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Enregistrement W4415363924 · doi:10.3389/fmed.2025.1705977

Editorial: Reviews in: regulatory science 2024-2025

2025· editorial· en· W4415363924 sur OpenAlexaboutno aff
Mette Due Theilade, Lisbeth E. Knudsen

Notice bibliographique

RevueFrontiers in Medicine · 2025
Typeeditorial
Langueen
DomaineMedicine
ThématiqueScience, Research, and Medicine
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésChinaRegulatory scienceRegulatory authorityTest (biology)Drug developmentDrug approvalAlternative medicineDrug industry

Résumé

récupéré en direct d'OpenAlex

The "Reviews in Regulatory Science" call presents a number of publications from 2024 and 2025, covering among other examples of regulatory approaches to GDPR and approaches to new technologies in pharmaceutical development. Oku and Someya (2025), both authors from the Japanese Pharmaceuticals and Medical Devices Agencies (PMDA), analyse the trends of GLP-compliant non-clinical studies submitted to PMDA from 2017 to 2023. USA/Canada contribute with most GLP studies submitted to PMDA, followed by Japan, Europe, and the United Kingdom. The number of GLP studies from China and Taiwan has been on the rise over the last 3 years, reflecting increased development in China of new drugs, particularly in the oncology field. China is not a part of the OECD Mutual Acceptance of Data (MAD) framework, where nonclinical safety studies conducted in test facilities successfully inspected by a competent GLP-compliance monitoring authority in one OECD country become accepted by other OECD countries. The PMDA accepts data from test facilities in non-MAD countries only if the product-based inspection of the studies conducted by the PMDA is successful. The authors found that a decrease in the percentage of studies conducted in Japan, suggesting reduced drug development activities in Japan. They also found a considerable time lag for many of the studies, which were submitted to the PMDA later than to the US or EU, and this is considered a serious issue for patients with life-threatening diseases in Japan. Christofidou et al (2025) provides an important contribution to the discussion on the practical application of informed consent when adhering to European legislation. The review investigates the gaps that the European General Data Protection Regulation (GDPR) has left regarding interpretation and practical application of consent towards the secondary use of health data and furthermore discusses potential solutions. The requirements for "informed consent" defined within the GDPR are not well defined in the context of e.g. genome research. The review proposes that the Data Governance Act ("DGA") through the concept of "data altruism" can be used as a cohesive solution to this. A systematic review by Chen et al (2025) utilised bibliometric and visualisation analyses of the core collection of Web of Science databases to evaluate the status and trends in the field of illicit drugs on the global level. The review includes a total of 5,797 publications between 2015 and 2024. Literature on substance abuse research mainly focused on addiction mechanisms, mental health impacts, and intervention strategies. Of interest is a rise in clinical applications of non-pharmacological approaches such as Mindfulness-Based Interventions and cognitive behavioural therapy. Although the United States has made substantial contributions to the field of illicit drug research, they do not play a significant role in global research cooperation. With a low level of international cooperation, research remains domestically oriented, which may impede the global impact and innovative capacity of research conducted. Data sharing, technological exchange, and collaborative actions among nations are instrumental in the establishment of a more efficient and coordinated global drug governance system, better equipping the international community to address the threats posed by drugs to public health and social security. Agyralides G (2024) discusses the impact of innovative technologies on the ecosystem of pharmaceutical development. Technology rapidly evolves, and it is dominated by artificial Intelligence including Machine Learning and the use of Big Data and Real-World Data (RWD) to produce Real-World Evidence (RWE). Nanotechnology is an inter-science field that gives new opportunities for the manufacturing of devices and products that have dimensions of a billionth of a meter. Artificial Neural Networks and Deep Learning are mimicking the use of the human brain, combining computer science with new theoretical foundations for complex systems. The author also discusses technologies like personalized medicines, gene therapy and CRISPR. The rapid development in new technologies contributes to significantly speeding up the process and reducing the costs for development of new medicines, and offers more options for better, safer, and more effective treatments, with a more solid, data-driven and evidence-concrete approach to be brought to the patient. However, focus should be maintained on a safe and ethical data-sharing culture. Nanotechnology is also the focus of the review by Rodríguez-Gómez et al (2025). The integration of nanotechnology into healthcare has introduced Nanotechnology-Enabled Health Products (NHPs) with promise of revolutionary advancements in medical treatments and diagnostics. NHPs show potential in four principal areas: nano-diagnosis, controlled drug delivery, treatment, and regenerative medicine. Despite their potential, the regulatory navigation for these products remains complex. Rodríguez-Gómez et al (2025) provides an excellent overview of the regulatory landscape for NHPs in the European Union and the United States, identifying applicable requirements and the main regulatory guidelines currently available for meeting regulatory expectations. The regulation of health technologies consistently lags behind rapid advancements in research and development, and the delay in establishing specific regulatory guidelines for NHPs is pronounced. The evolving regulatory landscape for NHPs across the EU and the US—and increasingly in emerging markets such as China and Japan—continues to face persistent hurdles. These include the absence of harmonized definitions, complex physicochemical characterization requirements, and intricacies in evaluating nanotoxicity. Altogether, efforts to modernise regulatory frameworks and encourage standardized testing, coupled with the emergence of AI-driven methodologies and the shift toward greener nanomanufacturing, signal a promising future for nanomedicine, however further collaboration across scientific, governmental, and industrial spheres is essential to fully harness these opportunities.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,013
score de la tête « metaresearch » (Gemma)0,053
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Éditorial · Signal consensuel: Éditorial
Score de désaccord entre enseignants0,041
Score d'incertitude au seuil0,137

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0130,053
Méta-épidémiologie (sens strict)0,0050,002
Méta-épidémiologie (sens large)0,0040,005
Bibliométrie0,0070,003
Études des sciences et des technologies0,0030,004
Communication savante0,0120,008
Science ouverte0,0070,003
Intégrité de la recherche0,0290,020
Charge utile insuffisante (le modèle a refusé de juger)0,0410,044

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,019
Tête enseignante GPT0,364
Écart entre enseignants0,345 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreÉditorial

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

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