CPT: Pharmacometrics & Systems Pharmacology最新文献

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.

Population pharmacokinetic (PK) models are widely used to inform drug development by pharmaceutical companies and facilitate drug evaluation by regulatory agencies. Developing a population PK model is a multi-step, challenging, and time-consuming process involving iterative manual model fitting and evaluation. A tool for fully automatic model development (AMD) of common population PK models is presented here. The AMD tool is implemented in Pharmpy, a versatile open-source library for pharmacometrics. It consists of different modules responsible for developing the different components of population PK models, including the structural model, the inter-individual variability (IIV) model, the inter-occasional variability (IOV) model, the residual unexplained variability (RUV) model, the covariate model, and the allometry model. The AMD tool was evaluated using 10 real PK datasets involving the structural, IIV, and RUV modules in three sequences. The different sequences yielded generally consistent structural models; however, there were variations in the results of the IIV and RUV models. The final models of the AMD tool showed lower Bayesian Information Criterion (BIC) values and similar visual predictive check plots compared with the available published models, indicating reasonable quality, in addition to reasonable run time. A similar conclusion was also drawn in a simulation study. The developed AMD tool serves as a promising tool for fast and fully automatic population PK model building with the potential to facilitate the use of modeling and simulation in drug development.

This study addresses the critical issue of drug-induced torsades de pointes (TdP) risk assessment, a vital aspect of new drug development due to its association with arrhythmia and sudden cardiac death. Existing methodologies, particularly those reliant on a single biomarker derived from CiPA O'Hara-Rudy (CiPAORdv1.0) ventricular cell model without the hERG dynamic as input to the individual machine learning model, have limitations in capturing the complexity inherent in the comprehensive range of factors influencing drug-induced TdP risk. This study aims to overcome these limitations by proposing a stacking ensemble machine learning approach by integrating multiple in silico biomarkers derived from the CiPAORdv1.0 with hERG dynamic characteristics. The ensemble machine learning model consisted of three artificial neural network (ANN) models as baseline model and support vector machine (SVM), logistic regression (LR), random forest (RF), and extreme gradient boosting (XGBoost) models as meta-classifier. The highest AUC score of 1.00 (0.90-1.00) for high risk, 0.97 (0.84-1.00) for intermediate risk, and 1.00 (0.87-1.00) for low risk were obtained using seven biomarkers derived from the CiPAORdv1.0 with hERG dynamic characteristics. Furthering our investigation, we explored the model's robustness by incorporating interindividual variability into the generation of in silico biomarkers from a population of human ventricular cell models. This study also enabled an analysis of TdP risk classification under high clinical exposure and therapeutic scenarios for several drugs. Additionally, from a sensitivity analysis, we revealed four important ion channels, namely, CaL, NaL, Na, and Kr channels that affect significantly the important biomarkers for TdP risk prediction.

By applying nonlinear mixed-effect (NLME) models, model-integrated evidence (MIE) approaches are able to analyze bioequivalence (BE) data with pharmacokinetic end points that have sparse sampling, which is problematic for non-compartmental analysis (NCA). However, MIE approaches may suffer from inflation of type I error due to underestimation of parameter uncertainty and to the assumption of asymptotic normality. In this study, we developed a MIE BE analysis method that is based on a pre-defined model and consists of several steps including model fitting, uncertainty assessment, simulation, and BE determination. The presented MIE approach has several improvements compared with the previously reported model-integrated methods: (1) treatment, sequence, and period effects are only added to absorption parameters (such as relative bioavailability and rate of absorption) instead of all PK parameters; (2) a simulation step is performed to generate confidence intervals of the pharmacokinetic metrics for BE assessment; and (3) in an effort to maintain type I error, two more advanced parameter uncertainty evaluation approaches are explored, a nonparametric (case resampling) bootstrap, and sampling importance resampling (SIR). To evaluate the developed method and compare the uncertainty assessment methods, simulation experiments were performed for BE studies using a two-way crossover design with different amounts of information (sparse to rich designs) and levels of variability. Based on the simulation results, the method using SIR for parameter uncertainty quantification controls type I error at the nominal level of 0.05 (i.e., the significance level set for BE evaluation) even for studies with small sample size and/or sparse sampling. As expected, our MIE approach for BE assessment exhibited higher power than the NCA-based method, especially as the data becomes sparser and/or more variable.

The main pathophysiological hallmark of Parkinson's disease (PD) is the accumulation of aggregated alpha-synuclein (αSyn). Microglial activation is an early event in PD and may play a key role in pathological αSyn aggregation and transmission, as well as in clearance of αSyn and immunotherapy efficacy. Our aim was to investigate how different proposed mechanisms of anti-αSyn immunotherapy may contribute to pathology reduction in various PD-like mouse models. Our mechanistic model of PD pathology in mouse includes αSyn production, aggregation, degradation and distribution in neurons, secretion into interstitial fluid, internalization, and subsequent clearance by neurons and microglia. It describes the influence of neuroinflammation on PD pathogenesis and dopaminergic neurodegeneration. Multiple data from mouse PD models were used for calibration and validation. Simulations of anti-αSyn passive immunotherapy adequately reproduce preclinical data and suggest that (1) immunotherapy is efficient in the reduction of aggregated αSyn in various models of PD-like pathology; (2) prevention of aSyn spread only does not reduce the pathology; (3) a decrease in microglial inflammatory activation and aSyn aggregation may be alternative therapy approaches in PD-like pathology.

HIV prevention with pre-exposure prophylaxis (PrEP) constitutes a major pillar in fighting the ongoing epidemic. While daily oral PrEP adherence may be challenging, long-acting (LA-)PrEP in oral or implant formulations could overcome frequent dosing with convenient administration. The novel drug islatravir (ISL) may be suitable for LA-PrEP, but dose-dependent reductions in $\mathrm{CD}{4}^{+}$ T cell and lymphocyte counts were observed at high doses. We developed a mathematical model to predict ISL pro-drug levels in plasma and active intracellular ISL-triphosphate concentrations after oral vs. subcutaneous implant dosing. Using phase II trial data, we simulated antiviral effects and estimated HIV risk reduction for multiple dosages and dosing frequencies. We then established exposure thresholds where no adverse effects on immune cells were observed. Our findings suggest that implants with 56-62 mg ISL offer effective HIV risk reduction without reducing lymphocyte counts. Oral 0.1 mg daily, 3-5 mg weekly, and 10 mg biweekly ISL provide comparable efficacy, but weekly and biweekly doses may affect lymphocyte counts, while daily dosing regimen offered no advantage over existing oral PrEP. Oral 0.5-1 mg on demand provided $>90\%$ protection, while not being suitable for post-exposure prophylaxis. These findings suggest ISL could be considered for further development as a promising and safe agent for implantable PrEP.

Valemetostat is an oral inhibitor of enhancer of zeste homolog (EZH) 2 and EZH1 approved in Japan for the treatment of adult T-cell leukemia/lymphoma (ATLL). To support the approved daily dose of 200 mg and inform dose adjustments in patients with ATLL, Bayesian exposure-response analyses were conducted using data from two clinical trials. The analyses included two efficacy endpoints, overall response by central and investigator assessments in patients with ATLL (n = 38, 150-200 mg), and six safety endpoints in patients with non-Hodgkin lymphoma (n = 102, 150-300 mg), which included grade ≥3 laboratory values for anemia, absolute neutrophil count decreased, and platelet count decreased; any grade ≥3 treatment-emergent adverse event (TEAE); and dose reductions and dose interruptions due to TEAEs. A slightly positive relationship was observed between unbound exposure and efficacy endpoints. A steeper relationship was observed in safety endpoints, compared with efficacy. Candidate covariate effects, except intercepts of the baseline laboratory values, were regularized via spike and slab priors in a Bayesian framework; only the laboratory values for corresponding hematologic TEAEs were shown to be of substantial impact. The target exposure range was established by defining a modified region of practical equivalence (184-887 ng·h/mL), which was expected to provide satisfactory efficacy and acceptable safety within the range of available exposure data. The simulated exposure range considering inter-individual variability showed that 200 mg could reach target exposure in the overall population and across subpopulations of interest, supporting the use of valemetostat 200 mg in patients with ATLL.