CPT: Pharmacometrics & Systems Pharmacology最新文献

The purpose of the analysis was to evaluate if 10 mg naloxone, administered intramuscularly, could reverse or prevent opioid-induced respiratory depression (OIRD), including OIRD associated with the administration of lethal doses of high-potency opioids. A naloxone population pharmacokinetic (PK) model was generated using data from two naloxone auto-injector (NAI) clinical PK studies. Mechanistic OIRD PK-pharmacodynamic (PD) models were constructed using published data for buprenorphine, morphine, and fentanyl. Due to the lack of published carfentanil data in humans, interspecies allometric scaling methods were used to predict carfentanil PK parameters in humans. A PD model of a combined effect-compartment and receptor kinetics model with a linear relationship between ventilation and carbon dioxide was used to predict the respiratory depression induced by carfentanil. Model-based simulations were performed using the naloxone population PK model and the constructed mechanistic OIRD PK–PD models. Changes in ventilation were assessed after opioid exposure and treatment with 2 mg naloxone or one or two doses of 10 mg naloxone. A higher percentage of subjects recovered back to the rescue ventilation thresholds and/or had a faster recovery to 40% or 70% of baseline ventilation with 10 mg compared with 2 mg naloxone. A second dose of 10 mg naloxone, administered 60 min post-opioid exposure, expedited recovery to 85% of baseline ventilation and delayed time to renarcotization compared with a single dose. In addition, when 10 mg naloxone was administered at 5, 15, 30, or 60 min before fentanyl or carfentanil exposure, rapid and profound OIRD was prevented.

In breastfeeding mothers, managing medical conditions presents unique challenges, particularly concerning medication use and breastfeeding practices. The transfer of drugs into breast milk and subsequent exposure to nursing infants raises important considerations for drug safety and efficacy. Modeling approaches are increasingly employed to predict infant exposure levels, crucial for assessing drug safety during breastfeeding. Physiologically-based pharmacokinetic (PBPK) modeling provides a valuable tool for predicting drug exposure in lactating individuals and their infants. This tutorial offers an overview of PBPK modeling in lactation research, covering key concepts, prediction approaches, and best practices for model development and application. We delve into milk composition dynamics and its influence on drug transfer into breast milk, addressing modeling considerations, knowledge gaps, and future research directions. Practical examples and case studies illustrate PBPK modeling application in lactation studies. We demonstrate how prediction algorithms for Milk-to-Plasma (M/P) ratios within a PBPK framework can support scenarios lacking clinical lactation data or extend the utility of available lactation clinical data to support further untested clinical scenarios. This tutorial aims to assist researchers and clinicians in understanding and applying PBPK modeling to understand and support clinical scenarios in breastfeeding mothers. Advances in PBPK modeling techniques, along with ongoing research on lactation physiology and drug disposition, promise further insights into drug transfer during lactation.

Patients with severe and sometimes moderate hemophilia B are prophylactically treated with factor IX concentrates to prevent bleeding. For some time now, various extended terminal half-life (EHL) recombinant factor IX concentrates are available allowing less frequent administration during prophylaxis in comparison to standard half-life recombinant FIX (rFIX). Especially, recombinant FIX-Fc fusion protein (rFIXFc; Alprolix®) exhibits a rapid distribution phase, potentially due to binding to type IV collagen (Col4) in the extravascular space. Studies suggest that the presence of extravascular rFIXFc is protective against bleeding as without measurable FIX activity in plasma, and no extra bleeding seems to occur. The physiologically based pharmacokinetic (PBPK) model for rFIXFc which we describe in this study, is able to accurately predict the observed concentration-time profiles of rFIXFc in plasma and is able to quantify the binding of rFIXFc to Col4 in the extravascular space after an intravenous dose of 50 IU/kg rFIXFc in a male population. Our model predicts that the total AUC of rFIXFc bound to Col4 in the extravascular space is approximately 19 times higher compared to the AUC of rFIXFc in plasma. This suggests that rFIXFc present in the extravascular compartment may play an important role in achieving hemostasis after rFIXFc administration. Further studies on extravascular distribution of rFIXFc and the distribution profile of other EHL-FIX concentrates are needed to evaluate the predictions of our PBPK model and to investigate its clinical relevance.

Zastaprazan (JP-1366) is a novel potassium-competitive acid blocker for the treatment of acid-related disorders. We aimed to establish a population pharmacokinetic (PK) model of zastaprazan, thereby characterizing the PK of zastaprazan in patients with gastroesophageal reflux disease (GERD) as well as evaluating the impact of various covariates, including CYP2C19 phenotypes, on zastaprazan PK. This population PK analysis included zastaprazan plasma concentration-time data from 92 patients with erosive GERD and 68 healthy volunteers without any gastrointestinal disorders and was performed using nonlinear mixed-effect modeling. Simulations were conducted to predict zastaprazan PK under various dosing regimens in patients with GERD. The plasma PK of zastaprazan was adequately described by a two-compartment model with Erlang-type absorption (six sequential compartments) and first-order elimination. CYP2C19 phenotypes had no significant effect on zastaprazan PK. The disease status was identified as a significant covariate on apparent clearance of zastaprazan, showing lower values in patients with GERD compared to healthy volunteers. However, the model-based simulation indicated that the impact of disease status on zastaprazan exposure was not clinically meaningful. Overall, the current population PK model successfully characterized the observed zastaprazan PK in both patients with GERD and healthy volunteers.

Integrated modeling of the pharmacokinetic (PK) and target binding, by means of a TMDD model, can provide valuable insights into the expected pharmacodynamic (PD) effects of monoclonal antibodies (mAbs). Optimal characterization of the human PK and target binding for mAbs requires data obtained after intravenous (IV) administration which can be combined with subcutaneous (SC) data to further this characterization. Integration of free and/or total target measurements in a population TMDD model will allow quantification of target engagement which is the first step in the cascade leading to efficacy. However, the assays for determination of free target concentrations are analytically challenging and are inherently biased to overpredict the true concentrations in the presence of mAb:target complexes. For that reason, the objective of the current research was to evaluate the predictive value of free target concentrations in a TMDD model developed using PK and total target observations only. Further, a secondary objective was to demonstrate that prediction of SC data is feasible, based on an existing IV model and typical values of mAb parameters reported for SC absorption. GSK3772847, a human immunoglobulin G2 sigma isotype (IgG2f) mAb that binds to the extracellular domain of the interleukin-33 receptor (IL-33R or ST2) and neutralizes IL-33-mediated ST2 signaling, was used as a model compound for mAbs in this study.

Artemisinin-based combination therapy (ACT) is the first-line recommended treatment for uncomplicated malaria. Pharmacokinetic (PK) properties in pregnant women are often based on small studies and need to be confirmed and validated in larger pregnant patient populations. This study aimed to evaluate the PK properties of amodiaquine and its active metabolite, desethylamodiaquine, and piperaquine in women in their second and third trimester of pregnancy with uncomplicated P. falciparum infections. Eligible pregnant women received either artesunate-amodiaquine (200/540 mg daily, n = 771) or dihydroartemisinin-piperaquine (40/960 mg daily, n = 755) for 3 days (NCT00852423). Population PK properties were evaluated using nonlinear mixed-effects modeling, and effect of gestational age and trimester was evaluated as covariates. 1071 amodiaquine and 1087 desethylamodiaquine plasma concentrations, and 976 piperaquine plasma concentrations, were included in the population PK analysis. Amodiaquine concentrations were described accurately with a one-compartment disposition model followed by a two-compartment disposition model of desethylamodiaquine. The relative bioavailability of amodiaquine increased with gestational age (1.25% per week). The predicted exposure to desethylamodiaquine was 2.8%–32.2% higher in pregnant women than that reported in non-pregnant women, while day 7 concentrations were comparable. Piperaquine concentrations were adequately described by a three-compartment disposition model. Neither gestational age nor trimester had significant impact on the PK of piperaquine. The predicted exposure and day 7 concentrations of piperaquine were similar to that reported in non-pregnant women. In conclusion, the exposure to desethylamodiaquine and piperaquine was similar to that in non-pregnant women. Dose adjustment is not warranted for women in their second and their trimester of pregnancy.

Omaveloxolone is a nuclear factor (erythroid-derived 2)-like 2 activator approved in the United States and the European Union for the treatment of patients with Friedreich ataxia aged ≥16 years, with a recommended dosage of 150 mg orally once daily on an empty stomach. The effect of the US Food and Drug Administration (FDA) high-fat breakfast on the pharmacokinetic profile of omaveloxolone observed in study 408-C-1703 (NCT03664453) deviated from the usual linear correlation between fed/fasted maximum plasma concentration (C_max) and area under the concentration–time curve (AUC) ratios reported for various oral drugs across 323 food effect studies. Here, physiologically based biopharmaceutics modeling (PBBM) was implemented to predict and explain the effect of the FDA high-fat breakfast on a 150-mg dose of omaveloxolone. The model was developed and validated based on dissolution and pharmacokinetic data available across dose-ranging, food effect, and drug–drug interaction clinical studies. PBBM predictions support clinical observations of the unique effect of a high-fat meal on omaveloxolone pharmacokinetic profile, in which the C_max increased by 350% with only a 15% increase in the AUC. Key parameters influencing omaveloxolone pharmacokinetics in the fasted state based on a parameter sensitivity analysis included bile salt solubilization, CYP3A4 activity, drug substance particle size distribution, and permeability. Mechanistically, in vivo omaveloxolone absorption was solubility and dissolution rate limited. However, in the fed state, higher bile salt solubilization led to more rapid dissolution with predominant absorption in the upper gastrointestinal tract, resulting in increased susceptibility to first-pass gut extraction; this accounts for the lack of correlation between C_max and AUC for omaveloxolone.

Certolizumab pegol (CZP; CIMZIA™) is the only Fc-free tumor necrosis factor inhibitor with data from a clinical study demonstrating no to minimal placental transfer. The pharmacokinetics (PK) of certolizumab pegol during pregnancy and postpartum in women with chronic inflammatory diseases were assessed using a population PK model based on data from the CHERISH study (NCT04163016), a longitudinal, prospective, open-label PK phase IB study. Model development was performed in NONMEM using a frequentist prior approach, with prior information based on a population PK model for certolizumab pegol in non-pregnant adult patients (NCT04740814). A one-compartment model with first-order absorption (K_a = 0.236 1/day) and linear elimination (CL/F = 0.416 L/day) from the central compartment (V/F = 7.86 L) best described certolizumab pegol PK in the CHERISH study. The structural model parameters were estimated with good precision (RSE < 25%). Baseline BW was included as a covariate on CL/F and V/F. Pregnancy trimester and time-varying log-transformed anti-drug antibody (ADA) titer were identified as the only significant covariates for CL/F with a comparable influence on CL/F. Individuals with higher ADA titer (75th percentile) during pregnancy exhibited CL/F up to 1.43-fold higher relative to individuals postpartum that showed median levels of ADA titer. However, the confidence interval for the combined effect of pregnancy stage and ADA titer effects on CL/F overlapped with the CL/F range of the typical individual postpartum. In addition, simulations showed a large overlap in certolizumab pegol concentrations between pregnant and non-pregnant adults. The findings of this population PK analysis support the maintenance of established certolizumab pegol dosing regimens throughout pregnancy.

Lecanemab (Leqembi®) was recently approved by health authorities in the United States, Japan, and China to treat early Alzheimer's disease (AD), including patients with mild cognitive impairment (MCI) or mild dementia due to Alzheimer's disease upon confirmation of amyloid beta pathology. Extensively and sparsely sampled PK profiles from 1619 AD subjects and 21,929 serum lecanemab observations from two phase I, one phase II, and one phase III studies were well characterized using a two-compartment model with first-order elimination. The final PK model quantified covariate effects of body weight and sex on clearance and central volume of distribution, ADA-positive status, and albumin on clearance, and of Japanese ethnicity on central and peripheral volumes of distribution. Exposure to lecanemab was comparable between two lecanemab-manufacturing processes. However, none of the identified covariates in the model had a clinically relevant impact on model-predicted lecanemab C_max or AUC at steady state following 10 mg/kg bi-weekly. Importantly, age, a well-recognized risk factor for AD, was not found to significantly affect lecanemab PK. The incidence of ARIA-E as a function of lecanemab exposure was modeled using a logit function with data pooled from 2641 subjects from the phase II and phase III studies, in which a total of 177 incidences of ARIA-E were observed. The probability of ARIA-E was significantly correlated with model-predicted C_max and predicted to be higher in subjects homozygous for APOE4. The incidence of isolated ARIA-H was not associated with lecanemab exposure and was similar between placebo and lecanemab-treated subjects.

Conventional approaches for establishing bioequivalence (BE) between test and reference formulations using non-compartmental analysis (NCA) may demonstrate low power in pharmacokinetic (PK) studies with sparse sampling. In this case, model-integrated evidence (MIE) approaches for BE assessment have been shown to increase power, but may suffer from selection bias problems if models are built on the same data used for BE assessment. This work presents model averaging methods for BE evaluation and compares the power and type I error of these methods to conventional BE approaches for simulated studies of oral and ophthalmic formulations. Two model averaging methods were examined: bootstrap model selection and weight-based model averaging with parameter uncertainty from three different sources, either from a sandwich covariance matrix, a bootstrap, or from sampling importance resampling (SIR). The proposed approaches increased power compared with conventional NCA-based BE approaches, especially for the ophthalmic formulation scenarios, and were simultaneously able to adequately control type I error. In the rich sampling scenario considered for oral formulation, the weight-based model averaging method with SIR uncertainty provided controlled type I error, that was closest to the target of 5%. In sparse-sampling designs, especially the single sample ophthalmic scenarios, the type I error was best controlled by the bootstrap model selection method.