CPT: Pharmacometrics & Systems Pharmacology最新文献

Xu R, Liu W, Ge W, He H, Jiang Q. CPT Pharmacometrics Syst Pharmacol. 2023;12(8):1093-1106.

In the title page, the author affiliation “Wenyuan Liu^1,3 and Weihong Ge^1,3” was incorrect. This should be changed to “Wenyuan Liu³ and Weihong Ge^3.”

We apologize for this error.

The recent SarsCov2 pandemic has disrupted healthcare system notably impacting intensive care units (ICU). In severe cases, the immune system is dysregulated, associating signs of hyperinflammation and immunosuppression. In the present work, we investigated, using a joint modeling approach, whether the trajectories of cellular immunological parameters were associated with survival of COVID-19 ICU patients. This study is based on the REA-IMMUNO-COVID cohort including 538 COVID-19 patients admitted to ICU between March 2020 and May 2022. Measurements of monocyte HLA-DR expression (mHLA-DR), counts of neutrophils, of total lymphocytes, and of CD4+ and CD8+ subsets were performed five times during the first month after ICU admission. Univariate joint models combining survival at day 28 (D28), hospital discharge and longitudinal analysis of those biomarkers’ kinetics with mixed-effects models were performed prior to the building of a multivariate joint model. We showed that a higher mHLA-DR value was associated with a lower risk of death. Predicted mHLA-DR nadir cutoff value that maximized the Youden index was 5414 Ab/C and led to an AUC = 0.70 confidence interval (95%CI) = [0.65; 0.75] regarding association with D28 mortality while dynamic predictions using mHLA-DR kinetics until D7, D12 and D20 showed AUCs of 0.82 [0.77; 0.87], 0.81 [0.75; 0.87] and 0.84 [0.75; 0.93]. Therefore, the final joint model provided adequate discrimination performances at D28 after collection of biomarker samples until D7, which improved as more samples were collected. After severe COVID-19, decreased mHLA-DR expression is associated with a greater risk of death at D28 independently of usual clinical confounders.

Pantoprazole is a proton pump inhibitor indicated for the treatment of gastroesophageal reflux disease, a condition that disproportionately affects children with obesity. Appropriately dosing pantoprazole in children with obesity requires understanding the body size metric that best guides dosing, but pharmacokinetic (PK) trials using traditional techniques are limited by the need for larger sample sizes and frequent blood sampling. Physiologically-based PK (PBPK) models are an attractive alternative that can account for physiologic-, genetic-, and drug-specific changes without the need for extensive clinical trial data. In this study, we explored the effect of obesity on pantoprazole PK and evaluated label-suggested dosing in this population. An adult PBPK model for pantoprazole was developed using data from the literature and accounting for genetic variation in CYP2C19. The adult PBPK model was scaled to children without obesity using age-associated changes in anatomical and physiological parameters. Lastly, the pediatric PBPK model was expanded to children with obesity. Three pantoprazole dosing strategies were evaluated: 1 mg/kg total body weight, 1.2 mg/kg lean body weight, and US Food and Drug Administration-recommended weight-tiered dosing. Simulated concentration–time profiles from our model were compared with data from a prospective cohort study (PAN01; NCT02186652). Weight-tiered dosing resulted in the most (>90%) children with pantoprazole exposures in the reference range, regardless of obesity status or CYP2C19 phenotype, confirming results from previously published population PK models. PBPK models may allow for the efficient study of physiologic and developmental effects of obesity on PK in special populations where clinical trial data may be limited.

Cytokine release syndrome (CRS) was associated with teclistamab treatment in the phase I/II MajesTEC-1 study. Cytokines, especially interleukin (IL)-6, are known suppressors of cytochrome P450 (CYP) enzymes' activity. A physiologically based pharmacokinetic model evaluated the impact of IL-6 serum levels on exposure of substrates of various CYP enzymes (1A2, 2C9, 2C19, 3A4, 3A5). Two IL-6 kinetics profiles were assessed, the mean IL-6 profile with a maximum concentration (C_max) of IL-6 (21 pg/mL) and the IL-6 profile of the patient presenting the highest IL-6 C_max (288 pg/mL) among patients receiving the recommended phase II dose of teclistamab in MajesTEC-1. For the mean IL-6 kinetics profile, teclistamab was predicted to result in a limited change in exposure of CYP substrates (area under the curve [AUC] mean ratio 0.87–1.20). For the maximum IL-6 kinetics profile, the impact on omeprazole, simvastatin, midazolam, and cyclosporine exposure was weak to moderate (mean AUC ratios 1.90–2.23), and minimal for caffeine and s-warfarin (mean AUC ratios 0.82–1.25). Maximum change in exposure for these substrates occurred 3–4 days after step-up dosing in cycle 1. These results suggest that after cycle 1, drug interaction from IL-6 effect has no meaningful impact on CYP activities, with minimal or moderate impact on CYP substrates. The highest risk of drug interaction is expected to occur during step-up dosing up to 7 days after the first treatment dose (1.5 mg/kg subcutaneously) and during and after CRS.

Cardiopulmonary bypass (CPB) can alter pharmacokinetic (PK) parameters and the drug may adsorb to the CPB device, altering exposure. Cefazolin is a beta-lactam antibiotic used for antimicrobial prophylaxis during cardiac surgery supported by CPB. Adsorption of cefazolin could result in therapeutic failure. An ex vivo study was undertaken using CPB devices primed and then dosed with cefazolin and samples were obtained over 1 hour of recirculation. Twelve experimental runs were conducted using different CPB device sizes (neonate, infant, child, and adult), device coatings (Xcoating™, Rheoparin®, PH.I.S.I.O), and priming solutions. The time course of saturable binding, using B_max (binding capacity), K_d (dissociation constant), and T2_off (half-time of dissociation), described cefazolin adsorption. B_max estimates for the device sizes were neonate 40.0 mg (95% CI 24.3, 67.4), infant 48.6 mg (95% CI 5.97, 80.2), child 77.8 mg (95% CI 54.9, 103), and adult 196 mg (95% CI 191, 199). The Xcoating™ K_d estimate was 139 mg/L (95% CI 27.0, 283) and the T2_off estimate was 98.4 min (95% CI 66.8, 129). The Rheoparin® and PH.I.S.I.O coatings had similar binding parameters with K_d and T2_off estimates of 0.169 mg/L (95% CI 0.01, 1.99) and 4.94 min (95% CI 0.17, 59.4). The B_max was small (< 10%) relative to a typical total patient dose during cardiac surgery supported by CPB. A dose adjustment for cefazolin based solely on drug adsorption is not required. This framework could be extended to other PK studies involving CPB.

Zavegepant is a novel gepant administered as a nasal spray approved in the United States at a 10 mg dose for the acute treatment of migraine with or without aura in adults. The cardiovascular safety of zavegepant nasal spray was assessed in both single-ascending dose (SAD) and multiple-ascending dose (MAD) studies in healthy participants. The SAD study included 72 participants (54 active/18 placebo) who received 0.1–40 mg zavegepant or placebo. The MAD study included 72 participants (56 active/16 placebo) who received 5–40 mg zavegepant or placebo for 1–14 days. Plasma zavegepant pharmacokinetics and electrocardiographic (ECG) parameters (Fridericia-corrected QT interval [QTcF], heart rate, PR interval, ventricular depolarization [QRS], T-wave morphology, and U-wave presence) were analyzed pre- and post-zavegepant administration. Using pooled data from the SAD and MAD studies, the relationship between time-matched plasma zavegepant concentrations and QTc interval was assessed using a linear mixed-effects model to evaluate the potential for QTc interval prolongation. Results showed that single and multiple doses of zavegepant had no significant impact on ECG parameters versus placebo, and there was no concentration-dependent effect on QTcF interval. The estimated slope of the plasma zavegepant concentration-QTcF model was −0.053 ms per ng/mL with a 90% confidence interval of −0.0955 to −0.0110 (p = 0.0415), which is not considered clinically meaningful. At doses up to four times the recommended daily dose, zavegepant does not prolong the QT interval to any clinically relevant extent.

Sotatercept is a breakthrough, first-in-class biologic, that is FDA-approved for the treatment of pulmonary arterial hypertension (PAH). A population pharmacokinetic (PopPK) model was developed using data from two phase 1 studies in healthy participants, and two phase 2 studies and one phase 3 study in participants with PAH. The pooled sotatercept PK data encompassed single intravenous (IV) or subcutaneous (SC) doses ranging from 0.01 to 3.0 mg/kg, as well as multiple SC doses ranging from 0.03 to 1.0 mg/kg, with PK samples collected up to a maximum of ~150 weeks following Q3W and Q4W dosing regimens. The final PopPK analysis included 350 participants, with 30 and 320 participants receiving sotatercept IV and SC, respectively. A two-compartment model with a first-order absorption rate constant and a linear disposition from central compartment well-described sotatercept PK. The estimated bioavailability is ~66%; bioavailability, clearance (CL), and central volume (VC) have low to moderate inter-individual variability. Time-varying body weight and baseline albumin concentration were statistically significant predictors of PK; CL and VC were predicted to increase with increasing body weight, while CL was predicted to decrease with increasing baseline albumin concentration. However, the magnitude of covariate effects is not predicted to meaningfully alter the disposition of sotatercept. Altogether, the PopPK modeling results demonstrate favorable PK characteristics (low to moderate variability and typical bioavailability), supporting sotatercept as a SC biological agent for the treatment of patients with PAH.

A physiologically-based pharmacokinetic (PBPK) model for tipifarnib, which included mechanistic absorption, was built and verified by integrating in vitro data and several clinical data in healthy subjects and cancer patients. The final PBPK model was able to recover the clinically observed single and multiple-dose plasma concentrations of tipifarnib in healthy subjects and cancer patients under several dosing conditions, such as co-administration with a strong CYP3A4 inhibitor and inducer, an acid-reducing agent (proton pump inhibitor and H2 receptor antagonist), and with a high-fat meal. In addition, the model was able to accurately predict the effect of mild or moderate hepatic impairment on tipifarnib exposure. The appropriately verified model was applied to prospectively simulate the liability of tipifarnib as a victim of CYP3A4 enzyme-based drug–drug interactions (DDIs) with a moderate inhibitor and inducer as well as tipifarnib as a perpetrator of DDIs with sensitive substrates of CYP3A4, CYP2B6, CYP2D6, CYP2C9, and CYP2C19 in healthy subjects and cancer patients. The effect of a high-fat meal, acid-reducing agent, and formulation change at the therapeutic dose was simulated. Finally, the model was used to predict the effect of mild, moderate, or severe hepatic, and renal impairment on tipifarnib PK. This multipronged approach of combining the available clinical data with PBPK modeling-guided dosing recommendations for tipifarnib under several conditions. This example showcases the totality of the data approach to gain a more thorough understanding of clinical pharmacology and biopharmaceutic properties of oncology drugs in development.

Immune checkpoint inhibitors block the interaction between a receptor on one cell and its ligand on another cell, thus preventing the transduction of an immunosuppressive signal. While inhibition of the receptor–ligand interaction is key to the pharmacological activity of these drugs, it can be technically challenging to measure these intercellular interactions directly. Instead, target engagement (or receptor occupancy) is commonly measured, but may not always be an accurate predictor of receptor–ligand inhibition, and can be misleading when used to inform clinical dose projections for this class of drugs. In this study, a mathematical model explicitly representing the intercellular receptor–ligand interaction is used to compare dose prediction based on target engagement or receptor–ligand inhibition for two checkpoint inhibitors, atezolizumab and magrolimab. For atezolizumab, there is little difference between target engagement and receptor–ligand inhibition, but for magrolimab, the model predicts that receptor–ligand inhibition is significantly less than target engagement. The key variables explaining the difference between these two drugs are the relative concentrations of the target receptors and their ligands. Drug-target affinity and receptor–ligand affinity can also have divergent effects on target engagement and inhibition. These results suggest that it is important to consider ligand–receptor inhibition in addition to target engagement and demonstrate the impact of using modeling for efficacious dose estimation.

The target-cell limited model, which is one of the mathematical modeling approaches providing a quantitative understanding of viral dynamics, has been applied to describe viral RNA profiles of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in previous studies. However, these models have been developed mainly using patient data from the early phase of the pandemic. Furthermore, no reports focused on the profiles of the viral titer. In this study, the dynamics of both viral RNA and viral titer were characterized using data reflecting the current clinical situation in which the Omicron variant has become epidemic and vaccines for SARS-CoV-2 have become available. Consecutive data for 5212 viral RNA levels and 5216 viral titers were obtained from 720 patients with coronavirus disease 2019 (COVID-19) in a phase II/III study for ensitrelvir. Our model assumed that productively infected cells would produce only infectious viruses, which could be transformed into non-infectious viruses, and has been used to describe the dynamics of both viral RNA levels and viral titer. The time from infection to symptom onset (t_inf) of unvaccinated patients was estimated to be 3.0 days, which was shorter than that of the vaccinated patients. The immune-related parameter as a power function for the vaccinated patients was 1.1 times stronger than that for the unvaccinated patients. Our model allows the prediction of the viral dynamics in patients with COVID-19 from the time of infection to symptom onset. Vaccination status was identified as a factor influencing t_inf and the immune function.