Journal of Pharmacokinetics and Pharmacodynamics最新文献

Autosomal dominant tubulointerstitial kidney disease (ADTKD), caused by mutations in UMOD and MUC1 genes, leads to tubular damage and fibrosis, ultimately resulting in kidney failure (KF). This study investigated clinical and genetic factors influencing the rate and severity of ADTKD progression by developing quantitative models. An estimated glomerular filtration rate (eGFR) of 10 mL/min/1.73 m² was used to define KF, corresponding to dialysis initiation. Natural history data from the Wake Forest University School of Medicine study were used to develop the models for UMOD (n = 371) and MUC1 (n = 233) disease types (age ≥ 18 years). Longitudinal change in eGFR and time-to-KF were quantified using nonlinear mixed-effects and parametric time-to-event modeling approaches, respectively, in Monolix (version 2024R1). Sigmoid I_max functions with steepness parameters varying before and after inflection points best captured eGFR decline. Patients with UMOD and MUC1 disease variants exhibited a similar initial shallow steepness ( $\approx$ 1), but after inflection, each declined rapidly. MUC1 patients progressed faster than UMOD during the post-inflection phase (γ₂ = 10.23 vs. 6.34). eGFR at first clinic visit (eGFR_FCV) and age at first clinic visit (AFCV) significantly affected between-subject variability in eGFR decline. A Weibull hazard function best described the time to KF. In UMOD, males reached Te (the age at which approximately 36.8% of individuals remain free from KF) 4 years earlier than females on average (β_Te_Male = -0.07), indicating faster progression in males. Older AFCV was associated with slower progression to KF (β_Te_AFCV = 0.59 for UMOD and 0.81 for MUC1). These models may help enable quantitative data-driven subgroup analysis in the future, optimizing inclusion/exclusion criteria for ADTKD clinical trials.

Drug discovery balances many factors as it identifies compounds for clinical testing, including compound efficacy, safety, pharmacokinetic (PK) properties, commercial feasibility, competitive positioning, and organizational pressures to move quickly with limited knowledge. When considering target engagement within clinically acceptable dosing constraints, design elements often balance potency requirements against the required extent of target engagement, which subsequently inform the PK design criteria (e.g. absorption and half-life considerations). Hence, an early understanding of the magnitude and duration of target engagement can focus design teams by providing well defined design criteria. To this end, an in vitro target engagement assay has been developed to bin targets and compounds by the type of target engagement profile required for efficacy (cellular anti-proliferation). This in turn directionally informs on the required concentration profile most aligned with the efficacy readout, bucketing results into three primary categories that drive efficacy: high transient concentrations, average concentrations, and threshold concentrations. This manuscript will outline the methodology developed for this early target coverage assessment and provide examples with selected compounds spanning molecularly targeted and cytotoxic oncology small molecules.

Posaconazole is an effective broad-spectrum triazole antifungal used as prophylaxis or to treat invasive Aspergillus and Candida infections in adults and pediatric patients. Posaconazole is a known strong inhibitor of cytochrome P4503A4 (CYP3A4) and substrate of P-glycoprotein (P-gp), which may lead to drug-drug interactions (DDIs) when co-administered with CYP3A4-sensitive substrates and warrants modified dosing of sensitive drugs when administered concomitantly with posaconazole. Given the long elimination half-life of posaconazole (26-35 h), there is the potential for DDIs caused by posaconazole after discontinuing the antifungal. Our clinical studies revealed that the half-life of posaconazole is significantly prolonged in subjects with a body mass index (BMI) ≥ 35 kg/m², which may put this population at an increased risk of DDIs after stopping posaconazole. This manuscript describes the development, verification, and validation of a whole-body, physiologically-based pharmacokinetic (PBPK) model which describes the concomitant use and washout DDIs of posaconazole delayed-release tablet (DRT) with victim drugs ranolazine and lurasidone in healthy volunteers of normal weight and with obesity. The key findings of this model are 1) the half-life of posaconazole is significantly prolonged in patients with BMI ≥ 35 kg/m² and 2) the mechanism of inhibition of CYP3A4 by posaconazole appears to be irreversible in vivo. This model may be used moving forward to assess the potential for washout DDIs with CYP3A4-sensitive substrates during concomitant use with, and after discontinuing posaconazole in subjects with normal weight and obesity.

This work focuses on design of experiments for Pharmacokinetic (PK) and Pharmacodynamic (PD) studies. Non-Linear Mixed Effects Models (NLMEM) modelling allows the identification and quantification of covariates that explain inter-individual variability (IIV). The Fisher Information Matrix (FIM), computed by linearization, has already been used to predict uncertainty on covariate parameters and power of test to detect statistical significance. A covariate effect is deemed statistically significant if it is different from 0 according to a Wald comparison test and clinically relevant if the ratio of change it causes in the parameter is relevant according to a test inspired by the two one-sided tests (TOST) as in bioequivalence studies. FIM calculation was extended by computing its expectation on the joint distribution of the covariates, discrete and continuous. Three methods were proposed: using a provided sample of covariate vectors, simulating covariate vectors, based on provided independent distributions or on estimated copulas. Thereafter, CI of ratios, power of tests and number of subjects needed to achieve desired confidence were derived. Methods were implemented in a working version of the R package PFIM6.1. A simulation study was conducted under various scenarios, including different sample sizes, sampling points, and IIV. Overall, uncertainty on covariate effects and power of tests were accurately predicted. The method was applied to a population PK model of the drug cabozantinib including 27 covariate relationships. Despite numerous relationships, limited representation of certain covariates, FIM correctly predicted uncertainty, and is therefore suitable for rapidly computing number of subjects needed to achieve given powers.

The questions and answers (Q&A) document for ICH E14/S7B provides the following advancements for QTc assessment: concentration-QTc modeling (C-QTc) as the primary analysis, accepting alternative approaches (Q&A 5.1 and 6.1) to thorough QT (TQT) studies, and incorporating an integrated nonclinical risk assessment as supporting evidence. Based on QT study reports reviewed by the FDA between 2016 and 2024, changes to the E14 guideline have resulted in a 34% decrease in the proportion of TQT studies, while the use of C-QTc analysis as the primary analysis has significantly increased. Studies using C-QTc instead of by-time analysis as the primary analysis reduced median sample sizes by 67%, 42%, and 35% for parallel, nested crossover, and crossover studies, respectively. The white paper C-QTc model was used for 60% of drugs that prolonged the QTc interval. From 2020 to 2024, reviews incorporating an integrated nonclinical risk assessment have also increased. The advancements in QTc assessments have streamlined QTc assessment and made clinical trials less resource-intensive. As the advancements continue to evolve the drug safety evaluation is likely to become even more adaptive and enable more precise and targeted QTc assessment.

Quantitative Systems Pharmacology (QSP) has emerged as a cornerstone of modern drug development, providing a robust framework to integrate data from preclinical and clinical studies, enhance decision-making, and optimize therapeutic strategies. By modeling biological systems and drug interactions, QSP enables predictions of outcomes, optimization of dosing regimens, and personalized medicine applications. Recent advancements in artificial intelligence (AI) and machine learning (ML) hold the potential to significantly transform QSP by enabling enhanced data extraction, fostering the development of hybrid mechanistic ML models, and supporting the introduction of surrogate models and digital twins. This manuscript explores the transformative role of AI and ML in reshaping QSP modeling workflows. AI/ML tools now enable automated literature mining, the generation of dynamic models from data, and the creation of hybrid frameworks that blend mechanistic insights with data-driven approaches. Large Language Models (LLMs) further revolutionize the field by transitioning AI/ML from merely a tool to becoming an active partner in QSP modeling. By facilitating interdisciplinary collaboration, lowering barriers to entry, and democratizing QSP workflows, LLMs empower researchers without deep coding expertise to engage in complex modeling tasks. Additionally, the integration of Artificial General Intelligence (AGI) holds the potential to autonomously propose, refine, and validate models, further accelerating innovation across multiscale biological processes. Key challenges remain in integrating AI/ML into QSP workflows, particularly in ensuring rigorous validation pipelines, addressing ethical considerations, and establishing robust regulatory frameworks to address the reliability and reproducibility of AI-assisted models. Moreover, the complexity of multiscale biological integration, effective data management, and fostering interdisciplinary collaboration present ongoing hurdles. Despite these challenges, the potential of AI/ML to enhance hybrid model development, improve model interpretability, and democratize QSP modeling offers an exciting opportunity to revolutionize drug development and therapeutic innovation. This work highlights a pathway toward a transformative era for QSP, leveraging advancements in AI and ML to address these challenges and drive innovation in the field.

Oligonucleotide therapeutics hold promise for targeted gene silencing, yet achieving optimal tissue-specific delivery remains challenging. This study introduces a mechanistic whole-body physiologically based pharmacokinetic (PBPK) model to predict tissue uptake dynamics of both conjugated (targeted) and unconjugated oligonucleotides across species. The model incorporates two uptake pathways: a non-saturable nonspecific pathway for all oligonucleotides and receptor-mediated endocytosis (RME) specific to conjugated molecules. Parameters for nonspecific uptake were derived from plasma and tissue concentration data of unconjugated antisense oligonucleotides (ASOs) in rats, while RME parameters for N-acetylgalactosamine (GalNAc)-conjugated oligonucleotides targeting the asialoglycoprotein receptor (ASGPR) were obtained from literature. Model validation against experimental data for conjugated and unconjugated ASOs and small interfering RNAs (siRNAs) in rats and mice demonstrated good predictive performance, with median predicted-to-observed AUC ratios of 0.84 (Interquartile range [IQR] 0.434-1.22) in rats and 0.629 (IQR 0.3-1.6) in mice. Local sensitivity analyses identified key parameters and processes influencing organ uptake, including the unbound plasma fraction and receptor-mediated uptake efficiency. Simulations highlighted the potential of sustained-release formulations to improve targeting specificity by mitigating receptor saturation. This is the first whole-body PBPK model to describe oligonucleotide pharmacokinetics across species and modalities. The model provides critical mechanistic insights to optimize tissue-specific delivery, guide formulation strategies, and enhance therapeutic outcomes for targeted oligonucleotide therapeutics.

Advancements in large language models (LLMs) have suggested their potential utility for diverse pharmacometrics tasks. This study investigated the performance of LLM for generating structure diagrams, publication-ready tables, analysis reports, and conducting simulations using output files from pharmacometrics models. Forty-four NONMEM output files were obtained from the GitHub software repository. The performance of Claude 3.5 Sonnet (Claude) and ChatGPT 4o was compared with two other candidate LLMs: Gemini 1.5 Pro and Llama 3.2. Prompt engineering was conducted for Claude for pharmacometrics tasks such as generating model structure diagrams, parameter tables, and analysis reports. Simulations were conducted using ChatGPT. Claude Artifacts was used to visualize model structure diagrams, parameter tables, and analysis reports. A web-based R Shiny application was implemented to provide an accessible interface for automating pharmacometric model structure diagrams, parameter tables, and analysis reports tasks. Claude was selected for investigation following performance comparisons with ChatGPT 4o, Gemini 1.5 Pro, and Llama on model structure diagram and parameter table generation tasks. Claude successfully generated the model structure diagrams for 40 (90.9%) of the 44 NONMEM output files with the initial prompts, and the remaining were resolved with an additional prompt. Claude consistently generated accurate parameter summary tables and succinct model analysis reports. Modest variability in model structure diagrams generated for replicate prompts was identified. ChatGPT demonstrated simulation capabilities but revealed limitations with complex PK/PD models. LLMs have the potential to enhance key pharmacometrics modeling tasks. However, expert review of the results generated is essential.

The aim of the analysis was to develop a phenomenological longitudinal population pharmacokinetics (PK)-anti-drug antibodies (ADA) model to enable an informed and quantitative framework for assessment of ADA influence. Data used were from seven clinical studies of avelumab across drug development phases in patients with several tumor types. ADA as covariate in a population PK model, and Markov models of ADA status (ADA+ or ADA-) were investigated. Finally, a joint PK-ADA model was developed. In the population PK models that evaluated ADA as a covariate, the clearance increase attributable to ADA+ status was 8.5% (time-varying ADA) to 19.9% (time-invariant ADA with inter-occasion variability in clearance). With a discrete-time Markov model (DTMM), tumor type was identified as a significant covariate on the probability of ADA- to ADA+ transition. When ADA time course predicted by the DTMM model was implemented as a covariate in the population PK model, an increase in avelumab clearance of 11-41% was estimated depending on tumor type. With a continuous-time Markov model (CTMM), in addition to tumor type, baseline ADA status was identified to significantly influence the ADA- to ADA+ transition rate constant. The joint PK-CTMM model estimated the maximal increase in CL due to ADA as 15% and a decrease in ADA- to ADA+ transition rate of up to 37% with increasing avelumab concentration, with 50% of the maximum decrease occurring at 349 µg/mL. The present work established a framework for the assessment of interactions between PK and immunogenicity for therapeutic proteins.

The assessment of drug-induced QT interval prolongation and associated proarrhythmic risks, such as Torsades de Pointes (TdP), has evolved significantly over the past decades. This review traces the development of nonclinical QT evaluation, highlighting key milestones and innovations that have shaped current practices in cardiac safety assessment. The emergence of regulatory guidelines, including International Council for Harmonization of Technical Requirements for Pharmaceuticals for Human Use (ICH) S7B, established a nonclinical framework for evaluating drug effects on cardiac repolarization, addressing concerns raised by drug withdrawals in the 1990s. Advances in in vitro, in vivo, and in silico models have enhanced the predictive accuracy of nonclinical studies, with the hERG assay and telemetry-based animal models becoming gold standards. Recent initiatives, such as the Comprehensive in vitro Proarrhythmia Assay (CiPA) and the Japan iPS Cardiac Safety Assessment (JiCSA), emphasize integrating mechanistic insights from human-derived cardiomyocyte models and computational approaches to refine risk predictions. The 2020s mark a shift toward integrated nonclinical-clinical risk assessments, as exemplified by the ICH E14/S7B Questions and Answers. These highlight the need of best practices for study design, data analysis, and interpretation to support regulatory decision-making. Furthermore, the adoption of New Approach Methodologies (NAMs) and reinforced adherence to 3Rs principles (Reduce, Refine, Replace) reflect a commitment to ethical and innovative safety science. This review underscores the importance of harmonized and translational approaches in cardiac safety evaluation, providing a foundation for advancing drug development while safeguarding patient safety. Future directions include further integration of advanced methodologies and regulatory harmonization to streamline nonclinical and clinical risk assessments.