CPT: Pharmacometrics & Systems Pharmacology最新文献

Hereditary angioedema (HAE) is a rare disorder linked to kallikrein-kinin system dysregulation, which leads to uncontrolled activation of plasma prekallikrein. Donidalorsen is an antisense oligonucleotide designed to selectively degrade prekallikrein messenger RNA and thereby reduce prekallikrein production. We aimed to develop population pharmacokinetic and pharmacokinetic/pharmacodynamic models of donidalorsen and evaluate the impact of potential intrinsic/extrinsic covariates on exposure and prekallikrein response. Plasma donidalorsen and prekallikrein data were obtained from phase 1 to 3 studies in healthy volunteers (NCT03263507, 721744-CS9) and adult and adolescent patients with HAE (NCT04030598, NCT05139810). The evaluated doses were 20, 40, 60, and 80 mg every 4 weeks (Q4W) and 80 mg every 8 weeks (Q8W), administered subcutaneously over 13-21 weeks. Donidalorsen pharmacokinetics were well described by a linear 2-compartment model with first-order absorption. The population terminal elimination half-life was 31.4 days. Prekallikrein was well described by an indirect response model with inhibition of prekallikrein production by donidalorsen. Covariate analysis identified body weight as the main factor affecting pharmacokinetic exposure; however, this effect was not considered clinically significant. The developed population pharmacokinetic/pharmacodynamic model well characterized the donidalorsen exposure-prekallikrein response relationship. Modeling analyses support that no dose adjustment is needed with respect to intrinsic/extrinsic factors in adults and adolescents with HAE. The nearly identical simulated pharmacokinetic or prekallikrein time courses for Q4W versus monthly dosing and for Q8W versus every-2-month dosing regimens support switching to more convenient regimens for patients.

Global efforts to eliminate onchocerciasis are hampered by the lack of a macrofilaricidal drug capable of killing adult parasites. Oxfendazole, a veterinary anthelminthic, exhibits macrofilaricidal activity and holds promise to shorten treatment durations. Phase 1 studies in healthy Caucasian adults demonstrated favorable pharmacokinetics and safety using a veterinary oral liquid formulation. More recently, a Phase 1 bioavailability trial (NCT04920292) evaluated a field-applicable tablet formulation in healthy African adults. This study presents a secondary analysis to (i) characterize the population pharmacokinetics of oxfendazole and its major metabolites in healthy African adults receiving the tablet formulation and (ii) propose a dosing regimen for Phase 2 evaluation in patients with onchocerciasis. Thirty healthy African adults were enrolled, and plasma concentration-time profiles of oxfendazole, fenbendazole, and oxfendazole sulfone were obtained from 24 participants who received oxfendazole (8 per dose group: 100 mg single dose, 400 mg single dose, 400 mg once daily for 5 days). All cohorts were pooled and analyzed using nonlinear mixed effects modeling. Oxfendazole absorption was best described by first-order kinetics with first-pass metabolism. Dose-limited bioavailability was evident. Disposition was best described by one-compartment models with linear elimination. Simulations suggested that 400 mg once daily (or 50 mg twice daily) for 5 days is required to achieve putative exposure targets (> 200 ng/mL for 5 days), with low risk of safety concerns. The population pharmacokinetic model adequately described oxfendazole pharmacokinetics in healthy African adults and supports dosing selection for future clinical trials. Trial Registration: ClinicalTrials.gov Identifier: NCT04920292.

Covariate selection in population pharmacokinetics modeling is essential for understanding interindividual variability in drug response and optimizing dosing. Traditional stepwise covariate modeling is often time-consuming, compared to the new machine learning alternatives. This study investigates the use of neural networks with stochastic gates for automated covariate selection, aiming to efficiently identify relevant covariates while penalizing excessive covariate inclusion. On various synthetic datasets, the approach demonstrated robustness in detecting important covariates, overcoming challenges such as high correlations, low covariate frequencies, high interindividual variability, and complex covariate dependencies. In real clinical data from a monalizumab study, the method successfully identified covariates that matched those found by experts. However, for tixagevimab/cilgavimab, it identified a superset of covariates, indicating a potential need for further pruning. This machine learning-based method enhances the covariate preselection process in population pharmacokinetics model development, offering significant time savings and improving efficiency even under challenging scenarios.

Vildagliptin (VDG) is a dipeptidyl-peptidase-4 (DPP-4) inhibitor used for type 2 diabetes (T2DM) treatment. Viewing to improve VDG treatment, a population pharmacokinetic (popPK) model was built to describe drug plasma, free liver and muscle concentrations determined by microdialysis in healthy and diabetic animals following 50 mg/kg i.v. bolus administration. A four-compartment popPK model with linear elimination and bidirectional transport between tissues and the central compartment described the data with diabetes as a covariate in Q₁ and Q_out,liver. The pharmacokinetic parameters of VDG were scaled to humans using allometry, and used to simulate VDG tissue concentrations in patients with T2DM and relate them with the DPP-4 inhibition by an I_max model. The efficacy of VDG was evaluated considering 80% and 92% DP-IV inhibition during the entire dosing interval. VDG 100 mg q24 h achieved 80% DPP-4 inhibition in plasma, but not in tissues. Although q12 h dosing interval reached 80% enzyme inhibition in plasma for > 25 mg doses, only the 100 mg reached this goal in muscle. The 92% enzyme inhibition was achieved in plasma for 50 and 100 mg q12 h but none of the dose regimens investigated reached this inhibition in tissues.

To characterize the relationship between cendakimab exposure and the longitudinal efficacy endpoint dysphagia days (DD), E–R analyses were performed using data from the EE-001 study (N = 427) with eosinophilic esophagitis. DD—a bounded, discrete endpoint assessed over 14-day period via modified daily symptom diary (mDSD)—was modeled using a latent variable indirect response (IDR) model coupled with a combined uniform-binomial (CUB) distribution. The latent variable, representing the underlying disease status, was dynamically modulated by placebo and drug effects (a function of individual-predicted exposure) to govern the binomial probability of DD, while the uniform component captured the residual variability in patient-reported outcomes. Inter-individual variability was estimated for baseline DD, maximum placebo effect, and maximum drug effect. Covariates, including steroid inadequate response or intolerance (Steroid IR/I) status and baseline DD, were incorporated in the final model based on the clinical relevance. The estimated placebo half-life was ~28 weeks, estimated EC₅₀ was 76.5 μg/mL, corresponding to an EC₉₀ of ~688 μg/mL, indicating steepness of the E_max curve. Model-based simulations showed that both 360 mg QW and QW-to-Q2W regimens reduced DD compared to placebo at Week 48, with mean reductions of ~1.65 and ~1.36 days, respectively. Covariate-stratified simulations suggested consistent responses across sex, age, and race. Steroid IR/I and baseline DD influenced treatment response magnitude but did not warrant dose modification. These findings support QW-to-Q2W as an effective maintenance posology and the utility of latent variable IDR models with appropriate likelihoods for modeling bounded, discrete longitudinal endpoints in E–R analyses.

Aspirin is one of the most commonly used medications in pregnancy, particularly for the prevention of hypertensive disorders. Despite aspirin's widespread use in pregnancy for preeclampsia prevention, its pharmacokinetics (PK) across all trimesters remain poorly characterized, complicating optimal dosing recommendations. To develop a pregnancy-specific physiologically based pharmacokinetic (PBPK) model for aspirin that could be individualized to patient-specific parameters, illustrating differences in aspirin PK across the different trimesters of pregnancy. A PBPK model was developed using GastroPlus (a mechanistically driven simulation software) for nonpregnant and pregnant people at each trimester of pregnancy. The nonpregnant PBPK model was first established and validated against existing data from healthy adult volunteers. Once validated, the model was adapted for pregnant people and verified using observed pharmacokinetic profiles. The simulated PK parameters of aspirin in pregnant and nonpregnant women closely matched the clinical observations reported in the literature, with fold errors ≤ 1.04 (less than 1.5 is considered an acceptable simulation model). The predicted systemic exposure (AUC_0-24h) of salicylic acid (SA), the active metabolite of aspirin decreased throughout gestation, showing a reduction of approximately 20% at 10 weeks and 30% at 40 weeks. An increase in clearance was observed as gestation progressed. The model predicted a modest decrease of 10% in systemic exposure in pregnant women and a 20% increase in fetal exposure to SA as pregnancy progresses. A PBPK model using GastroPlus was developed to describe the PK and pharmacodynamics of aspirin in both pregnant and nonpregnant healthy adults.

Quantitative systems pharmacology (QSP) models offer a useful platform to integrate drug pharmacology with knowledge about biological mechanisms across multiple scales and data sources into a unified quantitative framework. This makes them invaluable to address many relevant questions in drug research and development. Despite their potential, however, QSP models are seldom employed in the population analysis context due to their complexity and dimensionality. Model order reduction (MOR) techniques can be used to tackle this challenge. However, a single MOR technique might not be sufficient to achieve an applicable reduced model. Furthermore, to date there is no tool to judge whether the reduced model retains important mechanistic features of the original model. In this tutorial, we present a workflow employing index analysis that guides the selection and combination of MOR techniques and includes a check of the preservation of important mechanistic features by the reduced model. To demonstrate the value of the proposed approach, we first explain the concepts in the context of a small-scale example model and then expand to a well-known large-scale QSP model—the blood coagulation model.

Loperamide is a widely used nonprescription peripherally acting opioid indicated for diarrhea. Loperamide undergoes extensive first-pass metabolism, primarily by cytochrome (CYP) 3A and CYP2C8, with minor contributions from CYP2B6 and CYP2D6, and intestinal efflux by P-glycoprotein (P-gp). Increasing case reports have described exaggerated peripheral opioid effects and cardiac toxicities when ultra-high doses (> 70 mg) of loperamide are consumed alone and with CYP or P-gp inhibitors. A physiologically based pharmacokinetic (PBPK) model for loperamide was developed, verified, and applied by simulating interactions with select inhibitor drugs. The model successfully described the pharmacokinetics of loperamide (4, 8, 16 mg loperamide•HCl) in healthy adults. The predicted area under the plasma concentration–time curve (AUC) and maximum concentration (C_max) at all three doses were within 0.61–1.41-fold of observed values obtained from 10 clinical studies. The model independently well-captured the loperamide pharmacokinetic profile obtained from each of seven drug–drug interaction (DDI) studies. The inhibitor drugs tested included quinidine (P-gp), ritonavir (CYP3A/P-gp), gemfibrozil (CYP2C8), itraconazole (CYP3A/P-gp), gemfibrozil+itraconazole, and abemaciclib (CYP1A). Predicted AUC and C_max for loperamide from each DDI study were within 0.78–1.45-fold of observed values. Predicted AUC ratios (AUC of loperamide in the presence to absence of inhibitor) were within 0.78–1.09-fold of observed ratios. This novel PBPK model for loperamide could be used to guide loperamide dosing under untested DDI scenarios when the drug is coadministered with certain CYP/transporter inhibitors to minimize toxicity risk.

Model-based meta-analysis (MBMA) utilizes aggregate data (AD) and allows integration of information from multiple studies, which may provide more statistical power to detect clinically relevant treatment effects than an individual randomized controlled trial alone. Access to individual patient data (IPD) is often limited due to confidentiality; therefore, obtaining IPD associated with published literature data is challenging. Thus, to probe predictive covariates, one must rely on an adequate range of aggregate covariate data, or published stratified results could also be used. With access to IPD, or with access to published stratified results, estimates for predictive covariates could be improved. This work is primarily centered on quantifying the potential benefits of having access to IPD when performing MBMA. This was assessed using a 3-step approach. Two scenarios were explored: one to compare MBMAs with and without access to IPD, assuming no predictive covariates; and another to compare MBMAs with and without access to IPD, where a specific predictive covariate was known to be influential and was used to stratify IPD accordingly. The performance of the method was evaluated for different ratios of IPD studies versus AD studies. In the scenario where an MBMA with covariate was used, instead, the performance of the method was evaluated for different ratios of covariate stratified AD studies versus AD studies. Overall, the benefit of IPD over AD was not evident in the model without covariates, whereas including stratified IPD led to improved covariate model performance.

A high number of poorly soluble compounds are being developed; thus, understanding the factors that influence their absorption is critical. Intestinal bile salts, which facilitate micelle-mediated solubilization, are particularly important for drugs with low solubility and are reported to be highly variable. The aim of this study was to evaluate the effect of luminal bile salt concentrations on the absorption of poorly soluble compounds, using efavirenz, cinnarizine, and posaconazole as examples. Physiologically-based pharmacokinetic (PBPK) models were developed and validated using the Simcyp Simulator. Sensitivity analyzes were performed to assess the impact of bile salts and other gastrointestinal parameters on drug absorption. Simulations revealed that drug absorption in the fasted state was most sensitive to bile salt concentrations compared to other gastrointestinal parameters such as luminal pH, fluid volumes, and gastric emptying. The findings indicate that efavirenz, cinnarizine, and posaconazole exhibit high micelle-mediated solubility, with bile salts playing a critical role in their absorption, particularly in the fasted state. These results highlight the importance of considering bile salt concentrations in PBPK modeling of poorly soluble compounds.