Clinical Pharmacology & Therapeutics

Sotrovimab is a recombinant human monoclonal antibody that has been shown to prevent progression to hospitalization or death from severe disease in non-hospitalized high-risk patients with mild-to-moderate COVID-19 following either intravenous (IV) or intramuscular (IM) administration. Population pharmacokinetic (popPK) and exposure-response (ER) analyses were performed to characterize sotrovimab PK and the relationship between exposure and response (probability of progression), as well as covariates that may contribute to between-participant variability in sotrovimab PK and efficacy following IV or IM administration. Sotrovimab PK was described by a two-compartment model with linear elimination; IM absorption was characterized by a sigmoid absorption model. PopPK covariate analysis led to the addition of the effect of body weight on systemic clearance and peripheral volume of distribution, sex on IM bioavailability and first-order absorption rate (KA), and body mass index on KA. However, the magnitude of covariate effect was not pronounced and was therefore not expected to be clinically relevant based on available data to date. For ER analysis, sotrovimab exposure measures were predicted using the final popPK model. An ER model was developed using the exposure measure of sotrovimab concentration at 168 hours that described the relationship between exposure and probability of progression within the ER dataset for COMET-TAIL. The number of risk factors ([≤]1 vs >1) was incorporated as an additive shift on the model-estimated placebo response but had no impact on overall drug response. Limitations in the ER model may prevent generalization of these results to describe the sotrovimab exposure-progression relationship across SARS-COV-2 variants.

This study employed physiologically based pharmacokinetic (PBPK) modelling to compare the extent of fetal exposure between oral and long-acting injectable (LAI) aripiprazole and olanzapine. Adult and pregnancy PBPK models were developed and validated with relevant clinical data. Relevant indices of fetal exposure during pregnancy were predicted from concentration-time data at steady-state dosing for both oral and LAI formulations. Fetal Cmax for aripiprazole was 59-78% higher with LAI than oral, and 68-181% higher with LAI olanzapine than the oral formulation. Predicted C:M ratios (range) was 0.59-0.69 for oral aripiprazole and 0.61-0.66 for LAI aripiprazole, 0.34-0.64 for oral olanzapine and 0.89-0.96 for LAI olanzapine. Also, cumulative fetal exposure over 28 days from oral formulations were generally predicted to be lower compared with their therapeutic-equivalent LAI. As in utero fetal exposure to maternal drugs does not necessarily translate to risk, these data should be interpreted in a broader context that includes benefit-risk assessments.

Background: Mocravimod is an oral sphingosine-1-phosphate (S1P) receptor modulator. This Phase 1 multiple-ascending-dose study evaluated its safety, tolerability, pharmacokinetics (PK), and pharmacodynamics (PD) in healthy volunteers. Methods: In this double-blind, randomized, placebo-controlled, parallel-group trial, 60 healthy male volunteers were enrolled in five cohorts. Mocravimod was administered once daily at 0.3, 0.6, 1.2, or 3.0 mg for 14 days, or at 2.0 mg for 28 days. Safety assessments included adverse events (AEs), laboratory tests, vital signs, electrocardiography, and Holter monitoring. PK of mocravimod and its active metabolite, mocravimod-phosphate, and PD effects on absolute lymphocyte count (ALC) and leukocyte subsets were assessed. Results: Fifty-nine of 60 participants completed the study. One participant in the 3.0 mg cohort discontinued treatment because of asymptomatic transaminase elevation. No deaths or serious AEs occurred. AEs were mostly mild or moderate, transient, and showed no clear dose relationship. Mocravimod produced dose-dependent reductions in ALC from 0.6 mg onward, with maximum geometric mean reductions of 65%, 74%, 83%, and 77% at 0.6, 1.2, 2.0, and 3.0 mg, respectively. ALC values recovered to above the lower limit of normal during follow-up in all cohorts. Holter monitoring showed an initial placebo-corrected reduction in heart rate of approximately 10-15 beats/min at doses of 1.2-3.0 mg, which attenuated with continued dosing. One participant in the 3.0 mg cohort had a recurrent daytime second-degree atrioventricular block (Mobitz I/Wenckebach), reported as a mild non-dose-limiting AE. No QT prolongation was observed. Exposure to mocravimod and mocravimod-phosphate increased approximately dose-proportionally. Steady state was reached by Day 14 (Day 28 in the 2.0 mg cohort), accumulation was approximately five- to sevenfold, terminal half-lives were approximately 100-40 hours for both analytes, and parent-to-metabolite exposure ratios were close to 1. Conclusions: Once-daily mocravimod up to 3.0 mg for 14 days and 2.0 mg for 28 days was generally well tolerated and showed predictable S1P-modulator class effects on lymphocyte counts and heart rate, with PK properties supporting once-daily dosing and further clinical development.

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 Classical pharmacokinetic-pharmacodynamic (PK/PD) theory models exposure-effect in two dimensions: magnitude and time. Rate-dependent toxicity has been documented across therapeutic domains but never formalised as a conserved biological constraint. Methods We developed the Human Adaptive Rate Limit (HARL) framework, formalising the maximum tolerable velocity as |dS/dt|_max = sigma_max / tau. We validated HARL across five domains using published trial data and a reanalysis of the longitudinal biomarker data from the 202-patient CAR-T cohort of Wei et al (2023). An 8-ODE quantitative systems pharmacology model guided biomarker selection. Early biomarker velocities (maximum positive slope within days 0-5) were computed for ferritin and D-dimer. Patients were classified as high-risk only if both velocities exceeded their thresholds (dual-velocity classifier). Thresholds were identified by grid-search optimisation of the Youden index and assessed by leave-one-out cross-validation. Findings A prospective crossover study (Kleinbloesem 1987, n=8) demonstrated that matched steady-state nifedipine concentrations produce divergent haemodynamic responses depending solely on rate of rise, anticipating the dose-related mortality signal subsequently reported across ~8350 patients with coronary heart disease (Furberg 1995), a meta-analysis that was itself debated. Convergent evidence spans haematology (CHOIR, 1432 patients, hazard ratio [HR] 1.34 [1.03-1.74] for aggressive Hb correction), radiation (dose-rate effectiveness factor [DDREF] 1.5-2.0), and infusion pharmacology. In the CAR-T cohort, high-risk classification (ferritin >232 ng/mL per day AND D-dimer >1.21 mg/L per day) predicted severe CRS with 100% sensitivity (~78% specificity) in safety rule-out mode and 91.1% sensitivity (93.6% specificity, AUC 0.95 [95% CI 0.91-0.98]) in Youden-optimised mode. Median kinetic lead time was 4 days (range 3-7) before clinical decompensation. Interpretation Biological tolerability is three-dimensional. HARL unifies rate-dependent toxicity across domains spanning minutes to weeks. MTDyn--specifying target level and allowable rate of change--should supplement conventional dose-response assessment.

ObjectiveConventional pharmacodynamic (PD) modeling workflows require manual model selection, repeated equation rewriting, and empirical parameter adjustment, resulting in limited automation, high cross-scenario migration costs, and insufficient reproducibility. This study aims to develop PD Union, a unified, automated, and interpretable framework for mechanistic PD modeling. MethodsPD Union is built upon a unified continuous dynamical skeleton that organizes absorption and systemic exposure module, the receptor module, the drug input module, the first delay module, the primary pharmacodynamic function module, the primary pharmacodynamic state module, the downstream pharmacodynamic state module, the second delay module, the feedback module, the circadian modulation module, the biophase module, the direct effect module, the disease state module, the second PD axis first delay module, the second PD axis primary pharmacodynamic function module, the second PD axis primary pharmacodynamic state module, the second PD axis downstream pharmacodynamic state module, the second PD axis second delay module, and the second PD axis feedback module. A machine learning-based structure identification module is incorporated to recognize drug input modes and mechanism labels from population PK/PD time series, followed by constrained population parameter optimization, forming an integrated pipeline of structure identification, candidate generation, and parameter fitting. ResultsValidation was conducted at two levels. In standardized synthetic benchmarking across 14 representative single-endpoint scenarios, the structure identification model achieved an output mode accuracy(NRMSE) of 0.7600 and macro-average F1 of 0.6307; parameter fitting yielded an NRMSE mean of 0.146 and median of 0.117. In the unified reconstruction validation based on 15 population pharmacokinetics/pharmacodynamics (PK/PD) literature data, the mean NRMSE of PDUnion model for PD was 0.261, and the median was 0.228. Among the 15 studies, 14 performed better than the models provided in the original literature. ConclusionsPD Union demonstrates that interpretable mechanistic modularization combined with machine learning-assisted structure identification is feasible for automated PD modeling. The framework provides an executable methodological foundation for unified, reproducible, and extensible mechanistic PD modeling, with potential applicability to multi-endpoint and complex disease-state modeling scenarios.

AimsTo correlate potential links between the suspected adverse drug reaction (ADR) profile of licensed non-steroidal androgen receptor antagonists (NSARA) with their unique chemical properties and known off-target polypharmacology. MethodsPhysiochemical and polypharmacology data was curated from the Electronic Medicines Compendium, FDA New Drug Applications documents, and ChEMBL databases. System organ class (SOC, MedDRA) suspected ADRs and fatalities were curated from the United Kingdom Medicines and Healthcare products Regulatory Authority (MHRA) Yellow card spontaneous reporting scheme for their respective prescribing period; apalutamide (Jan 2019-), bicalutamide (Aug 2018-), enzalutamide (Aug 2018-), flutamide (Aug 2018-) and darolutamide (March 2019-) until Oct 2023. The number of daily doses (dd) was extracted from OpenPrescribing and NHS Digital secondary care medicines data. Data was standardised before comparison to suspected ADRs and fatality reports per 100,000 dd. ResultsA total of n = 2,480 suspected ADRs were associated with 42,903,000 dd of NSARAs in the United Kingdom. The highest number of ADRs were associated with enzalutamide (n = 1,091) and bicalutamide (n = 749). Enzalutamide was found to have the most off-target pharmacological interactions of the NSARAs studied (n = 5) including potent inhibition of {gamma}-aminobutyric acid, GABA receptor (IC50 = 2.6 {micro}M vs Cmax = 7.7 {micro}M) associated with nervous system disorders (n = 72, accounting for 73% of all NSARA ADRs in this SOC). Apalutamide, the only other GABA inhibitor (IC50 = 3 {micro}M vs Cmax = 2.9 {micro}M) had the highest relative rate of suspected nervous system ADRs at 1.08 per 100,000 dd. Apalutamide was also a modest inhibitor of the human Ether-a-go-go-Related Gene (hERG) ion channel (IC50 = 6 {micro}M vs Cmax = 2.9 {micro}M) and had the highest rate of suspected cardiac arrhythmia ADRs, 30-fold over, enzalutamide, a significantly weaker hERG inhibitor (15.7 {micro}M vs Cmax = 7.7 {micro}M). Darolutamide was the only NSARA to show effects at 5-HT (serotonin) receptor at < 10 {micro}M but did not translate to psychiatric disorders due to low clinical BBB penetration but a an association with hepatobiliary and cardiac disorders was identified based on this inhibitory axis. Suspected skin and subcutaneous SOC ADRs was associated with all NSARAs (except flutamide) but did not reach statistical significance (P = .25). A rationale for epidermis reactions relating to apalutamide containing a masked arylamine was explored but molecular matched pair (MMP) analysis with enzalutamide suggests it may not be a chemical cause. Statistical significance (P < .05) was identified in reported fatalities associated with NSARAs, flutamide had n = 24 or 897.5 fatalities per 100,000 dd which was likely due to both the indication and the small number of dd (n = 3,000) during the time period of the study. ConclusionsAn investigation of suspected ADRs, standardised to the number of dd for the novel NSARA drug class identified SOCs of potential interest. The highest number of reports related to enzalutamide and bicalutamide. Suspected skin and subcutaneous ADRs approached statistical significance and was interrogated for chemical and pharmacological connections for the first time with the aid of MMP analysis. A potential correlation to nervous system disorders and cardiac arrhythmia for the GABA and hERG inhibitors, enzalutamide and apalutamide, respectively was identified. Darolutamides interaction with 5-HT may influence ADRs associated with cardiac and hepatobiliary SOCs. Statistically significant number of suspected fatalities with flutamide was identified.

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.

Molecular quantitative trait loci (molQTL), which can provide functional evidence on the mechanisms underlying phenotype-genotype associations, are increasingly used in drug target validation and safety assessment. In particular, protein abundance QTLs (pQTLs) and gene expression QTLs (eQTLs) are the most commonly used for this purpose. However, questions remain on how to best consolidate results from pQTLs and eQTLs for target validation. In this study, we combined blood cell-derived eQTLs and plasma-derived pQTLs to form QTL pairs representing each gene and its product. We performed a series of enrichment analyses to identify features of QTL pairs that provide consistent evidence for drug targets based on the concordance of the direction of effect of the pQTL and eQTL. We repeated these analyses using eQTLs derived in 49 tissues. We found that 25-30% of blood-cell derived QTL pairs have discordant effects. The difference in tissues of origin for molecular markers contributes to, but is not likely a major source of, this observed discordance. Finally, druggable genes were as likely to have discordant QTL pairs as concordant. Our analyses suggest combining and consolidating evidence from pQTLs and eQTLs for drug target validation is crucial and should be done whenever possible, as many potential drug targets show discordance between the two molecular phenotypes that could be misleading if only one is considered. We also encourage investigating QTL tissue-specificity in target validation applications to help identify reasons for discordance and emphasise that concordance and discordance of QTL pairs across tissues are both informative in target validation.

Abstract Introduction The sharing of individual patient data is essential for advancing pharmacometrics but is strictly limited by privacy regulations (e.g., GDPR). While synthetic data generation offers a legally compliant alternative, its structural impact on complex nonlinear mixed-effects (NLME) modelling remains largely unexplored. This study aimed to benchmark five generative artificial intelligence algorithms by evaluating the balance between data privacy and the preservation of structural PK properties and clinical dosing guidance. Material & methods A daptomycin two-compartment PopPK model was used to simulate a reference cohort of 500 patients. Five generative algorithms (Modified AVATAR, Gaussian Copula, Synthpop, TVAE, and CTGAN) produced 100 independent synthetic datasets each. A two-stage evaluation framework was applied: first, a statistical indistinguishability test based on logistic regression (AUC ROC) was used as a macroscopic pre-selection criterion to determine algorithm eligibility for NLME modelling and privacy risk assessment. Privacy risk was independently quantified using the Anonymeter framework (Singling Out and Linkability attacks). Eligible algorithms were further evaluated on PK parameter recovery bias and clinical dosing simulations. Results Deep learning architectures (TVAE, CTGAN) were excluded at the pre-selection stage due to both biologically implausible covariate generation and high macroscopic detectability (mean AUC ROC = 0.837 and 0.986, respectively). Synthpop, AVATAR, and Gaussian Copula all passed the indistinguishability threshold (AUC ROC = 0.475 +- 0.033, 0.490 +- 0.013, and 0.619 +- 0.031, respectively) and proceeded to NLME evaluation. However, attack-based privacy assessment revealed that Synthpop carried an unacceptable singling-out risk (0.035), disqualifying it from privacy-preserving data sharing. AVATAR and Gaussian Copula demonstrated acceptable privacy profiles (singling-out = 0.004 and 0.001; linkability = 0.010 and 0.003, respectively). At the structural level, Gaussian Copula injected stochastic noise inflating residual error (+157.0%) and V1; (+25.9%), blunting predicted Cmax and predisposing to empirical dose escalation and risk of toxicity. AVATAR acted aSs a smoothing filter, deflating V2; (-48.3%) and underestimating CL (-12.9%). Forward clinical simulations confirmed directionally opposed prediction errors: Gaussian Copula consistently underestimated Cmax across standard and renally impaired profiles (-14.5% and -16.0%, respectively), predisposing to empirical dose escalation, whereas AVATAR- and Synthpop-derived models overestimated Cmax and Cmin in the obese infected patient (+14.7% and +8.2%, respectively), compounding the accumulation risk already present in this profile. Conclusion While no generative algorithm currently offers a perfect solution, AVATAR and Gaussian Copula represent the most viable candidates, being the only methods to satisfy both macroscopic indistinguishability and attack-based privacy criteria. These findings highlight the necessity of a structured, two-stage validation framework and suggest that, when coupled with therapeutic drug monitoring, synthetic datasets could significantly enhance multicentre collaboration while maintaining strict regulatory compliance

Investigating variation in genes involved in the absorption, distribution, metabolism, and excretion (ADME) of drugs are key to characterising pharmacogenomic (PGx) relationships. ADME gene variation is relatively well characterised in European and Asian populations, but African populations are under-studied - which has implications for safe and effective drug use in Africa. The genetic diversity of ADME genes across sub-Saharan African populations is large. The Southern African population cluster is most distinct from that of far West Africa. PGx strategies based on European variants will be of limited use in African populations. Although established variants are important, PGx must take into account the full range of African variation. This work urges further characterisation of variants in African populations including in vitro and in silico studies, and to consider the unique African ADME landscape when developing precision medicine guidelines and tools for African populations. Author summaryThe ADME genes are a group of genes that play a key role in absorption, distribution, metabolism and excretion of drugs. Variations in these genes can have a significant impact on drug safety and efficacy. Africa has a high level of genetic variation and is under-studied in drug development, which makes study of variations in these genes in African populations very important. Using a new data set of 458 high-coverage genomes from across Africa, we characterise the extent and impact of variation in the ADME genes, looking at both single nucleotide and copy number variations. We identified 343,368 variants, including 40,692 novel variants, and 930 coding variants which are predicted to have high impact on function. Our discovery curves indicate that there will be considerable value in sequencing more African genomes. Moreover, relatively few of these novel variants are captured on common genotyping arrays. We show that there is considerable diversity within Africa in some important genes, and this will have significant consequences for the emerging field of precision medicine in Africa.

Mocravimod (KRP203) is a selective sphingosine 1-phosphate (S1P) receptor modulator currently in development for patients with haematological malignancies undergoing allogenic haematopoietic cell transplantation (HCT). This first-in-human, randomised, double-blind, placebo-controlled, single ascending oral dose study evaluated the safety, tolerability, pharmacokinetics (PK), and pharmacodynamics (PD) of mocravimod in 136 healthy adult participants (EudraCT No. 2006-006814-13). Participants received single doses ranging from 0.01 to 40 mg or placebo, with a cohort dedicated to studying food-effect at 3 mg. Mocravimod demonstrated slow absorption (mean Tmax 6-11 hrs), extensive distribution, and a long terminal half-life (91-132 hrs). Exposure increased dose-proportionally for doses [&ge;]2 mg. The most common adverse events were headache, dizziness, and fatigue, all graded as mild or moderate; no serious adverse events or deaths occurred. Mocravimod-phosphate induced robust, dose-dependent reductions in lymphocyte counts, with significant decreases at doses [&ge;]2 mg and recovery to baseline observed in all but the highest dose groups. Cardiac effects included transient bradycardia and benign second-degree atrioventricular (AV) block at higher doses, without clinically significant arrhythmias. Food intake had minimal impact on PK. No clinically meaningful changes in pulmonary function or laboratory safety signals were detected. These results indicate that single oral doses of mocravimod up to 40 mg are safe and well tolerated in healthy adults, with predictable PK and expected PD effects. The findings support further clinical development of mocravimod as a targeted immunomodulator in settings such as allogeneic HCT for haematological malignancies.

BackgroundAGILE (NCT04746183) is a Phase Ib/IIa platform, evaluating candidates to treat COVID-19. CST-6 evaluated the safety and optimal dose of a novel intravenous (IV) formulation of favipiravir. MethodsCST-6 was a dose-escalating, open-label, randomised, controlled, Bayesian adaptive Phase Ib trial. Hospitalised adults with PCR-confirmed SARS-CoV-2 infection, within 14 days of symptomatic COVID-19 were randomised 2:1 in groups of 6 (n = 4 favipiravir, n = 2 standard of care (SoC)) to ascending doses of IV favipiravir twice daily (b.i.d.) for 7 days or SoC. Clinical data, safety evaluations, virology and pharmacokinetic (PK) samples were collected. The primary outcome was safety. Secondary outcomes included clinical, PK and virological endpoints. Results24 participants enrolled between 10/Sep/2022 and 01/Nov/2023 [10/24 female; median age 74 years (range 52-93)]. Favipiravir was well tolerated despite a high background rate of unrelated adverse events (AEs). No dose limiting toxicities (DLTs) were observed, with a model-predicted DLT risk of 16.8% and probability of unacceptable toxicity of 2.7% at the highest dose level. No SAEs were deemed related to favipiravir but an expected association with asymptomatic, transient hyperuricaemia was observed. PK exposures increased disproportionally to dose with significant accumulation in plasma, but with marked variability between participants within each cohort. ConclusionsA novel formulation of favipiravir was safe at sustained high doses that reached PK targets in a study group with frailty and complex health profiles. Plasma concentrations demonstrated accumulation. Significant variability in PK parameters between individuals was noted. We consider doses up to 2400mg b.i.d. to be safe for further evaluation. https://clinicaltrials.gov/study/NCT04746183 Key pointsO_LIA novel intravenous formulation of favipiravir, was safe and well tolerated in a frail and complex population, up to a dose of 2400mg b.i.d. C_LIO_LISignificant inter-individual variability in pharmacokinetic parameters was observed. C_LIO_LIPharmacokinetic modelling suggests pre-specified target concentrations were met. C_LI

BackgroundLarge language models (LLMs) are increasingly used in medical education and clinical decision-making, but their reliability in high-risk medication dosing remains unclear. Opioid rotation is a common task requiring precise calculations where errors may result in overdose or inadequate pain relief. MethodsThirteen LLMs were tested using an API-based framework to ensure independent queries across trials. First, fictional clinical scenarios were tested to simulate real-world clinical situations involving opioid rotation; to test the effects of changes in wording, scenarios were revised into 4 "vignettes" showing the same clinical situation. Next, opioid pairs were tested with a random-dose paradigm across a clinically-pertinent range (5-120 mg daily morphine equivalents). LLM outputs were compared with expected values derived from reference standards. Accuracy was assessed using predefined safety thresholds: tight accuracy (0.85-1.15x expected dose) and broad accuracy (0.6-1.7x). We tested models naively and with prompts augmented with reference tables and unit explanations. ResultsNaive models generally exhibited low tight-range accuracy across opioid pairs. For any given opioid pair, each model would consistently produce similar incorrect conversion ratios despite wide variability across opioid pairs and language models. Vignette wording changes accounted for 76% of within-scenario response variance. Reference-based prompt augmentation significantly improved performance, with over half of models achieving high proportions of conversions within tight accuracy for morphine-equivalent conversions. ConclusionsWhile commercial LLMs demonstrated variable accuracy in the native state, prompt augmentation significantly improved their performance.

Blinatumomab is a bispecific T-cell engager (BiTE) that binds to CD3 on T cells and CD19 on B cells. It has been approved for use in B-cell acute lymphoblastic leukemia (B-ALL) with a regimen that requires continuous infusion (cIV) for four weeks per treatment cycle. It is currently in clinical trials for Non-Hodgkin lymphoma (NHL) with cIV administration. Recently, there have been studies investigating dose-response after subcutaneous (SC) dosing in B-ALL and in NHL to determine whether this more convenient method of delivery would have a similar efficacy/safety profile as continuous infusion. We constructed mechanistic PKPD models of blinatumomab activity in B-ALL and NHL patients, investigating the amount of CD3:blinatumomab:CD19 trimers the drug forms at different dosing administrations and regimens. The modeling and analysis demonstrate that the explored SC doses in B-ALL and NHL achieve similar trimer numbers as the cIV doses in those indications. We further simulated various subcutaneous dosing regimens, and identified conditions where trimer formation dynamics are similar between constant infusion and subcutaneous dosing. Based on the model results, subcutaneous dosing is a viable and convenient strategy for blinatumomab and is projected to result in similar trimer numbers as constant infusion.

Pretomanid is a key component of the bedaquiline, pretomanid, linezolid with or without moxifloxacin (BPaL/M)regimen recommended for treatment of rifampicin-resistant tuberculosis (RR-TB). To support dose optimization and efficacy interpretation, we developed a pretomanid population pharmacokinetic (PK) model and evaluated exposure and probability of target attainment (PTA). Ninety-four RR-TB patients received daily oral pretomanid at 200 mg, and plasma samples were collected at multiple time points. Pretomanid concentrations were quantified using high-performance liquid chromatography-tandem mass spectrometry and PK modeling was performed using nlmixr2 in R. A one-compartment model with first-order absorption and elimination, and fat free mass allometric scaling best described the data. Typical clearance was 3.10 L/h, median AUC2 was 63,733 g{middle dot}h/L, and median trough concentration was 1,965 g/L. Pretomanid MICs for Mycobacterium tuberculosis in the TB-PRACTECAL trial were consistently below the provisional critical concentration, with a median of 0.125 mg/L. PK-Pharmacodynamic (PD) simulations indicated that nearly all participants achieved drug exposures exceeding %fT>MIC, supporting the regimens efficacy across the study population. We developed a pretomanid population PK model and facilitated exploring robust PK-PD targets for PTA that remain valuable to support dose optimisation. There is an urgent need for further research to identify the optimal clinically relevant PK-PD index for pretomanid, especially within the context of combination therapy.

ObjectiveTo identify post-marketing adverse event (AE) signals associated with isotretinoin using real-world data from the U.S. Food and Drug Administration (FDA) AE Reporting System (FAERS), aiming to provide references for clinical safety and further research. MethodsAE reports from the first quarter of 2004 to the third quarter of 2024 were extracted from the FAERS database. Four signal detection methods were employed: the Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Medicines and Healthcare products Regulatory Agency (MHRA) comprehensive criteria, Bayesian Confidence Propagation Neural Network (BCPNN), and Multi-item Gamma Poisson Shrinker (MGPS). ResultsA total of 142,160 AE reports involving isotretinoin were collected, corresponding to 50,519 patients. The four methods identified 469 common AE signals. The top five AEs ranked by descending ROR values were: inflammatory bowel disease (ROR=579.14; 95% CI: 554.95-604.39), gastrointestinal injury (ROR=412.80; 95% CI: 381.18-447.04), fulminant acne (ROR=321.42; 95% CI: 236.39-437.04), ulcerative proctitis (ROR=241.56; 95% CI: 201.70-289.30), and premature epiphyseal closure (ROR=221.22; 95% CI: 172.47-283.74). Among the top 30 AE signals, several conditions, including nasal vestibulitis, anal papilla hypertrophy, neonatal neuroblastoma, diverticular hernia, SAPHO syndrome, somatic delusional disorder, hypersomnia-bulimia syndrome, and hemihypertrophy, were not listed in the drugs prescribing information. The AE signals involved 25 system organ classes, predominantly psychiatric disorders (75, 15.99%), gastrointestinal disorders (58, 12.37%), and various congenital, familial, and genetic disorders (50, 10.66%). Additionally, strong signals related to pregnancy events were detected, notably unintended pregnancy (ROR=91.39; 95% CI: 86.78-96.26). ConclusionAE signals associated with isotretinoin involve a broad spectrum of system organ classes. Comprehensive monitoring during clinical use is essential, particularly concerning psychiatric and gastrointestinal disorders. Given the strong signals regarding teratogenicity and pregnancy-related events, strengthening preventive measures for pregnancy risks in patients is recommended.

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.