Clinical Pharmacology & Therapeutics

Background. Polypharmacy is an important source of phenoconversion caused by drug interactions potentially modulated by genetic variability. Aims. To develop a linear phenoconversion model for TDM data and provide quantitative estimates of drug-drug-gene interactions (DDGIs) in the pharmacogenetic phenotype groups of CYP2C19. Methods. Escitalopram TDM data in a large real-world sample (n=2,852) was analysed for phenoconversion of CYP2C19 activity. Co-medication was identified by reprocessing high-resolution mass-spectra (Orbitrap). We developed a statistical model to identify inhibition from co-medication in the CYP2C19 and in alternative elimination pathways. We extended the model to estimate the inhibition ensuing from individual co-medications, using a single model for all data to account for multiple co-medications and confounders simultaneously. A Bayesian approach allowed us to stabilize the fit and provide well-calibrated credibility intervals. Results. Reprocessing of TDM analyses identified 17 co-medications, which were shown to phenoconvert CYP2C19 activity proportionally to the activity in non-medicated phenotypes. Phenoconversion decreased the original CYP2C19 activity by about one third for a co-medication that corresponded to a 100% substrate of CYP2C19. The extent of CYP2C19 phenoconversion correlated strongly with the fractional contribution of CYP2C19 to the metabolism of the specific co-medication reported in the pharmacogenetic literature (R2=0.55) so long as the mechanism was competitive inhibition. Conclusion. We provide the statistical methodology to estimate phenoconversion from co-medication in TDM data and combine TDM and pharmacogenetic datasets in future studies aiming at establishing quantitative models of DDGIs.

Background: Sphingosine 1-phosphate receptor (S1PR) modulators can cause transient, dose-related negative chronotropic effects. Mocravimod is an oral S1PR modulator that is developed as a maintenance therapy in allogenic haematopoietic cell transplantation (allo-HCT). This phase I study evaluated whether two dose-titration regimens attenuate early bradycardia when initiating mocravimod while preserving pharmacokinetic (PK) and pharmacodynamic (PD) activity. Patients and methods: In this randomized, double-blind, placebo-controlled, parallel-group study, healthy adults received once-daily oral mocravimod using either dose titration (DT) regimen DT1 (0.3-2.0 mg with 4-day stepwise escalation) or regimen DT2 (0.5 mg to Day 14, 1.2 mg Days 15-18, then 2 mg), a fixed 2 mg regimen, or placebo for 21 days. The primary endpoint was the number of bradycardia episodes on treatment initiation and dose-escalation days derived from 24-hour Holter monitoring; PK of mocravimod and mocravimod-phosphate (whole blood) and PD effects (absolute lymphocyte count [ALC]) were assessed. Results: Fifty-six participants were randomized and 53 completed the study. Both titration regimens resulted in fewer bradycardia episodes than fixed initiation at 2 mg during the first week of treatment. Differences between titration and fixed dosing were no longer evident after Day 9, consistent with tolerance development. PK profiles were consistent with prior phase I data. By Day 21, DT1 achieved exposures close to the fixed 2 mg regimen, whereas DT2 yielded lower exposures, reflecting slower escalation. Peripheral lymphopenia developed in all active treatment groups and was comparable between regimens by Day 21, returning toward baseline by study end. Safety was similar between titration regimens and placebo, with similar distribution and incidence of adverse events. No serious adverse events occurred. Conclusion: Two practical titration regimens mitigated the early negative chronotropic effect observed with fixed-dose initiation of mocravimod at 2 mg once daily. Importantly, titration preserved the expected PK and PD profile, supporting dose escalation as an effective initiation strategy to improve early cardiac tolerability.

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.

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.

Importance Pharmacogenomic (PGx) guidelines can improve medication efficacy and reduce toxicity, but their application in pediatric intensive care units (PICUs) remains largely unexplored. Objective To determine the frequency of medications with established PGx guidelines administered in the PICU and assess the capacity of exome sequencing to capture PGx phenotypes for these medications. Design Retrospective cohort study integrating electronic medical record and exome sequencing data. Setting Morgan Stanley Children's Hospital of NewYork-Presbyterian, a single center tertiary care children's hospital. Participants A total of 4,939 children admitted to the PICU (2020 - 2024), and 192 children admitted to the PICU who underwent exome sequencing for research purposes (2015 - 2023). Exposure Critical illness requiring PICU admission. Main Outcomes and Measures Frequencies of administration of medications with established PGx guidelines in the PICU and the proportion of individuals with exome sequencing with identifiable PGx phenotypes. Results Among 4,939 PICU patients, 37.2% (n=1,837) received at least one medication with established PGx guidelines and 14.4% (n=712) received two or more such medications. Twenty PGx genes were implicated; CYP2C9 was most common (17.3%, n=853). An estimated 8.2% of patients received medications for which PGx-guided recommendations would have altered clinical management. Among 192 patients who underwent exome sequencing, at least one metabolizer phenotype was identified in 62% (n=119). Conclusions and Relevance Many critically ill children receive medications with established PGx guidelines. This study highlights an opportunity for more personalized medicine for critically ill children admitted to a tertiary care hospital and assesses the strengths and weaknesses of exome sequencing to uncover pertinent PGx phenotypes.

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.

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.

Accurate prediction of individual medical outcomes is essential for optimizing treatment allocation amid rising costs, coverage denials, and limited clinical resources. Traditional predictive models, including regression and neural networks, rely on average effects and cannot tailor predictions to the specific circumstances of individual cases. We present relevance-based prediction (RBP), a model-free method that predicts outcomes as weighted averages of observed cases, with weights determined by a rigorously defined measure of relevance. Unlike model-based methods that rely on fixed calibrated parameters, RBP revisits the original data for each prediction and customizes both the cases and variables used. Applied to opioid treatment, RBP provides case-specific insights unavailable from conventional models, including how each prior case informs a prediction, how each variable affects its reliability and value, and how reliable the prediction is before it is made. These individualized insights may prevent misleading average-based decisions and reduce harmful or suboptimal treatment.

Nociplastic pain features are common in systemic lupus erythematosus (SLE), yet its longitudinal trajectory remain poorly characterized. SLE patients in the FORWARD Databank were classified as Minimal, Type 1, Type 2, or Mixed using the Polysymptomatic Distress Scale (PSD[&ge;]8) and the Systemic Lupus Activity Questionnaire (SLAQ) inflammatory domain score ([&ge;]2). Cross-sectional analyses (N=372) compared clinical outcomes and medication use. Longitudinal analyses (n=301; median 3.7 years) characterized phenotype transitions using continuous-time Markov models and identified latent trajectories using joint group-based trajectory modeling (GBTM). At baseline, 29% were Minimal, 12% Type 1, 13% Type 2, and 47% Mixed. Functional impairment increased stepwise: from Minimal to Mixed, SF-36 physical component scores decreased from 49.7 to 30.0 and PROMIS Pain Interference scores increased from 46.2 to 63.6 (both p<0.001). Organ damage, depression, and opioid use were highest in Mixed. Longitudinally, Minimal and Mixed were persistent (mean duration 2.0 and 1.8 years; one-year retention 70%), while Type 1 and Type 2 were transient (~0.5 years; retention 18% and 28%). Exit trajectories were asymmetric: Type 1 moved preferentially to Minimal (49% of exits), whereas Type 2 moved to Mixed (65%; p<0.001). Population-average PSD was nearly flat (+0.014 SD/year, p=0.07); while opioid use declined to near zero in Minimal and Type 1 but remained high in Type 2 and Mixed. Joint GBTM identified four severity classes along a Minimal-to-Mixed diagonal. Nociplastic phenotypes in SLE are persistent, severity-stratified, with substantial functional, psychological, organ-damage, and opioid burdens. Transient Type 1 and Type 2 states have divergent longitudinal transitions.

Background. Compounded tirzepatide is prescribed at scale as a cheaper substitute for branded Mounjaro and Zepbound, yet the cost case is almost always built by setting one compounded price against one branded list price. That framing ignores the question that actually decides the answer: cheaper than which branded price the patient can reach. Branded tirzepatide is now sold at sharply different tiers, namely insurance copay (often $25-$150/month), LillyDirect Self Pay ($299-$449/month), and retail cash price ($1,000-$1,200/month). Whether compounded saves money turns entirely on which of these a given patient faces. A second open question is whether the two formulations even produce comparable effectiveness, since observed differences may reflect selection on insurance, baseline characteristics, and adherence rather than the drug. Methods. We conducted a retrospective cohort study of tirzepatide users in the Mochi Health telehealth program, classified by formulation from their refills as branded-only (Mounjaro/Zepbound; 6,238), compounded-only (71,683), or switchers (4,996); switchers were excluded from the formulation contrast. Among single-formulation patients with a documented six-month weight observation, the analytic cohort was 7,271 (869 branded, 6,402 compounded). The primary outcome was six-month percent body weight loss; the secondary outcome was >=10% response. We used 1:1 nearest-neighbor propensity-score matching (0.25 SD caliper) on baseline covariates only - age, sex, baseline BMI, baseline weight, comorbid diabetes, hypertension, dyslipidemia, prior bariatric surgery, and self-reported insurance coverage - deliberately excluding post-treatment variables such as adherence and time in program, which are mediators of the formulation effect. We pre-specified an equivalence margin of +/-2 percentage points on mean loss and tested equivalence with two one-sided tests (TOST). A directed acyclic graph (DAG) makes the identifying assumptions explicit; metformin use could not be reliably ascertained and is treated as an unmeasured confounder. The cost comparison reports the savings or premium of compounded versus branded under five branded price scenarios: retail list, LillyDirect Self Pay (two dose tiers), and insurance copay (typical and low end). It is a cost comparison (cost-minimization under demonstrated similar effectiveness), not a formal cost-effectiveness analysis: we computed no ICER, QALY, or discounting. Results. Branded and compounded patients had similar outcomes even before adjustment (mean loss 11.7% vs 11.5%; >=10% response 60.9% vs 58.8%). The largest baseline difference between the groups was insurance coverage (branded patients far more likely insured; standardized mean difference 0.67), which matching balanced to 0.01. After 1:1 matching (718 pairs, all |SMD| < 0.04), mean loss was 11.4% vs 11.4% (difference +0.08 pp, 95% CI -0.70 to +0.80) and >=10% response 59.3% vs 57.2% (difference +2.1 pp, 95% CI -3.1 to +7.1). The two formulations were statistically equivalent within the pre-specified +/-2 pp margin (TOST p < 0.001). Cost depends on the branded scenario: compounded saves $6,000 over six months versus retail list price, $1,494 versus LillyDirect maintenance-dose (5-15 mg) Self Pay, and $594 over a low-dose (2.5 mg) LillyDirect prescription, while it costs $300 more than branded under a typical insurance copay ($150/month) and is more expensive still at lower copays (savings turn negative below $200/month). Conclusions. Branded and compounded tirzepatide were statistically equivalent in six-month effectiveness within a pre-specified +/-2 pp margin, so the choice between them is essentially a cost decision - and that cost advantage is real but conditional on the branded price the patient can access. It is large against retail list price and shrinks to zero or reverses against LillyDirect Self Pay or a low insurance copay. Whether compounded is the lower-cost choice for an individual patient is, therefore, a question about which price tier that patient faces.

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

Objective: To evaluate the clinical efficacy of non-invasive electrical pulsed radiofrequency (PRF) stimulation on diagnostic thresholds and subjective pain in chronic, pedal diabetic peripheral neuropathy (DPN). Methods: A randomized, single-blind, placebo-controlled trial (ClinicalTrials.gov: NCT07725419) enrolled 92 patients with pedal DPN naive to PRF and scoring [&ge;] 4/10 on the Douleur Neuropathique 4 (DN4) test. Participants received either active PRF stimulation (n = 46) or a non-stimulating placebo (n = 46) applied bilaterally to the sciatic nerve in the popliteal fossa for 10 minutes per limb, once weekly for three weeks. The primary outcome was clinical neuropathic resolution (DN4 < 4). Secondary outcomes included subjective pain tracking via the Brief Pain Inventory-Short Form (BPI-SF) Worst Pain scale over a 6-month follow-up window. Missing data were handled via Non-Responder Imputation (NRI). Longitudinal continuous trajectories were modeled using Linear Mixed-Effects Models (LMMs) adjusted for age, gender, and baseline medication use. Results: In the Intention-to-Treat population (N = 92), a significant diagnostic responder effect occurred at 3 months, with 39.1% of active patients dropping below the diagnostic threshold for neuropathy (DN4 < 4) versus 19.6% of placebo controls (p = 0.039). For subjective pain, 47.7% of active patients achieved a Minimally Clinically Important Difference ([&ge;] 3-point reduction) in BPI Worst Pain at 1 month compared to 19.4% of placebo controls (p = 0.008). Multivariable logistic regression identified active treatment as a significant independent predictor of clinical response (Adjusted OR = 4.86; 95% CI: 1.56 to 17.53; p = 0.010). Continuous LMM tracking confirmed a statistically significant treatment-by-timepoint interaction for BPI Worst Pain at 1 month (p = 0.046). Conclusion: A brief, three-week course of non-invasive PRF stimulation serves as a safe, effective, non-pharmacological adjunct that aids in managing the diagnostic presentation of neuropathic pain and mitigates worst pain experiences in patients suffering from pedal DPN.

Summary Background Metronidazole (MET) resistance in Helicobacter pylori (H. pylori) exceeds 50-60% globally, yet MET-containing bismuth quadruple therapy (BQT) achieves &gt90% eradication in MET-resistant infections. We hypothesise this discordance stems from a structural limitation of two-fold dilution: a pharmacometric grey zone between the 128 and 256 &microg/mL breakpoints where treatable isolates are systematically misclassified as high-level resistance. Methods In a real-world cohort of 4610 treatment-na&iumlve children (2019-2024), checkerboard assays determined the bismuth-MET synergy factor (SF). Population PK/PD modelling simulated gastric MET exposure (AUC<sub&gt0-24</sub&gt/MIC &ge 70) at 1 g, 1&middot5 g and 2 g daily doses, incorporated bismuth SF, and estimated MIC coverage against the European Committee on Antimicrobial Susceptibility Testing (EUCAST) population distribution. Findings were validated against published adult trials. Findings 56&middot4% (485/860) of isolates were MET-resistant (MIC &gt 8 &microg/mL). At [&le;] 1 g/day MET, BQT offered no advantage over triple therapy for resistant infections. At 1&middot5 g/day, the synergy threshold was surpassed, achieving &gt90% eradication in MET-resistant infections, with efficacy independent of baseline susceptibility status. PK/PD modelling showed 1&middot5 g/day MET plus bismuth (median SF = 8) extended coverage to ~180 &microg/mL. To align modelled coverage with the &gt90% eradication rate, the dominant subpopulation of nominally high-resistant isolates must have a geometric mean MIC well below 256 &microg/mL (centred near 150 &microg/mL), consistent with a mixture of two log-normal subpopulations and confirming systematic upward misclassification by two-fold dilution in the grey zone. Interpretation This grey zone is a structural blind spot of two-fold dilution, not random measurement error. The &plusmn1-dilution reproducibility limit renders the interval inherently unresolvable. We propose a three-tier strategy: omit routine MET susceptibility testing for treatment-na&iumlve patients on optimised BQT; reserve categorical testing for BQT failures; and explore genomic/metabolomic biomarkers beyond MIC paradigms in this enriched population. Funding None. Keywords: Helicobacter pylori; Metronidazole resistance; Bismuth quadruple therapy; Antimicrobial susceptibility testing; Pharmacokinetic/pharmacodynamic modelling; Pharmacometric grey zone

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.

Introduction: Acute low back pain is a leading cause of disability worldwide. Clinical guidelines recommend non-pharmacological therapies as first-line treatment and advise caution with opioid prescribing. However pharmacological therapies, including opioids and gabapentinoids, remain commonly used. The comparative risks of subsequent opioid use disorder (OUD) and overdose diagnosis associated with initial treatment modality in large, real-world populations is not well characterized. We estimated the incidence of new-onset OUD and overdose diagnosis among opioid-naive, Medicaid-insured adults with newly diagnosed acute low back pain and estimated the association between initial treatment modalities and subsequent OUD and overdose diagnosis risk. Methods: We conducted a retrospective cohort study using Medicaid T-MSIS Analytic files from 25 states (2016-2019). We identified opioid-naive adults with a new diagnosis of acute low back pain who initiated pharmacologic or non-pharmacologic treatment within 1 month of diagnosis. The primary outcome was incident OUD and overdose diagnosis (based on diagnosis codes in claims) during follow-up. Associations between initial treatment modality and OUD and overdose diagnosis risk were estimated using a non-parametric, doubly robust estimator to adjust for measured confounding. Results: The cohort included 525,002 opioid-naive adults initiating treatment for low back pain. The cumulative incidence of OUD and overdose diagnosis was 1.5% and 2.4% at 7 and 13 months, respectively. Compared to non-use, use of gabapentinoids during the first month of treatment was associated with the highest relative risk (increasing risk) by 130.1%, 95% confidence interval (CI): 117.8%, 142.3%), the second-highest relative risk was estimated for higher-dose opioids, defined as > 50 daily Morphine Milligram Equivalents (MME) (118.1%, 95% CI: 99.2%, 137.0%). Lower-dose, short-duration opioids ([&le;] 50 MME, [&le;] 7 days) were also associated with elevated risk, though substantially smaller in magnitude (20.8%, 95% CI: 13.8%, 27.9%). In contrast, non-pharmacologic, non-interventional therapies were associated with reduced OUD and overdose diagnosis risk, with physical therapy demonstrating the largest relative reduction of 34.0% (95% CI: -40.9%, -27.1%). Discussion: In opioid-naive Medicaid patients with acute low back pain, initial non-pharmacologic treatment was associated with reduced OUD and overdose diagnosis risk. Gabapentinoids and opioids were each associated with increased risk; for opioids, the degree of risk increased with higher doses and durations. These results support guideline recommendations favoring non-pharmacologic treatment as first-line therapy and indicate the importance of cautious prescribing when pharmacologic treatment is considered.

Repeated low-dose psilocybin is being developed as a scalable outpatient treatment for mood and anxiety disorders, but chronic exposure raises concern because psilocin binds the cardiac serotonin 5-HT2B receptor, whose sustained agonism causes drug-induced valvular heart disease (VHD). We evaluated this risk using an exposure-response model that incorporates functional efficacy and exposure duration rather than binding affinity alone. Plasma psilocin concentrations were converted into the time-integrated increment in 5-HT2B Gq signaling above endogenous serotonergic tone ({Delta}TIA) and calibrated against drugs and conditions with known valvular outcomes. All modeled exposures known to cause human VHD scored {Delta}TIA [&ge;] +172 %{middle dot}h/day, whereas exposures not associated with VHD scored [&le;] +28. A candidate 3 mg daily psilocybin regimen scored {Delta}TIA +3, roughly two orders of magnitude below the weakest valvulopathic exposure. This safety margin arises from psilocins low-efficacy partial agonism at 5-HT2B (Emax {approx}51.8% of serotonin, compared with 96% for norfenfluramine) and its short half-life ({approx} 2.5 h), which prevents accumulation and produces brief daily receptor engagement. In support of the model, rats receiving continuous psilocin for 12 days at plasma concentrations {approx}2.4-fold above the projected human peak for 3 mg daily psilocybin showed no valvular lesions by blinded histopathology. This exposure duration however cannot exclude slowly developing fibrosis. Emerging human data, including serial echocardiography in repeated LSD microdosing and a large observational cohort, are also agreement with the model. Collectively, these findings suggest a favorable safety margin for daily, sub-hallucinogenic psilocybin use in clinical indications. Nevertheless, continued pharmacological and clinical investigations should include prospective echocardiographic monitoring to advance the clinical safety profile of sub-hallucinogenic psilocybin and support its evaluation across a broad array of therapeutic programs. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=54 SRC="FIGDIR/small/739440v1_ufig1.gif" ALT="Figure 1"> View larger version (10K): org.highwire.dtl.DTLVardef@1ef0508org.highwire.dtl.DTLVardef@1337d52org.highwire.dtl.DTLVardef@1689943org.highwire.dtl.DTLVardef@261605_HPS_FORMAT_FIGEXP M_FIG C_FIG Three key determinants of cardiac safety margins for repeated low-dose psilocybin are shown. Psilocin is a low-efficacy partial agonist at 5-HT2B (ceiling {approx}52% vs 96% for norfenfluramine; left). Its short half-life yields a brief daily pulse of receptor engagement rather than a sustained plateau (center). The resulting integrated 5-HT2B signal ({Delta}TIA) at 3 mg daily lies roughly two orders of magnitude below valvulopathic exposures, and continuous in vivo exposure produced no valvulopathy (right).

BACKGROUND. In patients with venous thromboembolism (VTE), renal impairment increases the risks for both recurrence and bleeding. Because these patients are underrepresented in clinical trials, we assessed whether standard direct factor Xa inhibitor (DXI) lead-in followed by early dose reduction was noninferior to standard anticoagulation in patients with acute VTE and moderate-to-severe renal impairment. METHODS. The VERDICT trial was a randomized, prospective, multicenter, open-label, blinded-endpoint, noninferiority trial. Consecutive patients with acute proximal deep-vein thrombosis or pulmonary embolism and chronic renal impairment (creatinine clearance 15?50 mL/min) were randomized 1:1 to an early DXI dose reduction strategy or standard therapy (heparin plus a vitamin K antagonist). Patients allocated to the DXI strategy underwent a second 1:1 randomization to apixaban or rivaroxaban, each administered at an initial standard lead-in dose followed by early dose reduction. The primary outcome was net clinical benefit at 3 months, defined as the composite of major bleeding and symptomatic recurrent VTE. RESULTS. Due to slow recruitment, the trial was prematurely terminated after enrolling 200 of the planned 800 patients (DXI: n=104; standard: n=96). The median age was 85.9 years, 31% were male, and 29.0% had severe renal impairment. The primary outcome occurred in 8 patients (7.7%) in the DXI group and 9 patients (9.3%) in the standard therapy group (adjusted subhazard ratio [sHR], 0.87; 95% CI, 0.26 to 2.87; P = 0.19 for noninferiority; noninferiority margin 1.30). Major bleeding occurred in 6.7% and 6.2% of patients and recurrent VTE occurred in 1.0% and 3.1% of patients, respectively. CONCLUSION. In patients with VTE and moderate-to-severe renal impairment, noninferiority of an early DXI dose-reduction strategy versus standard therapy could not be demonstrated for net clinical benefit. Although no major differences in efficacy or safety outcomes were observed between groups, the reduced sample size precludes definitive conclusions.

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

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.

mRNA-based therapeutics have emerged as a transformative class of medicines, yet their translation beyond infectious disease vaccines remains challenged by the absence of an integrated pharmacological framework accounting for the tri-component nature of these therapies - the lipid nanoparticle, the mRNA, and the expressed protein. Here, we present a modular, multiscale computational platform integrating two complementary mechanistic models covering the full pharmacological cascade of mRNA-based immunotherapies. The first is a Quantitative Systems Pharmacology (QSP) model describing the immunological response to mRNA vaccines, from antigen expression in antigen-presenting cells through B cell activation and circulating antibody production. The second is a Physiologically Based Pharmacokinetic (PBPK) model tracking whole-body disposition of mRNA-encoded therapeutic antibodies, incorporating a molecular layer resolving LNP uptake, endosomal mRNA escape, and intracellular translation. Both models are informed by a machine learning pipeline that maps IVT-mRNA nucleotide sequences directly onto kinetic parameters, enabling product-specific model simulations. We propose this platform as a step toward the quantitative pharmacological framework that mRNA therapeutics currently lack, and as a practical tool for model-informed design and development of this therapeutic class.