CPT: Pharmacometrics & Systems Pharmacology最新文献

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.

Physiologically-based pharmacokinetic (PBPK) models have become increasingly popular for model-informed drug development (MIDD) over the past decade. While several guidelines for model evaluation exist, these are by design often of a general and non-specific nature. It is clear what steps should be carried out but not necessarily how. For instance, a validation step needs to be performed to check if the predictions of a model indeed match with an external dataset. However, how to quantify this is yet unspecified. In this paper, we propose a more thorough validation approach based on the Continuous Ranked Probability Score (CRPS), a popular metric that explicitly quantifies how well a model recreates the distribution of observed data. Crucially, when applied to PBPK modeling, this metric can be used both in situations where we have individual level predictions and in situations where an entire virtual population is created. The CRPS can also be used to quantify the difference in predictive performance of two competing models. We applied this validation technique to compare two PBPK models. Additionally, we show that using a skill-score approach facilitates the validation of a single model. While our paper focuses on PBPK models, this metric is equally applicable to other models where the goal is to create a virtual population. Additionally, we provide an easily accessible online tool that can be used to perform the proposed validation method.

Mediation analyses can support biomarker development by quantifying the part of the total treatment effect on clinical outcome that is mediated by the biomarker. Given that pharmacometricians are often involved in the analysis of biomarker data with pharmacokinetic–pharmacodynamic (PK–PD) models, mediation analysis can be a valuable addition to the pharmacometrics toolbox. The generalized nature of causal mediation analysis makes it particularly suited for use in pharmacometrics, where complex models with non-linearities or interaction effects are common. This tutorial covers the concepts of causal mediation analysis and how these might be applied within a pharmacometric context using a simulation-based workflow, including example code and datasets.

Salmeterol is a commonly used β₂-agonist included on the List of Prohibited Substances and Methods published by the World Anti-Doping Agency (WADA). We developed a population pharmacokinetic (popPK) model to describe the PK of salmeterol including its major metabolite, α-hydroxysalmeterol, in plasma and urine after inhalation. The model was used to evaluate the ability of the current minimum reporting level (MRL) of 10 ng/mL for salmeterol to discriminate between permitted and prohibited use of salmeterol. Six studies on healthy participants, chronic asthmatics, or athletes were pooled and provided a total of 1175 concentrations (275 and 398 for salmeterol and 185 and 317 for α-hydroxysalmeterol in plasma and urine, respectively) from 92 individuals. A two-compartment model assuming intravenous-like bolus absorption best depicted plasma salmeterol PK, with a complete parent conversion into α-hydroxysalmeterol. Because urine volumes were only recorded in two studies, a separate urine compartment was defined to approximate physiologic micturition. Athletes had a 63% higher salmeterol plasma clearance and a 191% greater salmeterol urinary rate constant compared to other subjects, resulting in significantly higher salmeterol urine concentrations. Our popPK model suggests that salmeterol concentrations in urine at therapeutic doses (100 μg twice daily) are unlikely to be reported using the current MRL. However, to improve its sensitivity to detect cases of doping, an adjustment in the MRL and/or a different analytical target would be recommended.

Bilirubin is a breakdown product of erythrocytes and plays a crucial role in elimination of heme-containing proteins. After its synthesis in the reticuloendothelial system, unconjugated bilirubin is released into plasma and taken up into the liver. In hepatocytes, bilirubin is conjugated and excreted into the gastrointestinal tract via bile, where it is further converted to urobilinoids. There are various genetic factors causing abnormal bilirubin levels in plasma, such as Gilbert syndrome, Crigler-Najjar syndrome, Dubin-Johnson syndrome, and Rotor syndrome. To better understand bilirubin metabolism and its disorders, this study develops a physiologically based computational model incorporating published literature as well as real-world clinical data from the Explorys database. The model simulates bilirubin levels in both healthy individuals and patients with disorders of bilirubin metabolism. Population simulations show that Gilbert syndrome requires a substantial reduction in UDP-glucuronosyltransferase 1A1 activity, while Crigler-Najjar syndrome requires near-complete loss of its function. In contrast, Dubin-Johnson syndrome is characterized by a significant impairment of multidrug resistance-associated protein 2 activity. To also illustrate model behavior under targeted perturbations, we simulated administration of atazanavir in healthy individuals and patients with Gilbert syndrome to investigate its effect on bilirubin levels. Relative to baseline, unconjugated bilirubin maximum concentration (C_max) increased by 34% in healthy individuals but by 67% in Gilbert syndrome. Overall, this study provides a conceptual and mechanistically informed framework for studying bilirubin homeostasis and the functional consequences of drug administration in health and disease.

Lecanemab is a humanized IgG1 monoclonal antibody binding with high affinity to protofibrils of amyloid-beta (Aβ) protein. In clinical studies, lecanemab has been shown to reduce amyloid markers in early symptomatic Alzheimer's disease and slow decline on clinical endpoints of cognition and function. Nonlinear mixed-effects modeling assessed the correlation between amyloid PET and change in CDR-SB using data from lecanemab phase 2 study (Study 201) and phase 3 study (Study 301; Clarity AD). Data from placebo-treated subjects were used to establish a disease-progression model; the effect of amyloid reduction on disease progression was defined using data from lecanemab-treated subjects. CDR-SB scores were used with beta regression to fit a Richard's function parameterized in terms of baseline CDR-SB, intrinsic rate of disease progression, shape, and precision of the beta distribution. Simulations were conducted to evaluate the impact of lecanemab treatment over 4 years. Baseline CDR-SB was predicted by diagnosis and baseline mini-mental state examination (BMMSE) score. Intrinsic rate of disease progression was predicted by amyloid PET and BMMSE. Amyloid PET was a better predictor of drug effect than lecanemab exposure, demonstrating amyloid reduction as a surrogate marker of efficacy. Simulations projected the difference in CDR-SB between lecanemab and placebo treated subjects continued increasing over 4 years. Patients with low baseline amyloid and less severe disease were projected to have slower disease progression and better outcomes with lecanemab treatment.

Cachexia is a metabolic condition that accelerates the clearance of monoclonal antibodies in cancer patients and is a known mechanism causing time-dependent clearance. Successful anticancer treatment often ameliorates symptoms of cachexia, reducing the drug clearance over time especially in patients who respond. Serum albumin level is a common biomarker of cachexia that is frequently associated with antibody drug clearance. Physiologically based pharmacokinetic (PBPK) models of antibody drugs have incorporated albumin metabolism but have not been applied to describe time-varying clearance due to improvement in cachexia. The objective of this analysis was to evaluate albumin levels as a biomarker that is predictive of changes in antibody clearance due to cachexia. A PBPK model that jointly describes metabolism of albumin and biologic drugs was fitted to longitudinal albumin data from cancer patients treated with durvalumab and was used to predict changes in durvalumab clearance over time. PBPK model predictions were compared to empirical population pharmacokinetic (PK) models of durvalumab and other checkpoint inhibitors fitted directly to clinical PK. The model fitted the observed albumin data in cancer patients closely, and the three fitted parameters showed low uncertainty (RSE < 10%). By accounting for longitudinal albumin data in patients, the PBPK model recapitulated the observed magnitude of the change in clearance of durvalumab without fitting to clinical PK data. The model simulation demonstrated that utilization of albumin levels as a marker of cachexia in PBPK models can be used to mechanistically predict time-dependent clearance of monoclonal antibodies.

Physiologically Based Pharmacokinetic (PBPK) modeling is a powerful tool in drug development that integrates drug-specific information with physiological parameters to predict drug concentrations. However, parameter estimation in PBPK models presents significant challenges due to the large number of parameters involved and limited observed data. This tutorial introduces a novel approach coupling whole-body PBPK (WB-PBPK) models with population estimation methods (popWB-PBPK) to leverage individual data and estimate inter-individual variability on physiologically relevant parameters. The framework employs an optimized Stochastic Approximation Expectation–Maximization (SAEM) algorithm, reducing the estimation runtime through an adaptive parameter grid optimization and linear interpolation techniques. Using theophylline as a case study, we illustrate how this approach can accurately estimate drug-specific parameters (CYP1A2 clearance and lipophilicity) while incorporating covariate effects (smoking status). The optimized algorithm significantly reduces computational time compared to the standard SAEM algorithm. Our implementation in the saemixPBPK R package provides an accessible framework for parameter estimation in PBPK models, enabling more robust predictions of pharmacokinetic behavior leveraging individual data. This approach represents an important advancement in mechanistic modeling, allowing simultaneous estimation of population parameters, variability, and uncertainty while maintaining the physiological relevance of PBPK models.

Potassium-competitive acid blockers (PCABs) are emerging alternatives to proton pump inhibitors for the treatment of acid-related diseases. However, due to the complex, nonlinear interaction between drug exposure, food intake, and physiological rhythms, optimizing dosing strategies remains challenging. A multi-leveled population analysis was conducted using published pharmacokinetic and pharmacodynamic data on four representative PCABs: tegoprazan, YH4808, fexuprazan, and vonoprazan. A semi-mechanistic population PK/PD model was developed to account for food effects, circadian pH rhythms, and pH-dependent drug absorption. A multi-level nonlinear mixed-effects modeling framework was implemented to capture both inter-drug and inter-study variability. The model successfully described the time course of plasma concentration and intragastric pH for all four PCABs under various conditions. The model identified differences in pharmacokinetics and pharmacodynamic potency between drugs (with the relative in vitro potency ranked as vonoprazan > fexuprazan > YH4808 > tegoprazan), and simulations demonstrated that both pre- and post-meal administration enhanced pH control in early time period via potentially distinct mechanisms: the pre-meal effect may arise from temporally separated contributions of food- and drug-induced pH elevation, whereas the post-meal effect is likely driven by temporally overlapping, additive actions, particularly under low-dose or non-steady-state conditions. Predicted pH profiles and holding times above pH 4 closely matched reported clinical outcomes. The study demonstrates the application of a mechanistic, multi-level population approach for cross-drug PK/PD evaluation of PCABs. The findings support drug-specific dose optimization and highlight the clinical relevance of food–drug interactions. The modeling approach provides a model platform for pharmacotherapy or model-informed drug development (MIDD).

A physiologically-based pharmacokinetic (PBPK) model of niraparib and its primary metabolite using a relevant virtual cancer population is reported here. A series of in vitro experiments using liver S9, microsomes, and hepatocytes with various inhibitors and recombinant supersomes demonstrated that niraparib is specifically metabolized by carboxylesterase 1 via amide hydrolysis to an acid metabolite (M1). Available virtual cancer populations, along with reference populations, were applied to modeling simulations using fixed trial designs with demographic and clinical chemistry parameters from patients receiving niraparib in clinical studies. Simulations of niraparib and its metabolite M1 were verified across numerous available clinical studies and repeat dose ranges in cancer patients within 2-fold. The PBPK model was used to simulate exposures in moderately hepatic impaired, healthy Chinese and Japanese virtual populations as a surrogate of cancer comorbidity. The PBPK model confirmed minimal DDI liability with niraparib as a precipitant for most in vitro tested drug metabolizing enzymes and transporters. In vitro, niraparib lacks any CYP inhibition, induces CYP1A2 but not CYP3A4, and is not a CYP substrate, unlike some other PARPi's, which inhibit and induce numerous enzymes/transporters and are objects of CYP metabolism. At clinically relevant doses of niraparib ≥ 200 mg, a weak induction risk is predicted with sensitive CYP1A2 substrates, such as caffeine, and both niraparib and olaparib clinically increase serum creatinine in cancer patients, with up to a moderate inhibition risk predicted with MATE-1/-2K substrates, such as metformin, using a PBPK model of niraparib in the absence of a dedicated DDI study.