CPT: Pharmacometrics & Systems Pharmacology最新文献

Mavacamten is a cardiac myosin inhibitor for adults with obstructive hypertrophic cardiomyopathy (HCM). Dose optimization is performed 4 weeks after starting mavacamten, guided by periodic echo measurements of Valsalva left ventricular outflow tract gradient (VLVOTg) and left ventricular ejection fraction (LVEF). Previously, a population pharmacokinetic (PPK) model was developed and exposure-response (E-R) of VLVOTg (efficacy) and LVEF (safety) was used to identify the mavacamten titration regimen with the optimal benefit/risk ratio, now included in the US prescribing information. Mavacamten is metabolized primarily by cytochrome P450 2C19 (CYP2C19) (74%), a highly polymorphic enzyme. China has a higher prevalence of poor CYP2C19 metabolizer phenotype compared with the global population; therefore, a previous model was adapted to include Chinese patients with obstructive HCM to identify the optimal dosing regimen for this population. Data from a phase I (healthy Chinese volunteers) and a phase III (EXPLORER-CN, NCT05174416; Chinese patients with obstructive HCM) trial of mavacamten were added to the previous PPK and E-R models, and the observed VLVOTg and LVEF from EXPLORER-CN were successfully simulated. Next, five echocardiography-guided titration regimens (plus the EXPLORER-CN regimen) using representative or equal CYP2C19 phenotypes were simulated. The final simulated regimen recommended with an optimal benefit/risk profile across CYP2C19 phenotypes included: down-titration at Week 4 (if VLVOTg < 20 mmHg), restart at Week 12, and up-titration at Week 12 (for VLVOTg ≥ 30 mmHg and LVEF ≥ 55%), and every 12 weeks thereafter. This supports the previously recommended regimen for Chinese patients with obstructive HCM, now approved by the National Medicinal Products Administration.

Protein therapeutics have emerged as an exceedingly promising treatment modality in recent times but are predominantly given as intravenous administration. Transitioning to subcutaneous (SC) administration of these therapies could significantly enhance patient convenience by enabling at-home administration, thereby potentially reducing the overall cost of treatment. Approaches that enable sustained delivery of subcutaneously administered biologics offer further advantages in terms of less frequent dosing and better patient compliance. Controlled release technologies, such as hydrogels and subcutaneous implantable technologies, present exciting solutions by enabling the gradual release of biologics from the delivery system. Despite their substantial potential, significant hurdles remain in appropriately applying and integrating these technologies with the ongoing development of complex biologic-based therapies. We evaluate the potential impact of subcutaneously delivered controlled release systems on the downstream pharmacokinetics (PK) of several FDA-approved biologics by employing rigorous mathematical analysis and predictive PK simulations. By leveraging linear time-invariant (LTI) systems theory, we provide a robust framework for understanding and optimizing the release dynamics of these technologies. We demonstrate simple quantitative metrics and approaches that can inform the design and implementation of controlled release technologies. The findings highlight key opportunity areas to reduce dosing frequency, stabilize concentration profiles, and synergize the codelivery of biologics, calling for collaboration between drug delivery and PK scientists to create the most convenient, optimized, and effective precision therapies.

A growing number of covariate modeling methods have been proposed in the field of popPK modeling, but limited information exists on how they all compare. The objective of this study was to perform a systematic review of all popPK covariate modeling methods, focusing on assessing the existing knowledge on their performances. For each method of each article included in this review, evaluation setting, performance metrics along with their associated values, and relative computational times were reported when available. Evaluation settings report was done for uncertainty assessment of communicated results. Results showed that EBEs-based ML methods stood out as the best covariate selection methods. AALASSO, a hybrid genetic algorithm, FREM with a clinical significance criterion and SCM+ with stagewise filtering were the best covariate model selection techniques-AALASSO being the very best one. Results also showed a lack of consensus on how to benchmark simulated datasets of different scenarios when evaluating method performances, but also on which metrics to use for method evaluation. We propose to systematically report TPR (sensitivity), FPR (Type I error), FNR (Type II error), TNR (specificity), covariate parameter error bias (MPE) and precision (RMSE), clinical relevance, and model fitness by means of BIC, concentration prediction error bias (MPE), and precision (RMSE) of new proposed methods and compare them with SCM. We propose to systematically combine covariate selection techniques to SCM or FFEM to allow for comparison with SCM. We also highlight the need for an open-source benchmark of simulated datasets on a representative set of scenarios.

Type 2 diabetes (T2D) is a progressive metabolic disorder that could be an underlying cause of long-term complications that increase mortality. The assessment of the probability of such events could be essential for mortality risk management. This work aimed to establish a framework for risk predictions of macrovascular complications (MVC) and diabetic kidney disease (DKD) in patients with T2D, using real-world data from the Swedish National Diabetes Registry (NDR), in the presence of mortality as a competing risk. The study consisted of 41,517 patients with T2D registered in NDR between 2005 and 2013. At inclusion, patients were newly diagnosed (T2D < 1 year) and had no prior evidence of DKD or MVC. Using three-quarters of the data, a five-state multistate model was established to describe competing events of MVC, DKD, a combination thereof, and the terminal state, death. Two hypotheses were investigated: (1) the risk of MVC and DKD are mutually independent, and (2) mortality is independent of morbidities. At the end of the study, the majority of individuals remained in uncomplicated T2D; however, the probability of transition to complications and death increased over time. The mortality hazard depended on the presence of morbidities and was quantified as a life expectancy decreased by 5.0, 9.7, and 12.2 years for MVC, DKD, and the combined morbidity, respectively, compared to uncomplicated T2D. An established framework with a five-state model incorporating competing events was shown to be a useful tool for comorbidities risk assessment in newly diagnosed patients with T2D.

Ritlecitinib is an orally bioavailable, small molecule that has been approved by the U.S. Food and Drug Administration (FDA) as a once-daily oral treatment option for people 12 years of age and older with severe alopecia areata. This article assessed the exposure–response (ER) relationship of eyebrow and eyelash assessment (EBA/ELA) scores on ritlecitinib and compared them to the Severity of Alopecia Tool (SALT) score (primary endpoint) ER relationship on ritlecitinib. EBA and ELA both are numeric rating scales (NRS) with four levels (0 the most severe, 3 the normal). Longitudinal ER modeling with ordinal regression was conducted to describe ritlecitinib efficacy regarding the hair regrowth in eyebrows and eyelashes separately. The average concentration in the time interval between two adjacent EBA/ELA records was used as the exposure metric. The developed models described the longitudinal EBA/ELA profile and the responder rates adequately. The ER models and the model-based simulations implied that the tested doses in the phase IIb/III clinical trial are in the ascending region, but the magnitude of loading dose effect on earlier efficacy is different across the efficacy endpoints of EBA, ELA, and SALT scores (which could be explained by the estimated $��E�C_{50}�$ [concentration at half maximum effect]). The established longitudinal ER relationships supported the selection of 50 mg dose for overall Alopecia areata (AA) patients with impaired eyebrow and eyelash hairs. The presented analysis using the ordinal regression model can be utilized in any ER analysis where PD response is an ordinal categorical variable.

The antitumor efficacy of an intratumoral injection of a genetically engineered oncolytic vaccinia virus carrying human IL-7 and murine IL-12 genes (hIL-7/mIL-12-VV) was demonstrated in CT26.WT-bearing mice. In the CT26.WT-bearing mouse model, the efficacy of the combination of hIL-7/mIL-12-VV plus the anti-programmed cell death protein (PD)-1 antibody was determined to be correlated with the timing of administration: greater efficacy was observed when hIL-7/mIL-12-VV was administered before the anti-PD-1 agent instead of simultaneous administration. To identify an optimal dosing regimen for first-in-human clinical trials, a multiple model-informed drug-development (MIDD) approach was used through development of a quantitative systems pharmacology (QSP) model and an agent-based model (ABM). All models were built and verified using available literature and preclinical study data. Multiple dosing scenarios were explored using virtual populations by altering the interval between hIL-7/hIL-12-VV and pembrolizumab administration. In contrast with observations from preclinical studies, both the QSP and the ABM models demonstrated no antagonistic effect on the dose-dependent antitumor efficacy of hIL-7/hIL-12-VV by pembrolizumab in simulations of clinical therapy. Based on the MIDD strategy, it was recommended that the highest dose of hIL-7/hIL-12-VV and pembrolizumab should be administered on the same day, but with pembrolizumab administration following hIL-7/hIL-12-VV administration. Multiple different modeling approaches uniquely supported and informed the first-in-human clinical trial design by guiding the optimal dose and regimen selection.

Rheumatoid arthritis (RA) is a major public health concern, which can cause serious outcomes. Low-dose methotrexate (MTX) is a cornerstone in RA treatment, but there is significant heterogeneity in clinical response. To evaluate underlying sources of pharmacokinetic variability and clinical response of MTX, a physiologically based pharmacokinetic and pharmacodynamic (PBPK/PD) model was developed using PK-sim and Mobi (version 11.1). The PBPK model included metabolism and transportation by AXO1, FPGS, GGH, RFC, and MRP2, with renal and biliary excretion. We also developed various degrees of renal insufficiency populations with subsequent dosing optimizations. A total of 23 MTX plasma concentration-time profiles were used, with 97% of predicted plasma concentrations within a two-fold range compared to observed data. The PBPK/PD modeling and simulation demonstrated that variability in renal clearance and enzymes related to MTX are likely important drivers of PK variability and there is a quantitative relationship between MTX-PG3 and RA treatment response. The PBPK/PD model could be used to guide improvement in MTX dose regimens for RA patients.

Sotalol, a class III antiarrhythmic agent, is used to maintain sinus rhythm in patients with atrial fibrillation or atrial flutter (AFIB/AFL). Despite its efficacy, sotalol's use is limited by its potential to cause life-threatening ventricular arrhythmias due to QT interval prolongation. Traditionally, sotalol administration required hospitalization to monitor these risks. The FDA approval of intravenous (IV) sotalol for loading before oral maintenance aims to reduce hospitalization duration by facilitating an expedited loading dose, transitioning to oral maintenance therapy. This study evaluates the population pharmacokinetics (PK) and pharmacodynamics (PD) of sotalol using data from the Prospective Evaluation Analysis and Kinetics of IV Sotalol (PEAKS) Registry, which includes patients with atrial arrhythmias undergoing IV sotalol loading. A nonlinear mixed-effect modeling approach was used to describe sotalol PK, considering covariates such as age, weight, sex, and renal function. The study also examined the correlation between sotalol plasma concentrations and corrected QT interval (QTc) prolongation. Sotalol PK after IV loading and two oral maintenance doses was adequately described by a two-compartment model with first-order elimination in patients with atrial arrhythmias. Weight and creatinine clearance (CrCl) were identified as covariates with significant influence on sotalol PK. A linear regression model adequately described the relationship between QTc and plasma sotalol levels (R² = 0.27). The Monte Carlo simulations showed that the IV loading doses recommended in the prescribing information did not result in significant prolongation of QTc. The data from this study supports the current dosing recommendations of IV sotalol in patients with AFIB/AFL.

Patients with recurrent high-grade glioma (rHGG) have a poor prognosis with median progression-free survival (PFS) of <7 months. Responses to treatment are heterogenous, suggesting a clinical need for prognostic models. Bayesian data analysis can exploit individual patient follow-up imaging studies to adaptively predict the risk of progression. We propose a novel sample size analysis for Bayesian individual dynamic predictions and demonstrate proof of principle. We coupled a nonlinear mixed effects tumor growth inhibition model with a survival model. Longitudinal tumor volumes and time-to-progression were simulated for 2000 in silico rHGG patients. Bayesian individual dynamic predictions of PFS curves were evaluated using area under the receiver operating characteristic curve (AUC) and Brier skill score (BSS). We investigated the effects of sample size on AUC and BSS margins of error. A power law relationship was observed between sample size and margins of error of AUC and BSS. Sample size was also found to be negatively correlated with margins of error and landmark time. We explored the use of this sample size analysis as a clinical look-up table for prospective clinical trial design and retrospective clinical data analysis. Here, we motivate the application of Bayesian individual dynamic predictions as a clinical end point for clinical trial design. Doing so could aid in the development of study protocols with patient-specific adaptations (escalate or de-escalate dose or frequency of drug administration, increase or decrease the frequency of follow-up, or change therapeutic modality) according to patient-specific prognosis. Future developments of this approach will focus on further model development and validation.

Type 2 diabetes mellitus (T2DM), characterized by insulin resistance, is closely associated with Alzheimer's disease (AD). Cerebrovascular dysfunction is manifested in both T2DM and AD, and is often considered as a pathological link between the two diseases. Insulin signaling regulates critical functions of the blood-brain barrier (BBB), and endothelial insulin resistance could lead to BBB dysfunction, aggravating AD pathology. However, insulin signaling is intrinsically dynamic and involves interactions among numerous molecular mediators. Hence, a mechanistic systems biology model is needed to understand how insulin regulates BBB physiology and the consequences of its impairment in T2DM and AD. In this study, we investigated the pharmacodynamic effect of insulin on the expression of vascular cell adhesion molecule 1 (VCAM1), a marker of cerebrovascular inflammation. Intriguingly, normal insulin concentrations selectively activated the PI3K-AKT pathway, leading to decreased VCAM1 expression. However, exposure to supraphysiological insulin levels, which is present in insulin resistance, activated both PI3K-AKT and MEK-ERK pathways, and increased VCAM1 expression. We developed a mathematical model that adequately described the dynamics of various insulin signaling nodes and VCAM1 expression. Further, the model was integrated with in vitro proteomics and transcriptomics data from AD patients to simulate VCAM1 expression under pathological conditions. This approach allowed us to establish a quantitative systems pharmacology framework to investigate BBB dysfunction in AD and metabolic syndrome, thereby offering opportunities to identify specific disruptions in molecular networks that will enable us to identify novel therapeutic targets.