Therapie最新文献

Introduction: Acute musculoskeletal injuries are a common reason for primary care consultations. Nonsteroidal anti-inflammatory drugs (NSAIDs) are currently used in this context. Recent meta-analyses (M-A) showed a better benefit--risk profile for topical NSAIDs and no superiority of oral NSAIDs over paracetamol alone or in combination in terms of pain relief. It seems appropriate to evaluate rigorously NSAIDs' benefit-risk balance to support more consistent recommendations and promote appropriate evidence-based use of this drug class in trauma care.

Objective: This study's objective is to assess the efficacy and benefit-risk balance of oral and topical NSAIDs in the management of acute pain related to acute musculoskeletal injuries.

Materials and methods: A systematic review was conducted using public databases Medline (PubMed), CENTRAL (Cochrane) and Embase. Controlled randomised trials (RCTs) evaluating oral and topical NSAIDs (diclofenac, ibuprofen, ketoprofen and naproxen) for the treatment of acute pain from minor soft tissue injuries were included. These RCTs are from 2020 meta-analysis by Busse et al. bibliographic references and from complementary research from January 2020 to December 2023. Primary outcome is short-term pain (from 30minutes to 7days). Secondary outcomes are pain at rest, pain on movement (preferably on day 3), and safety analysis for oral and topical forms. Risk of bias was assessed using the Risk of Bias version 2 (RoB2) tool. Meta-analyses were done with Review Manager (RevMan) software. Levels of evidence were evaluated using both the REB and GRADE methods.

Results: The REB analysis concluded to a "solid evidence" for topical diclofenac (based on 10 RCTs and 7 confirmatory studies), to an "evidence requiring confirmation" evidence for topical ibuprofen (2 RCTs) and topical ketoprofen (2 RCTs). REB evaluation also concluded to a lack of proof for naproxen and for oral NSAIDs. GRADE analysis showed a low quality of evidence for topical NSAIDs efficacy and a high quality of evidence for their safety. It showed a very low quality of evidence for oral NSAIDs efficacy and safety.

Conclusion: According to REB methodology, topical NSAIDs, unlike oral formulations, have demonstrated efficacy in managing acute pain related to musculoskeletal injuries. These results reinforce recent meta-analyses favoring topical NSAIDs and recommending to limit the use of oral NSAIDs due to a lack of proof in this indication. Finally, NSAIDs impact on tissue healing has not been objectively studied by RCTs in humans, so further research is warranted. The use of NSAIDs in trauma care may potentially evolve as new data and knowledge emerge.

Objective: To assess the level of evidence supporting antibiotic therapy recommendations in primary care.

Method: After collecting the clinical practice guidelines (CPGs) listed on the Antibioclic® website (https://antibioclic.com), each recommendation regarding whether or not to prescribe antibiotics was evaluated based on whether it included a GRADE classification by the French National Authority for Health (HAS): A, B, C, or Expert Opinion (EO). The sources cited by the CPGs were reviewed. The recommendations and studies had to answer the question: "Should antibiotic therapy be prescribed in this situation?" The possible answers were "yes," "no," or "conditional yes," meaning delayed prescription. The primary outcome was the percentage of recommendations for which supporting studies were cited, in comparison to the stated GRADE.

Results: A total of 152 recommendations from 49 CPGs on antibiotic prescription were analyzed. In all, 71.7% did not mention a level of evidence, 3.3% were classified as GRADE A, and 9.2% as GRADE B. Upon reviewing the studies used to justify the recommendations, high-quality evidence was identified for only 7.9% of the recommendations, while 80.9% had no referenced studies.

Conclusion: Most French recommendations regarding antibiotic prescription in primary care do not provide the level of evidence supporting them.

Introduction: Precision medicine aims to tailor healthcare decisions and interventions to the unique biological and clinical characteristics of each patient. The recent convergence of artificial intelligence (AI) with advances in digital health, omics, and big data analytics has accelerated progress toward this goal. AI technologies - particularly machine learning, deep learning, natural language processing and generative large language models - enable the rapid and meaningful analysis of complex biomedical datasets, supporting more individualized care.

Purpose of review: In this narrative review, we provide an accessible overview of the core principles of AI for healthcare professionals and explore its practical applications across the spectrum of precision medicine. Real-world examples highlight how AI is being used to enhance early diagnosis, guide treatment selection, support disease prevention, and even contribute directly to therapeutic interventions. Alongside these advances, we discuss critical limitations and challenges, including ethical considerations, algorithmic bias, data privacy concerns, environmental impact, and practical barriers to clinical implementation.

Conclusion: This review offers both an introduction to AI and a practical overview of how it is being used, and where its limitations lie, in precision medicine, with the goal of helping healthcare professionals understand these evolving tools and use them efficiently and responsibly in clinical practice.

The advent of digital twins in pharmacology presents transformative potential for precision medicine, enabling personalized treatment optimization through dynamic computational simulations of drug interactions at molecular, cellular, and patient levels. These advanced virtual replicas of a patient's biological system are designed to predict individual therapeutic responses with high fidelity, thereby moving beyond the one-size-fits-all paradigm. This paper explores the concept of digital pharmacological twins, detailing how they can integrate heterogeneous data, including multi-omic, pharmacokinetic, pharmacodynamic, clinical, and environmental information, and employing a synergy of advanced mechanistic and machine learning models. Using illustrative examples from ongoing international initiatives, this work highlights the methodological frameworks necessary for developing and validating such comprehensive predictive tools. We underscore the critical importance of model interoperability, robust data integration strategies, and rigorous validation to ensure clinical utility. Ultimately, digital pharmacological twins promise to enhance therapeutic efficacy, minimize adverse drug reactions, and accelerate the translation of pharmacological science into tangible patient benefits.

Objective: To describe the clinical characteristics of depressive disorders and suicidality not associated with sexual dysfunction among users of finasteride 1mg/day for androgenetic alopecia.

Methods: A retrospective descriptive analysis was conducted using data from the French National Pharmacovigilance Database (BNPV) from 1985 to May 2024. Cases were selected based on the presence of depressive or suicidal symptoms, classified in Medical Dictionary for Regulatory Activities (MedDRA) high-level group terms, with no co-reported sexual dysfunction.

Results: Forty cases of depression or suicidality were identified in men treated with finasteride, with a median age of 31years. Most cases (62.5%) were classified as serious. In half of the cases, symptoms occurred within 9months of treatment initiation. Suicidality (ideation or attempts) was present in 40% of cases. Among patients who discontinued treatment, 45.2% reported symptom improvement. In unresolved cases (n=10), the median persistence of symptoms after withdrawal was 20.2months. A positive rechallenge was observed in two patients. Only 22.5% had a personal or family psychiatric history, and 17.5% reported a significant impact on quality of life.

Conclusion: While adverse psychiatric drug reactions, including depressive symptoms and suicidality, are often reported in conjunction with sexual dysfunction, this study highlights the severity of depressive effects associated with finasteride, particularly the risk of suicidality even in the absence of associated sexual dysfunction or psychiatric history. The persistence of depressive symptoms sometimes beyond 20months post-discontinuation, underscores the need for adapted management and long-term monitoring. Finally, these findings highlight the need for thorough psychiatric evaluation at the time of prescription and ongoing suicide risk assessment throughout the course of treatment.

In recent years, artificial intelligence (AI) has emerged as a powerful tool in healthcare and is becoming increasingly prevalent across all medical and paramedical disciplines. AI has numerous applications in pharmacology. This narrative review explores the increasing importance of AI in three key areas of pharmacology: pharmacokinetics (PK), pharmacodynamics (PD), and pharmacovigilance (PV), as well as pharmacology education. We conducted a literature review enhanced by the ARTIREV hybrid bibliometric tool to identify and analyze key advances, applications, and challenges with AI integration in this field. In PK, machine learning and hybrid approaches improve the prediction of individualized drug exposure, support model-informed precision dosing and handle irregular and sparse data through architectures such as recurrent neural networks and NeuralODEs. In PD, AI facilitates a shift towards an era of precision and personalized medicine by enabling the development of drug effect models and considering interindividual variability. It also makes it easier to implement adaptive dosing regimens that are tailored to various constraints. Regarding PV, AI enhances the detection of adverse drug reactions, the identification of safety signals at the population level and the assessment of preclinical toxicities through the analysis of unstructured data, particularly from electronic health records. Despite their potential, AI models face several significant limitations. These include the quality of training data, limited explainability due to the "black box" effect and a lack of external validation of the models developed. Altogether, this review emphasizes the role of AI in pharmacology and the necessity of training future professionals to ensure the safe and validated use of AI in personalized medical applications.