Frontiers in Pharmacology

Background: Adverse drug withdrawal events (ADWEs) are a key safety concern during deprescribing but remain poorly explored in pharmacovigilance systems. Objectives: To identify and compare ADWE signals across drug classes, different drugs within drug classes, and across patient characteristics, countries, and over time. Methods: A case/non-case disproportionality analysis was conducted in FDA-FAERS and EMA-EudraVigilance pharmacovigilance databases, with stratification by age (adults: 18-64, older adults: [≥]65), sex (male/female), reporting time (2004-2023 in 5-year intervals), and country (for EMA data). Disproportionality analysis (quantitative signal detection) was used to detect signals between ADWEs and drugs using the proportional reporting rate (PRR[≥]2), reporting odds ratio (ROR>1), and information component (IC>0) with case count [≥]5. Results: Overall, 158,501 reports (FDA-FAERS 145,514; EMA-EudraVigilance 12,987) included drug-event pairs related to ADWEs. In FDA-FAERS, clobetasone (IC=5.58; PRR=79.18; ROR=176.90) showed the strongest ADWE signals, followed by hydromorphone (4.85; 29.94; 37.37), hydrocodone, and paroxetine. In EMA-EudraVigilance, ethyl loflazepate (IC=6.01; PRR=119.80; ROR=197.53), clobetasone (5.39; 102.73; 155.10), veralipride, and levomethadone had the strongest signals. Most drugs maintained positive ADWE signals in analysis stratified into adults and older adults. However, among the top 10 drugs (based on highest IC values), buprenorphine/naloxone, desvenlafaxine, and baclofen in FDA-FAERS (ICs 4.95-6.05) showed stronger signals in older adults. A sex-based difference was observed, with paroxetine, venlafaxine, and buprenorphine/naloxone showing a stronger positive signal in females in both databases, whereas several opioids had stronger signals in males versus females across both databases. Conclusion: This study suggests ADWE signals for some medications differ by age and sex, potentially indicating different risks for withdrawal effects.

We propose two new tests to detect drug effects (DE) in trials of one to very few patients followed during two periods (before and after initiation of a treatment). Both methods use longitudinal natural history data to inform the estimation of each patient's DE. The first method uses a non linear mixed effect model (NLMEM) reflecting an expected natural history with a hypothetical drug effect, to estimate the Conditional Distribution of the Drug Effect (CDDE). The second method trains a Pareto Depth Analysis (PDA) algorithm, a machine learning based approach based on outlier detection, that we implement using data simulated under the NLMEM. We evaluated the two tests with a simulation study. We used data from the PROSPAX study in Autosomal Recessive Cerebellar Ataxias (ARCAs, to derive a NLMEM for the Scale for the Assessment and Rating of Ataxia score. The CDDE method provided controlled type I error and, in some scenarios, adequate corrected power, though sensitivity analyses showed vulnerability to misspecification. The PDA method demonstrated lower statistical power except with high score precision. These results highlight different strategies for quantifying treatment effects in ultra rare, patient' specific trials. They can inform methodological design for future ARCA precision therapies.

Importance: Immune checkpoint inhibitors (ICIs) produce diverse immune toxicities, but whether checkpoint blockade also modifies associations between other drugs and adverse events is poorly understood. Objective: To define ICI-associated toxicity organization and determine whether drug-associated adverse events and onset vary with ICI exposure and checkpoint pathway. Design and Setting: Cross-sectional analysis of deduplicated FAERS reports from 2016 through 2025; analyses performed in 2026. Participants: Among 13,701,106 deduplicated reports, 2,365,269 were cancer associated and 256,940 contained an ICI. Median age among cancer reports with observed age was 66 years (IQR, 56-75 years); 1,031,999 (43.6%) were female and 1,003,154 (42.4%) were male. Exposures: ICI exposure in any reported drug role, individual primary-suspect drugs, and checkpoint-pathway exposure. Main Outcomes and Measures: Reporting odds ratios (ORs), cross-organ adverse-event communities, adjusted primary-suspect drug x ICI interaction ORs for Stevens-Johnson syndrome/toxic epidermal necrolysis (SJS/TEN), drug reaction with eosinophilia and systemic symptoms (DRESS), acute generalized exanthematous pustulosis (AGEP), interstitial nephritis, drug-induced liver injury (DILI), and vomiting (VOM), and accelerated failure-time model time ratios for documented onset. Results: Of 3001 eligible Preferred Terms in cancer-associated reports, 2091 differed at a false discovery rate (FDR) less than .05. Four cross-organ toxicity communities were identified. Of 138 eligible drug-phenotype pairs, 65 had FDR-significant interactions, including moxifloxacin-SJS/TEN amplification (interaction OR, 101.72; 95% CI, 39.11-264.55), enfortumab vedotin-SJS/TEN attenuation (interaction OR, 0.17; 95% CI, 0.13-0.23), and omeprazole-interstitial nephritis amplification (interaction OR, 10.35; 95% CI, 7.62-14.05). Among 60,324 reports contributing to temporal analyses, ICI exposure was associated with longer adjusted documented time to onset for 5 of 6 phenotypes (time ratios, 1.37-1.59) but not AGEP (time ratio, 0.99; 95% CI, 0.67-1.46). Temporal associations also differed across checkpoint pathways. Conclusions and Relevance: ICIs were associated with a structured cross-organ toxicity landscape, phenotype-specific modification of drug-associated adverse events, and distinct temporal patterns across checkpoint pathways. These findings support checkpoint blockade as a modifier of drug-associated toxicity and motivate longitudinal and mechanistic validation.

Bayesian borrowing of external information can improve trial efficiency, particularly in pediatric and rare disease settings where patient populations are limited, but may introduce bias and inflate the Type~I error rate when the trial differs from external studies. Recent U.S. Food and Drug Administration (FDA) draft Bayesian guidance emphasizes careful evaluation of external information, prior specification, and assessment of operating characteristics. This paper compares three meta-analytic-predictive (MAP)-based methods for Bayesian borrowing: the MAP prior, robust MAP (RMAP) prior, and self-adapting mixture (SAM) prior. An adaptive platform trial design in Rett syndrome is used as a case study. Simulation studies evaluate frequentist operating characteristics under varying prior--data conflict, between-study heterogeneity, treatment effects, and clinically significant differences (CSDs) for the SAM prior. The MAP prior achieved the greatest efficiency when external and current data were compatible but exhibited the largest bias under substantial prior--data conflict. The RMAP priors improved robustness through fixed robust-component weights, whereas the SAM prior adaptively adjusted borrowing and was less sensitive to prior--data conflict while retaining efficiency gains when the data were compatible. Although the CSD influenced the degree of adaptive borrowing, as reflected by effective sample size, it had only a modest impact on frequentist operating characteristics. Sensitivity analyses using a skeptical robust component yielded similar qualitative conclusions, while accentuating the differences between the MAP and RMAP priors. These findings provide guidance for evaluating and selecting MAP-based borrowing strategies before trial implementation, particularly in rare disease settings, consistent with current FDA recommendations.

Inadequate autophagy promotes smoking-induced cellular senescence involved in chronic obstructive pulmonary disease (COPD) pathogenesis. Transcription factor EB (TFEB) is a master regulator of the autophagy-lysosome axis. For the first time, we investigated the therapeutic potential of pemafibrate, a putative TFEB inducer. COPD lung epithelial cells showed reduced TFEB expression. Pemafibrate enhanced autophagy/mitophagy flux and restored lysosomal acidification observed during cigarette smoke (CS) extract exposure in human bronchial epithelial cells, resulting in reduced cellular senescence. TFEB knockdown demonstrated involvement of pemafibrate-induced TFEB in these effects. Pemafibrate induced TFEB expression, mitigated alveolar enlargement and airflow obstruction, and attenuated the CS-induced increase in static lung compliance in a long-term CS-exposed mouse model. It reduced the CS exposure-induced cellular senescence, possibly through autophagy/mitophagy, as suggested by bulk RNA sequencing of mouse lungs. A retrospective cohort study showed that patients given pemafibrate displayed attenuated FEV1.0 decline compared with those given bezafibrate or fenofibrate. In conclusion, pemafibrate is a promising therapeutic agent for COPD, potentially exerting its effects through the regulation of the TFEB-autophagy/mitophagy-lysosome axis.

Background: Traditional real-world studies comparing SGLT2 and DPP4 inhibitors on renal outcomes rely on propensity score matching, which causes high-dimensional data loss. We used causal machine learning (Causal ML) to unmask heterogeneous treatment effects in diabetic kidney disease (DKD). Methods: Using data from 4,588 patients within the Japanese J-CKD-DB-Ex registry, we implemented a doubly robust (DR) learning framework (Linear DR-learner with XGBoost) to compare SGLT2 and DPP4 inhibitors. Outcomes included the chronic eGFR slope and a composite renal endpoint ([≥] 50% eGFR decline or end-stage kidney disease). Heterogeneity was explored via causal SHAP and decision trees. Results: At the population level, SGLT2 inhibitors modestly slowed chronic eGFR decline (average treatment effect [ATE] = 0.14 [95% CI: -0.86, 1.15] mL/min/1.73m^2/year) and reduced composite endpoint risk by 9% (ATE: -0.09 [-0.11, -0.08]) versus DPP4 inhibitors. However, individual-level counterfactual analysis suggested that for the chronic eGFR slope, non-glinide users with stable pre-treatment trajectories who were also taking ACE inhibitors had a greater benefit from SGLT2 inhibitors (ATE: 2.95 [-0.68, 6.58]). Conversely, glinide users with steep pre-treatment decline had a greater benefit from DPP4 inhibitors (ATE: -8.98 [-16.11, -1.85]). For composite renal events, SGLT2 inhibitors had a 28% absolute risk reduction within the algorithmically identified high-risk subgroup (eGFR [≤] 28.1 mL/min/1.73 m^2 and positive proteinuria; ATE: -0.28 [-0.33, -0.23]). Even non-proteinuric decliners demonstrated a 8% risk reduction with SGLT2 inhibitors (ATE: -0.08 [-0.10, -0.06]). Conclusion: Causal ML advances precision medicine in DKD, shifting from uniform prescribing to individualized, data-driven therapy targeting distinct intrarenal pathways.

Background: Prostate cancer enzalutamide treatment is approved at a standard dose of 160 mg daily. Concerns for real-world patients -- older and more fragile than those enrolled in clinical trials -- have prompted consideration of initiating treatment with lower doses, but the long-term efficacy of this approach remains unknown. We evaluate the long-term survival and longevity in patients treated with standard versus upfront low-dose enzalutamide. Methods: Retrospective analysis of 151 patients treated with enzalutamide (102 receiving 160 mg; 49 receiving [≤]80 mg) between 2014--2021 at the Centre Hospitalier Universitaire de Martinique, with complete follow-up through end of life (98.7% completeness of follow-up). Primary outcomes were overall survival (OS), progression-free survival (PFS), and longevity (attained age). Results: Doses [≤]80 mg were associated with longer median OS (36.3 vs. 20.7 months), improved restricted mean OS (difference of 0.7 years, p=0.05), and enhanced longevity (median 82.5 vs. 78.3 years, p=0.004). PSA response rate at 12 weeks was higher with lower-dose (71.4% vs. 48.8%, p=0.016). In multivariable models adjusted for prognostic factors, [≤]40 mg compared with 160 mg was non-inferior regarding OS (HR=0.61, 95% CI 0.36--1.06), superior regarding PFS (HR=0.59, 95% CI 0.35--0.99), and superior regarding longevity (HR=0.48, 95% CI 0.28--0.84). Bone metastasis, poor performance status, PSA response, time to PSA nadir, and disease duration were independent predictors of outcomes. A post-hoc analysis revealed a strong association between dose and physician-prescribing profiles, ranging from "endorse-lowest-dose" to "never-deviate-from-full-dose". Conclusions: Lower doses of enzalutamide were non-inferior to full-dose. Dose-adapted strategies warrant further investigation.

Importance: Evidence on GLP-1/GIP therapies is largely derived from trials enrolling selected populations or medical records that miss utilization outside healthcare channels. No nationally representative cohort has characterized real-world uptake, indications, and access. Objective: To characterize GLP-1/GIP prevalence, indication, clinical profile, and access. Design: Prospective cohort study with three GLP-1/GIP surveillance waves (March 2024, December 2024, October 2025). Setting: The Understanding America Study, an address-based, nationally representative panel of approximately 15,000 US adults aged 18+ years initiated in 2014. Participants: UAS participants responding to at least one surveillance wave (n=9150). Exposures: GLP-1/GIP use status (never vs any use, comprising current and former use), self-reported primary indication (diabetes, weight loss, or other), and access pathway (traditional vs non-traditional). Main Outcomes and Measures: Survey-weighted prevalence of GLP-1/GIP use, overall and by indication and access pathway; sociodemographic, cardiometabolic, treatment, and access characteristics; and smartwatch-derived resting heart rate, heart rate variability, maximum activity heart rate, step count, and sleep duration and variability. Results: Among n=9150 adults (1274 with any use; 60.9% female; median age 53 years), weighted prevalence increased 46%, from 8.2% (March 2024) to 12.0% (October 2025) representing 32 million. Weight-loss indications grew, reaching nearly half of use (4.1% to 5.6%); diabetes-indicated use was stable (5.3% to 5.4%). Users carried high cardiometabolic burden (obesity, 68.2%; diabetes, 53.6%) but diverged by indication: diabetes-indicated users were older (median, 59 vs 49 years), whereas weight-loss-indicated users were more often female (69.9% vs 51.3%) and healthier. One in three users (~9 million) had non-traditional access, especially in weight-loss-indicated users, of whom 33% had no conventional prescription; 41% used compounding, online, or foreign pharmacies; and, 43% lacked coverage. Non-traditional users were five times as likely to report an unlisted, likely compounded formulation (19.8% vs 4.1%). All p<0.05. Conclusions and Relevance: Real-world GLP-1/GIP use has grown rapidly and diversified substantially in indication, access, and population profile. One in 3 users obtained treatment through nontraditional channels largely invisible to claims data, raising long-term safety, efficacy, and coverage questions. GLIMMER provides a public, nationally representative longitudinal evidence base for future payer and provider decisions.

Background: Refractory and macrolide-resistant Mycoplasma pneumoniae pneumonia (MPP) has emerged as a major challenge in pediatric respiratory medicine, amplified by the post-2023 resurgence. However, a systematic overview of the research landscape specific to treatment-refractory and drugresistant disease in children remains lacking. Methods: Research articles and reviews on pediatric refractory or macrolide-resistant MPP published between 2000 and 2025 were retrieved from OpenAlex using Boolean searches. After screening, 2,286 records were quantitatively analyzed for annual output, contributing countries/institutions, thematic clusters, and citation-burst dynamics using Python. Results: Annual publications grew exponentially, with a pronounced surge after 2023 (n=378 in 2025). China produced the highest volume (45.1%) but recorded fewer citations per publication than the US, Japan, and Canada. The literature resolved into four clusters: macrolide resistance/molecular basis, epidemiology, etiology/co-infection, and refractory disease management. Burst analysis showed an evolution from earlier fronts like 23S rRNA mutations and azithromycin to recent emerging trends like pandemic-related co-circulation, genotype surveillance, and co-infection. Conclusions: Research on pediatric refractory and resistant MPP is expanding rapidly, shifting in emphasis from etiologic descriptions toward resistance mechanisms and clinical management. Standardizing the treatment of macrolide-unresponsive disease and post-pandemic epidemiological surveillance represent the principal directions for future work. Keywords: Mycoplasma pneumoniae; children; macrolide resistance; refractory pneumonia; bibliometric analysis; research trends

To evaluate the cost-utility and 5-year budget impact of first-line olaparib plus abiraterone versus abiraterone alone for metastatic castration-resistant prostate cancer (mCRPC) in China after the eleventh round of volume-based procurement (VBP). The intention-to-treat (ITT) population was assigned primary decision-analytic weight; the prespecified BRCA1/2-mutated (BRCAm) subgroup was a supporting analysis.

Background: A dose finding trial evaluated safety and adherence for pre-cardiac surgery administration of sodium valproate. Integrated multi-omics analyses of myocardium were used to characterise mechanisms underlying the treatment effects. Methods: Adults undergoing cardiac surgery were randomised 1:1:1:1 with concealed allocation to no treatment (Controls), sodium valproate 15mg/kg/day for 1-2 weeks, 15mg/kg/day for 4-6 weeks, or 25mg/kg/day for 4-6 weeks pre-surgery. The primary analysis evaluated adherence and toxicity. Myocardial injury was defined by high sensitivity serum troponin at 24 hours post-surgery. Single-nucleus Assay for Transposase-Accessible Chromatin with sequencing (snATACseq) and single nuclei RNA sequencing (snRNAseq) of myocardial biopsies collected at surgery assessed treatment effects on chromatin accessibility and gene expression. Candidate mechanisms were validated in in vitro. Results: The analysis cohort included 42 participants enrolled between January 2020 and August 2024. Non-compliance (38%) was highest with longer and higher dosing. Sodium valproate 15mg/kg/day for 1-2 weeks had the highest levels of complete treatment adherence (70%), with 20% experiencing moderate/severe drug related adverse effects. An as-treated analyses demonstrated reductions in troponin release in participants receiving Valproate[&le;]14 days. Myocardial biopsies from trial participants demonstrated activation of hormetic p53 and Akt-GSK-3{beta} ferroptosis protection pathways. Treatment effects were not attributable to chromatin accessibility. Treatment >14 days resulted in a heart failure phenotype with suppression of ferroptosis protection pathways, endothelial mesenchymal transition, and increased myocardial injury. Conclusions: Sodium valproate 15mg/kg/day for [&le;]14 days pre-surgery is well tolerated in adults awaiting cardiac surgery. This treatment was associated with upregulation of ferroptosis protection pathways and reductions in myocardial injury.

Background: Immunochemotherapy (ICT) is considered standard in regards to care for small-cell lung cancer (SCLC) in extensive stages, yet reliable biomarkers for treatment response remain elusive. While previous univariate analyses suggest specific peripheral lymphocyte subsets correlate with survival, the systemic immune response involves complex, multivariate interactions that require advanced analytical approaches. Methods: This paper analysed high-dimensional flow cytometry data from 32 patients with stage IV SCLC treated with carboplatin, etoposide, and atezolizumab. Peripheral blood was analysed at baseline (V0) and longitudinally during treatment. To identify potential early predictive biomarkers and mitigate sample attrition in later cycles, we focused on baseline and measurements after two cycles of ICT (V1). We employed a rigorous machine learning framework utilising nested cross-validation, bootstrapping, and permutation-based statistical testing to evaluate eleven different regression and survival models. Results: Under model-appropriate metrics, regressors did not generalise (R2 <0); conversely, censoring-aware Random Survival Forests (RSF) successfully extracted robust prognostic signatures. Baseline immune profiles (V0) achieved a concordance index (C-index) of 0.66 (p= 0.015), while dynamic changes from V0 to V1 ({triangleup}V) achieved a C-index of 0.65 (p= 0.022). Crucially, absolute values measured after two cycles of ICT (V1) yielded no significant signal (p= 0.445). Feature importance analysis confirmed the prognostic value of Th17 normalisation and identified Naive Regulatory T cells and Memory B cells as candidate components. Conclusion: Machine learning validation confirms a predictive signal in the peripheral immune profile of SCLC patients. Early dynamic shifts in the balance between regulatory and effector immune arms are associated with prognosis, contrasting with the lack of signal in absolute counts after two cycles of ICT. These findings establish a proof of concept for multivariate liquid biopsy immune profiling, warranting confirmation in larger cohorts and highlighting the necessity of integrating systemic and tumour-intrinsic data.

IMPORTANCE: A recent meta-analysis showed that chemoimmunotherapy was associated with improved overall survival (OS) compared with immune checkpoint inhibitor (ICI) monotherapy for programmed death-ligand 1 (PD-L1) tumor proportion score (TPS) [&ge;] 50% advanced non-small-cell lung cancer (NSCLC). However, whether this benefit reflects chemotherapy effect or ICI heterogeneity remains unclear. OBJECTIVE: To reassess the survival benefit of adding chemotherapy to ICI monotherapy using agent-stratified comparisons anchored to chemotherapy. DATA SOURCES: The 24 phase 3 randomized clinical trials included in the original meta-analysis (search date, August 3, 2025). DATA EXTRACTION AND SYNTHESIS: Hazard ratios (HRs) for OS and progression-free survival (PFS) were extracted from each trial in the original meta-analysis. Two analytic frameworks were used: within-agent comparisons (same ICI in both chemoimmunotherapy and monotherapy) and across-agent comparisons (ICI in one treatment strategy only). For within-agent comparisons, a two-stage random-effects meta-analysis was conducted. In stage 1, ICI-specific HRs for chemoimmunotherapy and ICI monotherapy versus chemotherapy were pooled and their ratio was calculated (RHR = HRchemoimmuno/HRmono; RHR < 1 favors chemoimmunotherapy). The RHRs were pooled in stage 2. For across-agent comparisons, RHR was derived from pooled HRs by treatment strategy. MAIN OUTCOMES AND MEASURES: Endpoints were OS and PFS. RESULTS: In within-agent comparisons (4 ICIs; 13 trials; N = 3252), pooled RHR was 0.94 (95% CI, 0.78-1.13; P = .48; I2 = 0.0%) for OS and 0.85 (95% CI, 0.68-1.06; P = .14; I2 = 0.0%) for PFS. In across-agent comparisons (7 ICIs; 11 trials; N = 2231), RHR favored chemoimmunotherapy for OS (0.68; 95% CI, 0.50-0.92; P = .01) and PFS (0.46; 95% CI, 0.37-0.58; P < .001). In a sensitivity analysis restricted to trials of NCCN-recommended regimens, pooled RHR was 1.02 (95% CI, 0.81-1.28; P = .87) for OS. CONCLUSIONS AND RELEVANCE: In the within-agent comparisons, adding chemotherapy to ICI monotherapy did not improve OS or PFS in patients with PD-L1 TPS [&ge;] 50% advanced NSCLC. The benefit in the original meta-analysis appears driven by across-ICI heterogeneity. These findings are consistent with ICI monotherapy as a standard first-line option and underscore the need for agent-level stratification in across-trial comparisons.

Objective. To determine the publication rate of abstracts presented at the American Diabetes Association Scientific Sessions and to evaluate the association between statistical significance of study results and subsequent publication. Research Design and Methods. We conducted a retrospective cohort study of abstracts presented at the 2018 American Diabetes Association Scientific Sessions. The primary exposure was study result category (statistically significant vs. non-statistically significant findings), and the primary outcome was publication in an indexed journal within 5 years after conference presentation. Publication status was determined through PubMed/MEDLINE and Scopus searches. Adjusted relative risks (RRs) and 95% CIs were estimated using generalized linear models with Poisson distribution and robust variance. Results. Among 541 included abstracts, 321 (59.3%) were subsequently published in indexed journals. Abstracts reporting statistically significant findings had a higher publication rate than those reporting non-statistically significant findings (61.9% vs. 42.3%; p=0.002). In the adjusted analysis, abstracts with non-statistically significant findings had a lower likelihood of publication compared with those reporting statistically significant findings (adjusted RR 0.71 [95% CI 0.55-0.93]; p=0.013). Conclusions. Approximately four in ten abstracts presented at the ADA Scientific Sessions were not published within 5 years. Abstracts reporting non-statistically significant findings had a lower likelihood of subsequent publication, suggesting persistent publication bias in diabetology research. Future initiatives promoting the interpretation of effect estimates, confidence intervals and clinical relevance, rather than statistical significance alone, may help reduce selective dissemination of evidence

Health facilities (HFs) routinely administer medicines and are expected to ensure patient safety by detecting, reporting, investigating, and analysing adverse events following exposure to drugs (AEFED). This study aimed to assess the implementation of pharmacovigilance activities in referral and regional health facilities in Cameroon and to identify pharmacovigilance training needs among healthcare personnel (HP). This was a cross-sectional descriptive study targeting referral and regional health facilities and healthcare personnel involved in patient care and pharmacovigilance activities in Cameroon. Health facilities were selected using stratified purposive sampling, while healthcare personnel were selected through exhaustive sampling. Data were collected using semi-structured electronic questionnaires administered face-to-face by trained enumerators. The questionnaires assessed the organization, resources, and implementation of pharmacovigilance activities at health facilities, as well as healthcare personnel knowledge of pharmacovigilance concepts, previous training, and perceived training needs. Of the 14 eligible health facilities, 10 (71.4%) consented to participate in the study. Of the 10 health facilities, 4 (40.0%) had an established pharmacovigilance unit, while 3 (30.0%) reported conducting neither detection nor notification activities. Among the 261 healthcare personnel approached, 214 (81.9%) participated. Only 41.6% had needed knowledge to detect an adverse event, while 72.9% were aware of adverse event notification procedures. Previous exposure to pharmacovigilance training was reported by 37.9% of healthcare personnel, and all participants expressed a need for additional training, particularly on national pharmacovigilance regulations (69.2%), organization of the pharmacovigilance system (67.3%), and adverse event detection (67.3%). The main reported challenges by healthcare personnel in the implementation of pharmacovigilance activities included insufficient budget allocation, limited access to pharmacovigilance training, lack of pharmacovigilance guidelines and insufficient qualified human resources. Pharmacovigilance implementation in referral and regional health facilities in Cameroon remains limited, with gaps in organizational structures, resources, healthcare personnel knowledge, and training. Strengthening pharmacovigilance systems through improved facility capacity, availability of essential tools, and targeted healthcare personnel training is needed to enhance drug safety surveillance.

Large language models perform well on medical examinations, but users routinely challenge their answers and invoke professional roles, and it is unclear what a system does when a medical credential and a stated task-specific accuracy point in opposite directions. In a factorial experiment on 480 items from four Polish specialty examination sets and three consumer large language model systems (ChatGPT, Claude, Gemini), each item and system received eleven independent conversations. Conditions crossed attributed source role (medical student, experienced specialist), stated prior accuracy on similar questions (2/10, 8/10) and suggestion correctness. The primary outcome was adoption of a prespecified incorrect option when the baseline answer matched the official key, comparing a specialist described as 2/10 with a student described as 8/10. Baseline agreement with the key was 87.2% across 15,683 analyzable conversations. The incorrect option was adopted more often from the specialist described as 2/10 than from the student described as 8/10 (10.2% vs. 7.6%; adjusted risk difference +2.82 percentage points, 95% CI +0.65 to +4.99). Estimates varied across the three systems and only one system-specific interval excluded zero. In a prespecified exploratory analysis with a shared eligibility rule, correct suggestions were adopted far more often than incorrect ones (risk difference +35.7 percentage points, 95% CI +30.8 to +40.7), indicating selective rather than indiscriminate compliance. An incorrect suggestion from a specialist with low stated accuracy was therefore slightly more influential than the same suggestion from a student with high stated accuracy, although the difference was modest and varied across systems. Agreement reached only after a user has disclosed a preferred answer should not automatically be treated as an independent second opinion, and medical large language model systems should be evaluated on how they revise answers after such disclosure, not solely on initial accuracy.

Introduction Miltefosine is the only approved oral antileishmanial agent, but its use in women of childbearing potential (WOCBP) is restricted due to preclinical teratogenicity. Current labeling recommends contraception during treatment and for at least five months thereafter, based solely on its long terminal elimination half-life. This study re-evaluated the required contraceptive duration using an exposure margin-based approach. Methods Virtual populations were generated from anthropometric data of 382 Indian, 4,462 Eastern African, and 4,019 Brazilian WOCBP with leishmaniasis. Published population pharmacokinetic models were used to simulate miltefosine exposure following 14-42-day regimens for visceral leishmaniasis (VL), post-kala-azar dermal leishmaniasis (PKDL), and cutaneous leishmaniasis (CL). A developmental safety exposure threshold was derived from the rat no-observed-adverse-effect level (0.6 mg/kg/day for 10 days) and adjusted using a 10-fold safety margin. Contraceptive durations resulting in median residual post-contraception exposure (AUCEOC-{infty}) below this threshold were considered supportive of contraceptive discontinuation. Results The developmental safety exposure threshold was estimated at 2.5 mg{middle dot}day/L. Despite pharmacokinetic differences across geographical regions and disease manifestations, required minimum contraceptive durations were consistent: three months from treatment initiation for the 14-day regimen, four months for 21- and 28-day regimens, and five months for the 42-day regimen. For the 14-day regimen, a single dose of a long-acting injectable contraceptive administered at treatment initiation would provide sufficient coverage. Conclusion An exposure margin-based approach supports shorter contraceptive durations than current recommendations. For the 14-day VL regimen, three months of contraception may provide a practical alternative to current labeling.

Purpose/Objective There is paucity of data reporting outcomes of breast cancers with initial internal mammary nodal involvement and no visceral metastases, treated with curative hypofractionated radiotherapy . We report the outcomes from a tertiary centre alongside spatial patterns of recurrences in the above group Material/Methods For this retrospective cross-sectional study, consecutive patients contoured as per the ESTRO 2013 guidelines, treated between 2016-2022 were eligible if their diagnostic imaging demonstrated involvement of the internal mammary nodes. Radiotherapy (40 Gy/15#/3 weeks) was delivered to the residual breast / thoracic wall, SCF region corresponding to the ESTRO lymph node level 4 and internal mammary chain nodes. Residual IMN/ level 4 nodes received a boost of 10Gy/5#. Spatial mapping of sites of recurrence at the local site and three nodal sites (axilla, SCF and IMN) was performed using deformable image registration. Sites of recurrence at the local site and three nodal levels were contoured separately. Volumetric intersection of the recurrent gross tumour volume (GTV_recurrence) with treated clinical target volume (CTV) was calculated. Actuarial overall (OS), disease free survival (DFS) & cumulative incidence of local (LR), regional (RR) and loco-regional recurrence(LRR) were calculated using Kaplan Meier method. Univariate comparison of outcomes with or without residual disease was performed using the log rank test. Results The median age of the 61 eligible women was 49 years. 77% received neoadjuvant chemotherapy and the rest adjuvant chemotherapy. 82% patients had a mastectomy. Axillary lymph node dissection was done in 96.7%. Boosts to residual IMN and SCF nodes were delivered to 21(34.4%) and 2 (3.3%) respectively. Median follow up was 3.6 years. Out of the 61 patients, 42 patients were disease free with an estimated 3 year disease free survival of 75% (95% CI 64, 88%). Spatial mapping of locoregional recurrence was possible in all but 1 patient with local (only) recurrence who was lost to follow-up after mammogram only. Among the patients with loco regional recurrence 1 had recurrence in local site + SCF +axilla, 3 had recurrence in the SCF+axilla, 2 in the SCF+IMN and 1 in the axilla+SCF+IMN. Only one patient had isolated axillary recurrence or isolated SCF recurrence. There were no IMN only recurrences. Among the 8 patients with nodal recurrence, a total of 27 individual GTV_recurrence were identified in the axilla(n=11), SCF(n=11) and IMN (n=5). IMN recurrences showed complete or partial overlap with CTV. SCF recurrences were a mix with predominantly in-field recurrences while axillary recurrences occurred outside the treated volume.Four (6.6%) patients had Grade 2 lymphoedema as documented late side effect. Conclusion Aggressive treatment of IMN disease with adjuvant radiation is effective with good locoregional control. Systemic recurrences are common and may benefit from intensification strategies.

Background. Cardiovascular-outcomes trials are lengthy, costly, and associated with substantial uncertainty prior to readout. In-silico trial simulation using real-world data (RWD) has emerged as a potential tool to support earlier decision-making; however, evidence of prospective predictive validity, generated prior to trial result disclosure, remains limited. Methods. We applied a semi-mechanistic machine learning framework integrating real-world patient data with biologically informed drug representations to prospectively simulate the VESALIUS-CV trial evaluating evolocumab versus placebo. The simulation model was trained on a combination of patient-level real-world data and a drug-centric knowledge graph and validated for both patient-level and trial-level retrospective predictive performance. The model was then used to simulate VESALIUS-CV before public disclosure of trial results, using a locked model and prespecified eligibility criteria and primary endpoint aligned with the clinical protocol. A patient-level time-to-event model was used to generate virtual trial arms, from which cumulative incidence curves, hazard ratios, confidence intervals, and p-values for major adverse cardiovascular events (MACE) were estimated. Results. In retrospective validation, the model demonstrated strong patient-level discrimination, with time-dependent ROC-AUC values ranging from 0.80 to 0.90 across follow-up horizons. For trial-level validation, 22 randomized cardiovascular-outcomes trials were simulated, and hazard ratios for 3-point MACE across 24 between-arm comparisons showed consistent directional agreement and quantitative correlation with published results such that the model accurately predicted trial success, achieving an F1 score of 0.83, with precision of 0.79 and sensitivity of 0.89. In a fully prospective application, the simulation predicted a statistically significant reduction in 3-point MACE with evolocumab versus placebo, estimating a hazard ratio of 0.78 (95% CI, 0.70-0.87) at 54 months. These predictions were consistent with the subsequently reported VESALIUS-CV results, which demonstrated a hazard ratio of 0.75 (95% CI, 0.65-0.86) at 55 months of median follow-up. Conclusions. In a fully prospective setting, a RWD-driven, AI-based simulation accurately predicted the direction, magnitude, and temporal dynamics of treatment effects observed in the VESALIUS-CV trial. These results demonstrate that in-silico trial simulation can anticipate clinical outcomes in the prospective setting, supporting its use as a complementary tool for early decision-making, trial design optimization, and de-risking in cardiovascular drug development.

Introduction: Polyendocrine metabolic ovarian syndrome (PMOS), formerly known as polycystic ovary syndrome (PCOS), is a common endocrine disorder affecting women of reproductive age. Besides reproductive and metabolic disturbances, PMOS negatively impacts psychological well-being and quality of life. Despite available treatment options, there remains a need for safe and effective therapies that improve both clinical symptoms and fertility outcomes. Aim: To compare the efficacy of VAMHA and MYRHA tablet combination therapy with standard non-hormonal therapy in restoring regular menstruation. Secondary objectives included assessment of ovulation, menstrual symptoms, polycystic ovarian morphology, hormonal and metabolic parameters, anthropometric measures, and skin manifestations. Study Design: Open-label, randomized, multicentre, prospective comparative clinical study. Methods: Seventy-one women with PMOS were randomized to Group A (n=37) or Group B (n=34). Group A received VAMHA and MYRHA tablets (2 tablets each), while Group B received Metformin 500 mg plus Myoinositol 600 mg (1 tablet), twice daily for 180 days. Data were recorded in Case Report Forms. Statistical Analysis: Continuous variables were summarized using mean and standard deviation, while categorical variables were expressed as frequencies and percentages. Appropriate statistical tests, including Chi-square, were used. A p-value [&le;]0.05 was considered significant. Results: Significantly more participants in Group A achieved regular menstrual cycles than Group B (31 vs. 22; p<0.05). Ovulation occurred in 16 participants in Group A compared with 6 in Group B (p<0.05). Both groups showed significant improvement in menstrual irregularity and related symptoms. Significant reductions in Anti-Mullerian Hormone (AMH), fasting insulin, and body mass index (BMI) were observed in both groups (p<0.05). Resolution of polycystic ovarian morphology occurred in 13 participants (38.23%) in Group A and 10 (33.33%) in Group B. Both treatments were well tolerated with no major safety concerns. Conclusions: VAMHA and MYRHA combination therapy was superior to standard non-hormonal therapy in improving menstrual regularity and ovulation. It also produced favourable metabolic, hormonal, and ultrasonographic outcomes, suggesting its potential as a safe and effective option for comprehensive PMOS management and fertility enhancement.