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Pharmaceutics
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Mechanism-Based Pharmacokinetic and Pharmacodynamic Modeling
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Mechanism-Based Pharmacokinetic and Pharmacodynamic Modeling

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

Deadline for manuscript submissions: 20 April 2026 | Viewed by 8359

Special Issue Editor

Dr. Artur Świerczek

E-Mail Website
Guest Editor
Department of Pharmacokinetics and Physical Pharmacy, Faculty of Pharmacy, Jagiellonian University Medical College, 9 Medyczna Street, 30-688 Krakow, Poland
Interests: PKPD; pharmacokinetics; pharmacometrics; mathematical modeling; QSP

Special Issue Information

Dear Colleagues,

Mechanistic pharmacokinetic and pharmacodynamic modeling is a methodology utilized at all stages of drug discovery and development. This approach enables the translation of results of preclinical investigations into clinical settings. Mechanistic physiologically based pharmacokinetic models (PBPK) describe the distribution of drugs within different tissues, cells and at the level of subcellular compartments. Mechanism-based pharmacokinetic/pharmacodynamic (PK/PD) models provide insights into the mechanisms of drug effects, pathophysiology of diseases and interrelations among drugs and various signaling molecules and biomarkers. PK/PD modeling allows for the quantitative assessment of drug effects in vivo and performing simulations in order to select the most appropriate dosing regimen that maximizes efficacy while minimizing the toxicity of medications. This approach may be also useful in the selection of first-in-human doses based on the results of preclinical studies.

Therefore, this Special Issue is dedicated to original research, as well as review articles related to mechanism-based PBPK, mechanistic PK/PD or quantitative system pharmacology (QSP) modeling. Both experimental and simulation-based studies are acceptable. Pharmaceutical scientists are cordially invited to share the results of their investigations covering the full spectrum of PK/PD modeling and simulation, including PBPK models of drugs in preclinical species and humans, disease progression modeling, PBPK/PD modeling and PBPK/QSP approaches.

Dr. Artur Świerczek
Guest Editor

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Pharmaceutics is an international peer-reviewed open access monthly journal published by MDPI.

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

  • PK/PD modeling
  • disease progression modeling
  • quantitative system pharmacology
  • physiologically based modeling

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Published Papers (5 papers)

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Research

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16 pages, 1221 KiB  
Article
Physiologically-Based Biopharmaceutics Modeling for Ibuprofen: Identifying Key Formulation Parameter and Virtual Bioequivalence Assessment
by Javier Zarzoso-Foj, Marina Cuquerella-Gilabert, Matilde Merino-Sanjuan, Javier Reig-Lopez, Víctor Mangas-Sanjuán and Alfredo Garcia-Arieta
Pharmaceutics 2025, 17(4), 408; https://doi.org/10.3390/pharmaceutics17040408 - 24 Mar 2025
Viewed by 661
Abstract
Background: Physiologically based pharmacokinetic (PBPK) modeling for biopharmaceutics applications (i.e., physiologically based biopharmaceutics modeling (PBBM)) enables mechanistic modeling from dissolution to absorption and disposition, facilitating the prediction of bioequivalence (BE) outcomes and the delimitation of the safe space. This study aims to [...] Read more.
Background: Physiologically based pharmacokinetic (PBPK) modeling for biopharmaceutics applications (i.e., physiologically based biopharmaceutics modeling (PBBM)) enables mechanistic modeling from dissolution to absorption and disposition, facilitating the prediction of bioequivalence (BE) outcomes and the delimitation of the safe space. This study aims to identify the product-related parameter driving ibuprofen dissolution to upgrade an existing PBPK model, so that an in vitro safe space and virtual BE (VBE) predictions of IR ibuprofen tablets can be performed. Methods: Cmax within- and between-subject variabilities of a previous PBPK model were optimized after identifying crucial physiological parameters for ibuprofen absorption and disposition. In vitro data modeling was performed to estimate the value of the parameter driving ibuprofen dissolution. A safe space was defined for this parameter and the sample size to declare BE was calculated. Finally, VBE simulations were performed to explore the effect of sample size as well as number of trial replicates and runs. Results: Cmax variability was adequately predicted after changing Vss and MRT in stomach and small intestine CV (%) to 10 and 150%, respectively. Particle surface pH was identified as the dissolution key parameter for ibuprofen. A safe space for test product surface pH values of 5.64–6.40 was defined in order to achieve a 90%CI for the Cmax ratio within the 80–125% range when the reference product surface pH is 6.02. R-ibuprofen was identified as the most discriminative enantiomer. VBE studies with 24 individuals showed BE outcomes that are sensitive to the number of trial replicates and runs. Conclusions: Ibuprofen particle surface pH has been identified as the in vitro parameter governing dissolution in maleate buffer 7 mM with HCl pH 2.0 pretreatment, allowing to establish an in vitro safe space useful for calculating sample sizes and to evaluate the BE success rate through PBBM/PBPK model-informed VBE simulations. Full article
(This article belongs to the Special Issue Mechanism-Based Pharmacokinetic and Pharmacodynamic Modeling)
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14 pages, 2555 KiB  
Article
Quantifying Heart Rate Changes After Delta-9-Tetrahydrocannabinol Administration Using a PBPK-PD Model in Healthy Adults
by Lixuan Qian and Zhu Zhou
Pharmaceutics 2025, 17(2), 237; https://doi.org/10.3390/pharmaceutics17020237 - 12 Feb 2025
Viewed by 1334
Abstract
Background: As cannabis becomes legal in several U.S. states, the risk of THC-induced tachycardia increases. This study aimed to develop and verify a physiologically based pharmacokinetic–pharmacodynamic (PBPK-PD) model to assess the impact of THC and its active metabolite, 11-hydroxy-THC (11-OH-THC), on the [...] Read more.
Background: As cannabis becomes legal in several U.S. states, the risk of THC-induced tachycardia increases. This study aimed to develop and verify a physiologically based pharmacokinetic–pharmacodynamic (PBPK-PD) model to assess the impact of THC and its active metabolite, 11-hydroxy-THC (11-OH-THC), on the heart rate of healthy adults. Methods: A PBPK-PD model for intravenous (IV) 11-OH-THC administration was first developed. Secondly, a PBPK-PD model for IV THC, combined with the metabolized 11-OH-THC, was established, verified, and validated. Direct PD models driven by the plasma, brain, and heart concentrations of THC and 11-OH-THC predicted using our previously verified PBPK model were tested for model development. Finally, the risks of tachycardia at a rest condition from various doses of oral and inhaled THC were simulated for 500 individuals aged 18–65 years, with a sex ratio of 1:1 and a baseline heart rate of 70 beats per minute. Results: The PD model was best described by a direct nonlinear Emax model driven by the sum of the total THC and 11-OH-THC concentrations in their effect compartments linked to their heart compartments. In 42 simulated dosing regimens with THC doses ranging from 2 to 69.4 mg, 97% of the observed heart rates or heart rate changes following THC administration fell within the 5th to 95th percentiles of the model-predicted values. Similarly, for two simulated 11-OH-THC IV doses, 93% of the observations fell within this range. Simulations indicated that half of the simulated population would experience tachycardia at doses of 60 mg and 15 mg of THC for oral and inhaled administration, respectively. The simulated risks of tachycardia based on specific conditions should be interpreted with caution. Conclusions: Our verified PBPK-PD model successfully describes the heart rate changes in healthy adults after IV, oral, and inhaled THC administration. This model provides a tool to predict the effects of THC and its primary metabolite on heart rates, offering valuable insights for assessing the risk of tachycardia in both clinical and recreational cannabis use. Full article
(This article belongs to the Special Issue Mechanism-Based Pharmacokinetic and Pharmacodynamic Modeling)
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15 pages, 1548 KiB  
Article
Pharmacodynamic Model of the Hemodynamic Effects of Propofol and Remifentanil and Their Interaction with Noxious Stimulation
by Maite Garraza-Obaldia, Sebastian Jaramillo, Zinnia P. Parra-Guillen, José F. Valencia, Pedro L. Gambús and Iñaki F. Trocóniz
Pharmaceutics 2024, 16(12), 1615; https://doi.org/10.3390/pharmaceutics16121615 - 19 Dec 2024
Cited by 1 | Viewed by 1245
Abstract
Background: Despite the known impact of propofol and remifentanil on hemodynamics and patient outcomes, there is a lack of comprehensive quantitative analysis, particularly in surgical settings, considering the influence of noxious stimuli. The aim of this study was to develop a quantitative [...] Read more.
Background: Despite the known impact of propofol and remifentanil on hemodynamics and patient outcomes, there is a lack of comprehensive quantitative analysis, particularly in surgical settings, considering the influence of noxious stimuli. The aim of this study was to develop a quantitative semi-mechanistic population model that characterized the time course changes in mean arterial pressure (MAP) and heart rate (HR) due to the effects of propofol, remifentanil, and different types of noxious stimulation related to the clinical routine. Methods: Data from a prospective study were used; the study analyzed the effects of propofol and remifentanil general anesthesia on female patients in physical status of I-II according to the American Society of Anesthesiologists (ASA I-II) undergoing gynecology surgery. Patients were consecutively assigned to different administration schemes of propofol and remifentanil targeted at different effect-site concentrations. Esophageal instrumentation, laryngeal mask airway insertion, hysteroscopy, and tetanus stimuli were applied. Data from patients with chronic hypertension were discarded. Results: MAP and HR observations from 77 patients were analyzed. The hemodynamic effects were described using turn-over models incorporating feedback mechanisms. Analyses revealed that propofol and remifentanil elicited effects on the turn-over of MAP and HR, respectively, with estimates of plasma drug concentrations causing an inhibition-half of the maximum effect (C50) of 8.79 µg∙mL−1 and 4.57 ng∙mL−1. Hysteroscopy exerted an increase in MAP (but not in HR), which was well-characterized by the model, with a predicted typical increase of 28 mmHg and a dissipation half-life of 33 min. The impact of other noxious stimuli on MAP or HR could not be identified. Model simulations indicated that propofol and remifentanil, titrated to inhibit the motor response to noxious stimuli, regardless of dose combinations, cause a significant risk of hypotension, especially following induction and at the end of surgery (when surgical intervention is completed, before the awakening phase). Conclusions: The developed semi-mechanistic and fully identifiable model provides quantitative information on how propofol, remifentanil, and surgical stimulus (hysteroscopy) interact to produce the hemodynamic changes (of MAP and HR) commonly observed in clinical practice. Full article
(This article belongs to the Special Issue Mechanism-Based Pharmacokinetic and Pharmacodynamic Modeling)
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12 pages, 2046 KiB  
Article
Toward Model-Informed Precision Dosing for Remimazolam: A Population Pharmacokinetic–Pharmacodynamic Analysis
by Yueting Chen, Cansheng Gong, Feng Liu, Zheng Jiao and Xiaochun Zheng
Pharmaceutics 2024, 16(9), 1122; https://doi.org/10.3390/pharmaceutics16091122 - 26 Aug 2024
Viewed by 1863
Abstract
Remimazolam, widely used for procedural sedation and general anesthesia, is a new ultra short-acting benzodiazepine for intravenous sedation and anesthesia. We aim to characterize the pharmacokinetics/pharmacodynamics (PK/PD) of remimazolam and its metabolite CNS 7054 in healthy Chinese volunteers using population analysis and suggest [...] Read more.
Remimazolam, widely used for procedural sedation and general anesthesia, is a new ultra short-acting benzodiazepine for intravenous sedation and anesthesia. We aim to characterize the pharmacokinetics/pharmacodynamics (PK/PD) of remimazolam and its metabolite CNS 7054 in healthy Chinese volunteers using population analysis and suggest an optimal dosing regimen for sedation therapy. Data were collected from a single-center, placebo-controlled, randomized, and dose–escalation clinical pharmacology study. Forty-six healthy volunteers received a single infusion dose of remimazolam, while nine healthy subjects received a continuous infusion of remimazolam. A population PK/PD model was established and RxODE and Shiny in R were used to design the remimazolam dosing regimens. A three-compartment model best described the PK of remimazolam and a two-compartment model with one transit compartment was adopted for CNS 7054. The relationship between exposure and the bispectral index was best described using an effect compartment model with an inhibitory sigmoid model. Additionally, a web-based dashboard was developed to provide individualized dosing regimens, complemented by a graphical illustration of the PK/PD profiles of the proposed dosing regimen. The established population PK/PD model characterized the dose–exposure–response relationship of remimazolam well, which could be applied to optimize individual dosing regimens. Full article
(This article belongs to the Special Issue Mechanism-Based Pharmacokinetic and Pharmacodynamic Modeling)
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Review

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54 pages, 991 KiB  
Review
The Role of Pharmacometrics in Advancing the Therapies for Autoimmune Diseases
by Artur Świerczek, Dominika Batko and Elżbieta Wyska
Pharmaceutics 2024, 16(12), 1559; https://doi.org/10.3390/pharmaceutics16121559 - 5 Dec 2024
Cited by 1 | Viewed by 2192
Abstract
Autoimmune diseases (AIDs) are a group of disorders in which the immune system attacks the body’s own tissues, leading to chronic inflammation and organ damage. These diseases are difficult to treat due to variability in drug PK among individuals, patient responses to treatment, [...] Read more.
Autoimmune diseases (AIDs) are a group of disorders in which the immune system attacks the body’s own tissues, leading to chronic inflammation and organ damage. These diseases are difficult to treat due to variability in drug PK among individuals, patient responses to treatment, and the side effects of long-term immunosuppressive therapies. In recent years, pharmacometrics has emerged as a critical tool in drug discovery and development (DDD) and precision medicine. The aim of this review is to explore the diverse roles that pharmacometrics has played in addressing the challenges associated with DDD and personalized therapies in the treatment of AIDs. Methods: This review synthesizes research from the past two decades on pharmacometric methodologies, including Physiologically Based Pharmacokinetic (PBPK) modeling, Pharmacokinetic/Pharmacodynamic (PK/PD) modeling, disease progression (DisP) modeling, population modeling, model-based meta-analysis (MBMA), and Quantitative Systems Pharmacology (QSP). The incorporation of artificial intelligence (AI) and machine learning (ML) into pharmacometrics is also discussed. Results: Pharmacometrics has demonstrated significant potential in optimizing dosing regimens, improving drug safety, and predicting patient-specific responses in AIDs. PBPK and PK/PD models have been instrumental in personalizing treatments, while DisP and QSP models provide insights into disease evolution and pathophysiological mechanisms in AIDs. AI/ML implementation has further enhanced the precision of these models. Conclusions: Pharmacometrics plays a crucial role in bridging pre-clinical findings and clinical applications, driving more personalized and effective treatments for AIDs. Its integration into DDD and translational science, in combination with AI and ML algorithms, holds promise for advancing therapeutic strategies and improving autoimmune patients’ outcomes. Full article
(This article belongs to the Special Issue Mechanism-Based Pharmacokinetic and Pharmacodynamic Modeling)
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