Journal of Pharmacokinetics and Pharmacodynamics最新文献

Pharmacokinetics and toxicological studies how the body reacts to a specific administered substance, such as a drug, toxin, or food. Each substance experiences these four steps: absorption, distribution, metabolism, and excretion, which are the main parameters in pharmacokinetics studies. Many toxic endpoints exist. There are three main ways to measure toxicology and pharmacokinetic parameters: in vivo, in vitro, and in-silico. Knowing toxicological and pharmacokinetic parameters before developing a new drug candidate could save time and resources, as clinical studies are highly cost-demanding. This review aims to gather studies using in-silico methodologies to predict pharmacokinetic properties.

Background: The ε4 allele of the apolipoprotein E gene (APOE4) is a major risk factor for developing sporadic Alzheimer's disease (AD). APOE4 homozygosity has been recently proposed as the defining signature of a genetic form of AD. The goal was to assess the role, if any, of APOE4 in AD progression using a mixed-effects disease progression model-informed approach.

Methods: Data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) were analyzed for 2092 participants categorized as cognitively normal (CN), subjective memory concerns (SMC), early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI), or AD. Each included subject had a median of 5.00 (IQR: 3-8) follow-ups; there were n = 13,699 follow-ups. Demographics, APOE4 genotype, cerebrospinal fluid biomarkers, MRI measures, and neuropsychological tests from baseline to 6-years of follow-up visits were analyzed. Linear mixed-effects models were used to evaluate the impact of the APOE4 genotype on disease progression.

Results: APOE4 heterozygous and homozygous frequencies were higher in AD vs. CN (p < 0.001). APOE4-positive groups were associated with lower levels of amyloid β1-42, higher levels of Tau and phosphorylated tau-181 proteins, lower hippocampus and entorhinal volumes, and worse AD Assessment Scale Cognitive-11 (ADAS-COG11), ADAS-COG13, and Mini-Mental State Examination neuropsychological test scores. The progression of the biomarkers over time was not associated with APOE4 positivity. The progression of all MRI measures and neuropsychological test scores was associated with APOE4 positivity.

Conclusions: APOE4 genotypes adversely influence the levels of biomarkers and the progression of neuroimaging and cognitive outcomes in AD.

Concentration-QTc (C-QTc) analysis was accepted to serve as an alternative to the by-time point analysis with intersection-union test (IUT) as the primary basis for decisions to classify the arrhythmogenic risk of a drug by ICH E14 Q&As (R3) in December 2015. Since then, this analysis method has been widely applied by industry as it significantly reduces the sample size to achieve the same power as with IUT. There are many model-based power calculation approaches available for C-QTc through simulation in the literature, however, there is still no standard method with a clear formula to determine the sample size for C-QTc analysis to exclude a small effect on the QTc interval. The current model-based simulation approaches are too complicated to prevent them from being widely used, which is not commensurate with the popular status. We have developed a systematic method based on t-tests to determine the sample size for different study designs using the C-QTc analysis method and applied it to many studies. The results of the sample sizes utilizing this method are consistent with simulation studies and validated by real analyses.

Proficiency in English is essential in scientific disciplines; however, it is unevenly distributed globally, creating barriers for those with limited training. Research in neuroscience supports the benefits of teaching in a student's native language. Consequently, pharmacometrics, a complex and growing field, stands to gain significantly from overcoming language barriers to better train future scientists. One effective strategy is to offer pharmacometrics education in various languages, particularly in regions with low English proficiency, such as French-speaking African countries. Recently, two French-led pharmacometrics training programs were conducted in Africa, demonstrating the positive impact of such initiatives. These programs are adaptable to other countries and languages, and ultimately, they could contribute to global health improvements by making pharmacometrics education more accessible worldwide.

Antibody-drug conjugates (ADCs) are advanced cancer therapeutics that link monoclonal antibodies to cytotoxic drugs, enhancing targeted delivery to tumors. Since the FDA's first ADC approval in 2000, 14 ADCs have received approval to date (March 2025), underscoring their therapeutic value across cancer types. A prolonged QT interval is a known risk factor for the development of torsades de pointes (TdP), a potentially fatal ventricular arrhythmia. Therefore, assessing and mitigating the potential for QT prolongation is a fundamental aspect of drug development, especially for oncology therapeutics where patients may already be at an increased risk of cardiovascular complications or receiving other QT-prolonging drugs. Traditional QT risk assessment, as outlined in the ICH E14 guidance, is challenging in oncology due the safety profile of anticancer drugs, which precludes study in healthy participants, and the ethical complications of placebo-controlled studies in patients with cancer; therefore, dedicated QT studies and/or concentration-corrected QT (QTc) assessments have been used as alternative approaches. This review investigates QT risk assessment for FDA-approved ADCs, examining nonclinical and clinical approaches and summarizing the strategies used in informing each ADC's labeling. Findings suggest that ADCs generally exhibit low proarrhythmic risk, attributed to the low systemic concentration of their payloads, and minimal QT effects have been observed in clinical settings. This analysis advocates a streamlined, fit-for-purpose QT risk assessment strategy in ADC development, reducing reliance on dedicated QT studies and promoting integrated assessments in early-phase trials. This approach can optimize ADC safety evaluation, supporting ongoing innovation and therapeutic application in oncology.

Concentration-QTc (C-QTc) analysis is a model-based method widely used to assess the impact of drugs on QT interval duration. C-QTc modelling was enabled to be used after the publication of the International Council for Harmonisation (ICH) E14 Questions and Answers guidance document in 2015, followed by the Scientific White Paper on C-QTc modelling (Garnett et al. J Pharmacokinet Pharmacodyn 45(3):383-397 2018), which included technical details and recommendations on how to perform and report the modelling. This hands-on tutorial aims to provide a practical implementation of the recommended C-QTc modelling methodology, including R code to perform the complete analysis, from data formatting to model predictions. The target audience is scientists who will perform C-QTc analyses. The tutorial uses real data from a previously published QT study by (Johannesen et al.Clin Pharmacol Ther 96(5):549-558 2014), focusing on two active treatments (dofetilide and verapamil) and placebo to illustrate positive and negative QT signals. The methodology implemented in this tutorial follows the recommendations outlined in the White paper. This tutorial includes practical steps for preparing an analysis-ready dataset, conducting exploratory data analysis, fitting the linear mixed effects (LME) model, assessing model performance and estimating the upper limit of the two-sided 90% confidence interval (CI) of baseline and placebo-corrected QTc (ΔΔQTc). Reproducibility of this workflow is ensured through the use of pkgr to manage R packages. The R codes provided as part of this tutorial were successfully used for several projects within the AstraZeneca portfolio and accepted by health authorities as part of QTc submissions.

Erythropoiesis is a complex process that results in the production of erythrocytes from hematopoietic stem cells in the bone marrow. This work aimed to develop a population pharmacokinetic-pharmacodynamic (PKPD) model describing erythropoiesis and hemoglobin synthesis following bitopertin, an inhibitor of glycine transporter 1 (GlyT1), administration. Data from a Phase 1 clinical trial in 67 healthy subjects administered bitopertin (10, 30, or 60 mg) or placebo for 120 days were analyzed. Hematological assessments included erythrocyte and reticulocyte counts, immature reticulocyte fraction, hemoglobin concentration, and mean corpuscular hemoglobin. The proposed semi-mechanistic model, which leverages data and physiological knowledge, was found to adequately simultaneously describe the dose- and time-dependent changes in the biomarkers. The framework was used to illustrate the potential outcome of hypothetical drug-target interactions at distinct stages of erythropoiesis and hemoglobin synthesis, exemplifying its usefulness in a clinical setting.

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.