Clinical Pharmacology & Therapeutics最新文献

Dupilumab, a fully human monoclonal antibody that blocks key drivers of type 2 inflammation, is approved across 8 diseases, including eosinophilic esophagitis. Subcutaneous dupilumab 300 mg weekly improved outcomes vs. placebo in adults (aged ≥ 18 years) and adolescents (aged ≥ 12 to < 18 years) during the phase III LIBERTY EoE TREET study (NCT03633617). The phase III EoE KIDS study (NCT04394351) demonstrated efficacy of a weight-tiered, higher exposure dupilumab dose approximating 300 mg weekly exposure in TREET vs. placebo in children aged ≥ 1 to < 12 years. To characterize dupilumab pharmacokinetics, inform clinical trial design, and optimize posology, a population pharmacokinetic model was developed using dupilumab concentration data from 6 single-dose studies in healthy adults and 3 eosinophilic esophagitis clinical trials. Healthy volunteer data were used to construct a stable base model; incorporation of patient data allowed estimation covariate effects for the disease population. Observational data from up to 52 weeks of treatment in eosinophilic esophagitis trials validated model performance. Significant effects were identified for body weight on clearance and volume of distribution, including time-varying weight in children and adolescents, and of albumin on clearance; age was not a significant covariate. Simulations identified alternative dupilumab regimens for patients weighing ≥ 5 to <15 kg (200 mg every 3 weeks) and ≥ 40 to <60 kg (300 mg weekly) with improved exposure matching to the 300 mg weekly dose in TREET. The final model was used to inform regulatory approval and potential future clinical trial design.

Benchmarking evidence from nonrandomized healthcare database studies against randomized clinical trials could strengthen confidence that database studies can reproduce known trial results and extend analyses to questions not directly addressed in trials. However, the process can be challenging if differences in effect modifier distributions persist despite the same eligibility criteria between trials and their emulations. Post hoc population standardization can address this by aligning observable population distributions. Across four cardiovascular outcome trials previously emulated in claims data by the RCT-DUPLICATE initiative, we implemented post hoc population standardization by potential effect modifiers to study whether effect estimates meaningfully move closer to the trial findings. Exposures and comparators were 1:1 propensity score-matched on > 100 baseline characteristics. In all trials, we standardized age and sex distributions to match those of the emulated RCTs and, additionally, some cardiovascular risk factors, data permitting. Standardization resulted in close alignment of the select baseline characteristics, yet produced minimal changes in hazard ratios (HRs) and 1-year risk differences. Variance increased in some analyses, reflecting large population differences in certain trial-database comparisons. The benefits of population standardization must be weighed against challenges such as weight variability, bias-variance tradeoff, and limited overlap in covariate distributions. Importantly, differences in effect modifier distributions may not alter effect estimates if modifiers do not change the exposure-outcome relationship on the multiplicative scale, underscoring the distinction between statistical and biological interactions. In our four example trials, post hoc population standardization equalized populations but did not result in closer alignment of treatment effect estimates and expanded 95% confidence intervals.

There is a paucity of data regarding the effects of prenatal disease-modifying therapies (DMTs) for multiple sclerosis (MS), on congenital anomalies in the offspring. Moreover, data on the association with neurodevelopmental disorders are lacking. This is an historical cohort study, within the Israeli Clalit Health Services database (2005-2024) that included all children, born to mothers carrying a diagnosis of MS. Mothers' demographic and clinical characteristics were used to account for potential confounders. A marginal model used for the association between prenatal dispensed DMTs and neurodevelopmental disorders. A log-binomial regression model used to estimate relative risks (RR), 95% CI for the association between exposure and major congenital anomalies (MCAs). The cohort included 1374 children: 484 exposed prenatally to DMTs (237, 85, 56, 49, 25, 32 exposed to interferon-β, glatiramer, monoclonal antibodies, fumarates, sphingosine-1-phosphate receptor (S1PR) modulators, or a combination of therapies, respectively). In a mean follow-up of 7.5 years (maximum 19.8 years), prenatal exposure to DMTs was not associated with significant risk for neurodevelopmental disorders, in univariate and in multivariate models. Within 1252 children with 2 years of follow-up, 50 (3.9%) children were identified with MCAs. There was higher rate of MCAs among S1PR modulator-exposed children 2(8%). Due to the small group risk estimates for S1PR modulators had wide CIs, with RR = 2.0 (95% CI 0.51-8.1, P = 0.314); RR = 5.16 (95% CI 1.47-18.2, P = 0.011) in the univariate and sensitivity analysis, respectively. In conclusion, in the current cohort, prenatal exposure to DMTs was not associated with neurodevelopmental disorders. Exposure to S1PR modulators in pregnancy was associated with higher risk for congenital anomalies. Prospective larger studies are warranted.

Claims-based analyses can suffer from residual and unmeasured confounding due to factors that are poorly captured in claims. Some of these factors may be measured in other data sources, such as in structured fields of electronic health records (EHR), for example, laboratory test results, or in free-text physician notes. We conducted a proof-of-principle study to demonstrate a process for evaluating the potential risk of confounding-factors poorly captured in claims data but measurable in the EHR as part of drug safety surveillance activities. In future practical applications, this approach could be used along with other sensitivity analyses to evaluate potential residual confounding (e.g., E-values, negative controls). We used claims-EHR linked data from the Mass General Brigham site of the US Food and Drug Administration's (FDA) Sentinel Real World Evidence Data Enterprise. We extracted a cohort that was previously used in a prototypical Sentinel claims-based query that compared initiators of sacubitril-valsartan vs. angiotensin-converting enzyme inhibitors or angiotensin receptor blockers on the risk of angioedema. In this cohort, we used EHR data to characterize angioedema risk factors poorly captured in claims and observed that claims-based proxies balanced most risk factors that were measurable only in EHR data. While quantitative bias analysis methods can be used to adjust for residual confounding using external information on magnitude and direction of bias, this was deemed unnecessary for this example due to the observed balance achieved on risk factors for angioedema measured in the EHR. A robust linked EHR-claims data infrastructure is crucial for routine application of these methods to evaluate and mitigate residual confounding in drug safety surveillance studies.

The global HIV response aims for widespread availability of affordable, quality-assured long-acting antiretroviral (LA ARV) drugs to achieve sustained epidemic control, particularly in low- and middle-income countries. This report summarizes key discussion points, findings, and outcomes from an international workshop on generic LA ARVs, held in Liverpool, United Kingdom, and co-organized by the Long-Acting/Extended-Release Antiretroviral Resource Program (LEAP) and the Centre of Excellence for Long-acting Therapeutics (CELT). The workshop brought together experts from across disciplines to evaluate the multifaceted challenges and opportunities for faster development and regulatory approval of affordable and accessible generic LA ARVs. Key topics included the application of novel pharmacokinetic end points to reduce study duration, the integration of community preferences into practice-based research in resource-limited settings, intellectual property considerations, formulation and manufacturing complexities that affect cost, scalability and implementation, and the growing role of model-integrated evidence in streamlining bioequivalence assessments. To reach the goal of timely and equitable global access to long-acting medicines, this workshop emphasized the need for strategic public-private engagement to promote data sharing, enhance regulatory efficiencies, and develop robust in vitro-in vivo correlation strategies that meet regulatory guidance for LAI products.

Investigator-initiated studies that include information collected by patients are rising, but limited data is available on patient and investigator experience in this setting. The I-CARE cohort included patients with inflammatory bowel disease (IBD) monthly collecting clinical information in 15 countries for up to 6 years. We describe patients and investigators' involvement in I-CARE and identify predictors of early withdrawal due to patient non-engagement. Patients' characteristics according to the number of electronic Patient-reported outcomes (ePRO) completed during follow-up were assessed. Predictors of early withdrawal due to patient non-engagement were identified using logistic regression. The coding of outcomes reported by patients and corrections by investigators on patients' ePROs were assessed. Among 12,846 patients included by 502 investigators, 79.3% and 77.3% filled more than one ePRO and at least one ePRO within 6 months before the study end date, respectively. All ePROs were completed in 72.8% and 56.4% of patients during year 1 and 3, respectively. Male gender, younger age (< 20), being unemployed or a student, and no previous history of abdominal surgery were associated with early withdrawal. Investigators corrected 52.5% of cancer or dysplasia reported by patients compared to 10% of serious infections. Investigators added or removed a treatment sequence in 19.6% of the 6708 patients treated with biologics. These results highlight the implication of patients in research and the importance of data validation by investigators alongside the challenge and potential of collecting medical data from patients. These findings can inform similar future initiatives in other diseases. (EudraCT, Number: 2014-004728-23; ClinicalTrials.gov, Number: NCT02377258).

ASCPT has a mission to advance clinical pharmacology and translational sciences to reduce health disparities. The development, regulatory, and outcomes (DRO) network within ASCPT strives to advance pharmacoequity globally. Pharmacoequity, coined by Utibe Essien (2021), is the principle that all individuals, regardless of race, ethnicity, geography, or socioeconomic status, should have access to high-quality and evidence-based therapies. This perspective spotlights the network's contributions toward this goal and identifies priority areas to build equitable health outcomes.

Traditionally, clinical outcomes measuring how a patient feels, functions, or survives are preferred endpoints in clinical trials; however, some may take a long time to manifest in slowly developing diseases. Biomarkers, if properly validated, can serve as surrogate endpoints, acting as substitutes for clinical outcomes. This study investigated the extent and type of evidence demonstrating the correlation between biomarkers and clinical outcomes available in the literature before these biomarkers were first used as surrogate primary endpoints in pivotal studies supporting marketing authorization of cardiovascular and metabolic medicines in the European Union. Fourteen biomarkers newly used as surrogate endpoints between 2008 and 2023 were identified from European public assessment reports, and systematic literature searches were conducted for each of the identified biomarkers in PubMed and Embase to review existing evidence for the correlation between biomarker surrogates and clinical outcomes. We found that such correlation had often not been established in the literature prior to their use as a primary endpoint, as validation studies were identified for only four of the biomarkers. Of the 14 identified novel biomarker surrogates, all but one (93%) were used in trials for medicines with an orphan designation. Regulators seem to accept the use of surrogates in trials for rare diseases, despite limited validation, and consider the totality of evidence submitted to support authorization of the medicine. Presentation of complete and explicit justification for the choice and acceptability of biomarker surrogates in public documentation, such as European public assessment reports, should be encouraged.

Regional differences in approved drug dosages and administration are shaped by complex factors, including clinical data, prior regulatory decisions, and region-specific considerations. This study investigated how such factors are associated with variations in approved doses across the United States Food and Drug Administration (FDA), European Medicines Agency (EMA), and Pharmaceuticals and Medical Devices Agency (PMDA) in the context of recent global regulatory practices. We analyzed 111 new molecular entities approved in the United States and Japan from 2017 to 2023, incorporating EMA data where available. Dose concordance, defined as agreement between regions on the labeled approved dosing regimen for the same indication, was observed in 77% between PMDA and FDA and 84% between EMA and FDA, higher than rates reported in earlier studies. Nevertheless, significant regional differences remain. Qualitative analysis suggested that discordance in approved doses was associated with a small number of high-level factors, including differences in benefit-risk perspectives, approaches to dose determination, population-related considerations. Quantitative analysis indicated associations with trial design and submission timing. In a subset of 11 products in which FDA approval preceded approvals by other agencies and involved FDA-led dose modifications, patterns were observed that were consistent with potential interdependence in regulatory decision making, while sponsor-driven harmonization and shared evidence bases may also underlie these patterns. These findings suggest that early regulatory engagement and international coordination may support efficient dose selection, although attention must be paid to differing benefit-risk assessment considerations across regions.

This review evaluates the available pharmacokinetic data on the plasma-to-breastmilk transfer of first- and second-line T2DM drugs against available clinical guideline recommendations. A list of drug therapies for treating T2DM was generated from national and international clinical guidelines. A systematic search of research articles reporting human plasma and breastmilk drug concentrations was conducted in Scopus, PubMed, Google Scholar, and LactMed® in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Studies evaluating breastmilk drug transfer in T2DM, with fully accessible abstract and main text reported in English, were included. Study quality was evaluated using the ClinPK checklist. Authors evaluated clinical guideline recommendations on the use of T2DM drugs in lactation and the basis upon which such recommendations were made. Only 5 out of 20 drugs (metformin, glyburide, glipizide, tolbutamide, and semaglutide) have clinical data on plasma-to-breastmilk transfer. Metformin and tolbutamide were detectable in maternal plasma and breastmilk. Half (51.7%) of guideline recommendations provide explicit guidance. Only 4.4% of recommendations were based on clinical evidence. Over half (57.8%) of recommendations were accessible online, and most guideline recommendations (78%) were against the use of antiglycemic agents while breastfeeding. The scarce clinical evidence to guide T2DM drug therapy during breastfeeding available has several design and methodological limitations. Published recommendations remain largely inconsistent, thus perpetuating uncertainty in the use of T2DM drug therapies in lactation. Addressing knowledge gaps is critical in developing clinical consensus to optimize T2DM drug therapy among breastfeeding mothers.