CPT: Pharmacometrics & Systems Pharmacology最新文献

Apixaban, a factor Xa inhibitor, is a direct oral anticoagulant with a well-balanced elimination; it is eliminated evenly via feces, urine (with no active secretion), and as metabolites after oral administration. The common understanding is that biliary secretion and enterohepatic circulation (EHC) of apixaban are limited in humans, and that fecal excretion may be attributable to intestinal secretion. However, a decrease in apixaban blood concentration with activated charcoal coadministration in humans suggests possible involvement of EHC. This study aimed to evaluate the contribution of biliary excretion, EHC, and intestinal secretion to apixaban pharmacokinetics (PK) using a physiologically-based pharmacokinetic (PBPK) model. A top-down analysis was performed using blood concentration and mass balance data from healthy volunteers. Model parameters were optimized using the Cluster-Gauss Newton method (CGNM), followed by the bootstrap method. The model accurately described observed data and indicated moderate to high biliary secretion relative to metabolic clearance. Simulated biliary secretion into the duodenum well predicted the biliary secretion data in humans (< 1% of dose at 8 h post-dose). Virtual knockout of EHC resulted in a shortened half-life from 8.7 to 2.9 h, and 17% and 55% decrease in area under the concentration curve (AUC) and fecal excretion after intravenous dosing, respectively, confirming the significant contribution of biliary excretion and EHC. The model also accurately described apixaban PK with activated charcoal coadministration at 2 or 6 h post-dose. Although further experimental validation (e.g., sandwich-cultured hepatocytes) would strengthen these findings, our study demonstrates that biliary secretion and EHC play a substantial role in apixaban elimination and disposition in humans.

A twice-daily administration of oral selumetinib (SLT) in the fasted state is the only approved pharmaceutical option for treating inoperable neurofibromatosis type I (NF-1) and plexiform neurofibromas (PN). In children, exposure to SLT is highly variable, and fasting presents a substantial burden. Therapeutic drug monitoring and pharmacokinetic modeling can support individualized therapy accompanied by a more rational alimentary routine. Twenty-eight children diagnosed with inoperable NF-1 or PN were recruited at a major pediatric oncological center. Twenty-two patients donated 156 blood samples in steady state for nonparametric population pharmacokinetic modeling. An equation was developed experimentally for estimating model error. Eleven three-compartment models were compared in terms of statistical performance. Monte Carlo simulations were performed to validate a limited external model using six additional patients and to compare the trough-to-peak SLT concentration ratios simulated for various dosing regimens to develop better control over exposure. A pharmacokinetic model that included total body weight as a covariate provided the best fit between predicted and observed concentrations (r = 0.994) and the best performance statistics. In the first Monte Carlo simulation, measured concentrations fell within the 0.01%–95% (median: 19.7%) quantiles of the simulated ranges. The second simulation revealed that 6-h (q6h), 8-h (q8h), and 12-h (q12h) dosing intervals would yield comparable trough-to-peak concentration ratios, with medians of 0.126 (range: 0.001–0.335), 0.104 (0.000–0.306), and 0.065 (0.000–0.279), respectively. The nonparametric population model provides efficient priors for making individual predictions of SLT concentrations. The simulation did not reveal any disadvantages of q6h or q8h dosing.

Patients with metabolic dysfunction-associated steatotic liver disease (MASLD) may exhibit altered pharmacokinetics (PK) and pharmacodynamics (PD) of drugs compared with healthy populations. However, no physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) model has been specifically developed for MASLD. Acetaminophen (APAP), a widely used analgesic, was selected to develop a PBPK/PD model predicting PK/PD changes of APAP and its metabolites in MASLD-related populations. Based on a comprehensive review of published APAP PK studies and examination of existing PBPK models, a simultaneous parent-metabolite PBPK model for APAP was developed and optimized in healthy people. The model simulated the dynamics of APAP and its five major metabolites: APAP-glucuronide (APAP-glu), APAP-sulfate (APAP-sul), N-acetyl-p-benzoquinone imine (NAPQI), APAP-cysteine (APAP-cys), and APAP-mercapturate (APAP-merc). The validated model was expanded to MASLD-related populations, including overweight, obese, nonalcoholic fatty liver disease (NAFLD), nonalcoholic steatohepatitis (NASH), and cirrhosis with different severities. Finally, a PD model was integrated to correlate APAP's PK with pain relief scores. The PBPK model reproduced published clinical PK data for APAP and its metabolites in healthy and MASLD-related populations. At therapeutic doses, the toxic NAPQI remained at very low levels. APAP's pain relief efficacy was retained, but onset time may change in MASLD-related populations. This PBPK/PD approach provides a strategy for projecting drug exposure in MASLD-related populations, even without specific PK or PD data. It highlights modeling's utility for personalized medicine in MASLD patients and MASLD treatment drug development.

Rituximab (RTX), an anti-CD20 monoclonal antibody, has been used to treat autoimmune diseases such as rheumatoid arthritis (RA). However, variability in therapeutic response to RTX remains a challenge. Here, a systems model is developed to mimic B cell differentiation leading to antibody-secreting cells (ASCs), including plasmablasts (PBs) and plasma cells (PCs). The model features the localization of B cell subsets in the bone marrow and secondary lymphoid organs and incorporates the internalization process of the CD20–RTX complex. To reproduce clinical data from patients with RA receiving RTX and glucocorticoids, pharmacokinetic models for the drugs were built and respective pharmacodynamic profiles of CD19⁺ and CD20⁺ cells and PBs were well captured by optimizing model parameters, which were estimated with good precision. As ASCs are the primary source of pathogenic autoantibodies in RA, the extent and duration of ASC depletion were hypothesized as drivers of therapeutic response to RTX. Global sensitivity analyses identified the CD20–RTX binding affinity and elimination rate constant (i.e., Fcγ-mediated degradation, internalization) as major determinants of both CD19⁺ cells and ASCs. The influence of baseline PBs and PCs on ASCs was also suggested, providing potential mechanisms underlying responder and non-responder variability. The model accurately reproduced the temporal changes in CD19⁺ cells after combination treatment with RTX and glucocorticoids suggesting successful model validation. This study provides a mechanistic framework and insights into key drivers of responses to CD20-depletion treatment using B cell dynamics as an indirect biomarker of clinical endpoints, which might ultimately improve therapeutic outcomes.

The tyrosine kinase inhibitor, nintedanib, reduces the rate of decline in forced vital capacity (FVC) in a comparable manner in patients with idiopathic pulmonary fibrosis (IPF), other forms of progressive pulmonary fibrosis (PPF), and systemic sclerosis-associated ILD (SSc-ILD). The recommended dose of nintedanib in all indications is 150 mg twice daily (BID). Data from Phase II and III trials in IPF, PPF, and SSc-ILD were incorporated into a meta-model to holistically investigate the relationship between nintedanib exposure and efficacy. Using data from 2642 patients with IPF, PPF, or SSc-ILD treated with nintedanib doses ranging from 50 to 150 mg BID, disease progression models with a maximum drug effect on the annual rate of change in absolute FVC (i.e., mL), FVC %predicted, and FVC Z-score were developed. The estimated plasma concentration producing 50% of the maximum drug effect (EC₅₀) ranged from 6.21 to 10.4 nM (with respect to nintedanib trough concentration) across the explored FVC-based endpoints. While the disease progression for absolute FVC (mL), FVC %predicted, and FVC Z-score was different between IPF and PPF patients compared to SSc-ILD patients, the relative treatment effect of nintedanib, described by a disease-modifying E_max effect, was comparable across indications. The majority of patients achieve exposure levels at or exceeding the EC₅₀ with the approved starting dose of 150 mg BID.

This work aimed to develop an appropriate model to evaluate the exposure–response relationship (ERR) for monthly migraine days (MMD) in atogepant's key migraine prevention clinical trials to support dose selection. The ERR between atogepant concentration and MMD over time was analyzed utilizing data from one phase 2b/3 and three phase 3 studies in patients with episodic or chronic migraine (EM/CM). Several distribution models were evaluated for placebo data, whereas two modified normal distributions were introduced enabling bounded MMD modeling. Exposure metrics and shapes for ERR were tested for the most suitable distribution. Stepwise covariate search, visual predictive checks, and plots of model-predicted MMD over the range of exposure metrics were utilized in model development, evaluation, and selection. The final MMD exposure–response model was able to model patients with EM/CM simultaneously and was based on a modified normal distribution with E_max ERR on C_min. The model adequately described the observed data over time. Due to the E_max relationship, MMD at Week 9–12 plateaued around their model-based atogepant C_min-EC₉₀ of 3.71 nM, which is similar to most C_min exposures seen at the 10 mg once-daily regimen. All approved atogepant dosages for EM/CM achieve effective concentrations to inhibit the calcitonin gene-peptide receptor by 90%. Patients who have been failed by conventional oral migraine preventive treatments or patients with a higher baseline MMD may require a longer treatment period to reach atogepant's maximal effect. No significant difference in efficacy was evident in patients exposed to prior oral migraine preventives compared to treatment-naïve patients.

Infliximab, a monoclonal antibody used for immune-mediated diseases, shows high interpatient pharmacokinetic variability. Prolonged exposure increases the risk of adverse effects and costs, making dose personalization essential to balance safety, efficacy, and cost-effectiveness. Population pharmacokinetic models support individualized dosing, but different models may predict varying drug exposure for the same patient. This study aims to identify compatible models for each patient and assess the impact of model selection on dosing. This retrospective study included adult Crohn's disease patients receiving infliximab. Published pharmacokinetic models were screened. Model-patient compatibility was evaluated using Multivariate Exact Discrepancy through 100,000 Monte Carlo simulations. The Metropolis-Hastings algorithm generated individual parameter distributions. For each model-patient pair, the median and 90% confidence interval of the dose required to achieve a target exposure of 2079 mg*day/L were computed. Sixteen models were tested. No model was compatible with all patients. Dosing was calculated only for compatible pairs. The average median dose was 9.25 mg/kg, with an average imprecision of 6.63 mg/kg. The highest median dose reached 23.21 mg/kg, reflecting inter-model differences, while the greatest imprecision (25.69 mg/kg) stemmed from patient variability. This concentration-based method personalizes dosing via pharmacokinetic profiling. Patients can be classified into three groups: (1) those for whom all models provide similar recommendations, indicating high reliability across models; (2) those incompatible with all models, for whom the posology recommended by the manufacturer should be prioritized; and (3) those for whom some models are compatible but intensified therapeutic drug monitoring is required.

Analyzing exposure-response (E-R) relationships for time-to-event (TTE) endpoints presents challenges due to the inherent time-dependent nature of the data. Some authors address these difficulties by using a fixed timepoint approach, where exposure is assessed at a predetermined time rather than dynamically over time. (e.g., initial exposure or last exposure). The aim of the current work is to compare the use of time-static and time-varying metrics to assess the E-R relationship through simulations. PK exposures were simulated from a one-compartment model and TTE data from a parametric proportional hazard model, involving the weekly average PK concentration as a time-varying covariate. Several scenarios were considered to handle the type of dosing (fixed or adaptive), the accumulation of the drug (low or strong), the type of event (efficacy, safety or independent), and the timing of the event onset (early or late). Wald tests on the exposure effect parameter were performed to assess the significance of the E-R relationship. For each simulation scenario, the type-I error and the power of the Wald tests were reported, revealing that no time-static metric consistently produced reliable results across all conditions. In order to ensure adequate statistical properties, we recommend using time-varying exposure, which shows good performance across all scenarios.

Pediatric extrapolation strategies issued by health authorities have streamlined pediatric drug development and reduced the unnecessary burden of conducting pediatric clinical studies. In line with these strategies, physiologically based pharmacokinetic (PBPK) models have been utilized extensively for initial dosing regimen and sampling timepoint selection for pediatric studies, as well as dose validation throughout pediatric drug development. Here, the status and challenges of PBPK modeling in pediatric drug development have been summarized by the IQ Pediatric PBPK Working Group. Our work reviews current practices for pediatric PBPK modeling across various therapeutic areas. To enable best practice, we propose an optimized workflow for pediatric PBPK modeling recommendations. Two selected key pediatric PBPK case examples are also described, where modeling impacted the drug label extension to pediatric patients. Moreover, we analyze the current gaps and challenges in our understanding of drug absorption, distribution, metabolism, and elimination in pediatric PBPK model development. Since neonates are the least studied and the most medically fragile, the depth of our understanding of their rapidly evolving physiological processes is limited and so there exist significant modeling gaps which we summarize here. Finally, we provide recommendations, including building a public data repository, leveraging real-world data, and implementing microdose studies for addressing pediatric PBPK modeling challenges.