Drug Safety

Objectives. To present DrugSet, a validated R Shiny application supporting the construction medicinal products codelists based on the Anatomical Therapeutic Chemical (ATC) system and their mapping to Clinical Practice Research Datalink (CPRD) Aurum prodcodes within a single interactive workflow. Materials and Methods. DrugSet comprises four modules: data preparation, ATC-based hierarchical code selection, string-based CPRD Aurum prodcodes mapping, and codelist export. Validation was conducted against World Health Organization (WHO) ATC reference codelists and manually curated prodcodes mappings across three drug classes: metformin, beta-blocking agents, and topical salicylic acid. Sensitivity, specificity, and Positive Predictive Values (PPV) were calculated for ATC codelist generation. Agreement proportions (overlapping against total identified codes) were calculated for prodcodes mapping. Time needed for codelist construction using DrugSet was recorded and compared to manual approaches. Results. DrugSet ATC codelist generation against WHO manual reference achieved 100% sensitivity, specificity, and PPV across all medicinal products. Prodcodes mapping agreement ranged from 89.2% to 98.3% with discrepancies due to missing data in the prodcodes input vocabulary. DrugSet completed codelist construction in 9 minutes compared to 3 hours and 10 minutes manually, across all medicinal products classes. Discussion. DrugSet provides a unified workflow that runs directly on ATC and source CPRD Aurum vocabulary files. The reduction in codelist construction time and export of the generated codelists supports reproducibility in pharmacoepidemiologic studies where codelist creation can represent a significant proportion of study setup time. Conclusion. DrugSet is an open-source, validated tool that improves accuracy, and efficiency of codelist construction for medicinal products based on ATC codes towards CPRD Aurum prodcodes.

Introduction: Adverse drug withdrawal events (ADWEs) are a key safety concern with deprescribing but are infrequently reported in trials. Although pharmacovigilance systems have advanced our understanding of medication-related harms, it is unclear how extensively these systems have been used for ADWEs. Objectives: To examine the reporting patterns of ADWEs for all drugs recorded in United States and European pharmacovigilance databases between 2004 and 2023. Methods: A retrospective study was conducted using two pharmacovigilance databases, the publicly available FDA-FAERS dataset and EMA-EV Level 2A (individual-level) dataset. ADWE cases were identified using relevant MedDRA preferred terms. Data on patient characteristics, reporter type, drugs, indication, ADWE outcomes, dechallenge/rechallenge, seriousness criteria, time to onset, duration, and causality were summarised. Results: A total of 158,505 ADWE reports were analysed (FDA-FAERS: 145,514; EMA-EV: 12,987), with mean ages of 46.1 (FDA; 55.3% female) and 45.5 years (EMA; 57.1% female). The frequently reported drug classes were opioids (FDA: oxycodone, 29.8%; EMA: buprenorphine, 19%), antidepressants (FDA: duloxetine, 32%; EMA: venlafaxine, 25.9%) and gabapentinoids (FDA: pregabalin, 6.7%; EMA: pregabalin, 6.0%). The most common adverse outcomes were other serious medical conditions (FDA=63.9%; EMA=46.0%), hospitalisation (FDA=15.9%; EMA=28.3%), and disability (FDA=13.3%; EMA=6.2%) and these outcomes varied significantly based on sex and age group (p<0.05). Conclusions: This study provides novel evidence of reporting patterns and characteristics of ADWEs across drugs in pharmacovigilance data. These findings emphasise that adverse drug reaction reporting systems need to accommodate ADWEs (i.e., clarity on terminologies, dechallenge/rechallenge, causality assessment) to effectively capture ADWE-related data to support evidence-based deprescribing practices for better patient safety

Background: Retrieval of adverse event reports based on coded drug-event co-occurrence enables large-scale pharmacovigilance analyses, but yields candidate reports rather than validated cases, risking misinterpretation if used alone. Aim: To develop and apply a framework for identification and characterization of clinically meaningful case series in pharmacovigilance. Methods: We conducted two case studies. The first developed and refined the framework in an information-rich setting, focusing on drug-induced impulsivity across selected drugs; the second tested its applicability in a more routine, information-poor setting, focusing on drug-induced suicidality. Results: In Case 1, non-relevant reports were frequent for drugs with uncertain evidence and negative controls ({approx}20-40%) compared to drugs with established causal roles (4%). The emerging framework assessed relevance based on exposure, event, drug-event relationship, and population. For suspected adverse drug reactions, relevant reports were further characterized by reporter suspicion and evidentiary qualifiers supporting or refuting causality; higher suspicion was associated with more supportive qualifiers. Applied to Case 2, the framework ruled out 69% of reports as non-relevant but highlighted substantial non-assessability (17%). Conclusions: In pharmacovigilance, retrieval is not equivalent to case identification. Relevance is question-specific and shaped by how reports are captured, processed, and retrieved. This can be especially critical for emerging or bias-prone safety questions. Transparent and reproducible case definition and adjudication are essential for interpretable analyses.

Introduction: Conventional substance-level disproportionality analysis may miss safety patterns specific to a dose form, route, or intended site. More granular analyses are hindered by incomplete, inconsistent reporting of product information. The Pharmaceutical Product Identifier (PhPID), representing products by substance, strength, and dose form, may support more granular analyses. Objective: To explore the use of PhPID-like dose form information for site-specific disproportionality analysis in dexamethasone. Methods: We evaluated VigiBase reports (January 1, 2001 - December 31, 2024) for completeness of dose form and route data. We standardized dexamethasone entries to PhPID Level 3 standards, representing substance and administrable dose form. Through disproportionality analysis (Information Component, IC) we compared substance-level and site-specific results. Results: Among 56.4 million suspected/interacting drugs, dose form was reported in 47.7%, route in 69.4%. Among 109,248 dexamethasone entries, 703 dose form and 80 route variations were mapped to 53 and 44 standard codes respectively; about half could be mapped unambiguously. Site-specific analyses revealed biologically plausible patterns not apparent in substance-level analyses. Ocular use showed higher ICs for glaucoma and cataract, while systemic use showed higher IC for psychiatric and endocrine events (e.g., depression, agitation, Cushing's syndrome). IC time-trends suggested that some signals (e.g., cataract with Ocular use) could emerge earlier in site-specific analyses. Conclusion: More granular product information, aligned with PhPID, may improve signal detection and characterization of site-specific safety issues. These findings support granular identifiers in pharmacovigilance while highlighting the need for better capture and standardization of dose form and route of administration data.

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: [&ge;]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[&ge;]2), reporting odds ratio (ROR>1), and information component (IC>0) with case count [&ge;]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.

INTRODUCTION: Drug-related-problems (DRPs) are encountered daily in prescription medicines at the community pharmacy but they are neither documented nor evaluated systematically. Population ageing in Greece is expected to increase polypharmacy and consequently DRPs, thus deteriorating patients` health and pressuring the already underfunded health system. Community pharmacists are ideally placed to review patients` medicines and in collaboration with physicians resolve identified issues. OBJECTIVES: The primary outcomes of the study were to define the number and nature of DRPs identified in prescription medicines at an urban community pharmacy in Greece. Secondary outcomes included the assessment of severity of errors and, for the evaluation part of the project, planned interventions to address them. METHODS: Our study was divided into two parts: the main part which included the evaluation of the current service and a following small-scale improvement project. For the first aspect, all prescription medicines dispensed from the pharmacy within one month were analyzed, using primary data extracted directly from pharmacy prescription and medication records. Medication characteristics as indicated in prescriptions (name of drug, dosage, formulation, dosing interval) and patients` medication records were utilized as sources of information. The identification of DRPs was based mainly on explicit criteria and reliable sources. Potential DRPs were classified following the Pharmaceutical Care Network Europe (PCNE) classification system (2019) while their severity was assessed and documented according to a tool developed by Abdel-Qader et al. (2010). A comprehensive approach of medication review as defined in `Polypharmacy Guidance: Realistic Prescribing` (Scottish Government Polypharmacy Model of Care Group, 2018) and patient interviews as an additional source was followed for the 4 selected patients in the second part of the study. RESULTS/DISCUSSION: In 635 prescriptions containing 1464 medicines we identified 364 DRPs in 529 different patients. The most common problem was related with a possible adverse drug event (P2.1, 62.1%) while 62% of the causes that led to problems belonged to inappropriate combination of drugs (C1.4), inappropriate drug (C1.2), drug dose too high (C3.2) and duration of treatment too long (C4.2). Most DRPs were characterized as significant (n=202, 55.5%) and minor (n=110, 30.2%) while the intervention `drug changed to` (I3.1, 25.8%) was the most frequent among the planned interventions. In the improvement project, 39 DRPs were identified in the 4 enrolled patients, including errors associated with potential inappropriate omissions and absence of monitoring not discovered in the first part of the study. CONCLUSION: We quantified the potential DRPs encountered in a community pharmacy and discovered that for every 4 medicines dispensed 1 potential DRP was identified. Even if all errors will not result in harm, they will create additional work and possibly lead to prescribing cascade. Additional sources of information, like the patient interviews we incorporated in the improvement project, are expected to increase the number and consistency of findings. Finally, the value of community pharmacists as a vital component of primary care was pointed out.

Drug shortages represent persistent supply disruptions in the U.S. pharmaceutical market, threatening patient access and increasing drug costs. Prior research commonly treats shortages as binary events and relies on static designs, limiting insight into how shortage characteristics drive cost escalation. This study uncovers the heterogeneity behind drug shortages and pharmacy acquisition costs of generic non-injectable drugs. FDA drug shortage records with weekly National Average Drug Acquisition Cost (NADAC) prices were fit with fixed-effects models, duration-specific models, and a double machine-learning framework to characterize heterogeneity in shortage-price associations by duration, market structure, and shortage reasons. In the baseline two-way fixed-effects model, active shortage designation alone was not associated with a significant increase in NADAC under clustered standard errors. In duration-specific models, shortages lasting more than four consecutive weeks were associated with approximately 7% higher NADAC, while each additional cumulative shortage week was associated with approximately 0.37% higher NADAC. Estimated CATEs varied widely across drugs in each week. Allocation restrictions, raw material and distribution disruptions, together with a lack of manufacturers, characterized shortages with higher estimated CATEs. These findings support monitoring both shortage persistence and supply-chain mechanisms to mitigate impacts on healthcare systems.

Background: This study aimed to evaluate real-world adverse event (AE) signals of EV to provide evidence-based guidance for its safe clinical application. Methods: Data from the FDA Adverse Event Reporting System (FAERS) database from the period of 2019 Q1-2025 Q3 were analyzed. Disproportionality analysis algorithms, including the reporting odds ratio (ROR), proportional reporting ratio (PRR), Bayesian confidence propagation neural network (BCPNN), and empirical Bayes geometric mean (EBGM), were utilized to mine safety signals.The time to onset (TTO) was evaluated using the Weibull distribution model. Results: Among 11,697,906 reports, 4,177 EV-treated patients experienced 14,511 AEs. The most common System Organ Classes (SOCs) were skin and subcutaneous tissue disorders (18.23%), general disorders and administration site conditions (13.17%).Multi-algorithm consensus identified 179 positive signals. Alongside known toxicities (rash, peripheral neuropathy, hyperglycemia), potential new signals emerged, including dysgeusia, atypical skin lesions, and myelosuppression. Median TTO was 14 days, with the Weibull {beta} of 0.736, confirming an "early failure" profile. Subgroup analysis revealed toxicity heterogeneity: patients aged [&ge;]65 and females exhibited stronger signals for fatal severe cutaneous adverse reactions, while patients aged < 65 and males showed higher susceptibility to neurological and metabolic toxicities. Conclusions: The real-world safety profile of EV confirms known toxicities, reveals new risks (e.g., dysgeusia), and shows toxicity concentrated in the first treatment cycle. Clinical practice requires proactive monitoring during the first two weeks using demographic-specific strategies: vigilance for fatal skin toxicity in elderly and female patients, and close follow-up of neurological and metabolic indicators in younger and male populations.

Background Large Language Models (LLMs) are increasingly explored for pharmacovigilance tasks, including information extraction, case documentation, and single-case causality assessment. However, their ability to support causality assessment at the case series level -- a complex, time-intensive task requiring clinical reasoning across multiple reports -- remains unexplored. Objective To investigate how a large-scale general-purpose LLM can support pharmacovigilance professionals in assessing causality in a case series, and to explore how prompt design influences the quality of the model's reasoning. Methods GPT-4o was used to assess causality for five drug - adverse event combinations, using an adaptation of the Bradford Hill viewpoints for case series assessment. The combinations represented varying drugs and vaccines, adverse events, and case series sizes (5-402 reports). One combination served as a negative control. Structured prompts were iteratively developed and refined using one combination, then applied to all combinations. LLM-generated assessments for each viewpoint were qualitatively evaluated by human annotators for accuracy (precision), and the LLM's coverage of key aspects from the original signal text was assessed for one combination (recall). Results Across all five combinations, annotators agreed with 79-92% of the LLM's output sentences. Full disagreement was consistently low (3-7%), with errors typically involving misinterpretation of complex report details rather than outright fabrication. Prompt design substantially influenced output quality; providing Bradford Hill viewpoint descriptions, including case series data, and adding explicit anti-hallucination instructions improved specificity and grounding. For the recall assessment, 15 of 23 key segments from the original signal text were reflected in the LLM output. The overall summary assessments demonstrated balanced reasoning, correctly distinguishing between positive safety signals and the negative control, and provided a coherent synthesis suitable as a starting point for human assessors. Conclusions LLMs have the potential to generate contextually nuanced and largely accurate preliminary causality assessments of case series aligned with the Bradford Hill viewpoints, with a low but non-zero hallucination rate. These findings support LLMs as a tool to augment, not replace, expert judgment in signal assessment. Future work should address larger and more diverse signal sets, improved evaluation frameworks for generative output, and the integration of pre-computed summary statistics to reduce errors.

Lithium has been the gold standard for the treatment and prevention of relapse in bipolar disorder for over 60 years. Guidance from the National Institute for Health and Clinical Excellence states explicitly to 'offer lithium as a first-line, long-term pharmacological treatment for bipolar disorder'. Yet, in the last two decades its use has been in decline with clinicians favouring anticonvulsants or antipsychotics when treating this condition. In this study, we have used three openly available datasets containing prescribing data from primary and secondary care to explore trends in the use of lithium in England, showing both regional and temporal variance between 2015-2024. We have shown that lithium use declined in primary care by 20.9% in the last ten years (2015-2024) and 10.9% overall in the last five years (2019 to 2025). We have also shown how there is some regional variation in the source of lithium for patients, although the vast majority is prescribed in primary care. Further research into clinical behaviour is needed to understand what is driving the decrease in lithium usage, and what barriers and enablers may influence its use across the country.

Cancer imposes a rising and largely out-of-pocket-financed burden in Nepal, yet patient-level data on how anticancer drugs are prescribed and what chemotherapy actually costs patients remain limited. This study described the drug utilization pattern and cost of anticancer drug therapy among cancer patients treated at a tertiary cancer hospital in Nepal. A descriptive, cross-sectional, record-based study was conducted at Bhaktapur Cancer Hospital, Bhaktapur, Nepal. Medical records of cancer patients aged above 18 years attending the day-care chemotherapy ward were reviewed using a structured proforma. Sociodemographic characteristics, diagnosis, regimen and cycle data, and WHO/INRUD prescribing indicators were recorded, along with the actual cost, maximum retail price, and Nepal Health Insurance Board reimbursement rate for anticancer drug therapy. Data were analyzed descriptively and, given the skewed distribution of cost, on a log-transformed cost variable using Pearson correlation, independent-samples t tests, and multiple linear regression in SPSS version 16. A total of 151 chemotherapy patients were analyzed (mean age 53.81 {+/-} 12.51 years; 67.5% female). Breast cancer (26.5%) was the most common diagnosis. Patients received a mean of 10.56 {+/-} 2.19 total drugs, of which 1.72 {+/-} 0.67 were anticancer agents; 99.85% of drugs were prescribed generically, and 63.2-67.7% were on the national essential medicines list. Mean actual cost of anticancer therapy per patient was NPR 15,245.28 {+/-} 15,672.33, substantially below the mean maximum retail price (NPR 38,803.22). In bivariate analysis, log-transformed cost was significantly higher among male patients (p =.038) and among patients not fully compliant with the essential medicines list (p =.028), but no patient, disease, or prescribing variable -- including sex -- independently predicted log-transformed cost in a multiple regression model that was not statistically significant overall (R{superscript 2} =.113, p =.323). Anticancer prescribing at this hospital was characterized by near-universal generic use but only partial alignment with the national essential medicines list, and chemotherapy cost varied widely across patients without being reliably explained by the patient or prescribing factors examined. These findings support continued WHO/INRUD-style prescribing surveillance and closer attention to essential-medicines-list adherence and drug pricing in this setting.

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.

Private pharmacies are ubiquitous yet underutilized for HIV pre- and post-exposure prophylaxis (PrEP and PEP) delivery. In a cluster-randomized trial in Kenya (NCT05842122), we randomized 60 pharmacies 1:1:1:1 to: client-sustained delivery (~$2/visit user fee); implementor-sustained delivery (~$2/visit reimbursement); counselor-supported delivery (task shifting; ~$1/visit reimbursement); or clinic referral (control; ~$1/referral reimbursement). Commodities were supplied free to pharmacies from government stock. Primary outcomes were PrEP initiation and one-month continuation (any dispensing or refilling, respectively), self-reported by clients 60 days post-enrollment (multiple-comparisons threshold: p=0.017). From June 2023-April 2025, 5,808 clients enrolled; 64% were PEP candidates. Compared to referral, the counselor-supported arm had significantly higher PrEP initiation (RR=6.5, 95% CI [2.6, 16], p<0.001) and continuation rates (RR=5.1, 95% CI [1.4, 19], p=0.016); all intervention arms had significantly higher PEP initiation rates. One seroconversion, one social harm, and two provider needlestick injuries occurred. Pharmacy PrEP/PEP delivery outperformed clinic referral, particularly when fully subsidized and counselor-supported.

Introduction: Clinical guidelines for managing opioid use disorder (OUD) recommend incremental dose titration for methadone and buprenorphine/naloxone to achieve a safe therapeutic maintenance dose. Dose titration recommendations are based on clinical experience, pharmacologic rationale, and expert consensus, with limited real-world evidence, especially in settings with widespread fentanyl use, where traditional titration schedules may be insufficient to address the higher opioid tolerance among people initiating treatment. This study aims to determine the comparative effectiveness of alternative titration schedules on completed induction and time to all-cause mortality. Methods and analysis: We will conduct a population-based retrospective cohort study using linked data from nine provincial health administrative databases. The study population will include adults ([&ge;]18 years) in British Columbia, Canada, who initiated methadone or buprenorphine/naloxone between 01/01/2010 and 30/06/2022. Primary outcomes are completed induction (defined as no dose increase for [&ge;]two weeks without any intervening decrease) and time to all-cause mortality, with overdose-related acute care visits or overdose-related death and treatment discontinuation as secondary outcomes. Using a target trial framework, this study will implement a clone-censor-weight approach to estimate the per-protocol effects of sustained titration schedules capturing adherence to and deviation from clinical guidelines. Sensitivity analyses will assess the robustness of findings through cohort and timeline restrictions as well as alternative exposure and outcome definitions. Discussion: This study will generate real-world evidence on the comparative effectiveness of alternative titration schedules of methadone and buprenorphine/naloxone on completed induction and all-cause mortality. The findings will support evidence-informed updates to OAT guidelines and clinical decision-making in British Columbia and other jurisdictions facing escalating opioid-related harms.

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.

Psilocybin shows therapeutic promise for several psychiatric disorders, but the acute perceptual and cognitive alterations produced by conventional doses (10-25 mg) require in-clinic supervision, which limits scalability. Whether the therapeutically relevant pharmacology of psilocybin can be separated from its hallucinogenic activity remains unresolved. To address this gap, we conducted a Phase 1, randomized, double-blind, placebo-controlled, single ascending dose study to characterize the safety, pharmacokinetics and pharmacodynamics of low doses of psilocybin. Fifty-six healthy adults received a single oral dose of psilocybin (0.5, 1.0, 1.5, 2.5, 3.5 or 4.0 mg) or matching placebo across seven sequential cohorts, with each dose escalation reviewed by a Drug Safety Review Committee. All participants completed the study with no serious adverse events or discontinuations. Treatment-emergent adverse events were comparable to placebo and most prominently arose as somnolence. Plasma psilocin appeared rapidly with a median time to maximum concentration < 1 h with dose-proportional exposure and a short terminal half-life. Subjective drug effects were dose-related and became distinguishable from placebo at doses at or below 2.5 mg. Peak subjective ratings increased with dose, while any signs of hallucinations or altered-states scores remained low and not different than placebo. Psychophysiological engagement was confirmed by a clear dose-dependent pupillary dilation while cognitive performance (attention, vigilance, working memory, impulse control) showed no dose-dependent decrement and state anxiety did not increase at any dose. These findings indicate that the perceptible pharmacology of psilocybin can be dissociated from significant perceptual alterations and cognitive impairment at low doses. They further support controlled investigations in outpatient Phase 2 studies evaluating the safety and feasibility of repeated, self-administered low-dose psilocybin. ClinicalTrials.gov #NCT07710027

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.

A global pandemic requires accelerated strategies to mitigate public healthcare risks and preserve societal stability, either by repurposing existing drugs or by rapidly developing novel drug candidates. Computational platforms have accelerated candidate identification for both routes. A critical gap is the translation from in vitro potency to predicted clinical efficacy, which determines whether a prioritized candidate can achieve therapeutic effect under realistic dosing. To address this, we developed the Pandemic Pharmacology Platform for COMPUTational Evaluation of anti-infectives (PPP-COMPUTE), a comprehensive pharmacokinetic/pharmacodynamic (PK/PD) simulation platform designed to support evaluation and prioritization of drug candidates and clinical trial design during a pandemic. PPP-COMPUTE integrates experimentally derived anti-infective potency data (e.g., EC50) with PK/PD modeling to evaluate whether clinical dosing regimens can achieve sufficient exposure for therapeutic efficacy in patients. The platform incorporates mechanism-based dynamic models with a focus on viral pathogens to simulate time-dependent viral load trajectories under various treatment scenarios, enabling quantitative assessment of antiviral response and optimization of dosing strategies. Probability of target attainment analyses further support evaluation of regimen feasibility against predefined pharmacological targets. The clinical trial module generates simulated virological endpoints to evaluate candidate clinical study designs. PPP-COMPUTE is an accessible and quantitative framework that links PK and preclinical anti-infective potency data with predicted clinical benefit, thereby supporting rapid drug evaluation during future pandemics.

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.

Objectives: This study aimed to examine patterns of deprescribing practices that are implemented after an emergency department (ED) visit due to antithrombotic-induced gastrointestinal (GI) bleeds among patients who concomitantly use antithrombotic and other medications. Methods: A retrospective cohort study design was used to identify a cohort of patients who visited an ED from the MarketScan claims database (2016-2023). Concomitant use of antithrombotics and other medications was assessed in the 30 days prior to the ED date (index date). Those who presented with GI bleeds on their ED visit were classified as exposed vs those without GI bleeds (unexposed). Antithrombotic deprescribing-defined as discontinuation ([&ge;]45 days gap between refills), switching, or dose reduction-was assessed from the index date through to end of data. Inverse probability of treatment (IPT)-weighted logistic regression models were used to compare the odds of deprescribing between the exposed vs. unexposed groups. Results: Of the 375,510 total concomitant users of antithrombotics and other agents, 9,145 had a GI bleed vs. not (366,365). The odds of antithrombotic deprescribing was significantly higher among the exposed vs unexposed, (odds ratio [OR], 1.39; 95% confidence interval [CI], 1.33, 1.46). There was no significant difference in the discontinuation of concomitant medications overall. However, among patients who continued antithrombotic use after the ED visit, the discontinuation of concomitant medications was relatively higher among the exposed (OR, 1.11; 95% CI, 1.05, 1.16). Conclusions: Antithrombotic agents were more likely to be deprescribed after an ED visit among patients who concomitantly used antithrombotics and other medications.