Clinical Pharmacology & Therapeutics

BackgroundNon-adherence to lipid-lowering therapy (LLT) affects up to half of patients and contributes substantially to preventable cardiovascular morbidity and mortality. Existing measures, such as the proportion of days covered, provide cross-sectional summaries but fail to capture the dynamic patterns of adherence over time. Although group-based trajectory modelling identifies distinct longitudinal adherence patterns, no approach currently predicts trajectory membership prospectively while incorporating patient-reported barriers. We developed BRIDGE, a barrier-informed Bayesian model to predict adherence trajectories and identify their underlying drivers. MethodsBRIDGE incorporates patient-reported barriers as structured prior information within a Bayesian framework for adherence-trajectory prediction. The model was designed not only to estimate which patients are likely to follow different adherence trajectories, but also to generate clinically interpretable probability estimates that help explain why those trajectories may arise and what modifiable factors may be most relevant for intervention. ResultsBRIDGE achieved a macro AUROC of 0.809 (95% CI 0.806 to 0.813), comparable to random forest (0.815 (95% CI 0.812 to 0.819)) and XGBoost (0.821 (95% CI 0.818 to 0.824)), two widely used machine-learning benchmarks for structured clinical prediction. Calibration was superior to random forest (Brier score 0.530 vs 0.545; ), and performance was stable across six independent training runs (AUROC SD = 0.003). Incorporating barrier-informed priors improved accuracy by 3.5% and calibration by 5.5% compared to flat priors, showing that incorporation of patient-reported barriers added value beyond electronic medical record data alone. Four clinically distinct adherence trajectories were identified: gradual decline associated with treatment deprioritisation amid polypharmacy (10.4%), early discontinuation linked to asymptomatic risk dismissal (40.5%), rapid decline associated with intolerance (28.8%), and persistent adherence (20.2%). Counterfactual analysis identified trajectory-specific intervention levers. ConclusionsBRIDGE provides accurate and well-calibrated prediction of adherence trajectories while offering clinically actionable insights into their underlying drivers. By integrating patient-reported barriers with routine clinical data, the model supports targeted, mechanism-informed interventions at the point of prescribing to improve adherence to cardioprotective therapies. FundingMRFF CVD Mission Grant 2017451 Evidence before this studyWe searched PubMed and Scopus from database inception to December 2025 using the terms "medication adherence", "trajectory", "prediction model", "Bayesian", "lipid-lowering therapy", and "barriers", with no language restrictions. Group-based trajectory modelling has consistently identified three to five adherence patterns across cardiovascular cohorts; however, these applications have been descriptive rather than predictive. Machine-learning models for adherence prediction achieve moderate discrimination but treat adherence as a binary or continuous outcome, thereby overlooking the clinically meaningful heterogeneity captured by trajectory approaches. One prior study applied a Bayesian dynamic linear model to examine adherence-outcome associations, but it did not predict adherence trajectories or incorporate patient-reported barriers. To our knowledge, no published model integrates patient-reported barriers into trajectory prediction. Added value of this studyBRIDGE is, to our knowledge, the first model to incorporate patient-reported adherence barriers as hierarchical domain-informed priors within a Bayesian framework for trajectory prediction. Using 108 predictors derived from routine electronic medical records, the model achieves discrimination comparable to state-of-the-art machine-learning approaches while additionally providing uncertainty quantification, barrier-level interpretability, and counterfactual insights to inform intervention strategies. The identified trajectories differed not only in adherence level but also in switching behaviour, drug-class evolution, and medication burden, suggesting distinct underlying mechanisms of non-adherence that may require tailored clinical responses. Implications of all the available evidenceEach adherence trajectory implies a distinct intervention target: asymptomatic risk communication for early discontinuers (40.5% of patients), proactive tolerability management for rapid decliners, medication simplification for patients with gradual decline associated with polypharmacy, and maintenance support for persistent adherers. By integrating routinely collected clinical data with patient-reported barriers, BRIDGE can be deployed within existing primary care EMR infrastructure to generate actionable, trajectory and patient--specific recommendations at the point of prescribing, helping to bridge the gap between adherence measurement and targeted adherence management.

Electronic health record (EHR) data are critical for clinical research but are challenging to share due to privacy and re-identification risks, particularly in sensitive domains such as opioid use disorder (OUD). Synthetic data generation offers a promising alternative; however, existing methods often struggle to preserve complex multivariate dependencies while maintaining a strong balance between data utility and privacy. The recently proposed MIIC-SDG framework leverages multivariate information theory and Bayesian network modeling to capture dependency structures and introduces Quality-Privacy Scores (QPS) to evaluate this trade-off, yet its capacity to model nonlinear relationships and support multi-task predictive settings remains limited. In this work, we propose a multi-task extension of TabGraphSyn, a graph-based generative framework for privacy-preserving EHR synthesis. The method constructs patient similarity graphs from high-dimensional tabular data and learns topology-aware embeddings via a graph convolutional network, which are then incorporated into a conditional variational autoencoder for synthetic data generation. Unlike prior approaches, our framework jointly models multiple clinically relevant OUD targets, including 180-day opioid abuse outcome, opioid concept group, and opioid source concept group, enabling preservation of label-dependent relationships across tasks. We evaluate TabGraphSyn against MIIC-SDG under a unified framework including multi-task predictive utility, distributional similarity, identifiability risk, membership inference risk, and QPS-based metrics. Results on the NIH All of Us dataset show that TabGraphSyn achieves a stronger overall utility-privacy balance, outperforming MIIC in most headline metrics, including higher synthetic multi-task ROC-AUC (0.5278 vs 0.4932), MetaQPS (AM: 0.0215 vs 0.0115; HM: 0.0391 vs 0.0223), while slightly underperforming in macro F1 (0.2321 vs 0.2840). These findings demonstrate improved modeling of nonlinear dependencies and more favorable quality-privacy trade-offs in multi-task settings, supporting its use for realistic and privacy-aware synthetic EHR data generation.

Introduction Large language models are increasingly being used in healthcare. In interventional pain medicine, clinical reasoning is essential for procedural planning. Prior studies show that simplified prompts reduce clinical detail in AI-generated responses. It remains unclear whether this reflects knowledge loss or simply prompt-driven suppression of information. Methods We performed a controlled comparative study using 15 standardized low back pain questions representing common interventional pain questions. Each question was submitted to ChatGPT under three conditions, professional-level prompt (DP), fourth-grade reading-level prompt (D4), and clinician-directed rewriting of the D4 response to a medical level (U4[->]MD). No follow-up prompting was allowed. Three physicians independently rated responses for accuracy using a 0-2 ordinal scale. Clinical completeness was determined by consensus. Word count and Flesch-Kincaid Grade Level (FKGL) were also measured. Paired t-tests compared conditions. Results Accuracy was highest with professional prompting (1.76). Accuracy declined with the fourth-grade prompt (1.33; p = 0.00086). When simplified responses were rewritten for clinicians, accuracy returned to baseline (1.76; p {approx} 1.00 vs DP). Clinical completeness followed the same pattern showing DP 80.0%, D4 6.7%, U4[->]MD 73.3%. Fourth-grade responses were shorter and less complex. Upscaled responses were more complex and similar in length to professional responses. Inter-rater reliability was low (Fleiss {kappa} = 0.17), but trends were consistent across conditions. Conclusions Reduced clinical detail under simplified prompts appears to reflect constrained output rather than loss of knowledge. Clinician-directed reframing restores omitted content. LLM performance in interventional pain depends strongly on prompt design and intended audience.

Background: Progression of transthyretin (TTR) amyloid cardiomyopathy (ATTR-CM) can lead to worsening congestion requiring diuretic intensification (DI), heart failure (HF)-related hospitalizations (HFH), and death. Tafamidis was the only approved ATTR-CM therapy in the US from 2019 until the 2024 approval of acoramidis, which achieves near-complete ([≥]90%) TTR stabilization. As head-to-head trials are lacking, real-world comparative effectiveness (CE) data are needed to guide treatment selection. Objective: To evaluate real-world CE of acoramidis versus tafamidis in newly treated patients with ATTR-CM. Methods: Retrospective study using Komodo Healthcare Map (R) US claims data tokenized to Claritas. Patients newly initiating acoramidis or tafamidis between 12/11/2024 and 04/30/2025 with [≥]1 prescription claim (first defined as index date) and [≥]6 months of continuous enrollment preindex date were included and followed until disenrollment, death, treatment switch, or study end date (07/31/2025). Outcomes included DI (initiation or dose-equivalent escalation of oral loop diuretics, parenteral loop diuretic use, or addition of thiazide-like diuretic) and a composite of DI, HFH (inpatient admission with a HF-related ICD-10-CM diagnosis code in any position), and mortality. Propensity score weighting balanced baseline characteristics, disease severity, comorbidity burden, and baseline medication use. Time-to-event outcomes were assessed using weighted Cox proportional hazards models. Results: After weighting, acoramidis (n=170) and tafamidis (weighted sample size=448) patients were comparable at baseline (mean age, 78.6 vs 78.7 years; male, 80.0% vs 80.2%) with mean follow-up of 139 and 143 days, respectively. DI cumulative incidence curves separated early and remained divergent, with acoramidis significantly reducing the hazard of DI events by 43% compared with tafamidis (11.8% vs 20.5%; HR, 0.57; 95% CI, 0.35-0.92; P=0.021). Acoramidis also had a significantly lower risk of composite events, with a 34% reduction in hazard compared with tafamidis (17.6% vs 26.4%; HR, 0.66; 95% CI, 0.44-0.99; P=0.046). Conclusions: In this first real-world CE study of newly treated patients, acoramidis had significantly lower risk of DI events and composite events of DI, HFH, and mortality than tafamidis, potentially supporting improved clinical stability with acoramidis initiation. Additional evaluation with longer follow-up, larger cohorts, and/or prospective clinical outcomes is warranted.

Background: Ageing is a systems level biological process underlying the onset and progression of multiple chronic disorders. Rather than arising from a single pathway, age related decline reflects interacting disturbances in metabolic regulation, inflammation, nutrient sensing, cellular stress responses, and tissue repair. Although GLP1 receptor agonists, sodium glucose cotransporter2 inhibitors, metformin, and rapamycin are usually evaluated against disease-specific endpoints. Objective: To develop an SBML compliant quantitative systems pharmacology model in which ageing is the primary pharmacological endpoint and to evaluate which combination therapy provides the greatest benefit for both metabolic and ageing related outcomes. Methods: We developed model comprising four layers: a metabolic/pharmacodynamic layer describing weight loss, HbA1c reduction, and nausea with tolerance; a drug layer capturing class-specific effects of GLP1 agonists, sodium glucose cotransporter2 inhibitors, metformin, and rapamycin; an ageing layer representing damage accumulation, repair capacity, frailty, and biological age gap; and a biomarker layer generating trajectories and estimated glucose disposal rate. Calibration was staged across semaglutide clinical endpoints. Bayesian hierarchical meta analysis, global sensitivity analysis, and practical identifiability analysis were used to assess robustness and interpretability. Results: The model reproduced semaglutide efficacy and tolerability dynamics and supported distinct drug-class profiles across metabolic and ageing axes. Rapamycin showed minimal glycaemic effect but emerged as a dominant driver of repair related ageing outcomes. Combination simulations predicted two distinct optima: one favouring metabolic improvement and one favouring ageing related benefit. Conclusion: The model supports the view that metabolic and ageing optimization are mechanistically distinct objectives and that weight loss and glycaemic improvement alone may be insufficient surrogates for health span benefit.

Serotonergic psychedelics such as N,N-dimethyltryptamine (DMT) and 4-phosphoryloxy-N,N-dimethyltryptamine (psilocybin) show therapeutic promise for psychiatric and neurodegenerative disorders but may be limited by liabilities from serotonin (5-HT)-2A mediated psychoactive effects and potential cardiotoxicity via 5-HT2B activation. To address these limitations, we designed and synthesized 2-halogenated derivatives of DMT and psilacetin to reduce 5-HT2A/5-HT2B activity while retaining engagement of therapeutically relevant targets, particularly 5-HT6, 5-HT2C, and 5-HT1B. This study demonstrated that 2-position halogenation decreased affinities, potencies, and efficacies at 5-HT2A and 5-HT1A receptors while preserving potent 5-HT6 agonism, especially for 2-Br-psilocin. The analogues exhibited reduced affinities at 5-HT2B and hERG ion channels, suggesting safer cardiac valve and cardiotoxic profiles. In C57BL/6J mice, 2-Br-psilacetin did not induce the head-twitch response and attenuated 2,5 dimethoxy-4-iodoamphetamine (DOI)-induced head-twitch behavior, suggesting a reduced potential for inducing psychedelic effects. Behavioral assays further revealed improvements in stress-induced affective measures and hippocampus-independent cued learning at intermediate doses. These findings identify 2-halogenated tryptamines as polypharmacological serotonergic ligands with reduced psychoactivity and cardiac valve and toxic liabilities, supporting their potential as next-generation psychedelic-inspired therapeutics. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=86 SRC="FIGDIR/small/718915v1_ufig1.gif" ALT="Figure 1"> View larger version (16K): org.highwire.dtl.DTLVardef@16aa5b2org.highwire.dtl.DTLVardef@a4813corg.highwire.dtl.DTLVardef@20c5f7org.highwire.dtl.DTLVardef@1a50a61_HPS_FORMAT_FIGEXP M_FIG C_FIG

Drug overdose deaths in the United States reached record levels during the fentanyl era before recently declining. A plausible hypothesis is that a sudden drop in fentanyl purity beginning in 2023 caused the downturn in overdose mortality. We evaluated this hypothesis by replicating a published analysis with regional overdose data, using models that account for time trends and autocorrelation, and negative control indicators to test for spurious correlation. When fentanyl purity was rising, the national purity series did not track overdose increases in most regions and showed only a modest association in the West. When both purity and mortality later declined, the observed associations were also seen with unrelated macroeconomic indicators that shared the same time pattern. National fentanyl purity alone does not provide a sufficient explanation for recent overdose declines.

Background: Chronic conditions such as hypertension can significantly disrupt daily life and emotional wellbeing. The interaction between patients' perceptions, adherence to antihypertensive medication and quality of life (QoL) remains underexplored outside structured clinical settings. Objectives: To capture unprompted patient perspectives and assess whether hypertension affects QoL and to investigate if patient reported experiences are associated with self-reported antihypertensive medication adherence. Methods: Social media listening (SML) study analyzing 86,368 anonymized posts from individuals with hypertension in 12 countries, collected between January 2022 and May 2024. Posts from 11 countries (n=81,368) were analyzed using artificial intelligence-enabled natural language processing. Posts from China (n=5,000) were analyzed separately using a harmonized framework. Quantitative and qualitative methods assessed variations by country, age, and gender, and associations between emotional expression and antihypertensive medication adherence. Results: Across the 11-country core sample, 45% of posts mentioned at least one QoL impact, most commonly worry/anxiety (11%). Impacts varied across countries. Among 8,096 posts with age identified, individuals <40 years reported emotional balance impacts in 28% of posts versus 22% among those aged 40+. Work/Education impacts were mentioned in 17% of posts by those <40 years vs 12% in 40+. Among 7968 posts explicitly referencing adherence, expressed worry was associated with stricter adherence (62% association score), as were structured routines (79% score), home monitoring (77%), dietary changes (77%), and exercise (71%). In contrast, sadness/depression was associated with inconsistent adherence (71%), as were forgetfulness (79%), side effects (73%), and cost/insurance concerns (65%). Conclusions: These results emphasize the importance of the psychological and emotional impact of hypertension, including on adherence to medication regimens, reinforcing the value of a holistic approach to patient care.

BackgroundHypertension is the leading modifiable risk factor for ischemic stroke, yet the adequacy of preventative hypertension care in routine clinical practice remains suboptimal. Whether gaps in hypertension management represent missed opportunities for stroke prevention remains unclear. ObjectiveTo evaluate the association between hypertension care delivery and the risk of incident ischemic stroke. MethodsWe conducted a retrospective, matched, nested case-control study among adults with hypertension using electronic health record data from a large regional health system (2010-2024). Patients with a first-ever ischemic stroke were matched 1:2 to controls on age, sex, race and ethnicity, and calendar time. Three care metrics were assessed during follow-up: (1) outpatient visits with blood pressure (BP) measurement per year; (2) number of antihypertensive medication ingredients; and (3) medication intensification score. Conditional logistic regression estimated adjusted odds ratios (aORs). ResultsThe study included 13,476 cases and 26,952 matched controls (N = 40,428). Mean (SD) age was 64.8 (12.2) years, 54.1% were female, and mean follow-up was 2,497 (1,308) days. Cases had fewer BP visits per year (median, 2.50 vs. 3.01; p < 0.001), similar number of medication ingredients (2.00 vs 2.00), and lower treatment intensification scores (-0.211 vs - 0.125). In adjusted models, >5 BP visits per year was associated with lower stroke odds (aOR, 0.55; 95% CI, 0.51-0.59) compared with [&le;]1 visit. Use of 2-3 medication ingredients (vs 0) was also associated with reduced stroke odds (aOR, 0.80; 95% CI, 0.75-0.86), whereas >3 ingredients was not significant. The highest quartile of treatment intensification showed the strongest association (aOR, 0.47; 95% CI, 0.44-0.51). Findings were consistent across subgroup and sensitivity analyses, including strata defined by baseline SBP and follow-up SBP. ConclusionsGreater engagement in hypertension care was associated with lower odds of ischemic stroke, suggesting that gaps in routine management may represent missed opportunities for prevention.

Background: Current medical large language model (LLM) evaluations largely rely on small collections of cases, whereas rigorous safety testing requires large-scale, diverse, and complex cases with verifiable ground truth. Multiple Sclerosis (MS) provides an ideal evaluation model, with validated diagnostic criteria and numerous paraclinical tests informing differential diagnosis, investigation, and management. Methods: We generated synthetic MS cases with ground-truth labels for diagnosis, localisation, and management. Four frontier LLMs (Gemini 3 Pro/Flash, GPT 5.2/5 mini) were instructed to analyse cases to provide anatomical localisation, differential diagnoses, investigations, and management plans. An automated evaluator compared these outputs to the ground-truth labels. Blinded subspecialty experts validated 70 cases for realism and automated evaluator accuracy. We then evaluated LLM decision-making across 1,000 cases and scaled to 10,000 to characterise rare, catastrophic failures. Results: Subspecialist expert review confirmed 100% synthetic case realism and 99.8% (95% CI 95.5 to 100) automated evaluation accuracy. Across 1,000 generated MS cases, all LLMs successfully included MS in the differential diagnoses for more than 91% cases. However, diagnostic competence did not associate with treatment safety. Gemini 3 models had low rates of clinically appropriate steroid recommendations (Flash: 7.2% 95% CI 5.6 to 8.8; Pro: 15.8% 95% CI 13.6 to 18.1) compared to GPT 5 mini (23.5% 95% CI 20.8 to 26.1), frequently overlooking contraindications like active infection. OpenAI models inappropriately recommended acute intravenous thrombolysis for MS cases (9.6% GPT 5.2; 6.4% GPT 5 mini) compared to below 1% for Gemini models. Expanded evaluation (to 10,000 cases) probed these errors in detail. Thrombolysis was recommended in 10.1% of cases lacking symptom timing information and paradoxically persisted (2.9%) even when symptoms were explicitly documented as more than 14 days old. Conclusion: Automated expert-level evaluation across 10,000 cases characterised artificial intelligence clinical blind spots hitherto invisible to small-scale testing. Massive-scale simulation and automated interrogation should become standard for uncovering serious failures and implementing safety guardrails before clinical deployment exposes patients to risk.

Purpose: Limited CNS bioavailability and pharmacodynamics are obstacles to effective systemic therapies for glioblastoma. One strategy to overcome these challenges is drug combinations enhancing CNS penetration and/or tumor chemosensitivity. LP-184, a synthetic acylfulvene class alkylator, induces DNA damage and inhibits glioblastoma cell viability in pre-clinical models. LP-184 is a prodrug converted to active metabolites by intracellular prostaglandin reductase 1 (PTGR1) that is over-expressed in >70% of glioblastoma. DNA damage induced by LP-184 is MGMT agnostic and reversed by transcription-dependent NER. Patients: LP-184 was evaluated in a Phase 1a study (NCT05933265) in 63 adult patients with advanced malignancies including 16 patients with recurrent glioblastoma. All patients with glioblastoma received prior standard-of-care therapy and most had received 1 or more additional therapies before enrollment. Results: Patients with glioblastoma experienced more frequent transaminitis, Grade 1-2 nausea and a trend towards more frequent and severe thrombocytopenia compared to the non-glioblastoma cohort. Otherwise, overall toxicity profiles were similar. Clinical pharmacokinetic analysis combined with published pre-clinical intra-tumoral bioavailability data (~20% penetration) predicted that LP-184 at the recommended dose for expansion (RDE) would achieve cytotoxic levels if combined with spironolactone, a BBB permeable ERCC3 degrader and TC-NER inhibitor that sensitizes glioblastoma cells to LP-184 3-6-fold. We show that three daily doses of spironolactone deplete orthotopic glioblastoma PDX ERCC3 protein by ~ 80% and increases tumor LP-184 cytotoxicity 2-fold. Conclusions: LP-184 is well tolerated at the RDE, and we establish a clinically translatable scheme for dosing spironolactone in combination with LP-184 for a future Phase 1b clinical trial.

BackgroundHeterozygous c.283+1G>A and c.283G>A variants in the THRB gene, encoding for thyroid hormone receptor (TR){beta}1 and {beta}2, lead to autosomal dominant macular dystrophy (ADMD). We report the detailed clinical characterization of two first-degree relatives with ADMD, heterozygous for THRB c.283+1G>A, and an unrelated ADMD patient with a novel variant, c.283G>C. The genomic and molecular consequences of both variants were studied. MethodsgDNA and mRNA were obtained from leukocytes. Clinical characterization included biochemistry, bone density and body composition, ECG, echocardiography, ultrasound, audiometry and color-vision. In vitro assays investigated TR function and DNA binding. ResultsThe patients manifested no resistance to thyroid hormone beta (RTH{beta}) and had normal FT4 and TSH. Detailed studies in two patients showed no goiter, tachycardia, hypercholesterinemia or hepatic steatosis. Hearing was not impaired. Both had impaired color vision and reduced bone density. RT-PCR from all three patients revealed skipping of exon 4 exclusive to TR{beta}1, producing a deletion of 87 amino acids in the N-terminal domain (TR{beta}1{Delta}NTD). In vitro, DNA-binding affinity of TR{beta}1{Delta}NTD to DR4-TRE with or without RXR was comparable to TR{beta}1WT. Surprisingly, TR{beta}1{Delta}NTD was transcriptionally twice more active than TR{beta}1WT with a similar EC50 for T3, demonstrating gain-of-function of TR{beta}1{Delta}NTD. THRA expression in leukocytes was increased by 3-fold compared to unrelated controls and different from RTH{beta} patients. ConclusionThese THRB splice site variants produce TR{beta}1 exon 4 skipping, resulting in a gain-of-function mutant, TR{beta}1{Delta}NTD. This explains the dominant ADMD phenotype devoid of RTH{beta} and suggests a TR{beta}1 gain-of-function syndrome.

Introduction: Smoking cessation after acute coronary syndrome (ACS) is a Class I recommendation, yet prescription pharmacotherapy use remains low and its real-world cardiovascular effectiveness when added to nicotine replacement therapy (NRT) is poorly characterized. Methods: We conducted a retrospective cohort study using the TriNetX US Collaborative Network (67 healthcare organizations). Adults hospitalized with ACS who received NRT within one month, serving as a proxy for active smoking status, were identified. Two co-primary propensity-matched (1:1, 50 covariates, caliper 0.10 SD) comparisons evaluated bupropion + NRT and varenicline + NRT individually versus NRT alone; a supportive analysis evaluated combined pharmacotherapy versus NRT alone. All-cause mortality was the primary endpoint. Secondary outcomes included MACE, heart failure exacerbations, major bleeding, TIA/stroke, emergency rehospitalizations, and cardiac rehabilitation utilization, assessed at 6 months and 1 year via Kaplan-Meier analysis. Hazard ratios (HRs) greater than 1.0 indicate higher hazard in the NRT-only group. Results: After matching, the combined analysis comprised 8,574 pairs, the bupropion analysis 4,654 pairs, and the varenicline analysis 2,126 pairs. At 1 year, the combined pharmacotherapy group had significantly lower all-cause mortality (HR 1.26, 95% CI 1.16-1.37), MACE (HR 1.16, 95% CI 1.12-1.21), heart failure exacerbations (HR 1.16, 95% CI 1.08-1.25), major bleeding (HR 1.18, 95% CI 1.08-1.28), and greater cardiac rehabilitation utilization (HR 0.82, 95% CI 0.74-0.92; all p < 0.001). TIA/stroke did not differ significantly. Six-month results were consistent. Both varenicline and bupropion individually showed lower mortality and MACE. A urinary tract infection falsification endpoint showed no between-group differences, supporting matching validity. The pharmacotherapy group had higher rates of new-onset depression, driven predominantly by bupropion recipients. Conclusions: In this propensity-matched real-world analysis, adding prescription smoking cessation pharmacotherapy to NRT after ACS was associated with lower mortality and fewer adverse cardiovascular events, supporting broader integration into post-ACS care pathways.

BackgroundChemokine receptor type 5 (CCR5) is expressed on hepatic stellate cells (HSCs), which, together with fibroblasts, are major producers of extracellular matrix during liver fibrosis. Leronlimab is a humanized IgG4{kappa} monoclonal antibody that binds to CCR5. The objective of the present study was to evaluate the antifibrotic effects of leronlimab in three independent preclinical studies using two mouse models of liver fibrosis. MethodsIn STAM (Stelic Animal Model) model 1, leronlimab was administered at doses of 5 or 10 mg/kg/week for 3 weeks. STAM model 2 was conducted as a confirmatory study to validate the antifibrotic effect observed with the 10 mg/kg/week dose in STAM model 1. In a third study, a carbon tetrachloride (CCl)-induced liver fibrosis mouse model was used to evaluate leronlimab administered at 10 mg/kg/week for 3 weeks. An isotype-matched control antibody was included in all studies for comparison. Evaluations included liver enzymes and histological assessment of liver fibrosis. ResultsIn STAM model 1, leronlimab at 10 mg/kg/week significantly reduced fibrosis area compared with the isotype control (p = 0.0005). These findings were confirmed in STAM model 2 (p < 0.0001). Consistent antifibrotic effects were also observed in the CCl-induced liver fibrosis model (p = 0.0006). ConclusionsCollectively, these preclinical results demonstrate that CCR5 blockade by leronlimab is associated with a significant reduction of established liver fibrosis in multiple mouse models and support further evaluation of leronlimab as a potential therapeutic option, either as monotherapy or in combination regimens, for chronic liver diseases with fibrosis.

Vaccine adjuvants are critical for enhancing immune responses and sustaining antibody production. Although their safety profiles are well established, assessments have largely focused on metabolic and excretory organs such as the liver and kidneys, with limited attention to the heart. Here, we systematically evaluated the cardiac effects of five representative adjuvants in mice: alum, MF59, AS03, Sigma Adjuvant Systems, and lipid A. None of the adjuvants impaired baseline cardiac contractile function. Notably, lipid A uniquely enhanced mitochondrial respiratory capacity in rat and human induced pluripotent stem cell-derived cardiomyocytes and promoted mitochondrial membrane hyperpolarization. We next examined its therapeutic potential in a doxorubicin (Dox)-induced heart failure model characterized by mitochondrial dysfunction. Co-administration of lipid A with influenza hemagglutinin (HA) antigen significantly ameliorated cardiac dysfunction. In parallel, lipid A prevented the Dox-induced decline in anti-HA antibody titers, an effect associated with preservation of splenic B cell populations. Collectively, these findings reveal a previously unappreciated cytoprotective dimension of lipid A, demonstrating that it not only potentiates immune responses but also counteracts chemotherapy-induced functional decline by enhancing mitochondrial activity.

Abstract Background: Hypertrophic (HCM) and dilated (DCM) cardiomyopathy constitute the principal phenotypes of primary cardiomyopathy, yet both lack sufficient therapeutic options. Integrating genetic insights with detailed cardiac phenotyping offers a promising strategy to prioritize targets and elucidate their mechanisms of action. Methods: We conducted an three-stage analysis. First, drug-target Mendelian randomization (MR) was performed using cis-acting protein (pQTL) and expression (eQTL) quantitative trait loci as genetic instruments for potential drug targets. Second, we examined causal associations between 82 cardiac magnetic resonance (CMR)-derived imaging traits and HCM/DCM risk in a CMR-based MR analysis. Third, mediation MR was employed to quantify the proportion of the genetic effect of prioritized drug targets on cardiomyopathy risk that was mediated through specific CMR phenotypes. Results: Our analyses identified 19 and 13 potential therapeutic targets for HCM and DCM, respectively. CMR-based MR revealed that HCM risk was causally associated with increased right ventricular ejection fraction (RVEF) and greater left ventricular wall thickness, whereas DCM risk was linked to ventricular dilation, impaired myocardial strain, and altered aortic dimensions. Critically, mediation analysis established that these CMR traits served as significant intermediate pathways. The protective effect of ALPK3 on HCM risk was mediated through a reduction in myocardial wall thickness. Conversely, the effects of PDLIM5, HSPA4, and FBXO32 on DCM risk were exerted in part via alterations in aortic dimensions. Conclusion: This integrative genetic and imaging study systematically identify candidate therapeutic targets for HCM and DCM and delineates the specific CMR phenotypes through which they likely exert their causal effects. Our findings advance the understanding of disease pathogenesis and highlight new possibilities for improving the diagnosis and management of cardiomyopathy.

Background: Few studies have examined racioethnic disparities in cardiovascular disease (CVD) in women after breast cancer treatment, who are at higher risk due to cardiotoxic cancer treatment. Methods: Based on the Pathways Heart Study of women with a history of breast cancer, this analysis examines the association between cardiometabolic risk factors (hypertension, diabetes, and dyslipidemia) and CVD events with self-reported race and ethnicity, as well as genetic similarity. Multivariable logistic and Cox proportional hazards regression models were used to test race and ethnicity and genetic similarity with prevalent and incident cardiometabolic risk factors and CVD events. Results: Of the 4,071 patients in this analysis, non-Hispanic Black (NHB), Asian, and Hispanic women were more likely to have prevalent and incident diabetes than non-Hispanic White (NHW) women. Analysis of genetic similarity revealed results consistent with self-reported race and ethnicity. For CVD risk, NHB women were more likely to develop heart failure and cardiomyopathy than NHW women. In contrast, Hispanic women were at lower risk of any incident CVD, serious CVD, arrhythmia, heart failure or cardiomyopathy, and ischemic heart disease, which was consistent with the associations found with Native American ancestry. Conclusions: This is the largest multi-ethnic study of disparities in CVD health in breast cancer survivors, demonstrating corroborating findings between self-reported race and ethnicity and genetic similarity. The results highlight disparities in cardiometabolic risk factors and CVD among breast cancer survivors that warrant more research and clinical attention in these distinct, high-risk populations.

Computational drug repurposing has largely been focused on rapid hypothesis generation, yet real-world applications span a far broader lifecycle, from drug candidate suggestion to designing experiments, analyzing assay data, and iteratively refining candidates. Here, we demonstrate that agentic AI can fulfill this entire scope. To this end, we developed RepurAgent, a hierarchical multi-agent AI system comprising a supervisor agent and a planning agent that coordinate four specialized sub-agents -- research, prediction, data, and report -- through a human-in-the-loop design, with episodic memory and retrieval-augmented generation. The system is grounded in data, tools, and standard operating procedures specific for drug repurposing, developed within the REMEDi4ALL consortium. We validated the agentic system across three scenarios spanning the various stages within the repurposing lifecycle: in Acute Myeloid Leukemia, RepurAgent recovered up to 97% of disease-relevant pathways identified by Google Co-Scientist, completing the workflow within 60 minutes; in a retrospective COVID-19 antiviral screen, RepurAgent acted as an adaptive experimental collaborator, prioritizing compounds with AUC-ROC up to 0.98 without predefined thresholds and flagging confounders missed in manual review; and for Multiple Sulfatase Deficiency, it prioritized 82 high-confidence candidates from 5000 compounds, which were further corroborated by domain experts. These results demonstrate that agentic AI can support across the full drug repurposing lifecycle, from hypothesis generation to experimental analysis. RepurAgent is open source and deployed at https://repuragent.serve.scilifelab.se/.

Confirming target engagement in tumor experimental models remains a major challenge in oncology drug development. Pharmacodynamic biomarkers can help address this, but few systematic resources link drug targets to candidate biomarkers. We developed TargetTrace, a comprehensive resource to identify and prioritize pharmacodynamic biomarkers across nine key target classes, including transcription factors/cofactors, kinases, phosphatases, ubiquitin ligases, deubiquitinases, acetyltransferases, deacetylases, methyltransferases, and demethylases. Biomarker candidates were gathered from curated molecular interaction resources and refined using external annotations to improve accuracy. For enzyme targets with measurable substrate changes, we applied a two-agent large language model workflow, followed by manual review, to harmonize antibody information from the antibody resources and ensure that the selected biomarkers are measurable with existing laboratory tests. From more than 92,000 input interactions and over 2,300 targets, we compiled 71,323 target-biomarker relationships involving 2,270 potential drug targets, encompassing both transcription factor/cofactor-target gene and enzyme-substrate interactions. Commercial antibodies were available for over 1,400 biomarkers, supporting laboratory validation. This resource provides a structured and reusable resource for systematic identification and prioritization of pharmacodynamic biomarkers in oncology.

Background Group A streptococcus (GAS) infections have been associated with neuropsychiatric disorders in epidemiologic studies and animal models, but data in US health care populations are limited. GAS is also associated with autoimmune sequelae, including acute rheumatic fever (ARF)/Sydenham chorea (SC), poststreptococcal reactive arthritis (PSRA), poststreptococcal glomerulonephritis (PSGN), and guttate psoriasis (GP). Epstein-Barr virus (EBV) has been linked to systemic lupus erythematosus (SLE) and multiple sclerosis (MS) and the complexity of these associations parallels that of GAS-associated conditions, providing a useful comparison. Objectives 1) Assess the association between a positive GAS test and incident neuropsychiatric diagnoses within 1 year in a large US health care database. 2) Assess the validity of the same database in detecting well-established disease associations while avoiding false associations. Design, Setting, Participants Retrospective cohort study using TriNetX data from US health care organizations. Patients with positive or negative tests were propensity score-matched (GAS cohort n=178,301; EBV cohort n=64,854). Patients with documented neuropsychiatric diagnoses prior to testing were excluded. To approximate a primary care population, inclusion required at least one well-visit. Exposures Positive vs negative GAS test; positive vs negative EBV test (separate cohorts). Main Outcomes and Validations Main outcome: incident neuropsychiatric diagnoses within 1 year of GAS testing. Positive control outcomes: ARF/SC, PSRA, PSGN, and GP (for GAS cohort); SLE and MS (for EBV cohort). Negative control outcomes: conditions without known association with GAS. Results After matching, a positive GAS test was associated with attention-deficit/hyperactivity disorder (ADHD) (RR: 1.09; 95% CI: 1.03-1.15). Among established poststreptococcal conditions, only GP was associated with prior GAS (RR: 1.75; 95% CI: 1.06-2.89). Case counts were insufficient to evaluate ARF/SC, PSRA, and PSGN. Negative control outcomes showed no association. In the EBV cohort, no association was observed with SLE, and MS showed a decreased risk. Conclusions and Relevance A positive GAS test was associated with ADHD but not with other neuropsychiatric disorders. The database detected poststreptococcal GP but did not identify most established postinfectious autoimmune associations, likely reflecting rarity, heterogeneity, and diagnostic complexity. These findings begin to describe the range of real-world health care databases to evaluate postinfectious neuropsychiatric risk.