Clinical Pharmacology & Therapeutics

BackgroundDrug suitability is determined by safety, efficacy, and pathological appropriateness. The pharmacogenomics of drug suitability can be assessed by analyzing drug response and drug choice in large population cohorts. MethodsWe investigated drug response and drug choice for dyslipidemia and hypertension using genetic, phenotypic, and prescribing data from the UK Biobank and the All of Us Research Program. Drug response was reassessed with rigorous biomarker scaling, while genome-wide association studies (GWAS) and polygenic scores were used to examine genetic factors influencing drug choice. ResultsConventional analyses showed that variants influencing baseline LDL cholesterol (LDL-C) were inversely associated with absolute LDL-C change but concordant with relative change following statin therapy; these signals disappeared after applying a variance-stabilizing Box-Cox transformation, indicating a methodological artifact in biomarker scaling. GWAS for drug choice identified several significant loci and unique genetic correlation patterns with cardiometabolic traits. Polygenic scores for drug choice yielded statistically significant predictive performance, which was enhanced by incorporating demographic factors, though prediction strength in clinical settings remains modest. ConclusionVariance-stabilizing transformation corrects spurious pharmacogenetic associations introduced by biomarker scaling. Genetic variation informs drug choice for dyslipidemia and hypertension, but current polygenic scores provide only modest benefits in clinical application.

Angioedema is a life-threatening adverse drug reaction associated with renin-angiotensin-aldosterone system (RAAS) inhibitors, characterized by localized swelling in the deep layers of the skin. Well-established evidence indicates an up to fivefold higher incidence of RAAS inhibitor-induced angioedema in self-identified Black patients compared to White patients. The mechanisms underlying this health disparity remain poorly understood and are often attributed to race, a poor proxy for interindividual genetic similarity and social stressors. Here, we investigate the genetic and social determinants of RAAS inhibitor-induced angioedema as well as the etiology of this racial difference. In particular, we (1) discovered OTULINL and CRABP1 as novel loci for RAAS inhibitor-induced angioedema, (2) confirmed the importance of bradykinin for this adverse drug reaction, (3) reported the first significant genome-wide association in self-identified Black participants, (4) identified alcohol use as an important social determinant, (5) demonstrated the strong role of variants enriched in 1000 Genomes African superpopulation-like genomes as the driver of racially differential angioedema risk, and (6) demonstrated the combined role of polygenic effect size and allele frequency differences in explaining these racial differences. Our results suggest that a clinical precision medicine tool may more precisely predict for whom RAAS inhibitors should be avoided (to prevent angioedema) compared to using race. These findings ultimately underscore the value of an evidence-based approach to removing race from treatment guidelines, which carries less potential harm than other removal strategies.

Topiramate (TPM) is approved for seizures and migraine prophylaxis and is used off-label for several neuropsychiatric conditions. The available dosage forms, including tablets and sprinkle capsules, are unsuitable for patients who may be unable to take medicine orally. The resulting potential treatment interruptions could have untoward consequences and underscores the importance of developing a parenteral formulation. In this study, we developed a population pharmacokinetic model of a novel, intravenous TPM formulation using data from a study in patients with epilepsy or migraine receiving a single intravenous dose of stable-labeled TPM. In total, 246 TPM concentrations from 20 adult patients were included for model development. A three-compartment pharmacokinetic model with linear elimination fit the concentration-time data best. Simulations for various loading and maintenance regimens for patients with and without enzyme-inducing comedications were performed. The final estimates(95% confidence interval (CI)) for CL (L/h), V1 (L), and the peripheral volumes, V2 and V3 for a 70 kg person were 1.31(1.01 - 1.53), 9.84 (8.49 - 11.0), 39.1 (36.5 - 41.8)L, and 9.01 (6.41 - 44.3) respectively. The use of enzyme-inducing co-medication was the only significant covariate, associated with a 63% increase in clearance .Goodness-of-fit plots and visual predictive checks indicate satisfactory model performance and prediction. The simulation results indicate that adjusting doses for patients receiving IV TPM can mitigate the changes in plasma TPM concentrations resulting from enzyme induction. This population pharmacokinetic model for intravenous topiramate can inform dosing decisions for patients with epilepsy when used as either initiation or bridging therapy.

GLP-1 receptor agonists (GLP-1 RAs) are effective in delaying progression of chronic kidney disease in individuals with type 2 diabetes mellitus (T2DM). We evaluated whether GLP-1 RA prescription is associated with reduced nephrotoxicity in adults receiving long-term lithium therapy. We conducted a retrospective, propensity score-matched cohort study using electronic health records from the TriNetX global network, which includes de-identified data from over 127 million patients across 109 healthcare organizations. The study population consisted of adults aged [≥]18 years with T2DM, with lithium exposure within the 2 years preceding the index date and at least one prescription for a GLP-1 RA. The primary efficacy outcome was the rate of renal nephrotoxicity in persons with T2DM prescribed lithium and a GLP-1 RA versus those with T2DM prescribed lithium but no GLP-1 RA or other antidiabetic agents. Nephrotoxicity was a composite of ICD-10 and CPT-coded renal disease. Incidence and time-to-event outcomes were assessed using Kaplan-Meier curves and Cox proportional hazards models. In our 24-month analysis, 462 matched patient pairs were included. Initiation of a GLP-1 RA during lithium therapy was associated with a lower incidence of renal events versus lithium alone (6{middle dot}1% vs 10{middle dot}4%), corresponding to a risk difference of -4.3% (95% CI -7{middle dot}86 to -0{middle dot}80), a risk ratio of 0{middle dot}58 (95% CI 0{middle dot}37-0{middle dot}91; p=0{middle dot}017), and higher event-free survival (89{middle dot}0% vs 83{middle dot}2%; log-rank p=0{middle dot}037). GLP-1 receptor agonist therapy was associated with a reduction in reports of lithium-associated nephrotoxicity. Our findings provide impetus to conduct mechanistic renal histopathologic studies combining GLP-1 RAs with lithium.

ObjectivesThe 2025 Global Consensus recommends continuing biologics throughout pregnancy in women with inflammatory bowel disease (IBD). Real-world evidence on biologic treatment patterns and outcomes remains limited. This study compared maternal and neonatal outcomes across different biologic use trajectories during pregnancy. MethodsA retrospective study was performed in pregnant women with IBD, treated and/or delivering at the University Hospitals Leuven, Belgium, between 2017 and 2025. Patients were categorized as continuers, discontinuers, non-users or initiators of biologics during pregnancy ResultsAmong 255 pregnancies, 103 (40.4%) were continuers, 68 (26.7%) discontinuers, 77 (30.2%) non-users, and 7 (2.7%) initiators. Before conception, 67.1% used biologics. Third-trimester disease activity was most frequent in initiators (42.9%, 3/7) and discontinuers (19.1%, 13/68), followed by non-users (14.3%, 11/77) and continuers (13.6%, 14/103). C-sections occurred more often in non-users (41.3%, 26/63) and discontinuers (39.4%, 26/66) than continuers (31.1%, 23/74). Preterm birth was more common among initiators (14.3%, 1/7) and discontinuers (12.1%, 8/66) than continuers (8.0%, 6/75) and non-users (3.2%, 2/62). Low birthweight occurred most in initiators (14.3%, 1/7), continuers (8.1%, 6/74) and discontinuers (6.1%, 4/66). Small-for-gestational-age infants were most frequent among continuers (14.9%, 11/74) and initiators (14.3%, 1/7) than discontinuers (7.6%, 5/66). ConclusionsWomen who discontinued biologics during pregnancy had higher rates of C-sections, preterm birth, and third-trimester disease activity than continuers, supporting continuation of biologics in pregnancy. The higher SGA rates among continuers, however, require further investigation. Initiators showed the poorest outcomes, highlighting the need for adequate disease control before and during pregnancy.

BackgroundGlucagon-like peptide-1 (GLP-1) receptor agonists, including liraglutide and semaglutide, are widely prescribed to treat type 2 diabetes mellitus and obesity. Recent anecdotal reports have suggested these agents might be associated with allodynia, a neuropathic pain syndrome, but large-scale epidemiologic evidence is lacking. MethodsTo investigate this potential link, a retrospective cohort study was conducted using the IQVIA PharMetrics(R) Plus database. Adults aged 18 and older who initiated liraglutide, semaglutide, or bupropion-naltrexone between 2006 and 2020 were included, excluding those with prior diabetes or antihyperglycemic therapy. Incident allodynia was identified via ICD-9/10 codes as the primary outcome. ResultsAmong 20,504 new users, those on GLP-1 receptor agonists had an allodynia incidence of 35 per 1,000 person-years, compared to 15 per 1,000 person-years for bupropion-naltrexone users. Adjusted analyses demonstrated over a twofold increased risk of allodynia with GLP-1 receptor agonists (aHR 2.15, 95% CI 1.57-2.96). ConclusionThese findings emphasize the need for heightened clinical vigilance and further research into mechanisms and management.

We designed a platform model that integrates physiologically-based pharmacokinetic (PBPK) modeling with quantitative systems pharmacology (QSP) to bridge translational challenges in antibody-drug conjugate (ADC) development. The PBPK-QSP platform model was developed for the ADC trastuzumab emtansine (T-DM1) in breast cancer patients. This mechanistic framework facilitates translation across preclinical in vitro experiments, in vivo studies, and clinical trials, supporting decision-making for novel ADCs. The PBPK-QSP model adequately predicts preclinical and clinical PK and PD data from two additional ADCs: trastuzumab deruxtecan (T-Dxd) and tusamitamab ravtansine. For within-target validation with T-Dxd in breast cancer, despite extensive preclinical calibration, efficacy predictions were initially overly optimistic compared to T-DM1 validation experience with the model and aggregated phase II trial data. Individual patient data from a phase II T-Dxd trial allowed evaluation of model performance and quantification of translational uncertainty in predicting clinical outcomes using preclinical experiments. Cross-pathway validation with tusamitamab ravtansine in non-small cell lung cancer has revealed the importance of incorporating a resistance module to describe clinical efficacy adequately. Clinical trial simulations for tusamitamab ravtansine subsequently inform that alternative fractional dosing could offer a potential efficacy advantage compared to existing clinical dosing. We integrated these insights into a practical recommended workflow for translational development programs, which addresses the key challenges in parameter estimation, data requirements, and uncertainty quantification in the key system parameters for each indication and cancer type. Ultimately, integrating an interactive modeling platform with a structured workflow to mitigate the risks of human translation and to potentially improve the clinical benefits of novel ADCs in oncology drug development.

BackgroundGabapentin prescriptions have increased 123% since 2010, reaching 15.5 million Americans annually. Recent studies suggest gabapentin-dementia associations, but whether concomitant medications modify this risk is unknown. Both gabapentin and calcium channel blockers (CCBs) affect neuronal calcium signaling through distinct mechanisms, raising the possibility of pharmacodynamic interaction. MethodsActive comparator new-user cohort study using Rutgers Clinical Research Data Warehouse (2015-2024). Adults [≥]40 years with hypertension initiating gabapentin (n=28,058) or pregabalin (n=5,733) were followed for incident dementia. Inverse probability of treatment weighted (IPTW) Cox models estimated hazard ratios stratified by baseline CCB exposure. Validation analyses tested CCB subtype specificity (dihydropyridine [DHP] vs verapamil), dementia subtypes (F03/G30/F01), frailty stratification (CKD, stroke), lag periods, falsification outcomes, and non-2{delta} anticonvulsant comparisons. ResultsAmong 33,791 patients (502 dementia events; median follow-up 1.22 years), we identified a novel drug-drug interaction: gabapentin was associated with substantially elevated dementia risk among CCB users (HR=2.22, 95% CI 1.42-3.47, p=0.0005) compared to non-users (HR=1.15, 95% CI 0.99-1.33; interaction p=0.004). A time-varying analysis confirmed this finding: among gabapentin users who initiated CCB during follow-up, CCB-exposed person-time showed 65% higher dementia incidence (Rate Ratio=1.65, 95% CI 1.19-2.29). This interaction showed striking CCB subtype specificity: DHP CCBs drove the signal (HR=3.20) while verapamil showed no interaction (insufficient events for analysis). The signal concentrated in F03 unspecified dementia (HR=1.68, p=0.004) with short latency (median 240 days), consistent with drug-induced cognitive impairment rather than neurodegeneration. Pre-index symptom balance analysis showed 6/6 symptom families balanced between groups, arguing against protopathic bias. The interaction was paradoxically weaker in frail patients (CKD ratio=0.25, stroke ratio=0.14), arguing against confounding by illness severity. Lag analyses showed strengthening over time (HR 2.22[->]3.72), falsification outcomes were largely null (4/7), and non-2{delta} anticonvulsants showed no CCB interaction. ConclusionsWe identified a novel drug-drug interaction whereby DHP CCB co-medication amplifies gabapentin-associated dementia risk, confirmed by time-varying analysis (Rate Ratio=1.65). The DHP-specific signal is biologically plausible given independent evidence that DHP CCBs may adversely affect cognition (DREAM consortium), while the absence of interaction with verapamil aligns with its potential neuroprotective properties identified in drug repurposing studies. The F03-specific pattern suggests drug-induced cognitive impairment that may be reversible. These hypothesis-generating findings identify gabapentin-DHP CCB combinations as a target for cognitive safety monitoring and warrant confirmation with concurrent exposure measurement.

Human genetics has become a cornerstone of drug target discovery, yet the value of Mendelian randomization (MR) for predicting clinical success remains uncertain. Here, we systematically evaluated MR across 11,482 target-indication pairs with documented Phase II clinical outcomes to assess its utility for drug development. We find that MR statistical significance alone does not enrich for Phase II success, in contrast to genome-wide association study (GWAS) support, which confers an increase in success probability. However, this apparent limitation reflects the heterogeneous nature of clinical failure and the fact that MR encodes information beyond P values. When MR-derived features, including instrument strength and explained variance, are integrated into machine learning models, predictive performance improves substantially. An MR-informed XGBoost classifier identifies target-indication pairs with a 55% overall approval rate, corresponding to a 6.4-fold enrichment over unstratified programs and a 2.8-fold improvement over GWAS- supported targets in Phase II. Notably, this enrichment is achieved without reliance on statistically significant MR results. Our findings demonstrate that MR is most informative when treated as a graded, context-dependent source of causal evidence rather than a binary hypothesis test, and that its integration with machine learning enables scalable, genetics-informed prioritization of drug targets across the clinical pipeline.

BackgroundBiomedical Large Language Models (LLMs) combined with prompt engineering offer domain-specific reasoning, yet their application to individual-level causality assessment remains unexplored. This study evaluated five combinations of biomedical LLMs, prompting strategies, and causality algorithms by comparing their agreement with two human expert evaluators. Research design and methodsA total of 150 Individual Case Safety Reports (ICSRs) were analyzed: 140 reports from Food and Drug Administration Adverse Event Reporting System (FAERS), and 10 myocarditis/pericarditis ICSRs from Vaccine AERS (VAERS). Assessments were conducted using the Naranjo and WHO-UMC algorithms. Biomedical LLMs tested included TinyLlama 1.1B, Medicine LLaMA-3 8B, and MedLLaMA v20, combined with Chain-of-Thought (CoT) or Decomposition prompting. Agreement was measured using Gwets Agreement Coefficient 1 (AC1) and percentage agreement, alongside performance metrics and qualitative error analysis. ResultsThe Medicine LLaMA-3 8B-Naranjo-CoT combination achieved the highest agreement with human assessors for the final classification of causality (64%). Biomedical LLMs demonstrated low inter-rater agreement on critical items of causality assessment such as identification of listed AE, temporal plausibility, alternative causes, and objective evidence of AEs. Frequent model failures included irrelevant responses. ConclusionsBiomedical LLMs showed improved performance over general purpose models previously tested but remain suboptimal for reliable causality assessment of ICSRs.

ImportanceGlucagon-like peptide-1 receptor agonists (GLP-1RAs) are fast-growing treatments for type 2 diabetes, obesity, and sleep apnea and are under investigation as potential treatments for many other conditions. The National Institutes of Healths (NIHs) All of Us Research Program offers a robust observational data source for studying questions related to GLP-1RA use in real-world settings. ObjectiveThis article describes key characteristics of All of Us participants who have been prescribed GLP-1RAs. The goals are to present the utility of the All of Us data and describe the strengths and limitations of using this resource for future research on GLP-1RAs. DesignUsing the All of Us Controlled Tier Curated Data Repository version 8 (CDRv8), we provide a descriptive analysis of the cohort with GLP-1RA records using cross-sectional surveys, longitudinal electronic health record (EHR) data, and longitudinal Fitbit data. SettingThe All of Us Research Program is a large, federally funded, longitudinal cohort study established in 2018 by NIH. Recruitment efforts are nationwide and target a range of populations to advance precision medicine for all. ParticipantsParticipants are U.S. residents, aged 18 or older at the time of study consent, who were enrolled between May 6, 2018, and October 1, 2023. ExposuresThe GLP-1RA cohort included participants with at least two GLP-1RA prescription records on different days at any time point based on their EHRs. Main OutcomesFrequencies and medians for a range of sociodemographic characteristics, health care utilization patterns, comorbid conditions, GLP-1RA prescription trends, laboratory and observation availability, and Fitbit data. ResultsThe All of Us GLP-1RA cohort is large (n=15 477), with high data availability across a range of relevant data types. These participants are older and have more comorbid conditions than the entire CDRv8 population. Prescription trends indicate rapid uptake of GLP-1RA drugs since 2014. Conclusions and RelevanceAll of Us CDRv8 is a valuable resource for research on GLP-1RAs across a large, heterogeneous cohort of participants. The variety and availability of data offer many possibilities for future observational, real-world research to address unanswered questions about GLP-1RA use and replicate recent findings generated from other datasets. Key pointsO_ST_ABSQuestionC_ST_ABSWhat data are available and what are the patterns of GLP-1 receptor agonist (GLP-1RA) prescriptions among participants in the All of Us Research Program? FindingsIn this descriptive cohort study of 633 534 All of Us participants, 15 477 participants had at least two records of GLP-1RA prescriptions. These participants tended to be older, have more comorbid conditions, and have higher health care utilization than the All of Us population as a whole. MeaningThe All of Us Research Program has a robust array of data to support observational studies of people receiving GLP-1RA prescriptions.

ContextErogothioniene (EGT), a potent natural antioxidant, exerts hepatoprotection. However, human clinical evidence remains absent. Objectiveto observe the clinical efficacy and safety studies of GeneIII(R) L-Ergothioneine Capsules in the Hepatoprotective Efficacy. Materials and methodsThirty subjects with some abnormal liver function indicators were selected to take 2 capsules of 30mg GeneIII(R) L-Ergothioneine Capsules per day, for 30 consecutive days. The changes in serum liver function biomarkers, clinical efficacy self-rating scale, and Pittsburgh sleep quality index scale (PSQI) from baseline in different visit cycles were compared by self-control. All adverse events that occured after receiving the trial medication were also tracked and the incidence of adverse events during the trial period was calculated. ResultsAll subjects completed treatment and follow-up. After taking GeneIII(R) L-Ergothioneine Capsules, the subjects aspartate aminotransferase (AST) levels were significantly reduced compared to baseline (p=0.0082). Additionally, alanine aminotransferase (ALT) levels were also significantly reduced (p=0.0025), gamma-glutamyl transferase (GGT) levels were significantly reduced compared to baseline (p=0.0270). Physical function scores on the Clinical Efficacy Self-Assessment Scale improved significantly compared to baseline. Physical function scores decreased significantly by 39.04% compared to baseline (p<0.0001). Additionally, the level of daytime functional impairment on the Pittsburgh Sleep Quality Index was significantly reduced by 51.56% compared to baseline (p=0.0038). No adverse events occurred throughout the study. No side effects were observed clinically, confirming the reliable safety of GeneIII(R) L-Ergothioneine Capsules under the current study design. ConclusionGeneIII(R) L-Ergothioneine Capsules have a significant effect on improving liver function, physical function, and sleep quality in subjects, and the overall safety is good.

BackgroundIn the past two decades, genetic studies have elucidated the contributions of key biological pathways to the pathogenesis of age-related macular degeneration (AMD), including complement activation, lipoprotein metabolism, angiogenesis, and extracellular matrix maintenance. Of these pathways, complement in particular has been observed to dominate the genetic architecture of AMD. Yet, clinical treatment of AMD with complement inhibitors has met with limited success. MethodsUsing data from four large-scale cohorts spanning 30,251 AMD cases and 438,016 AMD controls, we identified functional genetic variants to serve as proxies for complement inhibitor drug effects, and assessed their interaction with a pathway-specific AMD polygenic risk score (PRS). In each cohort, subjects were divided into low, medium, and high AMD risk groups based on quantiles of the PRS, such that each risk group included one-third of the cohorts AMD cases. Drug target variant associations with AMD were evaluated in each risk group, as well as in all-comers. Quantitative biomarker analysis leveraging retinal phenotypes derived from optical coherence tomography (OCT) data was also performed. ResultsGenetic proxies for C3 and CFB inhibition had an effect on AMD risk that was 1.6 to 2.3 times higher in the high PRS group compared to all-comers. Interactions between genetic drug proxies and the PRS was statistically significant, with replication across cohorts. Statistical support was strongest in three cohorts for C3 and across all four cohorts for CFB. Examining a retinal thickness phenotype (ISOS-RPE), genetic drug proxy by PRS interaction was nominally significant for CFB, and directionally consistent for C3. Our results point to a continuous relationship between genetic complement activation / inhibition and AMD risk, across disease stages, without threshold effects. ConclusionOur findings suggest that patient heterogeneity due to genetically-influenced complement activation may explain the limited efficacy of AMD treatment with complement inhibitors to date. Prospective studies are warranted to assess whether precision therapy with complement inhibitors may be achieved by enrichment of patients with high PRS in future trials.

Inflammatory pain is a major clinical challenge, yet appropriate human models mimicking local infection are lacking. We established a novel human pain model based on intradermal administration of lipopolysaccharide (LPS), a component of Gram-negative bacteria, and investigated the time course and underlying molecular mechanisms of inflammatory pain hypersensitivity. In a placebo-controlled pilot study with 12 healthy subjects, the intradermal LPS injection induced hyperaemia peaking at 4.5 h and mechanical hypersensitivity peaking at 6 h. Hypersensitivity to increasingly acidic injections and mechanical pinch lasted longer than hyperaemia. The double-blind, randomized, placebo-controlled full crossover main study was completed by 40 subjects and investigated the role of the Receptor for Advanced Glycation End-products (RAGE). Co-injection of the RAGE antagonist azeliragon largely reduced LPS-induced hyperaemia (-87%) and significantly attenuated hypersensitivity to mechanical (-55%) and increasingly acidic stimuli (-40%). In contrast, the Toll-like receptor 4 antagonist resatorvid had no effect on any readout. In both naive and inflamed skin, TRPV1 antagonist BCTC inhibited the majority of acid-induced pain. LPS-induced inflammation caused a substantial shift in the pH sensitivity of pain, suggesting that even mild tissue acidification contributes to inflammatory pain. The human LPS skin inflammation model is largely RAGE-dependent, highlighting its potential as a target in inflammation.

ImportanceAlcohol use is a leading cause of morbidity and mortality worldwide. Growing evidence suggests that glucagon-like peptide-1 receptor agonists (GLP-1RAs) may represent a novel potential pharmacotherapeutic tool for alcohol use disorder (AUD). ObjectiveTo examine the association between GLP-1RA prescriptions and alcohol use. DesignThis cohort study used a cross-sectional measure of alcohol consumption and longitudinal electronic health record (EHR) data collected between 1981 and October 2023 from NIHs All of Us Research Program participants. SettingAll of Us is a large program to recruit and collect surveys, EHR, genomic, and wearable data from a wide array of Americans. The data presented here are from the All of Us Curated Data Repository version 8. Participants393,596 All of Us participants with EHR data recruited across the United States. ExposureAt least two GLP-1RA prescription records in the EHR. Main Outcomes and MeasuresAlcohol Use Disorders Identification Test (AUDIT-C) scores and responses to individual AUDIT-C questions. ResultsAmong 15,447 participants with at least two recorded GLP-1RA prescriptions on separate days, 3650 had active GLP-1RA prescriptions, 5642 would have future GLP-1RA prescriptions (primary comparison group), and 544 had former GLP-1RA prescriptions. Those with active GLP-1RA prescriptions had statistically significant but modestly lower AUDIT-C scores on average compared with those with future prescriptions (incidence rate ratio [IRR] of 0.95; 95% CI, 0.91-0.99; P = 0.01). Participants with a former GLP-1RA prescription had lower AUDIT-C scores compared with those with future prescriptions, but this difference was not statistically significant. Results were similar using a propensity-score matched comparison group with a lower average AUDIT-C score for the current GLP-1RA group (IRR = 0.89; 95% CI, 0.85-0.93; P = <0.001) and no significant difference for the former prescription group. Analysis of individual AUDIT-C questions shows a significant association with GLP-1RA prescriptions and frequency of drinking but not drinks per occasion or binge drinking. Conclusions and RelevanceThis studys findings indicate that GLP-1RAs may reduce alcohol consumption by decreasing use frequency. Experimental studies and randomized controlled trials are needed to test the mechanisms and potential efficacy of GLP-1RAs in people with AUD. KEY POINTSO_ST_ABSQuestionC_ST_ABSIs there an association between glucagon-like peptide-1 receptor agonist (GLP-1RA) prescriptions and alcohol consumption? FindingsIn this observational cohort study of 15 447 people with GLP-1RA prescriptions in NIHs All of Us cohort, active GLP-1RA prescriptions were associated with significantly lower Alcohol Use Disorders Identification Test (AUDIT-C) scores compared with scores of those who would have GLP-1RAs in the future and of a matched comparison group with no GLP-1RAs. People with former GLP-1RAs did not have lower AUDIT-C scores than these comparison groups. MeaningActive GLP-1RA use may be effective in reducing alcohol consumption.

Reliable pediatric virtual patients are essential for model-informed simulations, including physiologically based pharmacokinetic (PBPK) modeling, to support dose selections in children and to evaluate drug exposure across developmental stages. Despite the availability of extensive pediatric physiological data and age- or size-based models, there remains a lack of well-established, flexible, and scalable approaches for integrating these data into realistic pediatric virtual patients that preserve multivariate physiological correlations and whole-body coherence across diverse clinical conditions and population needs. In this proof-of-concept study, we developed a physiology-informed conditional variational autoencoder (cVAE) to address this challenge. The model was trained using real-world pediatric data augmented with mechanistically derived physiological information and conditioned on age and sex. It generated realistic physiological parameters, including body size, estimated glomerular filtration rate, organ weights, and blood flows, while biological plausibility was maintained through embedded physiological constraints. The trained model demonstrated high reconstruction accuracy, with a mean absolute error of 0.0043 and an R{superscript 2} of 0.998, and the generated distributions closely matched those of the training data. All synthesized physiological profiles satisfied predefined physiological constraints, with total organ mass remaining below body weight and the sum of organ blood flows not exceeding cardiac output. Latent-space analyses further revealed smooth developmental patterns, enabling targeted physiological profile generation. The applicability of the generated physiological data was further demonstrated through PBPK simulations conducted across the pediatric age range using vancomycin as a testbed. Overall, this physiology-informed generative framework supports coherent pediatric virtual patient generation for PBPK modeling and model-informed dosing applications development. Study HighlightsO_ST_ABSWhat is the current knowledge on the topic?C_ST_ABSModel-informed simulation, including physiologically based pharmacokinetic (PBPK) modeling, provides useful estimation of pediatric drug disposition across developmental stages and supports pediatric dose selection. However, constructing physiologically coherent pediatric virtual populations remains challenging. Although real-world pediatric measurements and physiologically derived, function-based information are available, these data are typically obtained from heterogeneous sources. Integrating them to generate multivariate physiological profiles at the individual level, while maintaining internal coherence across interconnected organ systems, remains an open challenge in pediatric pharmacometric modeling. What question did this study address?Can a conditional generative modeling and latent representation learning framework that integrates real-world pediatric data with mechanistically derived physiological constraints generate biologically coherent, multivariate pediatric physiological profiles that are suitable for downstream PBPK modeling across the full pediatric age range? What does this study add to our knowledge?This study introduces a physiology-informed conditional variational autoencoder that learns a smooth, interpretable latent space of pediatric physiology conditioned on age and sex. By embedding physiological constraints directly into the training objective, the model generates virtual pediatric patients with internally consistent body size, renal function, organ weights, and blood flows. The utility of these generated profiles was demonstrated through latent-space inversion and vancomycin PBPK simulations that reproduced reported age-dependent exposure trends and variability. How might this change clinical pharmacology or translational science?This framework provides a scalable approach for generating physiologically coherent pediatric virtual populations, laying a foundation for robust and flexible mechanistic PK simulations, virtual clinical trials, and digital twin applications. It offers a practical bridge between real-world pediatric data and mechanistic modeling, supporting model-informed dosing and translational decision-making in pediatric patient care and drug development.

Virtual clinical trials (VCTs) hold significant promise for improving the drug development process, yet their predictive reliability depends critically on design decisions that remain poorly understood. This study examines how model complexity influences VCT outcomes, as well as how the choice of prior parameter distributions and virtual patient inclusion criteria affects those outcomes. Using oncolytic virotherapy treatment of murine tumors as a case study, we compared three mathematical models of varying complexity under different parameter priors (uniform and normal distributions) and two inclusion methods (accept-or-reject and accept-or-perturb). Our results demonstrate that the simplest model produces a plausible population that inadequately spans the feasible trajectory space, potentially missing critical interpatient heterogeneity. However, we found diminishing returns beyond intermediate model complexity, as both the intermediate and complex models captured similar ranges of patient responses across dosing protocols. Notably, the accept-or-reject method generated posterior parameter distributions that resembled the chosen priors, possibly overly reducing interpatient variability in treatment responses, particularly at high doses. In contrast, the accept-or-perturb inclusion criteria produced more robust results that were less sensitive to prior assumptions. These findings suggest that VCT design should prioritize models with sufficient biological detail to capture key mechanisms without unnecessary complexity, paired with inclusion criteria that avoid over-constraining plausible populations to match potentially unrealistic prior assumptions.

Hypoglycemia is a common issue in diabetes pharmacotherapy, with seasonal variations potentially influencing its occurrence. It has been reported that blood glucose levels exhibit seasonal variation, suggesting that seasonal factors may influence the occurrence of hypoglycemia. In this study, we examined the seasonal patterns of drug-related hypoglycemia according to causative drug categories using the Japanese Adverse Drug Event Report database. We assessed a total of 545,012 adverse event reports submitted between December 2004 and November 2024, among which 5332 cases of drug-related hypoglycemia were identified. The results indicated a consistent increase in hypoglycemia reports during the winter months, with the lowest percentage observed in August (0.78%), followed by a gradual rise, reaching a peak in February (1.13%). Diabetic medications, including sulfonylureas, alpha-glucosidase inhibitors, and thiazolidinediones, showed an increased frequency of hypoglycemia signals in winter. Some non-diabetic medications, such as fluoroquinolones and angiotensin II receptor blockers, exhibited similar seasonal trends. These findings suggest that seasonal factors may warrant consideration in the prevention of drug-related hypoglycemia.

Background Antibiotic pricing is a key determinant of access and stewardship in low- and middle-income countries (LMICs), yet empirical evidence on how prices are formed within pharmaceutical markets remains limited. However, there is little longitudinal evidence on how antibiotic prices behave within national pharmaceutical supply systems. This study evaluated the patterns and determinants of systemic antibiotic pricing in Tanzania using national regulatory import permit data. Methods We conducted a retrospective analysis of antibiotic importation records from the Tanzania Medicines and Medical Devices Authority for 2010-2016. Systemic antibiotics for human use imported via oral or parenteral routes were included. Unit prices (USD per smallest unit of measure) were summarized using the median and interquartile range (IQR). Prices were compared by route of administration, supplier country, and product naming practice (INN-named versus brand-named) using Mann-Whitney U and Kruskal-Wallis tests with false discovery rate adjustment. Results Of the 14,301 records, 10,894 (76.2%) met the inclusion criteria. Oral antibiotics predominated (89.6%). Although the median oral antibiotic prices declined over time, substantial price dispersion persisted across all study years. Parenteral antibiotics were consistently more expensive (USD 0.755-3.370) and more variable than oral antibiotics. Importation was concentrated in a few medicines, with amoxicillin-clavulanate (16.7%) and amoxicillin (11.4%) accounting for over one-quarter of records, and in a few supplier countries, with India representing 44.9% of the records. Significant price differences between INN-named and branded products were observed for amoxicillin (adjusted p<0.001) and ciprofloxacin (adjusted p=0.018), whereas prices differed significantly by supplier country across major medicines (adjusted p<0.05). Across medicines and years, wide within-product price distributions indicate persistent market segmentation rather than price convergence. Conclusions Antibiotic import prices in Tanzania exhibit systematic and reproducible variations associated with formulation type, supplier origin, and product naming practices. The findings indicate that procurement structure and supplier participation strongly influence pricing in the import-dependent pharmaceutical market. Monitoring import-level prices can serve as an upstream indicator of market conditions and support evidence-informed procurement, pricing regulations, and antimicrobial stewardship policies in LMIC settings.

Artificial intelligence (AI), particularly large language models (LLMs), is increasingly explored in healthcare, yet its real-world usability and safety in high-risk clinical pharmacy tasks remain uncertain. Vancomycin therapeutic drug monitoring (TDM), which requires precise pharmacokinetic calculations and context-sensitive interpretation within a narrow therapeutic window, provides a stringent test case for AI-assisted decision support. This proof-of-concept study developed and evaluated a hybrid clinical decision support system (TDM-AID) integrating a validated deterministic pharmacokinetic calculation engine, GPT-4o-based structured clinical interpretation, and retrieval-augmented guideline support. Thirty retrospective adult vancomycin TDM cases were assessed using a weighted six-domain rubric covering pharmacokinetic accuracy, AUC estimation, prospective prediction, timing recommendations, clinical judgment, and documentation quality. Two independent expert pharmacists evaluated system outputs against benchmark consultations. The overall median performance was 78% (IQR 12%), classified as Acceptable, and 73% (IQR 14%) when deterministic calculations were excluded. Foundational pharmacokinetic calculations achieved 100% accuracy. Clinical judgment demonstrated Good performance (83%), whereas prospective prediction was limited (58%), and timing recommendations were absent in all cases. Safety violations occurred in 17% of cases, including dose recommendations exceeding 4 g/day. Inter-rater reliability was good (ICC 0.87). These findings suggest that hybrid AI-driven decision support is technically feasible and usable as a pharmacist-augmenting draft generator; however, limitations in predictive reasoning, timing logistics, and safety enforcement necessitate deterministic safeguards and mandatory expert oversight before clinical implementation.