CPT: Pharmacometrics & Systems Pharmacology最新文献_第3页

A Combined Modeling Approach to Predict the Effect of Gastric Emptying Delay on the Pharmacokinetics of Small Molecules. 预测胃排空延迟对小分子药代动力学影响的联合建模方法。

IF 3 3区医学

CPT: Pharmacometrics & Systems Pharmacology Pub Date : 2025-08-21 DOI: 10.1002/psp4.70101

Maria M Posada, Karen B Schneck, Bridget L Morse, Luc R A Rougee, Lai San Tham, Jessica F Rehmel, Brian Thompson, Stephen D Stamatis, Stephen D Hall, Gemma L Dickinson

{"title":"A Combined Modeling Approach to Predict the Effect of Gastric Emptying Delay on the Pharmacokinetics of Small Molecules.","authors":"Maria M Posada, Karen B Schneck, Bridget L Morse, Luc R A Rougee, Lai San Tham, Jessica F Rehmel, Brian Thompson, Stephen D Stamatis, Stephen D Hall, Gemma L Dickinson","doi":"10.1002/psp4.70101","DOIUrl":"https://doi.org/10.1002/psp4.70101","url":null,"abstract":"Dulaglutide, a long-acting glucagon-like peptide-1 (GLP-1) receptor agonist, is approved for improving glycemic control and reducing cardiovascular risks in patients with type 2 diabetes mellitus (T2DM). This research investigates the effect of dulaglutide on gastric emptying and its impact on the pharmacokinetics (PK) of orally administered molecules utilizing a combination of population pharmacokinetic (PopPK) and physiologically based pharmacokinetic (PBPK) modeling approaches. In clinical studies, the gastric emptying delay (GED) was evaluated in healthy participants and patients with T2DM at various dose levels of dulaglutide. A PopPK model estimated the exposure-dependent delay in gastric emptying, which was then input into the orally administered small molecule PBPK models. These PBPK models, informed by internal clinical studies and publicly available data, quantified the effect of dulaglutide-induced GED on the area under the curve (AUC), maximum concentration (Cmax), and time to maximum concentration (tmax) of the co-administered drugs. The modeling approach was verified for reproducing observed GED-mediated drug-drug interactions (DDIs) at low doses of dulaglutide and to predict DDIs at a 4.5 mg dulaglutide dose. The clinical studies demonstrated that the 1.5 mg dulaglutide dose has no clinically relevant effect on the pharmacokinetics of small molecules, and the modeling led to a similar conclusion at 4.5 mg dulaglutide. This work demonstrates that modeling approaches can be used to predict potential GLP-1-mediated DDIs related to gastric emptying delay, increasing the efficiency of the clinical pharmacology programs.","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144945915","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Bayesian Approach to Compare Accumulating Survival Data From Clinical Practice With RCT Data: A Case Study in Non-Small Cell Lung Cancer Patients. 贝叶斯方法比较临床实践积累的生存数据与RCT数据：一个非小细胞肺癌患者的案例研究。

IF 3 3区医学

CPT: Pharmacometrics & Systems Pharmacology Pub Date : 2025-08-21 DOI: 10.1002/psp4.70075

Marjon V Verschueren, Daniel V Verschueren, Ewoudt M W van de Garde, Lourens T Bloem

{"title":"A Bayesian Approach to Compare Accumulating Survival Data From Clinical Practice With RCT Data: A Case Study in Non-Small Cell Lung Cancer Patients.","authors":"Marjon V Verschueren, Daniel V Verschueren, Ewoudt M W van de Garde, Lourens T Bloem","doi":"10.1002/psp4.70075","DOIUrl":"https://doi.org/10.1002/psp4.70075","url":null,"abstract":"Survival outcomes observed in randomized controlled trials (RCTs) may not always be generalizable to clinical practice. Evaluating whether treatment outcomes in clinical practice are similar to those in RCTs shortly after a new medicine is introduced is important for making informed decisions. Therefore, we aimed to develop a Bayesian model that compares survival data from clinical practice that accumulates over time with static survival data from RCTs, thereby providing rapid and easily interpretable results that can inform clinical and policy-related decision-making. We developed a Bayesian survival model that sequentially updates estimates as new data become available. We designed the model to incorporate static RCT data with accumulating clinical practice data. We used sequential hypothesis testing with Bayes factors to assess the strength of the evidence for different hazard ratio (HR) thresholds (i.e., ranging from HR > 1.0 to > 2.0 and HR < 0.5 to < 1.0). We applied the model to two datasets comprising survival data from clinical practice and an RCT for lung cancer patients treated with pembrolizumab plus chemotherapy (dataset 1) and pembrolizumab monotherapy (dataset 2). For dataset 1, the posterior model checks showed a misfit between the model and the data after 15 months, potentially due to channeling bias. The model fit should be improved before reliable estimates can be obtained. For dataset 2, the model estimated precise HRs 10 months before the end of data accumulation. Sequential hypothesis testing with Bayes factors provided easily interpretable results, with very strong evidence for an HR > 1.0 and strong evidence for an HR > 1.2. In conclusion, provided the posterior check shows an acceptable model fit, our Bayesian survival model with sequential hypothesis testing using Bayes factors can provide rapid and easily interpretable results for decision-making.","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144945955","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Toward a Quantitative Understanding of Aficamten Clinical Pharmacology: Population Pharmacokinetic Modeling. 对非洲临床药理学的定量理解：群体药代动力学模型。

IF 3 3区医学

CPT: Pharmacometrics & Systems Pharmacology Pub Date : 2025-08-17 DOI: 10.1002/psp4.70099

Donghong Xu, Hanbin Li, Stephen B Heitner, Daniel L Jacoby, Stuart Kupfer, Polina German, Justin Lutz

{"title":"Toward a Quantitative Understanding of Aficamten Clinical Pharmacology: Population Pharmacokinetic Modeling.","authors":"Donghong Xu, Hanbin Li, Stephen B Heitner, Daniel L Jacoby, Stuart Kupfer, Polina German, Justin Lutz","doi":"10.1002/psp4.70099","DOIUrl":"https://doi.org/10.1002/psp4.70099","url":null,"abstract":"Aficamten is a next-in-class, cardiac myosin inhibitor in development as a potential chronic oral treatment for patients with hypertrophic cardiomyopathy (HCM). A population pharmacokinetic (PK) model was developed using data from nine clinical studies to characterize aficamten PK and identify covariates that may alter aficamten exposure. Aficamten PK was best described by a 2-compartment model with linear elimination and first-order absorption following a time lag (Tlag). Population parameter estimates for a typical male participant with obstructive HCM (oHCM) and weighing 80 kg were: apparent clearance (CL/F), 2.62 L/h; apparent volume of the central compartment (Vc/F), 18.1 L; apparent intercompartmental clearance (Q/F), 57.6 L/h; and apparent volume of the peripheral compartment (Vp/F), 295 L. Estimated interindividual variability on CL/F and overall residual error (includes within-subject variability) was low; the coefficient of variation was 28.7% and 20.3%, respectively. Body weight on volume and clearance, population and sex on CL/F and Vp/F were identified as statistically significant covariates. A male patient with a baseline body weight of 56 kg (5th percentile of the population) exhibited a 23% higher AUCtau compared with a male patient with a typical body weight of 80 kg. Female patients demonstrated a 14.7% higher AUCtau than male patients of the same body weight. Healthy participants had a 23% lower AUCtau compared with participants with oHCM. This population PK analysis demonstrated that aficamten has a linear and predictable PK profile, with a favorable half-life and time-to-steady state, and low interindividual variability on CL/F and overall residual error (includes within-subject variability).","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144871853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Exposure-Response Analysis of Repotrectinib to Support the Dose Recommendation for Patients With ROS1-Positive NSCLC or NTRK-Positive Solid Tumors. Repotrectinib的暴露反应分析支持ros1阳性NSCLC或ntrk阳性实体瘤患者的剂量推荐

IF 3 3区医学

CPT: Pharmacometrics & Systems Pharmacology Pub Date : 2025-08-17 DOI: 10.1002/psp4.70102

Shengnan Du, Zheyi Hu, Jun Shen, Li Zhu, Amit Roy, Justine Lam, Ming Lu, Anna Kondic, Lora Hamuro

{"title":"Exposure-Response Analysis of Repotrectinib to Support the Dose Recommendation for Patients With ROS1-Positive NSCLC or NTRK-Positive Solid Tumors.","authors":"Shengnan Du, Zheyi Hu, Jun Shen, Li Zhu, Amit Roy, Justine Lam, Ming Lu, Anna Kondic, Lora Hamuro","doi":"10.1002/psp4.70102","DOIUrl":"https://doi.org/10.1002/psp4.70102","url":null,"abstract":"To support the benefit-risk assessment and dose justification of repotrectinib for patients with c-ros oncogene 1 (ROS1) positive non-small cell lung cancer (NSCLC) or neurotrophin receptor tyrosine kinase (NTRK)-positive solid tumors, exposure-response analyses were conducted. The analysis used data from the TRIDENT-1 trial for key clinical efficacy endpoints-objective response rate (ORR) and progression-free survival (PFS), as well as 5 clinical safety endpoints: Grade 2 or higher (Gr2+) dizziness, Gr2+ anemia, Grade 3 or higher (Gr3+) treatment-emergent adverse events (AEs), Gr2+ neurologic AEs, and dose reduction or interruption due to AEs. The exposure-response relationship for ORR was characterized by logistic regression with average repotrectinib exposure over the first 56 days of dosing; PFS or safety endpoints were evaluated by Cox proportional-hazards models with time-varying cumulative half-daily average drug concentration. The model predicted efficacy and safety were compared for 160 mg QD/BID (160 mg QD for 14 days, followed by 160 mg BID) and 160 mg QD under different food statuses. The recommended dose of 160 mg QD/BID demonstrated improved ORR and PFS over 160 mg QD in both ROS1-positive NSCLC and NTRK-positive solid tumors, while the increase in AEs was minimal. Predicted efficacy and safety were comparable across food conditions, supporting the administration of 160 mg QD/BID regardless of food. This work highlighted the importance of selecting appropriate exposure measures in exposure-response analyses, particularly when dose or dose frequencies change throughout treatment. The integrated exposure-response analyses provided a robust framework to support the repotrectinib dosing strategy.","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144871852","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Mathematical Modeling of Neuroinflammation in Neurodegenerative Diseases. 神经退行性疾病中神经炎症的数学建模。

IF 3 3区医学

CPT: Pharmacometrics & Systems Pharmacology Pub Date : 2025-08-13 DOI: 10.1002/psp4.70064

Alex Foster-Powell, Amin Rostami-Hodjegan, Guy Meno-Tetang, Donald E Mager, Kayode Ogungbenro

{"title":"Mathematical Modeling of Neuroinflammation in Neurodegenerative Diseases.","authors":"Alex Foster-Powell, Amin Rostami-Hodjegan, Guy Meno-Tetang, Donald E Mager, Kayode Ogungbenro","doi":"10.1002/psp4.70064","DOIUrl":"https://doi.org/10.1002/psp4.70064","url":null,"abstract":"Age-related neurodegenerative diseases such as amyotrophic lateral sclerosis (ALS), Alzheimer's disease (AD) and Parkinson's disease (PD) are an increasing public health concern. Whereas the pathology of these diseases is complex, chronic central inflammation, or neuroinflammation, is commonly observed across many neurodegenerative diseases. Despite a huge wealth of resources and promising preclinical testing, effective disease-modifying therapies do not exist. This failure is owing to a combination of poor biological understanding of this response, unsuitable animal models, and poor scaling from pathway up to clinical levels. In order to address these challenges, systems-level mathematical models may be utilized. Here, we provide a background on neuroinflammation and summarize available mathematical models of this response. Models described by ordinary, partial, and delay differential equations, and Boolean logic are introduced and discussed. The results as discussed in this review suggest logic-based modeling as a formalism capable of managing the challenges associated with the modeling of CNS diseases.","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144834410","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Perspective on the Use of Poisson Versus Logistic Regression in Exposure-Response Analysis: Insights and Considerations. 在暴露-反应分析中使用泊松与逻辑回归的观点：见解和考虑。

IF 3 3区医学

CPT: Pharmacometrics & Systems Pharmacology Pub Date : 2025-08-12 DOI: 10.1002/psp4.70100

Yiming Cheng, Ping Chen, Yan Li

引用次数: 0

A Systematic Comparative Analysis of Tumor Size Models Based on Erlotinib Clinical Data in Advanced NSCLC. 基于厄洛替尼的晚期非小细胞肺癌肿瘤大小模型临床数据的系统比较分析。

IF 3 3区医学

CPT: Pharmacometrics & Systems Pharmacology Pub Date : 2025-08-11 DOI: 10.1002/psp4.70095

Anna Mishina, Kirill Zhudenkov, Gabriel Helmlinger, Kirill Peskov

{"title":"A Systematic Comparative Analysis of Tumor Size Models Based on Erlotinib Clinical Data in Advanced NSCLC.","authors":"Anna Mishina, Kirill Zhudenkov, Gabriel Helmlinger, Kirill Peskov","doi":"10.1002/psp4.70095","DOIUrl":"https://doi.org/10.1002/psp4.70095","url":null,"abstract":"Early assessment of efficacy and dose optimization remain critical challenges in the development of anticancer therapies. Empirical models of solid tumor size dynamics-a key prognostic biomarker-have played a central role in addressing these challenges. However, a systematic comparison of commonly used tumor size models, in terms of descriptive and predictive performance as well as generalizability within a population framework, has not been conducted to date. The present research sought to develop a methodological framework for the optimization of tumor models, offering a basis for more accurate predictions of tumor dynamics. The corresponding modeling workflow was practically tested against clinical data of erlotinib, a treatment administered to patients with advanced NSCLC. Five widely used tumor size models were evaluated, of which only three-the Bi-Exponential (BiExp), the Linear-Exponential (LExp), and Claret's Tumor Growth Inhibition (TGI) model-demonstrated reproducibility of the base model during a repeated cross-validation approach. Among these, the TGI model exhibited superior descriptive and predictive performance. However, a thorough literature search showed that erlotinib clinical data in NSCLC have been analyzed using only the BiExp and LExp models. Furthermore, extrapolation from 3 to 16 months revealed outlier predictions for the BiExp and TGI models, while the LExp model showed higher consistency, suggesting that models utilizing an exponential growth function may have a more limited extrapolation range than those assuming linear growth. Despite a clear ranking of models based on descriptive and predictive performance, no hierarchy emerged with respect to discriminatory ability. All three models showed high accuracy in distinguishing RECIST-based objective responders, while accuracy in predicting the emergence of acquired resistance remained uniformly low. Trial Registration: Clinical trial number: NCT00364351.","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144816030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Target-Mediated Drug Disposition Revisited: Michaelis-Menten Approximations for Bivalent Drug Molecules. 靶标介导的药物处置重访：二价药物分子的Michaelis-Menten近似。

IF 3 3区医学

CPT: Pharmacometrics & Systems Pharmacology Pub Date : 2025-08-08 DOI: 10.1002/psp4.70094

Ronny Straube

引用次数: 0

A Case Study of Model-Informed Drug Development of a Novel PCSK9 Antisense Oligonucleotide. Part 2: Phase 2 to Phase 3. 一种新型PCSK9反义寡核苷酸基于模型的药物开发案例研究。第二部分：第二阶段至第三阶段。

IF 3 3区医学

CPT: Pharmacometrics & Systems Pharmacology Pub Date : 2025-08-05 DOI: 10.1002/psp4.70076

Jane Knöchel, Catarina Nilsson, Björn Carlsson, Alexis Hofherr, Per Johanson, Tina Rydén-Bergsten, Bengt Hamrén, Dinko Rekić

{"title":"A Case Study of Model-Informed Drug Development of a Novel PCSK9 Antisense Oligonucleotide. Part 2: Phase 2 to Phase 3.","authors":"Jane Knöchel, Catarina Nilsson, Björn Carlsson, Alexis Hofherr, Per Johanson, Tina Rydén-Bergsten, Bengt Hamrén, Dinko Rekić","doi":"10.1002/psp4.70076","DOIUrl":"https://doi.org/10.1002/psp4.70076","url":null,"abstract":"In this second part of a case study on the practical use of model-informed drug development (MIDD), we describe the clinical development of AZD8233, a novel proprotein convertase subtilisin/kexin type 9 (PCSK9) antisense oligonucleotide, from phase 2b to the start of phase 3. The case study exemplifies the use of MIDD to answer key design questions for the phase 3 program, including the design of a pivotal phase 3 study, a head-to-head study, and a cardiovascular outcome study informed by model-averaging analysis. Extensive phase 3 study simulations assessed the impact of drop-out, readout timing, dose frequency, and analysis method on study outcomes. The final phase 3 design assumed around 1% monthly drop-out (based on other PCSK9 inhibitor trials), used an EMA/FDA-approved analysis method, and set the primary readout at week 16. A simulated study predicted a reduction in low-density lipoprotein cholesterol (LDL-C) by week 16 of -69% with AZD8233 60 mg every 4 weeks. A virtual head-to-head study showed AZD8233 lowered LDL-C by 27% more than an active competitor (inclisiran) at day 270. Predicted cardiovascular relative risk reduction (RRR) for AZD8233 on top of statins ranged from 24% to 49% based on model choice; a model-averaging approach predicted an RRR of 27% assuming 63% LDL-C reduction from a 130 mg/dL baseline. This case study highlights the importance of cross-functional collaboration and other key MIDD enablers to ensure that MIDD extends beyond a simple simulation exercise and is instead considered an integral part of drug development dedicated to quantitative decision making.","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144788476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Predicting Systemic and Liver Bosentan Exposure Using Physiologically-Based Pharmacokinetic Modeling. 使用基于生理的药代动力学模型预测全身和肝脏波生坦暴露。

IF 3 3区医学

CPT: Pharmacometrics & Systems Pharmacology Pub Date : 2025-08-05 DOI: 10.1002/psp4.70085

Miao-Chan Huang, Julia Macente, Sofie Heylen, Chen Ning, Kristof De Vos, Neel Deferm, Pieter Annaert

{"title":"Predicting Systemic and Liver Bosentan Exposure Using Physiologically-Based Pharmacokinetic Modeling.","authors":"Miao-Chan Huang, Julia Macente, Sofie Heylen, Chen Ning, Kristof De Vos, Neel Deferm, Pieter Annaert","doi":"10.1002/psp4.70085","DOIUrl":"https://doi.org/10.1002/psp4.70085","url":null,"abstract":"Bosentan is the first approved oral medication for pulmonary arterial hypertension, yet the black-box warning on its labeling implies a substantial risk of liver injury associated with bosentan exposure. The risk assessment of bosentan-induced liver injury requires a thorough understanding of the underlying mechanisms, for which there is accumulating evidence. Integrating these mechanisms with clinical liver bosentan concentration would enable a more dynamic and relevant risk assessment. This study designed a workflow of physiologically-based pharmacokinetic (PBPK) model development to capture bosentan's hepatic disposition and predict the (intra)hepatic bosentan exposure. Specifically, clinical plasma and excretion data of bosentan were used to minimize the uncertainty in estimating the hepatic clearance. The model predictions were well overlapped with observations in the systemic circulation and excretion. Furthermore, the model-derived intrinsic hepatic clearance was comparable with the one derived from a clinical study. These results reflected confidence in the model's capability to predict hepatic bosentan exposure. The model-simulated steady-state unbound exposure to bosentan in hepatocytes and liver tissue ranged from 1.65 to 34.1 ng/mL following twice-daily 125-mg oral doses. The ratio of the simulated unbound concentration between the liver matrices and systemic plasma was between 0.80 and 2.93 across the therapeutic dosing regimens. In summary, a bosentan PBPK model was successfully developed with the designed workflow and was able to predict the hepatic disposition of bosentan. The developed model can be applied to generate hepatic bosentan exposure that bridges the toxicological mechanistic findings from in vitro to in vivo, assisting in risk assessment.","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144788478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0