CPT: Pharmacometrics & Systems Pharmacology最新文献

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.

Bosutinib is an orally available Src/Abl tyrosine kinase inhibitor and has been approved for the treatment of patients with Ph + chronic myelogenous leukemia. Bosutinib is a substrate of P-glycoprotein (P-gp) in vitro and is predominantly metabolized by CYP3A4 in humans with minimal urinary excretion. We present our perspective on using physiologically based pharmacokinetic modeling to understand the atypical changes in oral exposure of bosutinib, a CYP3A and P-gp substrate, in hepatic impairment patients.

Pegylated-interferon-α (Peg-IFNα) is a treatment option for chronic hepatitis B virus (HBV) infection. To quantify treatment response variability, we conducted a model-based meta-analysis (MBMA) of hepatitis B surface antigen (HBsAg) loss, defined as a binary outcome based on HBsAg levels falling below the limit of detection, with Peg-IFNα-based regimens at end-of-treatment (EOT) and 24 weeks post-treatment. A systematic review of HBsAg loss in chronic HBV infection was performed, searching Embase, MEDLINE, and Cochrane (January 2000–July 2022). Studies reporting only per-protocol results were excluded; intent-to-treat (ITT) or modified ITT results were prioritized. Models described the proportion achieving HBsAg loss with respect to treatment regimens, exploring baseline clinical and demographic covariates. For the EOT model, 83 study-strata-arms (11,493 participants) were included; for the 24-week model, 58 study-strata-arms (4267 participants) were included. In both models, Peg-IFNα duration and baseline HBsAg significantly predicted HBsAg loss (p < 0.001); baseline hepatitis B e-antigen (HBeAg) was an additional predictor at EOT (p = 0.007). These covariates reduced between-trial variance by 58.1% (EOT) and 77.6% (24-week), highlighting their role in explaining heterogeneity. This MBMA supports clinical trial design by simulating outcomes with Peg-IFNα across diverse populations, optimizing trial parameters, estimating sample sizes, and informing enrichment strategies. Notably, these findings have been applied to calibrate and validate in silico trials, demonstrating utility in advancing computational approaches for HBV drug development. This approach enhances precision in predicting treatment outcomes and sets a precedent for leveraging MBMA in chronic hepatitis B research, paving the way for more effective strategies.

Long-acting injectable medicinal products (LAIs) prolong drug release and thereby aim to enhance adherence and patient outcomes. European regulatory guidelines require the conduct of single- and multiple-dose trials to exclude differences in drug release between non-steady and steady state conditions. The complexity of these trials may however hamper the development of LAIs. This study aimed to examine whether drug release is different after single- and multiple-dose administration using clinical pharmacokinetic (PK) data of a sample of five regulatory-approved LAIs. Single- and multiple-dose data were extracted from an internal regulatory database. Population pharmacokinetic models with different absorption structures were developed using nonlinear mixed-effect modeling based on the single-dose data of every LAI. The best-fitting models were used to predict the pharmacokinetic profiles after multiple-dose administration. The absorption of LAIs after single-dose administration was best described with (parallel) first-order absorption structures (with and without lag-time). After multiple-dose administration, the mean model accuracy was 93% (minimum to maximum: 70%–122%), and 7 out of 10 observed pharmacokinetic variables (i.e., area under the plasma concentration—time curve, minimum and maximum concentration) met the pre-specified acceptance criteria. In conclusion, multiple-dose PK characteristics can be predicted using models developed from single-dose PK data, which indicates that drug release may not be very different between dosing conditions in this sample of regulatory-approved LAIs. Nevertheless, additional studies on other LAIs are required to test the generalizability of our findings and to increase our understanding of the limitations of the proposed model-based approach vis-à-vis the current evidentiary standard.

Modeling and simulations are indispensable tools to describe pharmacokinetics (PK) and pharmacodynamics to support monoclonal antibody (mAb) development. The linear PK of mAbs is commonly described by a 2-compartment PK model, while the nonlinear PK often observed at low doses is described by target mediated drug disposition (TMDD) models. Since target binding is the primary pharmacology of mAbs and receptor occupancy (RO) could impact PK through TMDD, it is desirable to have a simple mechanistic model to simultaneously describe both PK, including TMDD, and RO at the site of action (SoA). In this tutorial, we introduce a physiologically inspired PKRO (piPKRO) model for mAbs targeting membrane receptors. The linear PK part (referred to as the piPK model) is modified from the classical 2-compartment PK model to include using the physiological compartment volumes, adding drug clearance in extravascular compartments, describing mAb concentration in tissues reflecting measured partition coefficients, and target expression and drug binding based on the location of target expression. A few macroparameters including P_dist (partition coefficient) and t_dist (distribution half-time) are introduced to provide an intuitive understanding of the mAb distribution. Case studies of applying the model to real world data are provided. Analysis with the piPKRO model suggests that local drug depletion could occur due to significant target mediated drug clearance at the SoA in combination with relatively slow drug distribution. This local drug depletion can lead to much lower RO at the SoA compared to RO in the central compartment and subsequently impact efficacious dose predictions.

Chronic hepatitis B virus (HBV) infection remains a significant global health challenge. While the dynamic interplay between viral replication and host immune responses determines infection outcomes, the mechanisms driving the resolution of acute infection versus the emergence of chronicity remain incompletely understood. To address this challenge, we developed a detailed quantitative systems pharmacology (QSP) model of acute HBV infection capturing several key host immune and viral mechanisms absent in previous models. The model was parameterized using publicly available data and calibrated against clinical time-course datasets from multiple acute HBV case studies. Perturbation and local sensitivity analyses identified key drivers of biomarker dynamics, particularly hepatitis B virus DNA (HBV DNA), hepatitis B surface antigen (HBsAg), and alanine aminotransferase (ALT). These dynamics were most sensitive to parameters governing viral replication (e.g., HBV entry via the sodium taurocholate cotransporting polypeptide [NTCP] receptor, covalently closed circular DNA [cccDNA] formation, and hepatocyte turnover) and adaptive immune responses (e.g., CD8⁺ T cell activity, dendritic cell–mediated priming, and regulatory T cell [Treg]–driven immunosuppression). These influential parameters were used to generate a virtual population that reproduced the observed heterogeneity in biomarker trajectories. Notably, the magnitude and timing of biomarker peaks captured most of the variability, reflecting interindividual differences in individual immune responses and viral dynamics. While the current model nicely captures processes associated with acute HBV infections, it will be extended to different stages of chronic HBV with the objective of informing the rational design of novel therapies and supporting the development of curative HBV strategies.

Bispecific antibodies (bsAbs), for which each arm binds a distinct molecular target, are developed to engage soluble and cell surface targets in different therapeutic indications. Three key examples of mechanisms of action (MoA) for bsAbs are (1) immune cell engagers that foster immune cell interactions with target cells, (2) bispecifics that use one arm to increase specificity/localization to desired tissues to reduce on-target off-tissue toxicity, and (3) bispecifics that use two different arms to neutralize different disease targets. Understanding the pharmacokinetic (PK) profiles and target engagement of bsAbs poses unique challenges due to the existence of multiple targets with distinct biological properties. Here, we present a generalized minimal physiologically based pharmacokinetic (mPBPK) model to capture and predict the PK and target engagement of bsAbs across multiple tissues. First, we model the clinical PK and pharmacodynamic (PD) data for an anti-IL-13/IL-17 bsAb to capture and explain the PD response in the soluble cytokine target levels. Second, we model and simulate the PK-PD of the anti-CD20/CD3 T cell engaging antibody, mosunetuzumab, which acts via trans-binding between cell targets on B- and T-lymphocytes. Third, we use the model to explore case studies for other bsAb approaches to demonstrate the impact of binding affinity and avidity on both PK and target engagement and to provide insights into drug design. Overall, our work yields a model with example applications to advance the use of mechanistic modeling for early PK and target engagement predictions as well as for optimization of bsAb design.

Talazoparib is a poly(ADP-ribose) polymerase inhibitor approved for the treatment of breast and prostate cancer. Commercialization of a soft gelatin capsule (SGC) formulation developed post-approval required a bioequivalence (BE) and food effect (FE) study to bridge SGC with the initial commercial hard capsule (HC) formulation. Study execution and meeting BE criteria are challenging due to high variability in C_max, potentially higher C_max for SGC based on dissolution data, and the need to perform BE/FE assessment at steady state in cancer patients. Model-informed drug development (MIDD) was used to facilitate an efficient/feasible study design. Semi-mechanistic pharmacokinetic (PK)/pharmacodynamic (PD) modeling and simulations showed that AUC, instead of C_max, drove hematologic events, the main side effects of talazoparib. This supported a BE study powered for AUC equivalence only. Population PK simulation showed that following a 28-day treatment in the first period, 14 days in subsequent periods is sufficient for steady-state BE/FE assessments. Study results showed AUC met BE criteria while C_max was 37% higher for SGC relative to HC, which was deemed not clinically significant based on the PK/PD model. FE on SGC formulation was consistent with FE on HC formulation reported previously. The safety profile of the two formulations was generally consistent with the known safety profile. The totality of data (AUC equivalence, lack of impact of C_max on safety, observed safety data) supported bridging of the two formulations although C_max failed to meet BE criteria. MIDD was critical in study design optimization and supported approval of the SGC formulation.

Trial Registration: ClinicalTrials.gov Identifier: NCT04672460

Nintedanib reduces the rate of decline in forced vital capacity (FVC) in adult patients with idiopathic pulmonary fibrosis (IPF), chronic progressive-fibrosing interstitial lung diseases (ILDs) and systemic sclerosis-associated ILD (SSc-ILD). A pediatric Phase 3 trial (InPedILD) has been conducted in children 6–17 years, and partial extrapolation from adults to pediatrics was performed to support dose selection and benefit–risk assessment in pediatric patients with progressive-fibrosing ILDs. Previously developed population pharmacokinetic (popPK) and efficacy exposure-response (ER) meta-models across all non-oncologic pulmonary indications of nintedanib in adults were used as a basis for partial extrapolation to pediatric patients. Data from InPedILD and its open-label extension trial (InPedILD-ON) were incorporated and a Bayesian approach was utilized to extrapolate PK and FVC-based efficacy endpoints. PK of nintedanib was adequately described using a similar structure as the adult popPK model. The weight-based dosing scheme applied in InPedILD resulted in exposures similar to those in adult patients receiving the approved dose of nintedanib 150 mg twice daily. The ER models for FVC %predicted and FVC Z-score were similar to those developed in adults. Compared to adults, pediatric patients showed a slower estimated rate of decline for both endpoints. The estimated EC50 and Emax values in children and adolescents were comparable to those in adults. Partial extrapolation from adult to pediatric patients showed that the pre-defined pediatric dosing regimen resulted in similar nintedanib exposures compared to the efficacious exposures in adults. ER models suggested a similar beneficial treatment effect of nintedanib on FVC in children and adults.