Clinical Pharmacology & Therapeutics

A priori model informed precision dosing (MIPD) recommends an appropriate first dose based solely on the patients covariates enabling faster target attainment without required concentration measurements. Population pharmacokinetic model ensembling and machine learning (ML) approaches were developed and evaluated to predict a first dose of amoxicillin in intensive care. Following a bibliographic review, a virtual patient population was simulated based on cohorts from four published adult amoxicillin PopPK models. Model-development cohorts were reproduced, and steady-state trough concentrations were simulated using cohort-specific dosing regimens. As reference methods, weighted model ensembling (WME) and classification tree (CT)-informed ensembling were implemented. Two novel ensembling strategies were developed: regression tree (RT)-informed ensembling, using RT to predict the log individual prediction/observation ratio, and factor analysis of mixed data (FAMD), assigning model weights based on patient similarity to original model cohorts. In parallel, four ML algorithms (support vector machine, k-nearest neighbors, random forest, and XGBoost) were trained to predict the dose achieving target concentrations based on covariates and dosing scheme. All approaches were compared with single-model PopPK dosing, standard dosing, and a nomogram, and externally validated using clinical data. Most MIPD methods outperformed standard dosing. On simulated data, ensembling (30-42 % correct predictions) and ML (36-39 %) exceeded single-model approaches (14-32 %). RT-informed and FAMD ensembling improved performance by 6-10 % over uninformed ensembling on clinical data. In clinical patients receiving continuous infusion, ensembling further improved performance, with FAMD achieving 49 % correct predictions. ML-based ensembling eliminates the need for model selection and increase target attainment.

Background: Cysteamine is the only disease-modifying therapy for nephropathic cystinosis and has shown promise in mitochondrial disorders, but its clinical utility is limited by poor tolerability due to high peak concentrations with existing formulations. TTI-0102 is a novel natural controlled-release cysteamine prodrug designed to provide sustained cysteamine exposure with improved tolerability. Methods: A multi-center, randomized, single-blind, placebo-controlled Phase 2 trial enrolled 9 patients with MELAS syndrome caused by mtDNA m.3243A>G mutation (>50% heteroplasmy) and moderate disease severity (NMDAS score 15-45). Patients received placebo (n=3) or TTI-0102 at 2.75 g/day for one week then 5.5 g/day (n=6, equivalent to 2.5 g/day cysteamine base). Pharmacokinetic parameters, safety, and pharmacodynamic biomarkers including pyruvate, taurine, pantothenic acid, tryptophan, GSH/GSSG, lactate, GDF-15, and FGF-21 were assessed. Clinical efficacy was evaluated using the Modified Fatigue Impact Scale (MFIS) and 12-minute walk test. Results: TTI-0102 demonstrated expected gastrointestinal side effects (nausea, vomiting, diarrhea) consistent with the cysteamine class, with dropout occurring in patients 50 kg receiving fixed 5.5 g/day dosing. Weight-based dosing at 60 {+/-} 5 mg/kg TTI-0102 (~26 mg/kg cysteamine base equivalent) achieved sustained 24-hour cysteamine exposure with half the daily dose and peak concentrations lower than expected by dose proportionality, compared to approved formulations (Procysbi: 56 mg/kg, peak 2.5 mg/L vs. TTI-0102: 26 mg/kg, peak ~2 mg/L). TTI-0102 significantly elevated pantothenic acid (plateauing at 2 weeks) and taurine levels, providing mitochondrial cofactor support and antioxidant effects. Statistically significant pharmacodynamic effects included increased plasma pyruvate (p=0.03) without lactate elevation, suggesting enhanced glycolytic flux, and decreased tryptophan (p<0.01), potentially reducing oxidative stress from neurotoxic kynurenine pathway metabolites. Interestingly, increase in plasma pyruvate and decrease in tryptophan were negligible at doses up to 40 mg/kg/day, optimal at 60 mg/kg/day, and slightly less at 65 mg/kg/day. GSH/GSSG measurements were confounded by sample stability issues. GDF-15, FGF-21, and 12-minute walk distance showed no treatment-related changes. Most notably, MFIS total scores demonstrated significant improvement in TTI-0102-treated patients at 60 mg/kg/day average dose compared to placebo (p=0.04). Polynomial regression revealed therapeutic onset at ~4 weeks, maximal benefit at ~12 weeks, and subsequent plateau. Conclusions: This Phase 2 trial provides proof-of-concept that TTI-0102 is safe and well-tolerated in MELAS patients while treated with less than 65 mg/kg/day, with efficacy signals in fatigue reduction, a cardinal symptom affecting 71-100% of mitochondrial disease patients. The drug tri-faceted mechanism through sustained cysteamine, taurine, and pantothenic acid delivery addresses oxidative stress, mitochondrial energy metabolism, and cofactor deficiency. Significant MFIS improvement coupled with favorable modulation of pyruvate and tryptophan supports advancing TTI-0102 to larger Phase 2b/3 trials in mitochondrial disease employing weight-based dosing (60 {+/-} 5 mg/kg), validated patient-reported outcomes, and minimum 12-week treatment duration. The same mechanism of cysteamine/cystine thiol-disulfide exchange in lysosomes that may benefit mitochondrial diseases also supports cystinosis treatment. An investigator-initiated study in cystinosis will evaluate whether once-daily TTI-0102 at 60 {+/-} 5 mg/kg can maintain therapeutic WBC cystine levels, potentially offering improved adherence and quality of life compared to current twice-daily or four-times-daily regimens, and this weight-adjusted dosing strategy and pharmacodynamic biomarkers identified in the MELAS study are going to be used to inform the design of the planned Phase 2 study in Leigh syndrome, another mitochondrial disorder, in collaboration with the Childrens Hospital of Philadelphia (CHOP), with particular attention to dose optimization and biomarker-based assessment of pharmacological activity. Acknowledgement: We are very thankful to the patients and the clinical teams of Radboud University Nijmegen Medical Centre (Netherlands) and Centre Hospitalier Universitaire d'Angers (France) for their participation in this operationally challenging study.

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

Background and AimsPropionic acidemia (PA) is a rare autosomal recessive disorder caused by mutations in PCCA or PCCB, which encode the two subunits of propionyl-CoA carboxylase (PCC). PCC deficiency causes toxic metabolite accumulation and multi-organ damage. Current management, including dietary restriction, pharmacological support, and liver transplantation, does not restore enzymatic activity. We developed a dual-gene adeno-associated virus (AAV) therapy that delivers both PCC subunits to treat both PA subtypes. MethodsWe generated a clinically relevant PCCA-R73W knock-in mouse model and administered AAV8 vectors encoding native human PCCA and PCCB under the control of a liver-specific thyroxine-binding globulin promoter (AAV8-TBG-hPCCA-P2A-hPCCB). Metabolite levels and organ safety were longitudinally assessed. ResultsDual-gene therapy produced dose-dependent reductions in plasma C3/C2 ratio, 3-hydroxypropionic acid, 2-methylcitric acid, and propionylglycine, and significantly outperformed single-gene (PCCA-only) therapy. Neonatal facial-vein injection achieved metabolic correction comparable to or better than adult treatment. The longitudinal follow-up revealed sustained efficacy over a 16-week period, with no signs of hepatotoxicity or adverse effects. ConclusionsSingle-dose, dual-gene AAV therapy achieves sustained metabolic correction and demonstrates long-term safety in a clinically relevant PA model, supporting its translational potential for both type I and type II propionic acidemia.

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

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

BackgroundReliable population pharmacokinetic (PopPK) parameter estimation can be compromised by outliers under Gaussian residual error models. A common mitigation strategy is post hoc filtering based on conditional weighted residuals (CWRES); however, this approach can be insensitive due to model "masking" driven by variance inflation. Practical barriers to implementing robust likelihoods in standard software have motivated interest in computationally simpler exponential-tail alternatives such as the Laplace and exponential power distribution (EPD). MethodsWe implemented a one-compartment PopPK model using a custom likelihood workaround in Monolix to benchmark four residual error distributions: Normal, Laplace, Generalized Error Distribution (GED), and Students t. We assessed CWRES sensitivity under extreme contamination and compared estimation performance using theoretical tail-behavior analysis, controlled simulation studies spanning multiple contamination severities, and a real-world caffeine PK case study with influential terminal-phase deviations. ResultsSimulations showed that CWRES-based diagnostics can be unreliable: extreme outliers frequently produced |CWRES| < 6 because the Normal model inflated residual variance, thereby masking contamination. Exponential-tail models (Laplace, GED) improved robustness for mild to moderate outliers but failed under extreme deviations due to insufficiently heavy tails compared to power-law decay. In contrast, the Students t model, via power-law tail behavior, maintained stable and minimally biased structural parameter estimates across contamination scenarios. Consistent patterns were observed in the caffeine case study, where the Students t model provided improved fit and physiologically plausible parameter estimates. ConclusionsCWRES-driven outlier handling is methodologically fragile because influential contamination can be masked by variance inflation and induce biased inference. Among robust residual error models, exponential-tail distributions may be insufficient for extreme outliers, whereas the Students t distribution provides more stable inference across contamination severities. These findings support adopting Students t residual modeling as a default robust option in routine PopPK workflows when outlier contamination is plausible.

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

BackgroundCompounded versions of tirzepatide are widely available in the U.S. in the form of fixed-dose combinations of tirzepatide and various analogs of vitamin B12. These combinations are mass marketed in the U.S. and other countries as comparable to FDA-approved tirzepatide products even though they undergo no evaluation of their potency or impurity profiles. Research Design and MethodsSamples of compounded tirzepatide combined with B12 obtained from various sources in the U.S. market were tested using various analytical methods. Samples were assessed for unacceptable levels of peptide-related impurities. ResultsOur testing identified a widespread and previously unidentified impurity in compounded tirzepatide-B12 products resulting from a chemical reaction between tirzepatide and certain analogs of B12. ConclusionDespite the presence of this impurity, these products continue to be mass marketed as "personalized" treatments. Our findings underscore the importance of testing and FDA approval before new drugs are marketed and highlights potential risks for patients associated with untested combinations. A novel impurity, present at substantial levels in compounded tirzepatide/B12 products, highlights risks inherent in marketing complex therapies outside the drug-approval framework. Although clinical effects of this impurity are unknown, the identification of a widespread impurity adds to the existing quality concerns presented by compounded tirzepatide.

1.Pharmacokinetic studies in critically ill patients are often constrained by small sample sizes, limiting the strength and generalizability of conclusions drawn solely from observed data. Bayesian inference offers a powerful strategy to address this challenge by incorporating prior knowledge. In this study, we evaluated two model-based approaches for characterizing the population pharmacokinetics of ceftolozane and tazobactam in critically ill patients, comparing nonlinear mixed-effects modeling with Bayesian hierarchical analyses. The Bayesian methods incorporated literature-derived prior information. The data was collected from 13 critically ill patients receiving 3.0 g of ceftolozane combined with tazobactam (2:1) via intravenous infusion. Pharmacokinetic modeling was performed using NONMEM and Stan software with the Torsten extension. Model diagnostics and graphical analyses were conducted in RStudio with relevant packages. In the absence of prior information, a one-compartment model with a limited set of parameters describing inter-individual variability adequately characterized the pharmacokinetics of ceftolozane and tazobactam. When prior information was incorporated, a two-compartment model became feasible and yielded a characterization of parameter variability and correlations that was more consistent with published literature. The application of Bayesian inference ensured alignment with existing literature on ceftolozane and tazobactam pharmacokinetics and mitigated some systematic biases observed in the data-driven approaches. Moreover, the Bayesian approach enables direct decision-making by incorporating uncertainty into the analysis, as demonstrated by probability of target attainment analysis. Collectively, these results underscore the utility of Bayesian methods in pharmacokinetic modeling for critically ill patients, offering a robust framework for optimizing dosing strategies in data-limited settings.

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.

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

Background Plasma gelsolin (pGSN) is a non-immunosuppressive anti-inflammatory immunomodulator with demonstrated efficacy in animal models of acute lung injury. Its potential role in moderate-to-severe acute respiratory distress syndrome (ARDS) is currently under investigation. Methods We conducted a phase 1, randomized, double-blind, placebo-controlled study to evaluate the safety, tolerability, and pharmacokinetics of recombinant human pGSN (rhu-pGSN) following intravenous (IV) administration to healthy volunteers. Thirty-two participants were assigned to 4 sequentially ascending dose cohorts (6, 12, 18, 24 mg/kg of body weight) to receive five IV infusions of rhu-pGSN or saline placebo. Each cohort includes 8 subjects randomized 3:1 with rhu-pGSN or placebo. Doses were administered at 0 hours, 12 hours, 36 hours, 60 hours, and 84 hours. The primary outcome is the incidence and severity of clinical and laboratory AEs regardless of causality. Secondary outcomes include the pharmacokinetics of IV rhu-pGSN and the presence of anti-rhu-pGSN antibodies at Day 28. Results Overall, 10 subjects (41.7%) who received rhu-pGSN reported a total of 13 adverse events (AEs), and 1 subject (12.5%) who received placebo reported an AE. All AEs were mild or moderate. AEs in system organ classes that were reported by 2 or more subjects in either arm were skin and subcutaneous tissue disorders (12.5% rhu-pGSN; 0% placebo), gastrointestinal disorders (8.3% rhu-pGSN; 0% placebo), and nervous system disorders (12.5% rhu-pGSN; 12.5% placebo). No AEs by preferred term were reported by more than 1 subject in either arm. Three subjects (12.5%) experienced an AE assessed as related to study drug. No serious AEs occurred, and no AEs led to study discontinuation, dose interruption/reduction, or death. There were no apparent between-treatment differences in laboratory abnormalities, vital signs, or electrocardiogram findings. Conclusions Overall, in this study, IV rhu-pGSN (up to 24 mg/kg daily) appeared safe and well tolerated compared to placebo. The median half-life of rhu-pGSN exceeded 14 h across all dosing regimens, supporting once daily IV dosing in healthy subjects. Trial registration This study was registered with ClinicalTrials.gov on 2023-03-29 under the registration identifier NCT05789745.

BACKGROUNDSotalol loading intravenously enables achieving blood levels of sotalol that are observed at maximal steady-state concentration (Cmax ss) in one-day permitting the measurement of maximum QTc effects. Rapid evaluation of the QTc effects permits determination of arrhythmic risk and thus permits discharge in 24-hours instead of the usual three-day oral load hospitalization. Given the expense of IV Sotalol an oral loading test strategy is presented that also achieves Cmax ss blood levels rapidly, permitting a one-day hospitalization for QTc evaluation. METHODPharmacokinetic parameters referred to in the literature derived from normals as well as patients was utilized for population pharmacokinetic modeling and simulation.to obtain the Cmax ss concentrations for patients with normal renal function, creatinine clearance (CrCl) > 90 ml/min), as well as for patients with a CrCl of 60-89, 30-59, and 10-29 ml/min). Using pharmacokinetic simulations, an oral loading dose, as well as a second oral dose were determined that would reach the estimated Cmax ss in each of the groups based on renal function. RESULTSFor target dosing of 120 mg oral sotalol BID in patients with a CrCl >90 ml/min an oral loading dose of 200 mg provides a peak sotalol level of 1,420 ng/ml in 3-4 hours post dosing. The Cmax ss target is 1,299 ng/ml resulting in a 9% overshoot. The Cmax ss concentration provides a means of evaluating QTc effects within 24-hours. Oral loading regimens are described for varying additional renal function levels (CrCl 60-90, 30-59 and 10-29 ml/min) along with the time to first oral dose and follow-up dosing. The initial test dose can be based on an 80 or 120 mg oral sotalol maintenance dosing strategy. CONCLUSIONSEmploying an oral loading strategy may permit QTc evaluation and one-day discharge, preserving the pharmacoeconomic advantage of a Cmax ss test strategy. Clinical PerspectiveO_ST_ABSWhat is Known?C_ST_ABSO_LIIntravenously loading of sotalol enables achieving blood levels that are observed at maximal steady-state concentration (Cmax ss) in one-day permitting the measurement of maximum QTc effects. C_LIO_LIRapid evaluation of the QTc effects permits determination of arrhythmic risk and thus permits discharge in 24-hours instead of the usual three-day oral load hospitalization C_LI What the Study AddsO_LIWith oral sotalol loading, the Cmax ss can also be achieved in one-day permitting the measurement of maximum QTc effects and discharge from the hospital in 24-hours instead of the usual three-day inpatient initiation of oral sotalol. C_LI

Long-acting cabotegravir and rilpivirine combination (LA-CAB/RPV) is approved for HIV treatment whilst long-acting cabotegravir alone (LA-CAB) is approved for HIV prevention, both in adults. However, individuals who become pregnant might prefer to discontinue it due to lack of definitive data on safety. The aim of this study was to characterise the tail-phase maternal and fetal pharmacokinetics of LA-CAB/RPV following discontinuation at steady-state early in pregnancy. A virtual population of non-pregnant women (n = 100 per scenario) initiated intramuscular injections of LA-CAB/RPV at the approved dosage and continued maintenance dose (400/600 mg once monthly or 600/900 mg once every two months) until steady state. We simulated discontinuation at steady state after only one injection during pregnancy. Tail-phase pharmacokinetics of CAB and RPV from LA injections were characterised during gestation and until 6 months postpartum. Pharmacokinetic tails of LA-CAB/RPV were driven by the residual drug in the muscle depot which stabilised at steady state and reduced steadily upon dosing discontinuation. Upon discontinuation of the monthly dosing, predicted median (IQR) maternal plasma concentrations for LA-CAB were 415 (386-448) ng/mL at delivery and 125 (115-139) ng/mL 6 months postpartum. For LA RPV, these were 11.6 (11.0-12.6) ng/mL and 7.84 (7.30-8.49) ng/mL at delivery and 6 months postpartum, respectively. Pharmacokinetic tails of LA-CAB/RPV extend to several months postpartum, with levels falling below established minimum effective concentration in most women after gestation week 33. Potential strategies to minimise potential risks associated with LA-CAB/RPV discontinuation in this population are needed.

In the management of atrial fibrillation, the most frequently prescribed oral anticoagulant is apixaban, given at a fixed dose of 5mg BID. Apixaban is predominantly metabolized by cytochrome P4503A4 (CYP3A4) and is also a substrate for the drug efflux transporter P-glycoprotein (P-gp). In nearly 300,000 Medicare patients with AF receiving apixaban, we previously showed that concomitant therapy with drugs that inhibit both CYP3A4 and P-gp, specifically amiodarone or diltiazem, significantly increased serious bleeding that caused hospitalization and/or death. We hypothesized that this adverse effect was mediated by an increase in apixaban plasma concentrations caused by concomitant therapy that reduced drug elimination. Utilizing left-over samples obtained from clinically indicated blood draws that would typically be discarded, the Vanderbilt University Medical Center biobank BioVU contains >353,000 samples linked to de-identified electronic medical records (EMRs), with both DNA and plasma harvested. Of 35 samples drawn from patients taking apixaban 5mg BID, 5 were identified to be drawn from patients concomitantly taking drugs inhibiting both CYP3A4 and P-gp. Using a chromogenic anti-Xa assay, we found that plasma concentrations of apixaban were significantly higher (347{+/-}64 ng/mL; mean{+/-}SEM) for patients receiving concomitant CYP3A4/P-gp-inhibiting drugs compared to those not treated with these drugs (166{+/-}67 ng/mL; P=0.025, Mann Whitney). There were no differences between the 2 patient groups with respect to age, weight, or serum creatinine. The results of this pilot study provide preliminary data to support our hypothesis, and they demonstrate the practicality of obtaining pharmacokinetic data from a large cohort of plasma samples linked to deidentified EMRs. This approach could be used to define the role of apixaban levels in high-risk clinical scenarios and to better understand the relationship between drug levels and bleeding risk.

BackgroundAnthracyclines such as doxorubicin are effective chemotherapeutics but are limited by cardiotoxicity driven in part by mitochondrial dysfunction. Dysregulated mitochondrial dynamics, particularly excessive dynamin-related protein-1 (Drp1)-mediated fission, contribute to doxorubicin-induced cardiac injury and support selective survival of cancer cells. ObjectivesTo determine whether DRP1i2, a novel small molecule Drp1 inhibitor targeting a conserved domain shared between human and mouse, can function as a cardio-oncology therapeutic by reducing doxorubicin-induced cardiotoxicity while maintaining or enhancing anti-cancer efficacy. MethodsCardioprotective effects of DRP1i2 were evaluated in a murine model of chronic doxorubicin cardiotoxicity and in human induced pluripotent stem cell-derived cardiac microtissues exposed to acute doxorubicin injury. Anticancer activity was assessed across multiple cancer cell lines using 2D monolayers and 3D microtissues. ResultsIn vivo, DRP1i2 preserved left ventricular ejection fraction, reduced interstitial fibrosis and cardiomyocyte atrophy, and attenuated doxorubicin-induced myocardial proteomic remodelling. In human cardiac microtissues, DRP1i2 improved viability and restored contractile function despite persistent mitochondrial oxidative stress. DRP1i2 showed modest anticancer activity in MG63 osteosarcoma cells in both 2D and 3D systems and did not diminish doxorubicin efficacy in other cancer models (MDA-MB-231 breast, OVCAR3 ovarian, and A549 lung adenocarcinoma). Combined treatment further enhanced cytotoxicity selectively in MG63 cells. ConclusionsDRP1i2 exerts complementary cardioprotective and anticancer actions through modulation of shared mitochondrial pathways, identifying Drp1 as a druggable target in cardio-oncology. These findings support DRP1i2 as a first-in-class Drp1 inhibitor and highlight mitochondrial dynamics as a promising therapeutic axis to preserve anthracycline efficacy while reducing cardiotoxicity. Clinical PerspectivesExcessive Drp1-mediated mitochondrial fission links anthracycline cardiotoxicity with cancer cell survival. Inhibition with DRP1i2 preserved cardiac structure and function in a chronic doxorubicin cardiotoxicity model without compromising anti-cancer activity, representing mechanism-based cardioprotection, where the heart is protected by directly targeting the molecular processes driving injury. Translation will require pharmacologic profiling and testing in tumour-bearing and comorbid models, followed by early-phase trials to confirm safety and efficacy.

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

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

The current investigation introduces single-cell physiologically-based pharmacokinetic (scPBPK) models to gain insight into drug disposition at the cellular scale. The transition from standard PBPK (sPBPK) models to scPBPK models required depiction of expression-dependent (ED) processes, such as drug metabolism or membrane transport. ED processes utilize weighting functions - a defined or data-driven distribution -that yield heterogeneity in individual cell kinetics. Two scPBPK model examples are provided, one involving a drug (AZD1775) subject to 3 ED blood-brain barrier transport processes, and another drug (midazolam) with a single ED process of metabolism by hepatocytes. For both examples, the weighting function for each ED process was defined by a negative binomial distribution that is often used in scRNAseq analytics. The AZD1775 model simulations indicated a large degree of single cell drug concentration heterogeneity, whereas those for midazolam did not, due to high membrane transport relative to metabolism. scPBPK models offer a means to probe cellular pharmacokinetics compatible with modern omic technologies and may be extended to pharmacodynamic models. TeaserThe modeling framework to predict drug concentrations in single cells is presented.