Clinical Pharmacology & Therapeutics

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.

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.

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.

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.

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.

Background: Adverse drug withdrawal events (ADWEs) are a key safety concern during deprescribing but remain poorly explored in pharmacovigilance systems. Objectives: To identify and compare ADWE signals across drug classes, different drugs within drug classes, and across patient characteristics, countries, and over time. Methods: A case/non-case disproportionality analysis was conducted in FDA-FAERS and EMA-EudraVigilance pharmacovigilance databases, with stratification by age (adults: 18-64, older adults: [&ge;]65), sex (male/female), reporting time (2004-2023 in 5-year intervals), and country (for EMA data). Disproportionality analysis (quantitative signal detection) was used to detect signals between ADWEs and drugs using the proportional reporting rate (PRR[&ge;]2), reporting odds ratio (ROR>1), and information component (IC>0) with case count [&ge;]5. Results: Overall, 158,501 reports (FDA-FAERS 145,514; EMA-EudraVigilance 12,987) included drug-event pairs related to ADWEs. In FDA-FAERS, clobetasone (IC=5.58; PRR=79.18; ROR=176.90) showed the strongest ADWE signals, followed by hydromorphone (4.85; 29.94; 37.37), hydrocodone, and paroxetine. In EMA-EudraVigilance, ethyl loflazepate (IC=6.01; PRR=119.80; ROR=197.53), clobetasone (5.39; 102.73; 155.10), veralipride, and levomethadone had the strongest signals. Most drugs maintained positive ADWE signals in analysis stratified into adults and older adults. However, among the top 10 drugs (based on highest IC values), buprenorphine/naloxone, desvenlafaxine, and baclofen in FDA-FAERS (ICs 4.95-6.05) showed stronger signals in older adults. A sex-based difference was observed, with paroxetine, venlafaxine, and buprenorphine/naloxone showing a stronger positive signal in females in both databases, whereas several opioids had stronger signals in males versus females across both databases. Conclusion: This study suggests ADWE signals for some medications differ by age and sex, potentially indicating different risks for withdrawal effects.

Background. Pharmacogenetic (PGx) testing can guide drug prescribing but remains limited by the genomic assay used. Genotyping arrays are widely implemented yet limited to predefined variants, whereas low-pass whole-genome sequencing (LP-WGS) is not constrained by fixed probe design and may provide broader PGx variant availability after imputation. Methods. We compared Illumina Global Screening Array (GSA) v3 with ~1x LP-WGS for PGx profiling in 500 hospital biobank participants with electronic health record evidence of exposure to pharmacogenetically actionable drugs and reported adverse drug reactions. Concordance was evaluated genome-wide, at 20 actionable pharmacogenes for PharmCAT-derived star alleles and metabolizer phenotypes, and for HLA alleles. Results. Genome-wide concordance between imputed array and LP-WGS data was high (median 99.63%; interquartile range, 99.59%-99.64%). For pharmacogenetically relevant variants, LP-WGS captured a larger fraction, particularly rare alleles absent from the array data, whilst maintaining high concordance at shared sites. Predicted phenotype concordance exceeded 98% for most genes, although gene-specific differences in phenotype classification were observed. LP-WGS reduced missing phenotype assignments for selected loci, particularly CYP2C19 and NAT2, by improving resolution of star-allele structure. However, in structurally complex or incompletely characterized genes such as CYP2C9 and CYP2D6, broader variant recovery increased indeterminate classifications rather than consistently improving clinical interpretability. For HLA loci, concordance varied by imputation strategy, with SNP2HLA performing marginally better utilizing the GSA array compared to the LP-WGS approach. Conclusions. Overall, LP-WGS provides broader variant coverage and improved resolution for selected pharmacogenes but did not resolve all clinically important loci. These findings support further evaluation of LP-WGS as a scalable PGx screening approach, especially where long-term genomic data reuse is a priority.

Importance: Immune checkpoint inhibitors (ICIs) produce diverse immune toxicities, but whether checkpoint blockade also modifies associations between other drugs and adverse events is poorly understood. Objective: To define ICI-associated toxicity organization and determine whether drug-associated adverse events and onset vary with ICI exposure and checkpoint pathway. Design and Setting: Cross-sectional analysis of deduplicated FAERS reports from 2016 through 2025; analyses performed in 2026. Participants: Among 13,701,106 deduplicated reports, 2,365,269 were cancer associated and 256,940 contained an ICI. Median age among cancer reports with observed age was 66 years (IQR, 56-75 years); 1,031,999 (43.6%) were female and 1,003,154 (42.4%) were male. Exposures: ICI exposure in any reported drug role, individual primary-suspect drugs, and checkpoint-pathway exposure. Main Outcomes and Measures: Reporting odds ratios (ORs), cross-organ adverse-event communities, adjusted primary-suspect drug x ICI interaction ORs for Stevens-Johnson syndrome/toxic epidermal necrolysis (SJS/TEN), drug reaction with eosinophilia and systemic symptoms (DRESS), acute generalized exanthematous pustulosis (AGEP), interstitial nephritis, drug-induced liver injury (DILI), and vomiting (VOM), and accelerated failure-time model time ratios for documented onset. Results: Of 3001 eligible Preferred Terms in cancer-associated reports, 2091 differed at a false discovery rate (FDR) less than .05. Four cross-organ toxicity communities were identified. Of 138 eligible drug-phenotype pairs, 65 had FDR-significant interactions, including moxifloxacin-SJS/TEN amplification (interaction OR, 101.72; 95% CI, 39.11-264.55), enfortumab vedotin-SJS/TEN attenuation (interaction OR, 0.17; 95% CI, 0.13-0.23), and omeprazole-interstitial nephritis amplification (interaction OR, 10.35; 95% CI, 7.62-14.05). Among 60,324 reports contributing to temporal analyses, ICI exposure was associated with longer adjusted documented time to onset for 5 of 6 phenotypes (time ratios, 1.37-1.59) but not AGEP (time ratio, 0.99; 95% CI, 0.67-1.46). Temporal associations also differed across checkpoint pathways. Conclusions and Relevance: ICIs were associated with a structured cross-organ toxicity landscape, phenotype-specific modification of drug-associated adverse events, and distinct temporal patterns across checkpoint pathways. These findings support checkpoint blockade as a modifier of drug-associated toxicity and motivate longitudinal and mechanistic validation.

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

Sub-Saharan African populations carry pharmacogenomic alleles poorly represented in the European-derived reference panels underlying most clinical genotyping tools. We present a curated, machine-readable catalog of nine actionable alleles across six pharmacogenes (CYP2D6, CYP2B6, CYP2C9, CYP2C19, CYP3A5, NAT2) with African-specific frequency ranges, functional annotations, and evidence levels derived from reanalysis of 661 high-coverage whole-genome sequences across seven 1000 Genomes Project African populations. Direct comparison against PharmCAT v3.4.0 shows that CYP2D6 produces zero diplotype calls (0/661 samples callable) due to monomorphic reference positions absent from standard variant-only VCF output, a known limitation whose consequences for African allele carriers had not been reported. afripharmagen's reduced-position strategy identifies 243 CYP2D617 and 134 CYP2D629 carriers from the same input. For CYP2B6, CYP2C9, CYP2C19, and NAT2, both tools show concordance of 95-100%. Frequency gradients (CYP2B66: 30-50%; CYP2D617: 15-35% in West Africa; CYP3A5*1: 60-95%) translate directly into prescribing risk for efavirenz, tramadol, tacrolimus, and isoniazid. Pharmacogenomic decision support in African settings must incorporate population-specific allele definitions and input-format-aware strategies.

Accurately predicting the effects of pharmacogenomic variants is essential for the development of personalized therapeutic strategies, as genetic variability can influence drug response differently across patients. Here, we assessed several computational approaches using a dataset of pharmacogenomic variants with either clinical annotations or functional characterization by deep mutational scanning, compiled from the literature, with an additional focus on CYP2C9, a clinically relevant drug-metabolizing enzyme. Our results show that, despite recent methodological advances, substantial room for improvement remains. In particular, current methods struggle to distinguish gain-of-function variants associated with increased drug clearance and fast-metabolizer phenotypes from neutral variants, whereas loss-of-function variants that reduce drug clearance are predicted more accurately. The integration of structural and evolutionary information appears to be a key strategy for improving performance, with the coevolution-based StructureDCA method achieving the highest accuracy compared with classical genetic variant-effect predictors and recent deep learning approaches, including the pathogenic-variant predictor AlphaMissense and general protein language model-based methods. Finally, our results indicate that computational models can complement in vitro experiments in clinical variant interpretation, as StructureDCA predictions showed better agreement with clinically annotated phenotypes than large-scale deep mutational scanning data in several cases.

Long-term in vivo transcriptomic time courses are costly, limiting assessment of chronic molecular responses from short studies. We developed a pharmacokinetics-informed transcriptomic ordinary differential equation model (PKT-ODE) that links an oral pharmacokinetic profile and Hill drug-effect function to first-order turnover of co-expression modules. The model was fitted to rat liver responses to fenofibrate at three doses in Open TG-GATEs through day 8. At the held-out day-29 endpoint, PKT-ODE achieved Pearson r = 0.960 and mean squared error (MSE) = 0.148. In this dataset, these values achieved lower prediction error and higher correlation than four statistical baselines and validation-selected linear and multilayer-perceptron transition models. Literature-curated peroxisome proliferator-activated receptor target genes occurred only in modules with positive fitted drug effects. These results provide a proof of concept for pharmacokinetics-informed transcriptomic extrapolation; cross-compound, cross-organ and alternative-regimen performance remain to be tested.

Background Treatment of Raynaud phenomenon(RP) with oral vasodilators(calcium channel block-ers and phosphodiesterase type 5 inhibitors) has shown moderate efficacy, may not benefit to all patients, and adverse effects often compromise long-term treatment. In addition, a large placebo effect may jeopardize the assessment of treatment benefits. Pharmaconutritional strategies aiming at increasing nitric oxide bioavailability (beet-root juice and L-citrulline) may be promising alternatives, and we further hypothesized that patient preference for a treatment could be a driver of the response. Methods This study consisted of a series of randomized, double-blind, N-of-1 trials conducted in outpa-tients with primary or secondary RP. Each patient underwent a multiple crossover design with repeated blocks of randomized treatments periods: 2 weeks of placebo, 2 weeks of active treat-ments, and 1 week of washout. Outcomes included the Raynaud Condition Score(RCS), fre-quency and daily duration of attacks. Each patient prespecified its preferred primary outcome, efficacy threshold and preferred treatment, which was used for stratified randomization. Gener-alized linear mixed-effects models were used to determine individual and aggregated efficacy. Results Twenty-one patients completed 2 to 8 treatment blocks. Seventeen patients tested L-citrulline, 17 beetroot juice and 13 both treatments. Ten patients selected RCS as a primary outcome, 6 patients the number of attacks and 5 the duration of attacks. Me-dian threshold for considering treatment efficacy chosen by patients was 50% (min-max 20% to 75%) reduction of symptoms. Using individual criteria to define efficacy neither L-citrulline nor beetroot juice showed significant efficacy compared to baseline. Based on the aggregated data, our results show no significant difference between L-citrulline and the L-citrulline-based placebo, nor between beetroot juice and nitrate-depleted beetroot juice, with the exception of the daily duration of RP attacks with beetroot juice (p=0.002). Finally, there was a marked placebo response, notably when patients received their preferred treatment. Conclusions: Our study did not show significant beetroot juice or L-citrulline efficacy in RP. However, we found that individual preference for one treatment over another maximizes responses to both placebo and active treatments, particularly with regard to the frequency and duration of RP attacks, thus suggesting that a real and modifiable placebo effect exists in RP.

Background: Polymorphisms in the N-acetyltransferase 2 (NAT2) gene explain much of the interindividual variation in isoniazid (INH) metabolism and determine risk of toxicities. However, there is limited evidence to guide INH dose adjustment according to the NAT2 acetylator profile in weekly rifapentine-INH tuberculosis preventive therapy (TPT). Methods: In a prospective, multicenter, within-subject PK trial (NCT05413551), adults initiating 3HP in Brazil were assigned genotype-guided INH doses (slow: 5 mg/kg <=300 mg; intermediate: 15 mg/kg <=900 mg; rapid: 25 mg/kg <=1,500 mg) alongside a standard 900 mg flat dose on an alternate occasion. AUC0-24 and C24 were estimated from serial blood samples; a two-compartment Michaelis-Menten population PK model characterized NAT2 effects on clearance. Results: Among 228 participants, 47.4% (108/228) were intermediate, 43.4% (99/228) slow, and 9.2% (21/228) rapid acetylators. Genotype-guided dosing reduced AUC0-24 variability approximately two-fold versus standard dosing (CV 58.8% vs 76.8%) and increased exposure uniformity (median AUC0-24 27.2 [IQR 18.8-41.3] vs 43.2 [27.3-71.0] mg h/L). Among slow acetylators, C24 >0.15 ug/mL decreased from 27/42 (64%) with standard dosing to 1/42 (2%) with genotype-guided dosing (P<0.0001). In 104 participants with intensive PK sampling, rapid acetylators receiving guided doses had AUC0-24 similar to standard-dose intermediate acetylators (42.8 vs 39.5 mg h/L; P=.63). Monte Carlo simulations supported doses of 600, 900, and 1,200 mg for slow, intermediate, and rapid acetylators, respectively. Conclusions: NAT2-guided isoniazid dosing reduced variation in drug levels, averting very low and high AUC and C24. These findings inform genotype-stratified dosing of INH for TPT, which might reduce toxicities and improve outcomes.

Objective: Erythropoietic protoporphyria (EPP) is a rare photodermatosis marked by multi-year diagnostic delays. We developed and externally validated machine learning models to identify patients with EPP earlier from longitudinal electronic health record (EHR) data and estimate undiagnosed disease burden. Materials and Methods: In a retrospective case-control study at two San Francisco health systems, an academic referral center (UCSF) and a safety-net hospital (ZSFG) we identified 74 confirmed EPP cases using combined diagnostic coding, biochemical criteria, and specialty chart review. Symptom-enriched controls were sampled at a 40:1 ratio. Longitudinal diagnoses, laboratory results, medications, procedures, and encounters preceding the outcome date were modeled with a gradient-boosting classifier (CatBoost) and a state-space sequence model (MAMBA). The best model was deployed across the UCSF population and externally validated at ZSFG without retraining. Results: On the UCSF held-out test set (n=1,865; 43 cases), MAMBA outperformed CatBoost (AUC ROC 0.91 vs 0.89; average precision 0.42 vs 0.27; precision 65% vs 20%), flagging cases a median of 229 days before documented diagnosis. Deployed across 297,967 symptom-compatible patients, it identified 310 high-risk individuals, implying a prevalence approaching genetic estimates. External validation at ZSFG showed attenuated performance (AUC ROC 0.72; average precision 0.10) while preserving early detection (median 264 days). Discussion: A sequence model integrating temporal EHR signals detected EPP months before clinical recognition, corroborating genetic evidence of substantial underdiagnosis. Cross-site attenuation reflects population and documentation differences and underscores the need for local recalibration. Conclusion: Longitudinal EHR-based machine learning can shorten EPP diagnostic delay and prioritize patients for confirmatory testing, supporting proactive rare-disease case finding.

Background: In Denmark, perioperative high-dose glucocorticoid treatment were step-wisely implemented for total hip arthroplasty (THA), total knee arthroplasty (TKA), and unicompartmental knee arthroplasty (UKA). We aimed to estimate the effect of a single high dose of glucocorticoids on opioid consumption following primary THA, TKA, and UKA. Methods: This was a prespecified analysis of a multicenter natural experiment using electronic health record data. We included elective THA, TKA, or UKA surgeries performed in Eastern Denmark from 2017-2025. At each center, surgeries before implementation of high-dose glucocorticoids served as controls, whereas surgeries after implementation comprised the intervention group. The primary outcome was the between-group difference in cumulative 0-24h opioid consumption, which included preemptive end-of-surgery doses. The predefined minimal important difference was set at 5 mg IV morphine equivalents. Secondary outcomes were maximum 0-10 numerical rating scale (NRS) pain score and incidence of opioid-related adverse events within 24 hours, hospital length of stay, and days alive and out of hospital at 30 days. Results: A total of 47,317 surgeries performed at nine centers were analyzed: 13,010 controls and 34,307 in the intervention group. During the study period, five centers implemented high-dose glucocorticoids for THA patients, two for TKA/UKA patients. High-dose glucocorticoids were administered to 6% of patients before implementation versus 92% after. High-dose glucocorticoids resulted in a mean reduction of 3.8 mg intravenous (IV) morphine equivalents (95% CI 3.3;4.3). The intervention also reduced the maximum 0-24h NRS pain score by 0.8 points (99% CI 0.7;0.9), but there was no difference in adverse events, length of stay, or days alive and out of hospital. Conclusions: Implementation of high-dose glucocorticoids reduced 0-24-hour opioid consumption by 3.8 mg IV morphine equivalents after elective hip and knee arthroplasty. This difference was below the prespecified minimal important difference threshold. Online registration: https://doi.org/10.1101/2025.11.11.25339982

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

Background: Parametric time-to-event models require specification of a baseline hazard function, which may influence prediction when the underlying hazard shape is uncertain. This study compared conventional joint longitudinal time-to-event models with mechanistic Multi-Task Logistic Regression, which directly models the survival distribution without selecting a continuous parametric hazard family. Methods: A simulated dataset of 100 individuals with longitudinal sum of longest diameters and event outcomes was analyzed using a shared mechanistic tumor shrinkage regrowth model. Event submodels comprised exponential, Gompertz, Weibull, log-normal, log-logistic, and circadian hazards, mechanistic Multi-Task Logistic Regression, and a hybrid neural-mechanistic extension. All models were estimated jointly using shared patient-specific random effects and longitudinal data. Models were evaluated using longitudinal goodness-of-fit, visual predictive checks, five-fold cross-validated inverse-probability-of-censoring-weighted dynamic area under the curve and Brier scores, integrated Brier score, calibration, and event-interval negative log score. Results: Longitudinal parameter estimates and diagnostics were comparable across models. All conventional hazard models produced identical dynamic area under the curve values within prediction windows, although probabilistic accuracy differed. The log-normal hazard achieved the lowest overall integrated Brier score (0.1928). Mechanistic Multi-Task Logistic Regression achieved the highest later landmark discrimination (area under the curve 0.867 versus 0.798 for all hazard models) and the lowest mean event-interval negative log score (2.362). The hybrid model improved intermediate-landmark discrimination but not overall probabilistic accuracy. Conclusions: Mechanistic Multi-Task Logistic Regression provided competitive joint time-to-event prediction while avoiding baseline hazard-family selection. It represents a practical complementary alternative to parametric hazard modeling, particularly when hazard shape is uncertain and dynamic discrimination is important.

Background: A dose finding trial evaluated safety and adherence for pre-cardiac surgery administration of sodium valproate. Integrated multi-omics analyses of myocardium were used to characterise mechanisms underlying the treatment effects. Methods: Adults undergoing cardiac surgery were randomised 1:1:1:1 with concealed allocation to no treatment (Controls), sodium valproate 15mg/kg/day for 1-2 weeks, 15mg/kg/day for 4-6 weeks, or 25mg/kg/day for 4-6 weeks pre-surgery. The primary analysis evaluated adherence and toxicity. Myocardial injury was defined by high sensitivity serum troponin at 24 hours post-surgery. Single-nucleus Assay for Transposase-Accessible Chromatin with sequencing (snATACseq) and single nuclei RNA sequencing (snRNAseq) of myocardial biopsies collected at surgery assessed treatment effects on chromatin accessibility and gene expression. Candidate mechanisms were validated in in vitro. Results: The analysis cohort included 42 participants enrolled between January 2020 and August 2024. Non-compliance (38%) was highest with longer and higher dosing. Sodium valproate 15mg/kg/day for 1-2 weeks had the highest levels of complete treatment adherence (70%), with 20% experiencing moderate/severe drug related adverse effects. An as-treated analyses demonstrated reductions in troponin release in participants receiving Valproate[&le;]14 days. Myocardial biopsies from trial participants demonstrated activation of hormetic p53 and Akt-GSK-3{beta} ferroptosis protection pathways. Treatment effects were not attributable to chromatin accessibility. Treatment >14 days resulted in a heart failure phenotype with suppression of ferroptosis protection pathways, endothelial mesenchymal transition, and increased myocardial injury. Conclusions: Sodium valproate 15mg/kg/day for [&le;]14 days pre-surgery is well tolerated in adults awaiting cardiac surgery. This treatment was associated with upregulation of ferroptosis protection pathways and reductions in myocardial injury.