CPT: Pharmacometrics & Systems Pharmacology最新文献

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.

Model-informed drug development (MIDD) has been increasingly applied to guide decision-making, ameliorate efficiency, and enhance the likelihood of successful trials. The development of ligelizumab, a humanized anti-IgE monoclonal antibody, in chronic spontaneous urticaria (CSU) illustrated how MIDD can be applied to support central aspects of drug development, such as dose selection and trial design, pediatric drug development and extrapolation, generation of evidence to support potential labeling, optimizing treatment outcomes, and enhancing patient access. In this manuscript, we provide an overview of the key modeling and simulation analyses that were part of the MIDD approach for the development of ligelizumab in CSU and how they were staggered around the availability of interim and final data from the Phase 2 and Phase 3 studies. Furthermore, we present details of the non-linear mixed-effects models characterizing the population pharmacokinetics and exposure-response relationship of ligelizumab for efficacy in adolescent and adult patients with CSU.

Pegbing (peginterferon alpha-2b) is a polyethylene glycol-modified interferon α-2b injection that has demonstrated favorable efficacy and safety profiles in the treatment of chronic hepatitis B (CHB). This study aimed to develop a population pharmacokinetic (PopPK) model of Pegbing in both healthy subjects and CHB patients and to investigate the influence of covariates on its pharmacokinetic behavior. Pharmacokinetic data were obtained from a Phase I trial in healthy volunteers and a Phase II trial in CHB patients. A one-compartment model with a target-mediated drug disposition (TMDD) component incorporating IFN receptor downregulation was established to describe the pooled data from 28 healthy subjects and 39 CHB patients. Physiologically reasonable parameters were estimated, providing a good description and prediction of the model. Furthermore, the final PopPK model was externally validated using an independent dataset of 115 CHB patients. In the covariate analysis, health status (healthy v.s. CHB) was a significant covariate, affecting the Pegbing absorption rate, creatinine clearance was associated with clearance, and body weight affected the volume of distribution. Compared with healthy subjects, CHB patients exhibited a consistent area under the curve (AUC) but a higher C_max. A PopPK model of Pegbing in both healthy volunteers and CHB patients was successfully established. Based on the model simulation, covariate-based dose adjustment is unnecessary for CHB patients with normal renal function.

Survival outcomes observed in randomized controlled trials (RCTs) may not always be generalizable to clinical practice. Evaluating whether treatment outcomes in clinical practice are similar to those in RCTs shortly after a new medicine is introduced is important for making informed decisions. Therefore, we aimed to develop a Bayesian model that compares survival data from clinical practice that accumulates over time with static survival data from RCTs, thereby providing rapid and easily interpretable results that can inform clinical and policy-related decision-making. We developed a Bayesian survival model that sequentially updates estimates as new data become available. We designed the model to incorporate static RCT data with accumulating clinical practice data. We used sequential hypothesis testing with Bayes factors to assess the strength of the evidence for different hazard ratio (HR) thresholds (i.e., ranging from HR > 1.0 to > 2.0 and HR < 0.5 to < 1.0). We applied the model to two datasets comprising survival data from clinical practice and an RCT for lung cancer patients treated with pembrolizumab plus chemotherapy (dataset 1) and pembrolizumab monotherapy (dataset 2). For dataset 1, the posterior model checks showed a misfit between the model and the data after 15 months, potentially due to channeling bias. The model fit should be improved before reliable estimates can be obtained. For dataset 2, the model estimated precise HRs 10 months before the end of data accumulation. Sequential hypothesis testing with Bayes factors provided easily interpretable results, with very strong evidence for an HR > 1.0 and strong evidence for an HR > 1.2. In conclusion, provided the posterior check shows an acceptable model fit, our Bayesian survival model with sequential hypothesis testing using Bayes factors can provide rapid and easily interpretable results for decision-making.

Dulaglutide, a long-acting glucagon-like peptide-1 (GLP-1) receptor agonist, is approved for improving glycemic control and reducing cardiovascular risks in patients with type 2 diabetes mellitus (T2DM). This research investigates the effect of dulaglutide on gastric emptying and its impact on the pharmacokinetics (PK) of orally administered molecules utilizing a combination of population pharmacokinetic (PopPK) and physiologically based pharmacokinetic (PBPK) modeling approaches. In clinical studies, the gastric emptying delay (GED) was evaluated in healthy participants and patients with T2DM at various dose levels of dulaglutide. A PopPK model estimated the exposure-dependent delay in gastric emptying, which was then input into the orally administered small molecule PBPK models. These PBPK models, informed by internal clinical studies and publicly available data, quantified the effect of dulaglutide-induced GED on the area under the curve (AUC), maximum concentration (C_max), and time to maximum concentration (t_max) of the co-administered drugs. The modeling approach was verified for reproducing observed GED-mediated drug–drug interactions (DDIs) at low doses of dulaglutide and to predict DDIs at a 4.5 mg dulaglutide dose. The clinical studies demonstrated that the 1.5 mg dulaglutide dose has no clinically relevant effect on the pharmacokinetics of small molecules, and the modeling led to a similar conclusion at 4.5 mg dulaglutide. This work demonstrates that modeling approaches can be used to predict potential GLP-1-mediated DDIs related to gastric emptying delay, increasing the efficiency of the clinical pharmacology programs.