Clinical and Translational Science

Chronic wounds, such as diabetic and ischemic ulcers, involve impaired perfusion and delayed healing. TOP-N53 is a novel bifunctional molecule combining nitric oxide (NO) release with phosphodiesterase-5 (PDE5) inhibition to enhance local NO-cGMP signalling, resulting in vasodilation and angiogenesis. This first-in-human, randomized, double-blind, vehicle-controlled Phase I trial assessed the safety, tolerability, pharmacokinetics (PK), and pharmacodynamics (PD) of single subcutaneous TOP-N53 doses in 29 healthy male volunteers. Each participant received injections of TOP-N53 and vehicle in the same forearm, but either at the proximal or at the distal site in an intra-individually blinded manner. Safety assessments included local and systemic parameters. PK and PD responses were evaluated by analysis of TOPN53 and its bioactivation metabolite TOP-52 in plasma, and by Laser Speckle Contrast Imaging (LSCI), a non-invasive method to measure skin perfusion, respectively. TOP-N53 was safe and well tolerated, with no serious adverse events or local or systemic adverse reactions. Plasma concentrations remained below the quantification limit and LSCI showed sustained dose-dependent increases in local skin perfusion at doses of 4.84 ug and 9.075 ug TOP-N53 SC for up to 24 h post injection when compared to vehicle. These findings support the favourable safety and tolerability profile of TOP-N53 associated with locally improved skin perfusion, encouraging its further clinical development as a topical treatment for chronic wounds with microvascular dysfunction.

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.

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.

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.

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.

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.

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.

BackgroundPersonalized pharmacotherapy requires systematic consideration of genetic factors influencing drug efficacy and safety. The accumulation of large-scale whole-exome sequencing (WES) data provides an opportunity to assess population frequencies of clinically significant pharmacogenetic variants; however, the diagnostic applicability of exome data for pharmacogenomics remains insufficiently studied. Materials and MethodsA retrospective analysis of 6,102 anonymized sequencing datasets obtained between 2020 and 2025 was performed using the DNBSEQ-G400 (MGI) platform and Agilent SureSelect Human All Exon v6/v7/v8 enrichment kits. SNV and indel detection, CNV analysis, high-resolution HLA typing, and diplotype assignment for key pharmacogenes were conducted. Pharmacogenomic annotations were derived from PharmGKB (levels of evidence 1A-2B), CPIC, and PharmVar. Additionally, WES limitations and the feasibility of imputing non-coding pharmacogenetic variants were evaluated. ResultsPopulation frequencies of alleles and metabolic phenotypes were determined for 13 Very Important Pharmacogenes (VIPs), along with the distribution of HLA class I and II alleles. The highest allelic and phenotypic variability was observed in CYP family genes, particularly CYP2D6, CYP2C19, and CYP2B6. A total of 663 pharmacogenomic annotations were identified, predominantly related to drug metabolism (50.38%) and toxicity (29.56%), including psychotropic agents, anticoagulants, statins, opioid analgesics, antineoplastic agents, and immunosuppressants. At least 32 drugs require pharmacogenetic testing based on variants located in non-coding regions, as well as accurate CYP2D6 copy number determination. Linkage disequilibrium analysis demonstrated the inability to reliably impute most non-coding pharmacogenetic variants from WES data. ConclusionThese findings represent one of the largest reference assessments to date of pharmacogenetically significant variant and HLA allele frequencies in the Russian population. The results confirm the utility of WES for population pharmacogenomic screening while simultaneously highlighting its fundamental limitations and the need for alternative genetic diagnostic methods in selected cases.

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.

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.

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.

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.

BackgroundRoutinely collected prescribing and medicine-related data in Scotland are comprehensive and of high quality. However, they are generated across multiple healthcare settings and stored in complex source systems that are not optimised for longitudinal or outcomes-focused research. To maximise the research value of these data, there is a need for curated, analysis-ready resources that provide consistent representations of medicines exposure and enable linkage to clinical outcomes. The Medicines in Acute and Chronic care Scotland (MACCS) provides standardised, curated medicines data to support longitudinal analyses of medicine-related exposure across NHS healthcare systems. MethodsMACCS resource is a national individual-level medicines dataset for adults (18 years of age and older), derived from routinely collected prescribing and medicine-related data held by Public Health Scotland (PHS). It integrates data from the Hospital Electronic Prescribing and Medicines Administration (HEPMA), Prescribing Information System (PIS), and Homecare Medicines (HCM) datasets, which are linked at the individual level to eleven other national clinical records; including Scottish Morbidity Records (SMR00/01/02/04/06), laboratory data and mortality records; using the unique NHS Scotland person identifier. Data are curated, harmonised and pre-linked within the National Safe Haven and accessed by approved researchers through secure Trusted Research Environments. ResultsMACCS contains individual-level information on adults receiving NHS Scotland care, including patient demographics (such as age, sex and geographical indicators) and detailed records of medicines prescribing in community pharmacies as well as those administered in hospitals and through homecare services. Medicines-related data captures exposure dates and, where available, details on formulation, strength and dose. In addition, MACCS includes cancer registry data, renal registry data, laboratory test results, microbiology surveillance and mortality records. The earliest dates of data availability vary by source dataset. ConclusionMACCS provides a sustainable, longitudinal medicines research resource that simplifies access to complex national prescribing data and enables robust linkage to health outcomes. By supporting population-scale analyses across care settings, MACCS enhances the capacity for high-quality research to inform clinical practice, health policy, and medicines optimisation in Scotland. Key FeaturesO_LIThe Medicines in Acute and Chronic Care in Scotland (MACCS) data resource was established in 2025 to integrate medicine-related data with other electronic data from Scottish healthcare systems, creating a national, linked, routinely updated data resource at population level. C_LIO_LIMACCS provides pre-linked data from multiple routinely collected national datasets within NHS Scotland including, but not limited to, prescribing records, hospital episodes, laboratory results, and death records, within a single secure environment. C_LIO_LIMACCS includes patient demographics, data on medicines prescribing and administration/supply, key biochemistry and haematology test results (e.g., kidney and liver function tests), data on hospital admissions and surgical procedures, and date and cause of death. C_LIO_LIThe data resource provides longitudinal follow-up of the adult population ([&ge;]18 years of age) receiving medicines through NHS Scotland since 2010, covering approximately 4.6 million individuals, and supports pharmacoepidemiological studies, drug utilisation research, pharmacovigilance projects, as well as health services research. C_LIO_LIApproved researchers can apply through a streamlined process to access the linked MACCS data resource through established NHS Scotland governance processes, with data accessed within a Trusted Research Environment. C_LI

BackgroundAntibiotic pricing is a key determinant of access and stewardship in low- and middle-income countries (LMICs), yet empirical evidence on how prices are formed within pharmaceutical markets remains limited. However, there is little longitudinal evidence on how antibiotic prices behave within national pharmaceutical supply systems. This study evaluated the patterns and determinants of systemic antibiotic pricing in Tanzania using national regulatory import permit data. MethodsWe conducted a retrospective analysis of antibiotic importation records from the Tanzania Medicines and Medical Devices Authority for 2010-2016. Systemic antibiotics for human use imported via oral or parenteral routes were included. Unit prices (USD per smallest unit of measure) were summarized using the median and interquartile range (IQR). Prices were compared by route of administration, supplier country, and product naming practice (INN-named versus brand-named) using Mann-Whitney U and Kruskal-Wallis tests with false discovery rate adjustment. ResultsOf the 14,301 records, 10,894 (76.2%) met the inclusion criteria. Oral antibiotics predominated (89.6%). Although the median oral antibiotic prices declined over time, substantial price dispersion persisted across all study years. Parenteral antibiotics were consistently more expensive (USD 0.755-3.370) and more variable than oral antibiotics. Importation was concentrated in a few medicines, with amoxicillin-clavulanate (16.7%) and amoxicillin (11.4%) accounting for over one-quarter of records, and in a few supplier countries, with India representing 44.9% of the records. Significant price differences between INN-named and branded products were observed for amoxicillin (adjusted p<0.001) and ciprofloxacin (adjusted p=0.018), whereas prices differed significantly by supplier country across major medicines (adjusted p<0.05). Across medicines and years, wide within-product price distributions indicate persistent market segmentation rather than price convergence. ConclusionsAntibiotic import prices in Tanzania exhibit systematic and reproducible variations associated with formulation type, supplier origin, and product naming practices. The findings indicate that procurement structure and supplier participation strongly influence pricing in the import-dependent pharmaceutical market. Monitoring import-level prices can serve as an upstream indicator of market conditions and support evidence-informed procurement, pricing regulations, and antimicrobial stewardship policies in LMIC settings.

IntroductionPatients undergoing anterior cruciate ligament (ACL) reconstruction experience substantial postoperative pain, which delays recovery and leads to both immediate and long-term opioid use. In other knee procedures, infiltration between the popliteal artery and the capsule of the posterior knee (IPACK) has demonstrated analgesic and opioid reducing effects. However, the effect in patients undergoing ACL reconstruction has not been investigated. We aimed to investigate the real-world effect of IPACK in patients undergoing ACL reconstruction on immediate postoperative opioid consumption. ParticipantsIn this single-centre difference-in-differences cohort study, all patients who underwent ACL reconstruction surgery at Bispebjerg Hospital, Denmark, from 1 February 2024 to 30 June 2025 are included. The study further includes a similar reference cohort, comprising all patients who underwent trochleaplasty, Elmslie-Trillat, or medial patellofemoral ligament reconstruction during the same period, and at the same hospital. InterventionThe primary exposure is the implementation of IPACK as part of perioperative management for ACL reconstruction on 1 January 2025. The IPACK was performed under ultrasound guidance, immediately before surgery, administering 20 mL of ropivacaine 0.5% between the popliteal artery and the posterior knee capsule. OutcomesThe primary outcome is the cumulative opioid consumption from surgical incision to 2 hours postoperatively. Secondary outcomes include the cumulative opioid consumption from incision to 24 hours postoperatively, the worst reported pain score at 0-24h postoperatively, occurrence of postoperative nausea or vomiting (PONV) 0-24h postoperatively, length of PACU stay, length of hospital stay, and nerve injuries. As an exploratory outcome, carbon dioxide emissions will be investigated. Statistical analysisThe main analysis will be a standard two-way fixed effects DiD regression assessing the changes occurring at the time of implementation of IPACK in the ACL cohort, with adjustment for the underlying time trend. Continuous outcomes are reported as mean difference (95% confidence interval [CI]), and binary outcomes as absolute and relative risks (95% CI).

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.

BackgroundThe strength and transparency of clinical trial evidence supporting drug approvals has become increasingly scrutinized, particularly considering the increased use of regulatory flexibility and expedited pathways. While U.S. Food and Drug Administration (FDA) standards have been extensively analyzed, evidence standards at the European Medicines Agency (EMA) remain less well-characterized. Thus, this study aims to systematically assess the design, quality, and outcomes of pivotal efficacy trials supporting EMA drug approvals between 2020 and 2023. MethodsWe conducted a cross-sectional analysis of new medicines and biosimilars receiving positive opinions from the EMAs Committee for Medicinal Products for Human Use (CHMP) and subsequent approval by the European Commission between January 2020 and December 2023. Data were extracted from European Public Assessment Reports (EPARs) and EMA medicine databases. Key variables included trial design features, primary endpoint type and achievement status, and justification for approval in cases of failed efficacy endpoints. ResultsBetween 2020 and 2023, 232 drugs were approved by the EMA for 281 indications. Of these, 205 (88.4%) were new active substances and 65 (28.0%) were granted orphan designation. Forty-six products (19.8%) were approved via a special regulatory program, most commonly Conditional Approval (26 products; 11.2%). Cancer was the leading therapeutic area, accounting for 61 approvals (26.3%). Approvals were supported by 393 pivotal clinical trials. Of these, 327 (83.2%) were randomized controlled trials (RCTs) and 218 (66.6% of RCTs) had a superiority design. A total of 232/393 trials (59.0%) relied on surrogate endpoints. Overall, 22 approvals (9.5%) were supported by at least one pivotal trial in which at least one primary endpoint was not met; in seven of these cases (31.8%), the failed trial was the sole pivotal trial. The most common rationale for approval despite null primary results was reliance on the totality of evidence, secondary endpoints, or clinical judgment (9 products; 40.9%). ConclusionsOur findings reveal substantial variability in the design and evidentiary strength of pivotal trials supporting EMA approvals between 2020 and 2023. While the majority of studies were RCTs, reliance on surrogate endpoints was common. That 10% of approvals were based on pivotal trials with null primary endpoints highlights the nuanced role of regulatory judgment in therapeutic evaluation. These findings prompt reflection on evolving evidence standards in drug regulation and underscore the need for transparency and consistent justifications.

IntroductionAdverse drug reactions (ADRs) remain a major public health issue, and genetic factors contribute importantly to interindividual variability in drug response. Pharmacogenetic testing helps reduce ADR risk by optimizing drug selection and dosage, particularly in monogenic disorders. Material and MethodsWhole-exome sequencing of 6,739 samples from the Russian population was performed using the MGIEasy Universal DNA Library Prep Set on the DNBSEQ-G400 platform (MGI). Variants in 48 genes were examined, focusing on inherited arrhythmias (Long QT syndrome, Short QT syndrome, Timothy syndrome, Andersen-Tawil syndrome, Brugada syndrome, Atrial fibrillation, Catecholaminergic polymorphic ventricular tachycardia), enzyme deficiencies (Glucose-6-Phosphate Dehydrogenase Deficiency [G6PDD], Porphyrias), Dravet Syndrome (DS) and Malignant Hyperthermia (MH). All identified variants had been reported at least once as pathogenic (P) or likely pathogenic (LP) in ClinVar, along with those occasionally classified as variants of uncertain significance (VUS). Each variant was manually re-evaluated according to ACMG criteria. ResultsA total of 75 unique variants in 18 genes were observed in 119 individuals (1.77%), including 21 carriers and 13 women with a G6PD mutation. Of these, 46 variants were classified as P, 21 as LP, and 8 as VUS. Missense variants accounted for the largest proportion (73.33%). The most affected genes were KCNQ1 (24/119), which exhibited the highest number of unique variants (18), G6PD (20/119), SCN1A (15/119), and RYR1 (14/119). Regarding associated conditions, mutations linked to arrhythmias were found in 51 individuals, MH in 27, G6PDD in 20, DS in 15, and Porphyrias in 6. ConclusionsIncorporating genetic information on both common and rare clinically actionable variants into therapeutic decision-making has the potential to improve medication safety, reduce preventable ADRs, and enhance the effectiveness of personalized pharmacotherapy.

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.

BackgroundPatients undergoing hematopoietic stem cell transplantation (HSCT) often receive multiple antibiotics and antifungal agents concurrently, making it crucial to understand potential pharmacokinetic interactions. We report here an interaction between the glycopeptide antibiotic teicoplanin (TEIC) and sulfo-butyl ether-{beta}-cyclodextrin (SBECD), a solubilizing excipient in the intravenous formulation of posaconazole (PSCZ). MethodsWe performed a single-center retrospective analysis of HSCT patients who received oral and intravenous PSCZ during TEIC therapy. Associations between PSCZ administration and TEIC concentration-to-dose (C/D) ratios were evaluated using linear mixed-effects models. In rats, we examined the effects of intravenous PSCZ and SBECD on TEIC pharmacokinetics by assessing the area under the concentration-time curve (AUC) and urinary excretion of total TEIC and its components. Molecular docking and in vitro protein-binding assays were also conducted to investigate the interaction between TEIC and SBECD. ResultsIn HSCT patients, TEIC C/D ratios were significantly lower during intravenous PSCZ administration but not during oral PSCZ use. In rats, both intravenous PSCZ and SBECD decreased TEIC AUC and increased urinary excretion, particularly for the A2 group. Docking simulations indicated that the hydrophobic side chain of TEIC A2-2 fit within the SBECD cavity, and in vitro assays confirmed SBECD concentration-dependent increases in TEIC unbound fractions. ConclusionCo-administration of intravenous PSCZ containing SBECD may reduce TEIC protein binding, thereby enhancing renal elimination.