CPT: Pharmacometrics & Systems Pharmacology最新文献

Drug-target binding determines a drug's pharmacodynamics but can also have a profound impact on a drug's pharmacokinetics, known as target-mediated drug disposition (TMDD). TMDD models describe the influence of drug-target binding and target turnover on unbound drug concentrations and are frequently used for biologics and drugs with nonlinear plasma pharmacokinetics. For drug targets expressed in tissues, the effect of TMDD may not be detected when analyzing plasma concentration curves, but it might still affect tissue concentrations and occupancy. This review aimed to investigate the likeliness of such a scenario by reviewing the literature for a typical range of TMDD parameter values and their impact on local drug concentrations and target occupancy in a whole-body PBPK model with TMDD. Our analysis demonstrated that tissue drug concentrations are impacted and significantly depleted in many physiological scenarios. In contrast, the effect on plasma concentrations is much lower, specifically for smaller organs with lower perfusion. Moreover, in scenarios with fast internalization of the drug-target complex, the distribution of large molecules from plasma to tissue interstitial space emerges as a rate-limiting step for the drug-target interaction. These factors may lead to overpredicting local drug concentrations when considering only plasma pharmacokinetics. A sensitivity analysis revealed the high and not always intuitive impact of drug-specific parameters, including the drug molecule hydrodynamic radius, dissociation constant (K_d), drug-target complex internalization rate constant (k_int), and target dissociation rate constant (k_off), on the drug's pharmacokinetics. Our analysis demonstrated that tissue TMDD needs to be considered even if plasma pharmacokinetics are linear.

Edoxaban is an orally active inhibitor of activated factor X (FXa). Population pharmacokinetic (PK) and pharmacodynamic (PD) analyses were performed to characterize the PK and PK-PD relationships of edoxaban in pediatric patients to identify the covariates that may contribute to inter-subject variability in PK and PD of edoxaban in pediatric patients, and to compare the PK and PD data between pediatric and adult patients. The pediatric PK of edoxaban was best described by a two-compartment model with transit compartments, first-order oral absorption, and linear elimination. The estimated glomerular filtration rate (eGFR), body weight, and post-menstrual age were the significant covariates explaining variability in edoxaban PK among pediatric patients. A function based on renal maturation was applied to edoxaban clearance. The clearance for a 70 kg patient with an eGFR of 110 mL/min/1.73 m² was estimated to be 42.9 L/h (CV ~ 31.8%). PK simulation showed that exposures across five pediatric age groups were comparable to that in adult patients receiving 60 mg once daily dose. The PK-PD relationship for anti-factor Xa was best fit with an E_max (8.65 IU/mL) model with an EC₅₀ of 631 ng/mL. The PK-PD relationships for activated partial thromboplastin time and prothrombin time were best fit with linear models (slopes of 0.0467, and 0.0415 s mL/ng, respectively). In addition, due to the small number of efficacy and safety events, an exploratory analysis did not detect a correlation between efficacy events (recurrent venous thromboembolism) or safety events (clinically relevant bleeding) and edoxaban exposure.

Ritonavir-boosted atazanavir (ATV/r) and rifampicin are mainstays of second-line antiretroviral and multiple anti-TB regimens, respectively. Rifampicin induces CYP3A4, a major enzyme involved in atazanavir metabolism, causing a drug–drug interaction (DDI) which might be exaggerated in pregnancy. Having demonstrated that increasing the dose of ATV/r from once daily (OD) to twice daily (BD) in non-pregnant adults can safely overcome this DDI, we developed a pregnancy physiologically based pharmacokinetic (PBPK) model to explore the impact of pregnancy. Predicted pharmacokinetic parameters were validated with separate clinical datasets of ATV/r alone (NCT03923231) and rifampicin alone in pregnant women. The pregnancy model was considered validated when the absolute average fold error (AAFE) for C_trough and AUC_0-24 of both drugs were <2 when comparing predicted vs. observed data. Thereafter, predicted atazanavir C_trough was compared against its protein-adjusted IC₉₀ (14 ng/mL) when simulating the co-administration of ATV/r 300/100 mg OD and rifampicin 600 mg OD. Pregnancy was predicted to increase the rifampicin DDI effect on atazanavir. For the dosing regimens of ATV/r 300/100 mg OD, ATV/r 300/200 mg OD, and ATV/r 300/100 mg BD (all with rifampicin 600 mg OD), predicted atazanavir C_trough was above 14 ng/mL in 29%, 71%, and 100%; and 32%, 73% and 100% of the population in second and third trimesters, respectively. Thus, PBPK modeling suggests ATV/r 300/100 mg BD could maintain antiviral efficacy when co-administered with rifampicin 600 mg OD in pregnancy. Clinical studies are warranted to confirm safety and efficacy in pregnancy.

Despite the initial success of single-targeted chimeric-antigen receptor (CAR) T-cell therapy in hematological malignancies, its long-term effectiveness is often hindered by antigen heterogeneity and escape. As a result, there is a growing interest in cell therapies targeting multiple antigens (≥2). However, the dose-exposure-response relationship and specific factors influencing the pharmacology of dual-targeted CAR-T-cell therapy remain unclear. In this study, we have developed a multiscale cellular kinetic-pharmacodynamic (CK-PD) model using case studies from CD19/CD22 and GPRC5D/BCMA autologous CAR-Ts. Initially, an in vitro tumor-killing model characterized the impact of individual binder affinities and their contribution to overall potency across varying (1) effector: target (ET) ratios and (2) tumor-associated antigen (TAA) expressing cell lines. Subsequently, an integrated CK-PD model was developed in pediatric acute lymphoblastic leukemia (ALL) patients, which accounted for CAR-T-cell product composition and relative antigen abundance in patients' tumor burden to characterize patient-level multiphasic cellular kinetics using multiple bioanalytical assays (e.g., flow and qPCR-based readouts). Global sensitivity analysis highlighted relative antigen expression, maximum killing rate constant, and CAR-T expansion rate constant as major determinants for observed exposure of dual-targeted CAR-T-cell therapy. This modeling framework could facilitate dose-optimization and construct refinement for dual-targeted bicistronic CAR-T-cell therapies, serving as a valuable tool for both forward and reverse translation in drug development.

In drug development, quantitative systems pharmacology (QSP) models are becoming an increasingly important mathematical tool for understanding response variability and for generating predictions to inform development decisions. Virtual populations are essential for sampling uncertainty and potential variability in QSP model predictions, but many clinical efficacy endpoints can be difficult to capture with QSP models that typically rely on mechanistic biomarkers. In oncology, challenges are particularly significant when connecting tumor size with time-to-event endpoints like progression-free survival while also accounting for censoring due to consent withdrawal, loss in follow-up, or safety criteria. Here, we expand on our prior work and propose an extended virtual population selection algorithm that can jointly match tumor burden dynamics and progression-free survival times in the presence of censoring. We illustrate the core components of our algorithm through simulation and calibration of a signaling pathway model that was fitted to clinical data for a small molecule targeted inhibitor. This methodology provides an approach that can be tailored to other virtual population simulations aiming to match survival endpoints for solid-tumor clinical datasets.

Around 1.2 million women living with HIV give birth annually, majority of whom will breastfeed their infants while receiving antiretroviral therapy (ART). Lamivudine, a component of first-line ART regimens crosses from maternal plasma to breast milk, with measurable concentrations in some breastfed infants. Wide variability in plasma-to-breast milk transfer has been reported within- or across studies, probably due to differences in sampling framework. This work sought to characterize the milk-to-plasma transfer of lamivudine, quantify inter-patient variability and associated factors, and predict exposure of a breastfed infant. We explored data from an observational pharmacokinetic study that included 35 Ugandan mothers and their infants. Mothers received lamivudine doses of 150 mg twice daily or 300 mg once daily as part of their antiretroviral regimen. Pharmacokinetic sampling was undertaken across two visits approximately 8 weeks apart, providing 248 maternal plasma, 256 breast milk-, and 151 infant blood concentrations, measured across a 24-h sampling interval. A one-compartmental model best described the plasma disposition of lamivudine, with first-order absorption, interindividual variability on clearance and volume of distribution, and a proportional residual error model. A lag in time of plasma-to-breast milk drug accumulation was described using an effect compartment model with a milk-to-plasma ratio of 1.77. An estimated daily infant dose of 179.3 μg/kg (range: 125.8, 282.3) closely predicted the observed infant steady-state concentrations and translated into 3.34% (2.13, 7.20) and 3.35% (1.10, 7.15) of the standard daily maternal dose in visits 1 and 2, respectively. The established modeling framework can be extended to other drugs.

The probability of target attainment (PTA) is a common metric in drug dose optimization, but it requires a specific known target concentration threshold. Such target thresholds are not always available for some treatments, and patient and disease groups, particularly when treating children. This study performed pharmacokinetic and pharmacokinetic-pharmacodynamic (PKPD) simulations to explore different statistical approaches for determining the optimal dose for unknown PK and PKPD targets. To determine an optimal dose, PK and PKPD outcomes in typical patients with a standard adult dosing regimen were simulated and set as the reference profile, and compared to simulated outcomes for different dosing regimens in the population of interest. Statistical distances between the empirical cumulative distribution functions of the outcomes from all possible dosing regimens were calculated and compared to the reference profile. An optimal dose for known PK and PKPD target outcomes was selected to maintain the outcome above the assigned target, while optimal dosing in a population of interest with an unknown target was selected to generate equivalent PK and PKPD outcomes as the typical population. All of the dose optimization methods with commonly used PK and PKPD models and covariates were implemented as an open source freely available Shiny web-application. The developed pharmacometric method for dose optimization in populations with known and unknown target levels were robust and reproducible, and the implementation of a freely accessible Shiny web-application ensures widespread use and could be a useful tool for dose optimization in populations of interest.

Sensitivity analyses are important components of physiologically based pharmacokinetic (PBPK) model development and are required by regulatory agencies for PBPK submissions. They assess the impact of parametric uncertainty and variability on model estimates, aid model optimization by identifying parameters requiring calibration, and enable the testing of assumptions within PBPK models. One-at-a-time (OAT) sensitivity analyses quantify the impact on a model output in response to changes in a single parameter while holding others fixed. Global sensitivity analysis (GSA) methods provide more comprehensive assessments by accounting for changes in all uncertain or variable parameters, though at a higher computational cost. This tutorial article presents a software package for conducting both OAT and GSA of PBPK models built in the Open Systems Pharmacology (OSP) Suite. The tool is accessible through either an R script or a graphical user interface, and the outputs consist of sensitivity metrics of pharmacokinetic (PK) parameters, such as C_max and AUC, evaluated with respect to model input parameters. Results are formatted according to regulatory standards. The OAT analysis methods comprise two-way local sensitivity analyses and probabilistic uncertainty analyses, whereas the GSA methods include the Morris, Sobol, and EFAST methods. These analyses can be conducted on single PBPK models or pairs of models for the evaluation of the sensitivity of PK parameter ratios in drug-drug interaction studies. The practical application of the package is demonstrated through three illustrative case studies.

Balcinrenone (AZD9977) is a selective mineralocorticoid receptor modulator in development in combination with dapagliflozin for treatment of heart failure with impaired kidney function and chronic kidney disease. A prespecified concentration-QT analysis was performed based on data from a phase I single ascending dose study prospectively designed as a thorough QT study substitute. Oral single doses of balcinrenone of 5-800 mg, plus fractionated doses of 800 and 1200 mg, were evaluated in 62 healthy male participants. Time-matched 12-lead digital electrocardiogram and plasma concentrations were measured pre-dose and up to 48 h postdose in the participants. Analysis was performed using a linear mixed-effect model, where baseline-adjusted Fridericia-corrected QT interval (ΔQTcF) was a dependent variable and time-matched balcinrenone concentration an independent variable. The model fit was deemed adequate by evaluation of goodness-of-fit plots, and the slope of the concentration-ΔQTcF relationship was nonsignificant (-0.003 ms/μmol/L; 95% confidence interval [CI]: -0.181, 0.176). The high clinical exposure scenario was defined as the maximum concentration (2.156 μmol/L) following the highest expected therapeutic dose (40 mg once daily) under fed conditions and in presence of a strong cytochrome P450 3A4 inhibitor. Estimated baseline-adjusted and placebo-corrected QTcF interval (ΔΔQTcF) at this concentration was 0.35 ms (90% CI: -1.29, 2.00). Furthermore, the upper 90% ΔΔQTcF CI was estimated to be below the threshold of regulatory concern of 10 ms at all observed concentrations (up to 16.7 μmol/L). Hence, it can be concluded that balcinrenone does not induce QTcF prolongation at the high clinical exposure scenario.

Pegcetacoplan is a complement C3/C3b inhibitor indicated for the treatment of geographic atrophy (GA). A population pharmacokinetic (PK)/pharmacodynamic (PD) analysis of pegcetacoplan used GA lesion area measurements from three clinical studies to determine the effect of pegcetacoplan exposure on GA progression. A base disease progression model was developed using data from sham-treated eyes and untreated fellow eyes, followed by treatment effect assessment in dose-response and PK/PD models. In total, 1501 patients from FILLY (NCT02503332), OAKS (NCT03525613), and DERBY (NCT03525600) received intravitreal pegcetacoplan 15 mg monthly or every other month (EOM) or sham treatment monthly or EOM and were included in the population analysis of lesion area. Disease progression over time was adequately described as linear-with-time over the 24-month maximal study duration. Disease-specific covariates associated with slower lesion growth were unilateral, unifocal, and subfoveal GA lesions and >20 intermediate or large drusen groups (≥63 μm) at baseline. The dose-response model estimated 0.80-fold (95% CI: 0.75, 0.84) and 0.83-fold (95% CI: 0.78, 0.87) reductions in GA lesion growth rate with pegcetacoplan monthly and EOM, respectively, versus sham. A relationship between vitreous humor concentration and GA lesion growth rate was quantified as 2.6% per unit of log-transformed vitreous pegcetacoplan concentration in the PK/PD model. PK/PD predictions of treatment effect based on exposure (pegcetacoplan monthly: 0.80 [90% CI: 0.77, 0.84]; pegcetacoplan EOM: 0.83 [90% CI: 0.80, 0.86]) were consistent with predictions based on dose response. These results support the benefit of pegcetacoplan administered monthly or EOM in slowing GA lesion growth.