Clinical Pharmacology & Therapeutics最新文献

The US FDA's Biomarker Qualification Program (BQP) aims to validate biomarkers for use in the therapeutic development process. Applicants to this program are not restricted to just pharmaceutical firms and a variety of incentives may underlie applicants' decision to participate. Of the 80 biomarker qualification programs that were initiated through February 2025, we find that academic organizations (70.0%) are the most common applicants, followed by pharmaceuticals-related industries (55%), government entities (51.25%), and pharmaceutical firms (50%), although much of this activity is in the context of multi-party consortia. With respect to stage of development, all phase-I-related biomarkers are developed in some form of partnership, while academic institutions alone and foundations devote slightly more attention to trial endpoints as a total share of BQP activities. These findings shed light on the incentives at play and the types of stakeholders that have been active in the development of these tools to date.

The 125th anniversary of the American Society for Clinical Pharmacology and Therapeutics (ASCPT) marks a significant milestone, providing an opportunity to reflect on the Society's rich history and pivotal role in advancing science, education, and collaboration.

Early-career scientists face unique challenges as they progress in their professional journeys, from navigating new environments and shifting career paths to managing competing priorities. This perspective highlights ASCPT's programs and initiatives that support early-career scientists in building a strong professional foundation. It also emphasizes the value of volunteering, networking, and leadership within the society, which can promote career development and strengthen connections within the broader scientific community.

Measuring medication discontinuation in claims data primarily relies on the gaps between prescription fills, but such definitions are rarely validated. This study aimed to establish a natural language processing (NLP)-based validation framework to evaluate the performance of claims-based discontinuation algorithms for commonly used medications against NLP-based reference standards from electronic health records (EHRs). A total of 36,656 patients receiving antipsychotic medications (APMs), benzodiazepines (BZDs), warfarin, or direct oral anticoagulants (DOACs) were identified from the Mass General Brigham EHRs in 2007-2020. These EHR data were linked with 97,900 Medicare Part D claims. An NLP-aided chart review was applied to determine medication discontinuation from EHR (reference standard). In claims data, discontinuation was defined by having a prescription gap larger than 15-90 days (claims-based algorithms). Sensitivity, specificity, and predictive values of claims-based algorithms against the reference standard were measured. The sensitivity and specificity of 90-day-gap-based algorithms were 0.46 and 0.79 for haloperidol, 0.41 and 0.85 for atypical APMs, 0.47 and 0.75 for BZDs, 0.33 and 0.80 for warfarin, and 0.38 and 0.87 for DOACs, respectively. The corresponding estimates for 15-day-gap-based algorithms were 0.68 and 0.55 for haloperidol, 0.59 and 0.62 for atypical APMs, 0.71 and 0.45 for BZDs, 0.61 and 0.49 for warfarin, and 0.58 and 0.64 for DOACs, respectively. Positive predictive values were primarily affected by medication discontinuation rates and less by gap lengths. The overall accuracy of claims-based discontinuation algorithms differs by medications. This study demonstrates the scalability and utility of the NLP-based validation framework for multiple medications.

Therapeutic antibodies are often prescribed off-label to pregnant patients to treat inflammatory, autoimmune, or malignant conditions. Despite their broad use, the extent of fetal exposure to such therapeutic antibodies and the risk to fetal development remain largely unknown. Given the ethical challenges to conduct randomized trials in pregnant patients, modeling and simulation approaches offer an opportunity to yield mechanistic insights using data from observational studies. In this study, a physiologically based pharmacokinetic (PBPK) modeling framework was developed to predict maternal and fetal therapeutic antibody exposures throughout pregnancy. The model incorporates expression data on the placental neonatal Fc receptor (FcRn), a receptor critical to transplacental IgG transfer. FcRn-mediated transplacental antibody transfer was described by three endosomal compartments: (1) maternal vascular endothelial cells; (2) syncytiotrophoblast cells; and (3) fetal vascular endothelial cells. The model was calibrated and validated using endogenous IgG concentrations and pharmacokinetic data from > 2,000 non-pregnant subjects, 167 pregnant women, and 268 infants. Overall, the minimal PBPK model adequately captured the observations, with predictions falling within a twofold range of maternal and fetal concentrations as follows: infliximab (54% and 50%), adalimumab (100% and 70%), ustekinumab (38% and 41%), vedolizumab (92% and 77%), and etanercept (75% and 33%). In addition, the PBPK framework supported the evaluation of infliximab and adalimumab dosing regimens that maintain maternal therapeutic levels while minimizing fetal exposure. This study provides a generalizable PBPK framework including FcRn ontogeny, implemented in a user-friendly tool, to predict transplacental transfer of many biologics and to support appropriate dosing regimens throughout pregnancy.