconomic and related variables for Maine's forestry and logging industry change in the future? To answer this, we forecast key macroeconomic and related variables with a vector error correction (VEC model) to assess past and predict future economic contributions from Maine's forestry and logging industry. The forecasting results imply that although the contribution of the industry in Maine would likely remain stable due to level prices and a slight increase in output, local Maine communities could be worse off due to decreases in employment and firms. We then incorporated these forecasts into a 3-stage modeling process to analyze how a negative shock to exchange rates from an increase in tariffs could affect Maine's employment and output. Our results suggest that increased tariffs will reduce output and increase employment volatility in Maine. Rising uncertainty and costs of business operations suggest care should be taken when changing tariffs and trade restrictions, especially when changes to business operations can harm markets and communities.

Sharareh Mirzaei, Stephanie Bunt, Susan M Bogus

As the construction industry undergoes rapid digital transformation, ensuring that new technologies enhance rather than hinder human experience has become essential. The inclusion of Building Information Modeling (BIM) plays a central role in this shift, yet its influence on job satisfaction remains underexplored. In response, this study developed a human-centered measurement model for evaluating job satisfaction in BIM work environments by adapting Hackman and Oldham's Job Characteristics Model for the architecture, engineering, and construction (AEC) industry to create a survey that captured industry perspectives on BIM use and job satisfaction. The model uses Partial Least Squares Structural Equation Modeling to analyze the survey results and identify what dimensions of BIM-related work affect job satisfaction. While it was hypothesized that BIM use increases job satisfaction, the results show that only some dimensions of BIM use positively impact BIM job satisfaction; the use of BIM does not guarantee an increase in overall job satisfaction. Additionally, more frequent BIM use was not associated with higher satisfaction levels. These findings suggest that in the AEC industry, sustainable job satisfaction depends less on technological autonomy and more on human-centric factors, particularly collaboration and meaningful engagement within digital workflows.