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Pharmaceutics
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Recent Advances in Physiologically Based Pharmacokinetics
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Recent Advances in Physiologically Based Pharmacokinetics

A special issue of Pharmaceutics (ISSN 1999-4923). This special issue belongs to the section "Pharmacokinetics and Pharmacodynamics".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 7256

Special Issue Editor

Prof. Dr. Li Liu

E-Mail Website
Guest Editor
Center of Drug Metabolism and Pharmacokinetics, China Pharmaceutical University, Nanjing 210009, China
Interests: PBPK-PD model; metabolism/transport systems and drug disposition

Special Issue Information

Dear Colleagues,

The physiologically based pharmacokinetic (PBPK) model employs differential equations to analyze drug dynamics across various body tissues. This modeling approach, extensively applied within the pharmaceutical industry, is instrumental in predicting and evaluating drug efficacy and safety profiles. Focusing on accurately forecasting alterations in enzyme and transporter protein functions within organs, PBPK modeling offers significant advantages for studying special populations, including the elderly, pregnant women, neonates, and patients with hepatic or renal dysfunction, as well as for assessing drug–drug interactions. Recently, pharmacometrics has become a standard regulatory tool, facilitating analyses of surrogate endpoints to support accelerated drug approvals. Furthermore, it plays a pivotal role in critical trial phases for dose selection, dosing strategies for special populations (e.g., pediatric patients), dose regimen optimization, efficacy predictions, and dosing in unstudied patient populations. Furthermore, the PBPK model integrates in vitro mechanistic data to predict exposure profiles of new drug formulations. It can also incorporate individual patient characteristics to optimize personalized treatment regimens, thereby reducing the risk of adverse reactions, even toxic side effects.

Prof. Dr. Li Liu
Guest Editor

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • physiologically based pharmacokinetic (PBPK) model
  • pharmacometrics
  • drug–drug interaction
  • pharmacokinetics and pharmacodynamics

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Published Papers (5 papers)

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Research

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25 pages, 2783 KB  
Article
Pharmacokinetics of CYP2C19- and CYP3A4-Metabolized Drugs in Cirrhosis Using a Whole-Body PBPK Approach
by Ruijing Mu, Jingjing Gao, Xiaoli Wang, Jing Ling, Nan Hu and Hanyu Yang
Pharmaceutics 2025, 17(12), 1582; https://doi.org/10.3390/pharmaceutics17121582 - 8 Dec 2025
Abstract
Background/Objectives: Cirrhosis significantly alters physiological function and drug metabolism, particularly for medications primarily metabolized by CYP2C19 and CYP3A4. This study aims to establish a physiologically based pharmacokinetic (PBPK) modelling framework capable of predicting pharmacokinetic changes across different stages of cirrhosis, and to [...] Read more.
Background/Objectives: Cirrhosis significantly alters physiological function and drug metabolism, particularly for medications primarily metabolized by CYP2C19 and CYP3A4. This study aims to establish a physiologically based pharmacokinetic (PBPK) modelling framework capable of predicting pharmacokinetic changes across different stages of cirrhosis, and to determine optimal dosing regimens that achieve drug exposure levels comparable to those in healthy individuals. Methods: We constructed a physiologically based pharmacokinetic (PBPK) model that incorporates six drugs, including omeprazole, lansoprazole, midazolam, ondansetron, verapamil, and alfentanil, which are metabolized primarily by CYP2C19 or CYP3A4. The pharmacokinetics of these drugs following oral or injectable administration were simulated in 1000 virtual healthy subjects, and the PBPK model was validated using clinical data. The model was further adapted to account for physiological changes in cirrhotic patients, extending its application to a population of 1000 virtual patients with liver cirrhosis. Results: Most observed data fell within the 5th and 95th percentiles of the virtual patient simulation results. Additionally, for most simulations, the area under the concentration-time curve (AUC) and peak concentration (Cmax) were within 0.5- to 2-fold of the observed values. Sensitivity analysis indicated that the reduced expression of metabolizing enzymes increased plasma concentrations of drugs, which was a major factor contributing to the elevated drug exposure in patients with cirrhosis. The clinical dosing regimens of the CYP2C19 substrate omeprazole and the CYP3A4 substrate ondansetron were optimized for use in cirrhotic patients. Conclusions: The developed PBPK model successfully predicted the pharmacokinetics of CYP2C19 and CYP3A4 substrates in both healthy individuals and cirrhotic patients and can be effectively used for dose optimization in cirrhotic populations. Full article
(This article belongs to the Special Issue Recent Advances in Physiologically Based Pharmacokinetics)
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19 pages, 2340 KB  
Article
Predicting Pharmacokinetics of Drugs in Patients with Heart Failure and Optimizing Their Dosing Strategies Using a Physiologically Based Pharmacokinetic Model
by Weiye Gu, Qingxuan Shao and Ling Jiang
Pharmaceutics 2025, 17(11), 1394; https://doi.org/10.3390/pharmaceutics17111394 - 28 Oct 2025
Viewed by 764
Abstract
Background: Heart failure (HF), as the end stage of various cardiac diseases, alters blood flow to key organs responsible for drug clearance. This can lead to unpredictable and often suboptimal drug exposure, creating a critical need for quantitative tools to guide precise dosing [...] Read more.
Background: Heart failure (HF), as the end stage of various cardiac diseases, alters blood flow to key organs responsible for drug clearance. This can lead to unpredictable and often suboptimal drug exposure, creating a critical need for quantitative tools to guide precise dosing in this vulnerable population. Methods: This study aimed to establish a whole-body physiologically based pharmacokinetic (PBPK) model for characterizing drug pharmacokinetics in both healthy subjects and patients across the HF severity spectrum. Eight commonly used drugs (digoxin, furosemide, bumetanide, torasemide, captopril, valsartan, felodipine and midazolam) for treating HF and its comorbidities were selected. Following successful validation against clinical data from healthy subjects, the PBPK model was extrapolated to HF patients. Pharmacokinetics of the eight drugs in 1000 virtual HF patients were simulated by replacing tissue blood flows and compared using clinical observations. Results: Most of the observed concentrations were encompassed within the 5th–95th percentiles of simulated values from 1000 virtual HF patients. Predicted area under the concentration–time curve and maximum plasma concentration fell within the 0.5~2.0-fold range relative to clinical observations. Sensitivity analysis demonstrated that intrinsic renal clearance, unbound fraction in blood, muscular blood flow, and effective permeability coefficient significantly impact plasma exposure of digoxin at a steady state. Oral digoxin dosing regimens for HF patients were optimized via the validated PBPK model to ensure that steady-state plasma concentrations in all HF patients remain below the toxicity threshold (2.0 ng/mL). Conclusions: A PBPK model was successfully developed to predict the plasma concentration–time profiles of the eight tested drugs in both healthy subjects and HF patients. Furthermore, this model may also be applied to guide digoxin dose optimization for HF patients. Full article
(This article belongs to the Special Issue Recent Advances in Physiologically Based Pharmacokinetics)
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17 pages, 2746 KB  
Article
Development of PBPK Population Model for End-Stage Renal Disease Patients to Inform OATP1B-, BCRP-, P-gp-, and CYP3A4-Mediated Drug Disposition with Individual Influencing Factors
by Yujie Wu, Weijie Kong, Jiayu Li, Xiaoqiang Xiang, Hao Liang and Dongyang Liu
Pharmaceutics 2025, 17(8), 1078; https://doi.org/10.3390/pharmaceutics17081078 - 20 Aug 2025
Viewed by 1217
Abstract
Background/Objective: Physiologically based pharmacokinetic (PBPK) modeling is a powerful tool for predicting pharmacokinetics (PK) to support drug development and precision medicine. However, it has not been established for non-renal clearance pathways in patients with end-stage renal disease (ESRD), a population that bears heavy [...] Read more.
Background/Objective: Physiologically based pharmacokinetic (PBPK) modeling is a powerful tool for predicting pharmacokinetics (PK) to support drug development and precision medicine. However, it has not been established for non-renal clearance pathways in patients with end-stage renal disease (ESRD), a population that bears heavy medication burden and is thereby at high risk for drug–drug–disease interactions (DDDIs). Furthermore, the pronounced inter-individual variability in PK observed in ESRD patients highlights the urgent need for individualized PBPK models. Methods: In this study, we developed a PBPK population model for ESRD patients, incorporating functional changes in key drug-metabolizing enzymes and transporters (DMETs), including CYP3A4, OATP1B1/3, P-gp, and BCRP. The model was initially constructed using the recalibrated demographic and physiological parameters of ESRD patients. Then, we used five well-validated substrates (midazolam, dabigatran etexilate, pitavastatin, rosuvastatin, and atorvastatin) and their corresponding PK profiles from ESRD patients taking a microdose cocktail regimen to simultaneously estimate the abundance of all these DMETs. Lastly, machine learning was employed to identify potential factors influencing individual clearance. Results: Our study suggested a significant reduction in hepatic OATP1B1/3 (75%) and intestinal P-gp abundance (34%) in ESRD patients. Ileum BCRP abundance was estimated to increase by 100%, while change in hepatic CYP3A4 abundance is minimal. Notably, simulations of drug combinations revealed potential DDDI risks that were not observed in healthy volunteers. Machine learning further identified Clostridium XVIII and Escherichia genus abundances as significant factors influencing dabigatran clearance. For rosuvastatin, aspartate aminotransferase, total bilirubin, Bacteroides, and Megamonas genus abundances were key influencers. No significant factors were identified for midazolam, pitavastatin, or atorvastatin. Conclusions: Our study proposes a feasible strategy for individualized PK prediction by integrating PBPK modeling with machine learning to support the development and precise use of the aforementioned DMET substrates in ESRD patients. Full article
(This article belongs to the Special Issue Recent Advances in Physiologically Based Pharmacokinetics)
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26 pages, 3462 KB  
Article
Prediction of Pharmacokinetics for CYP3A4-Metabolized Drugs in Pediatrics and Geriatrics Using Dynamic Age-Dependent Physiologically Based Pharmacokinetic Models
by Jing Han, Zexin Zhang, Xiaodong Liu, Hanyu Yang and Li Liu
Pharmaceutics 2025, 17(2), 214; https://doi.org/10.3390/pharmaceutics17020214 - 7 Feb 2025
Cited by 1 | Viewed by 2180
Abstract
Background/Objectives: The use of medicines in pediatrics and geriatrics is widespread. However, information on pharmacokinetics of therapeutic drugs mainly comes from healthy adults, and the pharmacokinetic parameters of therapeutic drugs in other age stages, including pediatrics and geriatrics, are limited. The aim [...] Read more.
Background/Objectives: The use of medicines in pediatrics and geriatrics is widespread. However, information on pharmacokinetics of therapeutic drugs mainly comes from healthy adults, and the pharmacokinetic parameters of therapeutic drugs in other age stages, including pediatrics and geriatrics, are limited. The aim of the study was to develop a dynamic age-dependent physiologically based pharmacokinetic (PBPK) model to predict the pharmacokinetics of drugs in humans at different ages. Method: The PBPK models characterizing dynamic age-dependence were developed in adults (20–59 years old) and 1000 virtual individuals were constructed. Four CYP3A substrates, namely midazolam, fentanyl, alfentanil and sufentanil, served as model drugs. Following validation using clinic observations in adult populations, the developed PBPK models were extrapolated to other age populations, such as pediatrics and geriatrics, via replacing their physiological parameters and pharmacokinetic parameters, such as organ volume, organ blood flow, clearance, fu,b and Kt:p. The simulations were compared with clinic observations in corresponding age populations. Midazolam served as an example, the dose transitions between adult pediatrics and adult geriatrics were visualized using the developed PBPK models. Results: Most of observed plasma concentrations fell within the 5th–95th percentile of the predicted values in the 1000 virtual individuals, and the predicted AUC0–t and Cmax were almost within between 0.5 and 2 times of the observations. The optimization of dosages in pediatrics and geriatrics were further documented. Conclusions: The developed PBPK model may be successfully used to predict the pharmacokinetics of CYP3A4-metabolized drugs in different age groups and to optimize their dosage regiments in pediatrics and geriatrics. Full article
(This article belongs to the Special Issue Recent Advances in Physiologically Based Pharmacokinetics)
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Review

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19 pages, 1436 KB  
Review
The Evolution and Future Directions of PBPK Modeling in FDA Regulatory Review
by Yangkexin Li, Henry Sun and Zuoli Zhang
Pharmaceutics 2025, 17(11), 1413; https://doi.org/10.3390/pharmaceutics17111413 - 31 Oct 2025
Viewed by 2252
Abstract
Background: Physiologically based pharmacokinetic (PBPK) modeling is a mathematical approach that integrates human physiological parameters with drug-specific characteristics (including both active pharmaceutical ingredients and excipients), and it has emerged as one of the core technologies for optimizing the efficiency and reliability of drug [...] Read more.
Background: Physiologically based pharmacokinetic (PBPK) modeling is a mathematical approach that integrates human physiological parameters with drug-specific characteristics (including both active pharmaceutical ingredients and excipients), and it has emerged as one of the core technologies for optimizing the efficiency and reliability of drug development. Methods: This study synthesizes applications of PBPK models in FDA-approved drugs (2020–2024), systematically analyzing model utilization frequency, indication distribution, application domains and choice of modeling platforms, to reveal their substantive contributions to regulatory submissions. Additionally, we conducted an in-depth analysis of the PBPK models for 2024, classifying models into three tiers based on critical assessment of FDA reviewer comments. Results: Among 245 FDA-approved new drugs during this period, 65 NDAs/BLAs (26.5%) submitted PBPK models as pivotal evidence. Oncology drugs accounted for the highest proportion (42%). In application scenarios, drug–drug interaction (DDI) was predominant (81.9%), followed by dose recommendations for patients with organ impairment (7.0%), pediatric population dosing prediction (2.6%), and food-effect evaluation. Regarding modeling platforms, Simcyp® emerged as the industry-preferred modeling platform, with an 80% usage rate. In terms of regulatory evaluation, a core concern for reviewers is whether the model establishes a complete and credible chain of evidence from in vitro parameters to clinical predictions. Conclusions: Detailed regulatory reviews demonstrate that although some PBPK models exhibit certain limitations and shortcomings, this does not preclude them from demonstrating notable strengths and practical value in critical applications. Benefiting from the strong support these successful implementations provide for regulatory decision-making, the technology is gaining increasing recognition across the industry. Looking forward, the integration of PBPK modeling with artificial intelligence (AI) and multi-omics data will unprecedentedly enhance predictive accuracy, thereby providing critical and actionable insights for decision-making in precision medicine and global regulatory strategies. Full article
(This article belongs to the Special Issue Recent Advances in Physiologically Based Pharmacokinetics)
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