Drug Safety

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.

Objective As antimicrobial resistance (AMR) continues to threaten global public health, bacteriophage therapy products (BTPs) offer a promising alternative to conventional antimicrobials. However, translation into routine clinical practice requires best practice standards for manufacturing and quality control to ensure the consistent safety, quality, and reliability of personalised BTPs produced for individual patients or small cohorts. Design A modified Delphi methodology was used to develop consensus statements, engaging experts from Australia's National Bacteriophage Therapy Regulatory Working Group across the fields of clinical microbiology, phage biology, good manufacturing practice (GMP), regulatory science, and government. The process comprised three iterative phases: (1) structured statement development, (2) an anonymous REDCap survey, and (3) a hybrid consensus meeting. The strength of evidence and recommendations was assessed using the GRADE (Grading of Recommendations Assessment, Development and Evaluation) framework. Results Consensus was reached on 35 statements to provide best practice manufacture and quality control guidance for BTPs. These statements address requirements for phage identification and characterisation; define the point at which GMP-aligned processes commence for ubiquitous phages; outline quality control expectations for phage active pharmaceutical ingredient (pAPI) production and maintenance of BTP and host cell repositories. Additional guidance covers quality management systems, including documentation, traceability, and governance. Conclusion These consensus statements provide comprehensive best practice recommendations for the manufacture and quality control of BTPs in Australia. By promoting consistent, safe, and quality-assured approaches to personalised BTPs, they aim to facilitate clinical implementation while remaining aligned with existing international pharmacopoeial standards and regulatory frameworks.

Introduction: The application of pharmacogenomics (PGx) in pediatrics is limited by the lack of age-oriented interpretation approaches, as algorithms developed for adults do not account for ontogenetic changes in the activity of drug-metabolizing enzymes and transport proteins. The aim of this study was to evaluate the clinical applicability of pharmacogenomic data in Russian children, assess the concordance between genotype-based recommendations and the ontogenetic status of drug-metabolizing enzymes, and develop recommendations for the generation of age-oriented PGx reports. Methods: We analyzed whole-exome sequencing (WES) data from 524 pediatric patients and 635 newborns, filtering pharmacogenomic annotations according to PharmGKB/ClinPGx evidence levels (1A-2B) and the presence of the 'Pediatrics' tag. The concordance between genotype-based recommendations and the ontogenetic status of drug-metabolizing enzymes was assessed in newborns. In a pediatric subgroup of 100 patients, a retrospective analysis of medical records was performed to evaluate the structure of pharmacotherapy and the frequency of adverse drug reactions (ADRs). A 'PGx-ADR-cost' database was created, and the relative population burden index was calculated for 27 gene-variant-drug-ADR associations. Results: Clinically relevant annotations (requiring drug avoidance or dose modification) accounted for only 5% of all initial pharmacogenomic annotations in both cohorts; 67.6% (pediatric cohort) and 67.2% (neonatal cohort) of these were related to alleles with altered function. Concordance between genotype-based recommendations and the ontogenetic status of drug-metabolizing enzymes in newborns was observed in only 5 of 14 (35.71%) gene-drug pairs. ADRs were identified in 21% of the 100 pediatric patients; however, only two cases could be explained by high-evidence PharmGKB/ClinPGx annotations. Ranking by relative population burden identified UGT1A1*28-irinotecan-induced neutropenia and HLA-A*31:01-carbamazepine-induced severe cutaneous reactions as priority associations. Conclusions: Age represents a critical factor in the interpretation of pharmacogenomic data in children, as current approaches to PGx reporting do not adequately incorporate the ontogenetic context. We propose a pediatric PGx interpretation model that includes mandatory reporting of patient age, ontogenetic adjustment, evidence-level stratification, and multidisciplinary clinical assessment. Prospective validation is required to confirm the clinical utility of the proposed approach.

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.

Background Glucagon-like peptide-1 receptor agonists (GLP-1RAs) are widely prescribed for type 2 diabetes and obesity, yet their neuropsychiatric safety profile remains incompletely characterized. We aimed to systematically evaluate neuro-adverse event (AE) signals for six GLP-1RAs and to validate key findings using population-based data. Methods We conducted disproportionality analysis of FAERS data for semaglutide, liraglutide, dulaglutide, tirzepatide, exenatide, and lixisenatide. RORs were calculated for 93 predefined neuro-AE MedDRA PTs across 11 neurological categories. External validation used NHANES 2013-2018 (n=17,057; 70 GLP-1RA users) with survey-weighted regression. Results We identified 41 significant neuro-AE signals. Semaglutide showed the strongest neuromuscular signal, muscle atrophy (ROR 3.94; 95%CI 3.42-4.54), corroborated by tirzepatide (ROR 2.35; 95%CI 2.04-2.71). Exenatide generated the highest psychiatric signal: nervousness (ROR 4.03; 95%CI 3.70-4.40). NHANES confirmed higher depression odds (OR 2.05; 95%CI 1.32-3.19; P=0.001) and reduced sleep hours (beta -0.35; P=0.033). Conclusions GLP-1RAs carry multiple neuropsychiatric safety signals, including muscle atrophy as a potential class effect and depression risk corroborated by population-level data. These findings support heightened clinical monitoring.

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.

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.

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.

Oregon (OR) and Colorado (CO) were the first states to enact regulations for provision of psilocybin with support of licensed "facilitators." As more states and countries adopt similar policies, informed public policy decisions require that client characteristics and rationale for using psilocybin, psilocybin dosing practices, mental health outcomes, and adverse events are understood. We performed a retrospective observational study of responses for 2363 individuals receiving psilocybin at OR and CO regulated service centers. Clients and facilitators entered data before and after receiving psilocybin, including the Mystical Experience Questionnaire-30 (MEQ-30), Patient Health Questionnaire-9 (PHQ-9), Generalized Anxiety Disorder-7 (GAD-7), and World Health Organization Well-Being Index-5 (WHO-5). Preexisting mental health issues were common (66%) in participants. Psilocybin doses ranged from 1-95 mg, with a mean total of 28*8 mg. We observed improvements of 49% in PHQ-9 scores, 51% in GAD-7 scores, and 22% in WHO-5 scores at two-weeks after dosing. MEQ-30 scores were dose-dependent. Changes in PHQ-9 and GAD-7 scores were not different for psilocybin doses [&le;]30 mg and >30 mg, and only weakly correlated with MEQ-30 scores. There were 94 mild adverse events during and after dosing, five more serious events not clearly related to treatment, and evidence of possible risk of increased suicidality. Study limitations include open label administration, self-reporting, loss of participants for follow-up, and a short 2-week post-dosing end-point. We conclude that psilocybin services, delivered within these regulated frameworks, is associated with improvements in mental health in real world populations, however, more robust monitoring is needed to ensure safety.

Background. Long COVID affects an estimated 400 million people worldwide, and is associated with low quality of life. Nearly all completed Long COVID clinical trials reported no benefit, and most required participants to travel to study sites. This requirement systematically excludes severely affected patients. Because there are numerous candidate therapeutics with established safety profiles and regulatory approvals for other indications, scaled, efficient evaluation of therapeutics is needed. Methods. We designed and are conducting a double-blind, placebo-controlled, phase two trial of tirzepatide for Long COVID fatigue, using an entirely remote infrastructure. Design elements included electronic consent, identity and diagnosis verification through document upload, cold-chain delivery of an injectable study drug through a central pharmacy, shared decision-making for dose titration, repeated at-home capillary blood collection in a biospecimen subcohort, weekly participant touch points through study application, wrist-worn wearable monitoring, and clinical support. The trial is operating under FDA Investigational New Drug authorization. Results. This trial enrolled 1,058 participants in 73 days, at least double the rate of any other Long COVID trial. Mean baseline metrics include mean Fatigue Severity Scale of 59.3 (standard deviation [SD] 4.9), daily step count of 3,611 (SD 2,706, general population reference mean 7,731), EQ-5D-5L of 0.6 (SD 0.2), and FUNCAP27 4.0 (SD 1.0), which was a more severely affected population than other clinical trials that collected comparable data. Study processes are working as designed. Participants use existing advocacy and support channels to gather and communicate. Conclusions. A direct-to-participant, siteless infrastructure can support a double-blind placebo-controlled trial of an injectable drug at scale, accelerate accrual, and reach severely affected participants who are routinely excluded by site-based designs. Modernizing drug distribution and regulatory pathways is needed to realize the full potential of decentralized infrastructure for drug repurposing clinical trials.

Objectives: To characterize how completely and promptly summary results are reported for registered hypertension trials on ClinicalTrials.gov, and whether reporting correlates with the observable obligation to report. Methods: Cross-sectional analysis of completed or terminated interventional trials for hypertension, retrieved through the ClinicalTrials.gov API version 2. Trials required a primary completion date of type ACTUAL at least 12 months before extraction. Reporting was timed from primary completion to first results submission and classified as timely at 365 days or fewer. Applicability was approximated requiring interventional design, phase 2 or later, a United States site, and an FDA-regulated drug or device, assigned flag-confirmed or inferred. Proportions are reported with Wilson 95% confidence intervals, time to reporting by Kaplan-Meier, and adjusted associations by logistic regression clustered on lead sponsor. Results: Of 5,851 trials, 5,396 were due to report. Timely reporting was 9.1% (95% CI 8.3-9.9) and any-time reporting 28.8% (95% CI 27.6-30.0). Reporting was graded by applicability, with flag-confirmed trials reporting timely at 36.9% (95% CI 31.6-42.5) and non-applicable trials at 6.3% (95% CI 5.6-7.1). A United States site carried the strongest adjusted association with timely reporting (OR 4.03, 95% CI 2.99-5.42). Among unreported trials, 7.8% had a sponsor-tagged publication and 36.4% under a broader definition. Conclusion: Prompt registry reporting of hypertension trial results remains uncommon, and reporting is most closely associated with the observable obligation to report.

Background: Warfarin therapy requires repetitive dose adjustments based on INR (International Normalised Ratio) monitoring. We evaluated the long-term real-world performance of Forsante Warfarin Advisor (WA), a CE-marked class IIb guideline-based decision support and care automation medical device used in anticoagulation management. Methods: Retrospective real-world data from routine clinical use between 2016 and 2026 were analysed. Treatment quality was assessed using Time in Therapeutic Range (TTR). Recommendation performance was evaluated by comparing achievement of target INR after clinician acceptance or modification of Warfarin Advisor recommendations. Results: Among 1348 patients in March 2026 median TTR was 83%, compared with 70% in March 2016. Dosages congruent with Warfarin Advisor recommendations were strongly associated with achieving target INR at follow-up in INR target ranges of 2.0-3.0 and 2.5-3.5. Treatment quality remained consistently high across years of deployment. No serious device-attributable safety incidents, regulatory incident reports, or CAPA cases were identified during 12 calendar years and 82,709 patient years of routine use. Conclusions: The findings provide real-world long-term evidence that a guideline-based warfarin dosing and care automation system can support sustained high-quality anticoagulation control in routine clinical practice. The findings support the feasibility of deploying workflow-integrated execution of selected guideline-driven clinical processes, while the causal effects on clinical outcomes require prospective confirmation. Keywords: Clinical decision support systems, Guideline execution, Real-world evidence, Warfarin, Anticoagulation

Pharmacogenomics (PGx) can improve safety and effectiveness of commonly dispensed medicines, but its value at the population level depends on how often clinically actionable PGx phenotypes co-occur with the medicines they affect. We assessed this co-occurrence in a cross-sectional analysis of Our Future Health (OFH), a new UK national biobank, by applying Pharmacogenomics Clinical Annotation Tool (PharmCAT v3.1.1) to imputed genotypes from 738,531 participants across 17 pharmacogenes with established PGx prescribing guidelines. Every participant had at least one actionable PGx phenotype, with a mean of 6.1 (SD 1.3). The number of actionable PGx phenotypes was similar across genetically inferred ancestry groups, although the pharmacogenes contributing to that count differed between groups. Using linked primary care dispensing records, 36.8% (95% CI 36.7-36.9) had been dispensed at least one medicine between April 2018 and June 2025 matched to a gene for which they carried an actionable PGx phenotype. Co-occurrence rose with age, ranging from 43.7% to 58.9% across ancestry groups among those aged [&ge;]70 years. Participants carried an actionable PGx phenotype for a mean of 13.8 (SD 6.5) of the 33 medicines dispensed in English primary care with PGx prescribing guidance, of which a mean of 0.6 (SD 1.0) had been dispensed. Co-occurrence was concentrated in a few widely dispensed classes, principally proton-pump inhibitors and antidepressants acting through CYP2C19 and statins through SLCO1B1. These findings highlight opportunities to optimise treatment for a large proportion of patients receiving routine medications and identify where pre-emptive PGx testing could have the greatest clinical benefit.

Adverse drug event (ADE) evidence is produced across patient-generated, clinical, and scientific settings that differ in language, documentation purpose, terminology, and degree of standardization. These differences shape both which adverse experiences become visible to pharmacovigilance systems and how readily they can be linked to curated drug-safety knowledge. We examine these relationships across five corpora representing distinct data-production settings: ADE Corpus V2 (medical case reports), SMM4H-2026 Task 1 (multi-lingual user-generated health content), CADEC V2 (patient-forum narratives), the Dutch ADE Corpus (EHR clinical notes), and TwiMed-PubMed (biomedical literature). A shared BERTopic analysis of ADE-positive texts concerning antidepressants and antihypertensives across the four English-language corpora identified nine interpretable topics. CADEC V2 contained a more differentiated distribution of symptom-specific themes, including sexual effects, suicidal or panic-related thoughts, vivid dreams, and memory difficulties, whereas SMM4H-2026, TwiMed-PubMed, and ADE Corpus V2 were dominated by a broader medication, sleep, tiredness, and pain theme. These patterns indicate that data-production context shapes what adverse experiences are expressed and standardized, with patient-generated narratives surfacing subjective, symptom-specific experience largely absent from clinical and scientific sources. We further show that this context shapes how readily real-world drug mentions can be linked to curated pharmacovigilance knowledge. Using SIDER 4.1 as a retrieval resource, we find substantial cross-corpus mismatches between real-world drug mentions and SIDER's predominantly English, generic-name vocabulary: CADEC V2 achieved only 9.5% exact-match coverage, with unmatched mentions frequently involving brand names, misspellings, and language-specific variants, compared to 91.0% coverage in TwiMed-PubMed's formally standardized biomedical literature. To probe how these representational differences interact with automated detection, we compare corpus-specific QLoRA fine-tuning of Llama-3.2-3B with retrieval-augmented inference using Llama-3.1-70B and Llama-3.1-405B grounded in SIDER-retrieved evidence. QLoRA-Llama-3B achieved the highest micro-averaged F1 scores on ADE Corpus V2 (0.91), CADEC V2 (0.88), and SMM4H-2026 (0.80), whereas SIDER-grounded inference with Llama-3.1-405B achieved the highest scores on Dutch ADE (0.95) and TwiMed-PubMed (0.91); these corpus-dependent patterns should not be interpreted as a controlled comparison of adaptation strategies, since model scale, task formulation, and available supervision differ across datasets. Together, our findings indicate that data-production context influences what adverse experiences are expressed, how they are standardized, and how readily they can be retrieved and computationally detected. Pharmacovigilance systems should therefore combine source-sensitive supervision with external knowledge grounding while explicitly monitoring gaps between real-world language and curated drug-safety resources.

Background: Systematic reviews of clinical prediction models increasingly include studies using artificial intelligence (AI) and machine learning (ML) methods alongside traditional multivariable regression approaches. A previously published Excel tool enabled standardised data extraction using the CHARMS checklist and risk of bias assessment using PROBAST. The recent publication of the PROBAST+AI framework, which distinguishes the assessment of model development quality from the assessment of model evaluation risk of bias and assesses applicability in both parts, necessitates an updated digital instrument applicable across prediction modelling methods. Methods: We updated an open-access Excel tool to incorporate the full PROBAST+AI framework. The updated template incorporates structural separation between assessment of model development quality and model evaluation risk of bias, with applicability assessed in both parts. It also incorporates updated signalling questions, including those addressing methodological issues particularly relevant to AI/ML, and automates the generation of summary tables and graphical displays. Results: The updated tool (CHARMS & PROBAST+AI Template) contains 11 worksheets and supports data extraction and appraisal for up to 30 prediction models. Dedicated, linked worksheets enable separate assessment of model development and model evaluation, with Domain 4 distinguishing among Apparent, Internal, and External evaluation settings. Key updates include dedicated assessments for predictor pre-processing, class imbalance handling and recalibration, data leakage prevention, and replication of the full model development pipeline within resampling procedures. Automated sheets dynamically format tables and summary charts covering PROBAST+AI parts. Conclusions: The CHARMS & PROBAST+AI Excel template provides a standardised, user-friendly, and rigorous digital framework for systematic reviewers appraising traditional statistical and AI-driven clinical prediction models.

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.

Introduction Hepatitis E virus (HEV) in pregnancy is associated with high maternal and perinatal morbidity and mortality. The safety and efficacy of the recombinant protein HEV vaccine (HEV239, Hecolin) have been established in non-pregnant adult populations but there is limited information among pregnant women. This trial has two co-primary objectives: 1) to assess pregnancy-related and/or serious safety events among pregnant women between 14 and 34 weeks of gestation receiving two Hecolin doses four weeks apart compared to placebo recipients, and 2) to determine immune non-inferiority of pregnant recipients of two Hecolin doses four weeks apart compared to non-pregnant women. Methods and Analysis This is a multi-site, randomized, observer-blinded, placebo-controlled vaccine safety and immunogenicity trial in pregnant women and non-pregnant women of reproductive age in Karachi, Pakistan. A total of 2,358 healthy women will be enrolled, including 2,208 pregnant women between 14 and 34 weeks of gestation, who will be randomized in a 1:1 ratio (stratified by gestational age, 14-27 and 28-34 weeks) to receive either Hecolin or a normal saline placebo in two doses administered 1 month apart during pregnancy and a third dose administered postpartum, approximately 5 months after the second dose. A third arm of 150 non-pregnant women aged 16-45 years will receive Hecolin on 0, 1, and 6 months. The co-primary outcomes will be (i) the proportion of pregnancy-related AESIs and SAEs in pregnant participants from the first dose until the end of study follow-up, compared with placebo, and (ii) the geometric mean concentration (GMC) of anti-HEV IgG at four weeks after the second dose, comparing pregnant vaccine recipients with non-pregnant vaccine recipients (non-inferiority margin of 0.67 for the GMC ratio). Immunogenicity will be evaluated in a pre-specified subset of 300 participants receiving Hecolin, including 150 pregnant participants and 150 non-pregnant participants. Secondary outcomes will include maternal, neonatal, and infant safety outcomes, as well as immunogenicity according to the number of Hecolin doses received and the trimester of vaccination. Ethics and Dissemination The trial was approved by the National Bioethics Committee (NBC) of Pakistan (Reference number: 4-87/NBC-910), the institutional Ethics Review Committee (ERC) of the Aga Khan University (Reference number: 8298), and the Institutional Review Board (IRB) of the International Vaccine Institute (IVI) (Reference number: 2022-007). All participants will provide written informed consent in accordance with Good Clinical Practice. The results will be submitted to World Health Organization (WHO) Strategic Advisory Group of Experts in Immunization (SAGE), and disseminated through conference presentations, and peer-reviewed publications.

Introduction: Self-rated health is an important patient-centred measure of health. The relationship between cannabis use and self-rated health has been previously studied, although with methodologic concerns which we aimed to address in this investigation. Methods: Propensity score weighted analyses of the National Health and Nutrition Examination Survey (NHANES) 2009-2018 were conducted. The primary exposure was self-reported cannabis use in the 30 days prior to survey response. The primary outcome was self-rated health measured on a five-level ordinal scale. Secondary outcomes included the number of days in the past months with: i) poor physical health, ii) poor mental health, and iii) activity limitations related to poor health. A weighted proportional odds regression model was used for the primary analysis and weighted zero-inflated negative binomial regression models were used for each secondary analysis. Results: Among 22,055 adults aged 20-59 responding to the NHANES cannabis questionnaire, 14.4% endorsed use in the past 30 days. After reweighting the sample to balance cannabis users and non-users across sociodemographic, medical, and lifestyle characteristics, there was no statistically significant association between recent cannabis use and higher levels of self-rated health (OR 0.90, 95% CI 0.80-1.01). Cannabis use was associated with poor mental health and activity limitations in the past month, but not poor physical health. Conclusions: Recent cannabis use was not associated with self-rated health but was associated with poor mental health and activity limitations in the past month. Cannabis users at risk of poor mental health should be connected with clinicians to help guide therapy.

Background: High intensity statins are foundational after acute coronary syndrome (ACS), yet intensive care unit prescribing occurs while renal reserve, perfusion, and interacting therapies are changing. We tested a renal safety checkpoint integrating kidney status, hemodynamic instability, and drug interaction burden to identify when statin intensity may become nonexchangeable. Methods: We emulated an active-comparator target trial across MIMIC-IV, eICU, and MIMIC-III. Critically ill adults with ACS, acute myocardial infarction, or percutaneous coronary intervention who received high- or moderate-intensity statins within 24 hours were included. The primary outcome was 7-day KDIGO stage 2 or 3 acute kidney injury or incident renal replacement therapy. Eligibility, time zero, treatment assignment, and follow-up were aligned. Database-specific propensity scores, overlap weighting, and standardization addressed confounding and treatment overlap. Safety domains, longitudinal analyses, bootstrap resampling, source omission, and endpoint sensitivities assessed robustness. Results: Among 5,178 patients, 761 developed the primary outcome, including 223 who initiated renal replacement therapy. Standardized risks were 17.40% with high-intensity therapy and 15.01% with moderate-intensity therapy (risk difference, 2.39 percentage points [95% confidence interval (CI), -0.23 to 5.05]; risk ratio, 1.16 [95% CI, 0.99 to 1.39]). Risk separation was greatest with high hemodynamic instability (5.78 percentage points [95% CI, 1.56 to 9.74]) and high drug-interaction burden (6.24 percentage points [95% CI, -0.44 to 12.19]). Renal replacement therapy showed a 1.33-point risk difference (95% CI, 0.18 to 2.67). Conclusions: This study moves statin safety assessment beyond fixed dose label or isolated creatinine measurement. The findings support a clinically actionable monitoring strategy in which early statin intensity is reassessed against evolving perfusion, kidney status, and interaction burden. This approach preserves intensive lipid lowering for physiologically suitable patients while identifying a high risk window in which temporary moderation.

Context: Most individuals in the United States have commercial health insurance, yet costs for tuberculosis (TB) care have focused on the public sector. Objective: To quantify 12 month all cause healthcare costs and identify predictors of expenditure among commercially insured persons with TB disease in the United States. Design/Setting: Retrospective cohort study using Merative (TM) MarketScan (R) Commercial Claims Database (2013 to 2018). Participants: Adults 18 years old with TB disease Main Outcome Measure: Total 12 month all cause healthcare costs (outpatient, inpatient, pharmacy) were calculated from the date of diagnosis. Adjusted cost ratios (aCR) were estimated using a Gamma generalized linear model. Results: We included 303 individuals diagnosed with TB disease, median age 46 years, 158 (52%) male, 16 (5%) with HIV, 12 (4%) with hepatitis B (HBV), and 13 (4%) with a drug use disorder. Mean total 12-month costs were $32,404 (median $8,075; SD $78,829). Median 12-month costs were substantially higher among persons with any comorbidity (HIV, HBV, hepatitis C (HCV), alcohol use disorder, drug use disorder, or Charlson score >0) compared to those without ($11,930 [IQR $4,194 to $36,073] vs $3,385 [IQR $1,506 to $8,609]; p<0.001). HIV coinfection and drug use disorder were the strongest independent predictors. HIV coinfection was associated with 4.7 fold higher costs (aCR 4.70, p<.001), driven predominantly by pharmacy expenditure (aCR 16.4). Drug use disorder was associated with 3.2 fold higher costs (aCR 2.62, p=.03). Comorbidity burden was a continuous independent predictor (aCR 1.36 per Charlson point, p<.001). Conclusions: Healthcare costs are high among persons with TB who have commercial insurance, and are further increased with comorbidities including HIV coinfection and drug use disorder. Improved screening, care coordination and management of TB and high risk comorbidities could yield significant cost savings.