CPT: Pharmacometrics & Systems Pharmacology最新文献

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.

The Pharmpy Automatic Model Development (AMD) tool automates the building of population pharmacokinetic (popPK) models by utilizing a systematic stepwise process. In this study, the performance of the AMD tool was assessed using simulated datasets. Ten true models mimicking classical popPK models were created. From each true model, dataset replicates were simulated assuming a typical phase I study design—single and multiple ascending doses with/without dichotomous food effect, with rich PK sampling. For every dataset replicate, the AMD tool automatically built an AMD model utilizing NONMEM for parameter estimation. The AMD models were compared to the true and reference models (true model fitted to simulated datasets) based on their model components, predicted population and individual secondary PK parameters (SP) (AUC_0-24, c_max, c_trough), and model quality metrics (e.g., model convergence, parameter relative standard errors (RSEs), Bayesian Information Criterion (BIC)). The models selected by the AMD tool closely resembled the true models, particularly in terms of distribution and elimination, although differences were observed in absorption and inter-individual variability components. Bias associated with the derived SP was low. In general, discrepancies between AMD and true SP were also observed for reference models and therefore were attributed to the inherent stochasticity in simulations. In summary, the AMD tool was found to be a valuable asset in automating repetitive modeling tasks, yielding reliable PK models in the scenarios assessed. This tool has the potential to save time during early clinical drug development that can be invested in more complex modeling activities within model-informed drug development.

It is often a goal of model development to predict data from which a variety of outcomes can be derived, such as threshold-based categorization or change from baseline (CFB) transformations. This approach can improve power or support multiple decisions. Because these derivations are indirectly predicted from the model, they are valuable tests for misspecification when used in visual or numeric predictive checks (V/NPCs). However, derived outcome V/NPCs (especially if primary or key secondary) are often overly scrutinized and held to an uncommon standard when comparing model predictions to point estimates, even if by conventional standards both the directly and indirectly modeled data are captured well. Here, simulations of directly modeled data were used to determine where apparent issues in V/NPCs of derived outcomes are expected. Two types of datasets were simulated: (1) a simple pre–post study and (2) pharmacokinetic/pharmacodynamic data from a dose-ranging study. A psoriasis exposure–response model case study was also assessed. V/NPCs were generated on the raw data, CFB data, and placebo-corrected CFB (dCFB) data, and binned summary statistics of the observed data for each trial were graded as being strongly or weakly supportive of a predictive model (within the interquartile range or the 95% central distribution of all simulated trials, respectively). In all cases, the strength of support in direct data V/NPCs was minimally related to that in derived outcome V/NPCs. There are myriad benefits to modeling the underlying data of a derived measure, and these results support caution in discarding adequate models based on overly strict derived measure predictive checks.

Mavacamten is a selective, allosteric, reversible cardiac myosin inhibitor that has been developed for the treatment of adults with symptomatic obstructive hypertrophic cardiomyopathy (HCM). A population pharmacokinetic (PopPK) model was developed to characterize mavacamten pharmacokinetics (PK) and the variation in mavacamten exposure associated with intrinsic and extrinsic factors. Data from 12 clinical studies (phases 1, 2, and 3) were used. Evaluable participants were those who had at least one mavacamten concentration measurement with associated sampling time and dosing information. The base model included key covariates: body weight, cytochrome P450 isozyme 2C19 (CYP2C19) phenotype with respect to PK, and formulation. The final model was generated using stepwise covariate testing and refinement processes. Simulations were performed to evaluate PK: apparent clearance (CL/F); apparent central and peripheral volumes of distribution; and steady-state average, trough, and maximum concentrations. Overall, 9244 measurable PK observations from 497 participants were included. A two-compartment model structure was selected. After stepwise covariate model building and refinement, additional covariates included were: specified mavacamten dose, omeprazole or esomeprazole administration, health/disease status, estimated glomerular filtration rate, fed status, and sex. The final PopPK model accurately characterized mavacamten concentrations. At any given dose, CYP2C19 phenotype was the most influential covariate on exposure parameters (e.g., median CL/F was reduced by 72% in CYP2C19:poor metabolizers compared with the reference participant [CYP2C19:normal metabolizer]). CL/F was also approximately 16% higher in women than in men but lower in participants receiving concomitant omeprazole or esomeprazole (by 33% and 42%, respectively) than in participants not receiving such concomitant therapy.