CPT: Pharmacometrics & Systems Pharmacology最新文献

To establish bioequivalence (BE) of a generic test formulation with respect to a reference listed drug, it is necessary to demonstrate a comparable rate and extent to which active ingredients reach the site of action. To decrease unnecessary human testing and simulate scenarios involving specific populations or challenges with recruitment or study design, industry and regulators are increasingly considering in silico virtual bioequivalence (VBE) approaches. This tutorial introduces the VBEToolbox R package: a toolbox within the Open Systems Pharmacology framework to streamline and standardize computational VBE workflows. The package integrates in vitro and in vivo data to train pharmacokinetic models through inference of inter-individual variability from clinical data and establishment of in vitro to in vivo extrapolations. A nonparametric approach is adopted to account for uncertainties from parameter non-identifiability. The trained model is then applied to determine the study size with statistical power needed to demonstrate BE virtually. The use of the VBE tool is illustrated with two case studies. The first evaluates the VBE of petrolatum and ethylene glycol dermal formulations of testosterone by integrating in vitro skin permeation tests, vehicle/skin partitioning data, testosterone solubility data, and in vivo absorption data in a mechanistic in vitro/in vivo dermal absorption model. The second assesses the VBE of two oral bupropion formulations by integrating in vitro dissolution data in a physiologically based pharmacokinetic model. These case studies highlight essential considerations for model development, training, and extrapolation toward application for VBE assessment.

Efavirenz induces and inhibits multiple drug-metabolizing enzymes, contributing to significant drug–drug interactions. This study quantified the impact of multiple doses of efavirenz on CYP3A activity via midazolam metabolism using a population pharmacokinetic approach. Healthy volunteers received 1 mg midazolam orally in two sessions: first with a single 600 mg efavirenz dose and then after chronic efavirenz dosing (600 mg/day for 17 days). Midazolam and 1′-hydroxymidazolam were quantified via LC–MS/MS, and CYP2B6, CYP3A4, and CYP3A5 genotypes were assessed using TaqMan assays. Seventy-two volunteers (n = 72) completed sampling after the initial dose, and 58 completed both occasions. Non-linear mixed effects modeling was performed using the stochastic approximation expectation maximization estimation method in NONMEM. The base pharmacokinetic model employed was a two-compartment model with first-order absorption and first-order elimination, incorporating proportional error terms for midazolam and 1′-hydroxymidazolam. Covariate analysis utilized a full model approach to assess the impact of CYP3A4 and CYP3A5 genotypes, self-reported sex, and multiple doses of efavirenz as covariates affecting the formation clearance of 1-OH midazolam. CYP3A5 expressors exhibited a 1.27-fold increase in midazolam clearance compared to non-expressors, while CYP3A4 intermediate metabolizers showed a 0.94-fold decrease relative to normal metabolizers. Clearance was also 1.30-fold higher in females compared to males. Multiple doses of efavirenz increased midazolam clearance by 1.92-fold (95% CI 1.65–2.28) after accounting for inter-individual variability caused by other covariates. Furthermore, K_a and bioavailability (F) increased with repeated efavirenz exposure. In conclusion, this population pharmacokinetic analysis effectively quantified the specific induction effect of multiple doses of efavirenz on CYP3A compared to a single dose of efavirenz.

Trial Registration: ClinicalTrials.gov identifier: NCT00668395

The EryDex System (EDS) is a drug/device combination, which has been tested in clinical trials for ataxia telangiectasia (AT). EDS encapsulates dexamethasone sodium phosphate (DSP) solution in autologous erythrocytes at the point of care, and encapsulated DSP (eDSP) is infused back into the patient. Low doses of dexamethasone are released from erythrocytes over a 30-day period. This study aimed to (1) characterize the pharmacokinetics (PK) of dexamethasone released from intravenously infused eDSP based on data collected in clinical trials of healthy adults and pediatric AT patients, and to (2) simulate and extrapolate exposure measures of dexamethasone following intravenous infusion of eDSP administered once per month over 6 months in a pediatric population. The population PK model was developed using dense PK data from a phase 1 study in healthy adults and sparse PK data from a phase 3 study in pediatric AT patients. Three dose levels were studied, and the overall PK population included 24 healthy adults and 109 AT patients. The PK of dexamethasone released from eDSP was described using a simplified two-compartment model, adequate for estimating systemic exposure despite not fully capturing RBC release kinetics indicative of a triphasic pattern. The model showed a good fit, and future refinement will include mechanistic release modeling as more in vitro and in vivo data become available. Monte Carlo simulations of eDSP showed a rapid peak at 0.67 h, followed by sustained dexamethasone release; faster in the first 24 h, then slower over 20–30 days. No accumulation occurred with once-monthly dosing.

Drug–drug interaction (DDI) is a common cause of adverse drug events (ADEs). Despite real-world data-based studies have developed knowledge on DDI, the precise relationships between doses of two-drug combinations exposure and the risks of ADEs remain largely unknown. The estimation of the dose-risk relationship (DRR) under commonly used regression models could be subject to model misspecification or overspecification. We developed a dose-aware model (DAM) for revealing DRR. DAM could improve the DRR estimation by identifying the optimal model from a large number of meaningful models of doses of two-drug combinations exposure and risks of ADE. We compared DAM with commonly used models (e.g., exposed-versus-unexposed model, dose-response model, and saturated model), in which DAM had higher performance metrics on model fitting in real-world data analyses and DRR estimation in a simulation study. In conclusion, DAM is a powerful tool for estimating DRR for potential adverse two-drug combinations, which could be used to mitigate DDI-induced harm.

Nipocalimab is a fully human immunoglobulin G (IgG)1 monoclonal antibody (mAb) designed to selectively block the IgG binding site of neonatal fragment crystallizable receptor (FcRn) to inhibit IgG recycling and decrease circulating IgG, including pathogenic IgG autoantibodies (such as antiacetylcholine receptor, anti-muscle-specific kinase, and anti-low-density lipoprotein-related protein 4 antibodies in generalized myasthenia gravis [gMG]). A mechanistic model, integrating serum nipocalimab concentrations, FcRn occupancy, and total serum IgG data from five phase 1 studies in healthy adult participants and one phase 2 (Vivacity-MG) study in adult participants with gMG, was developed. The relationship between total serum IgG reduction and placebo-corrected MG-Activities of Daily Living score change from baseline in participants with gMG was also characterized. Nipocalimab exhibited nonlinear target (FcRn)-mediated disposition, causing rapid, reversible, and concentration-dependent FcRn occupancy and IgG reduction (up to 85%) in healthy participants and participants with gMG. The PK of nipocalimab after a single intravenous (IV) administration is consistent with that after repeated IV administrations, with no accumulation following every 2 weeks (Q2W) dosing. The PK of nipocalimab and its effect on IgG reduction were similar between healthy participants and participants with gMG. Model-based simulations indicated that the IV dose of 15 mg/kg Q2W, starting 2 weeks after a 30 mg/kg IV loading dose, was the lowest Q2W maintenance dose predicted to achieve the target of 70% median of the average change in IgG reduction in participants with gMG and was the recommended dose for the pivotal phase 3 Vivacity-MG3 study in a gMG population.

Quantitative pharmacology research application (QPRapp) is a web-based, interactive, and easy to use Shiny for Python interface, which facilitates evaluation of dose-exposure relationships, pharmacokinetic/pharmacodynamic (PK/PD) assessment of small and large molecules, and calculation of target occupancy for mono-, bi-, and tri-specific molecules. The dashboard sidebar offers a streamlined approach that incorporates multiple inputs, with various drop-down options to conduct respective simulations. The user can specify the type of molecule (small or large), number of model's compartments (one or two), and for large molecules, the number of drug's targets (one, two, or three). Additionally, the user can choose among the four indirect response PD models and execute the corresponding PK/PD simulations for small molecules. The platform application allows users to easily export simulated scenarios as CSV files for further analysis. Boasting features such as target-mediated drug disposition (TMDD) and early feasibility analysis (EFA) for multi-specific molecules, this application can assist project teams with limited computational expertise in applying model-informed drug development (MIDD) during the early stages of drug discovery and development.

Therapeutic oligonucleotides (TOs) represent an emerging modality, which offers a promising alternative treatment option, particularly for intracellular targets. The two types of TOs, antisense oligonucleotides (ASO) and small interfering RNAs (siRNAs), distribute highly into tissues, especially into the liver and the kidneys. However, molecular processes at the cellular level such as the uptake into the cell, endosomal escape, binding to the target mRNA, and redistribution back to the systemic circulation are not well characterized because experimental data and assays are lacking. We developed a whole-body PBPK model for TOs and verified the predictive performance against clinically observed data for three ASOs and five siRNAs. The predicted concentration-time profiles were in accordance with the clinically observed data for all investigated TOs, and all pharmacokinetic parameters were predicted within twofold. Sensitivity analysis with the evaluated PBPK model revealed that the endocytosis uptake rate and the fraction unbound in plasma impact the peak concentration (C_max), time to C_max (t_max), and the area under the curve (AUC) of a subcutaneously administered ASO, whereas the redistribution rate and the nuclease clearance had minor to no impact. The mathematical model can guide the development of required in vitro assays for key parameters to better understand the pharmacokinetics of TOs. PBPK models, parameterized with reliable in vitro data, could be used in the future to predict the pharmacokinetics in special populations with limited clinical data to ensure a safe and effective therapy.

We thank Dr. Grevel for his interest in our article [1] and appreciate the opportunity to further clarify and elaborate on aspects of our analysis.

First, however, we would like to address a few apparent misunderstandings in Dr. Grevel's commentary [2]. Contrary to his assertion, the dataset we analyzed was not limited to the ten-year component of the FDPS. As detailed in the Data section of our article, the analysis also incorporated long-term follow-up data, which is further described in reference [3].

Dr. Grevel also noted that our model did not account for time-dependent hazards within transient states. However, our model does indeed incorporate such a feature, as the hazard functions for all transient states include time-varying age in a Gompertz-Makeham component.

Regarding the dependence of transitions on prior states, Dr. Grevel suggested that our model omits potential pathway-dependent transitions. While such modeling considerations can be appropriate when patients may reach a transient state via multiple distinct routes, this is not applicable in our case. In our model, there is no heterogeneity in the upstream pathways leading to any of the transient states.

Dr. Grevel notes the absence of certain figures that resemble those in a previous publication he co-authored [4]. One such figure corresponds closely to our figure 2. However, in our presentation, each trajectory is shown in a separate panel, enabling a clearer depiction of the agreement between model predictions and observed data. Also, Dr. Grevel would like us to display the sojourn times of the subjects in the study, which is not possible in our case due to the interval censoring. Finally, Dr. Grevel wondered whether the model could predict in which state a patient with a certain set of apparently significant covariates will most likely be, for example, 10 years after being assigned to one of the two intervention groups. This is already available in figure 2, which includes the model simulation of the probabilities of the different states over the whole study and follow-up period under the observed study design and covariates.

The authors declare no conflicts of interest.

Rosuvastatin (RSV), a potent HMG-CoA reductase inhibitor, is widely used for the management of hyperlipidemia and prevention of cardiovascular disease. Its absorption and disposition are primarily transporter-mediated, involving intestinal absorption by OATP2B1 and efflux by BCRP; hepatic uptake by OATP1B1, OATP1B3, OATP2B1, and NTCP; and biliary excretion by BCRP and MRP2. Given its minimal metabolism, RSV serves as a model substrate for transporter-based drug absorption, disposition, and DDI studies. We developed and verified a PBPK model of RSV using a middle-out approach, incorporating extensive in vitro and in vivo data. The model was verified with > 75 datasets, including plasma and hepatic RSV concentrations from PET imaging studies. The model successfully captured RSV PK profiles in the Caucasian, Chinese, Malay, Japanese, and Korean populations. It also accurately captured the interconversion of RSV and RSV-lactone, changes in RSV PK due to OATP1B1 and BCRP polymorphisms, and DDI with rifampin or cyclosporine. Sensitivity analyses revealed that reduced hepatic OATP1B1 activity and/or intestinal BCRP efflux are likely determinants of altered RSV PK in the third trimester. Compared to previous models, our model extensively incorporates genetic polymorphisms, ethnic variability, reversible metabolism to the lactone, DDI, and pregnancy, allowing its use in the future to facilitate RSV dose optimization in multiple populations, including pregnant individuals.