CPT: Pharmacometrics & Systems Pharmacology最新文献

Non-Hodgkin lymphoma (NHL) is the fifth most common malignancy and accounts for 5% of all cancers in the US, with the largest proportion being B-cell CD20 positive NHL. Odronextamab is a CD20xCD3 IgG4 bispecific T-cell-engaging monoclonal antibody under development for the treatment of relapsed or refractory (R/R) B-NHL. The objectives of this analysis were to characterize the pharmacokinetics (PK) of odronextamab in adult patients, and elucidate sources and correlates of variability. PK data of 507 patients with R/R B-NHL from ELM-1 (NCT02290951, Phase I; n = 167) and ELM-2 (NCT03888105, Phase II; n = 340) were analyzed. Odronextamab concentration–time profiles following intravenous administration of 0.03 mg to 320 mg doses were described by a bi-exponential decline with parallel linear (first-order) and non-linear (Michaelis–Menten) elimination processes. The modified Michaelis–Menten or target-mediated elimination was not only concentration–dependent but also time-dependent. A reduction in target-mediated clearance over time suggests a reduction in target abundance to a larger extent than associated with concentration alone, which is consistent with the treatment-induced depletion of the B cells observed in patients who underwent assessment. Linear clearance (CL) and steady-state volume of distribution were 0.189 L/day and 9.41 L, respectively. Target-mediated clearance was ~5 L/day at baseline, with an asymptote of ~0.03 L/day at steady state. With the largest covariate effect on odronextamab exposure, baseline body weight was directly correlated with CL and volume of distribution, albumin was inversely correlated with CL and volume of distribution, and baseline interleukin-10 was inversely correlated with CL.

Model-based meta-analysis (MBMA) informs key drug development decisions by integrating data, published or unpublished, from multiple studies. Due to these various sources of information and the use of summary-level data (e.g., mean responses over treatment arms or percent responders), MBMA models require careful implementation. This tutorial provides a comprehensive guide for conducting an MBMA with MonolixSuite, focusing on longitudinal continuous and categorical data. Two case studies are presented: the first examining naproxen in osteoarthritis and the second evaluating canakinumab compared to existing treatments in rheumatoid arthritis. The tutorial explains the process of model building and handling study heterogeneity in Monolix, including how to include between-study variability and between-treatment-arm variability. It also shows how to apply appropriate weighting due to the use of summary-level data. For model evaluation, the tutorial demonstrates the use of automatically generated diagnostic plots, statistical tests, and convergence assessment tools. Furthermore, it illustrates how to use the model in Simulx to support the decision-making process, such as by simulating clinical trials. This step-by-step guidance offers practical insights for leveraging MBMA in model-informed drug development.

We present a framework for maximum likelihood analysis on count observations that begin high and quickly drop to zero, for example, from hollow fiber drug comparison studies. This simulation study focuses on treating observed counts as Poisson or normally distributed for the purpose of estimating infection rebound after effective treatment. CFU profiles were simulated from inoculation to 96 h post-treatment. The PK-PD link was an Emax inhibitory model. Random parameters were pathogen growth and natural decay rates, drug concentration for half-maximal effect, and drug pathogen kill rate. Other parameters, including PK, were fixed. Parameters were adjusted to attain 67% efficacy at 24 h. Random parameter values were optimized for profiles observed at 24, 48, 72, and 96 h assuming each of four probability assumptions: (1) all CFU measurements were Poisson distributed (truth); (2) CFU < 128 were Poisson, higher values were normally distributed; (3) all observations were normally distributed; and (4) observations were normally distributed but CFU < 10 were censored. CFU-time profiles were re-simulated using the optimized parameter densities. Rebound percentage (CFU ≥ 10 at 24 h post-treatment) was best predicted using strategy 2, above. For limited periodically collected time series count data that quickly fall to 0, the true proportion reaching 0 (lack of rebound) was best modeled by assuming Poisson distribution at low counts. At higher counts (≥ 128), assuming normality is reasonable. Censoring observations leads to biased models.

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.

Physiologically based pharmacokinetic (PBPK) modeling of placental drug transfer is an evolving tool for predicting fetal drug exposure. In this study, a pregnancy-specific metformin PBPK model was developed, and the following four approaches were evaluated to predict metformin placental transfer: (1) perfusion-limited model, and permeability-limited models using (2) ex vivo cotyledon open system apparent clearance, (3) ex vivo cotyledon closed system data fit to a three-compartment model to estimate clearance, and (4) active transport kinetics and passive clearance. Simulated metformin maternal plasma concentrations (MPCs) and umbilical cord venous plasma concentrations (UCCs) were compared to observed in vivo data from subjects with gestational diabetes mellitus taking metformin 500 mg twice daily. Model selection criteria were determined by the percentage of observed clinical data falling within the 5th to 95th percentiles of the simulated population. Among the approaches, the model that included passive permeability and in vitro intrinsic transporter clearances (Approach 4) best described placental metformin transfer, with 92% of UCCs falling within the 5th to 95th percentiles of the simulated population. Furthermore, maternal uptake transport had the largest influence on predicted UCCs. A two-fold increase in maternal uptake transport increased the predicted population mean UCC C_max by 97%, whereas a 0.5-fold decrease resulted in a 49% decrease in UCC C_max. This refined PBPK model offers a valuable framework for predicting placental transfer and fetal exposure of metformin when placental transporters are altered throughout pregnancy and/or with pathological conditions.

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.