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Pharmaceutics
Special Issues
In Vitro–In Vivo Correlation and Mechanistic Pharmacokinetic Models: Bridging Drug...
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In Vitro–In Vivo Correlation and Mechanistic Pharmacokinetic Models: Bridging Drug In Vitro Characteristics and In Vivo Behaviors

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

Deadline for manuscript submissions: closed (30 May 2025) | Viewed by 1906

Special Issue Editor

Dr. Zhijun Kevin Wang

E-Mail Website
Guest Editor
Department of Clinical Pharmacy Practice, School of Pharmacy and Pharmaceutical Sciences, University of California, Irvine, CA 92697, USA
Interests: pharmacokinetics; bioanalytical assay development; pharmaceutics; herbal medicine; drug delivery
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Pharmacokinetic modeling can assist in the understanding of drug behavior in the body and thus plays an essential role in new drug development. Establishing in vitro–in vivo correlation (IVIVC) bridges the gap between in vitro characteristics and in vivo drug behaviors, which can provide valuable insights for formulation development and predicting clinical outcomes. Therefore, the reliable link provided by the robust model between in vitro data and in vivo performance has the potential to reduce the costs and enhance the efficiency of preclinical and clinical studies. In recent years, significant progress has been made in various modeling techniques, particularly in the areas of IVIVC and mechanistic pharmacokinetic modeling.

This Special Issue aims to explore the most recent advancements and practical applications related to IVIVC and mechanistic pharmacokinetic modeling, including, but not limited to, the following:

  1. Development and validation of IVIVC models;
  2. Integration of in vitro data with in vivo pharmacokinetic profiles;
  3. Advancements in mechanistic pharmacokinetic modeling techniques;
  4. Applications of pharmacokinetic modeling in drug–drug interactions;
  5. Utilization of IVIVC and mechanistic pharmacokinetic modeling in regulatory submissions;
  6. Case studies demonstrating the translational potential of IVIVC and mechanistic pharmacokinetic modeling in pharmacy practice.

In this Special Issue, original research articles and reviews are welcome.

I look forward to receiving your contributions.

Dr. Zhijun Kevin Wang
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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

  • IVIVC
  • PBPK
  • pharmacokinetic modeling
  • mechanistic modeling
  • drug development
  • drug–drug interactions
  • sensitivity analysis
  • pharmacometrics

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Published Papers (2 papers)

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Research

17 pages, 2454 KiB  
Article
Translational Pharmacokinetic-Pharmacodynamic Modeling of a Novel Oral Dihydroorotate Dehydrogenase (DHODH) Inhibitor, HOSU-53 (JBZ-001)
by Joo Young Na, Min Hai, Kyeongmin Kim, Sandip M. Vibhute, Chad E. Bennett, Christopher C. Coss and Mitch A. Phelps
Pharmaceutics 2025, 17(4), 412; https://doi.org/10.3390/pharmaceutics17040412 - 25 Mar 2025
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Abstract
Background: HOSU-53 (JBZ-001), an orally bioavailable new chemical entity, represents a highly potent dihydroorotate dehydrogenase (DHODH) inhibitor in late preclinical development for application in cancer therapy. Methods: Multiple Good Laboratory Practice (GLP) and non-GLP preclinical studies were conducted in mice, rats, and dogs. [...] Read more.
Background: HOSU-53 (JBZ-001), an orally bioavailable new chemical entity, represents a highly potent dihydroorotate dehydrogenase (DHODH) inhibitor in late preclinical development for application in cancer therapy. Methods: Multiple Good Laboratory Practice (GLP) and non-GLP preclinical studies were conducted in mice, rats, and dogs. Plasma samples of HOSU-53 and dihydroorotate (DHO), the substrate of DHODH, were collected for pharmacokinetic (PK) and pharmacodynamic (PD) assessment and modeling. Two modeling approaches were utilized to understand the PK/PD properties of HOSU-53 and to recommend a first-in-human (FIH) dose. Results: A population PK/PD model was developed using a stochastic approximation of the expectation-maximization method and evaluated using graphical and numerical methods. The PK of HOSU-53 was well described by a two-compartment model with a first-order absorption and linear elimination, and the PD was described by a turnover model. No covariates were considered significant on PK/PD parameters. This model was subsequently used to predict DHO exposures in humans across a range of doses. Additionally, predicted human hepatocellular HOSU-53 concentrations were obtained from a physiologically based PK model constructed in PK-Sim. Conclusions: A first-in-human starting dose of 5 mg once daily was established from the model approaches and will be utilized in the upcoming FIH clinical study. Full article
(This article belongs to the Special Issue In Vitro–In Vivo Correlation and Mechanistic Pharmacokinetic Models: Bridging Drug In Vitro Characteristics and In Vivo Behaviors)
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13 pages, 1832 KiB  
Article
Evaluation of Complex Drug Interactions Between Elexacaftor-Tezacaftor-Ivacaftor and Statins Using Physiologically Based Pharmacokinetic Modeling
by Eunjin Hong, Peter S. Chung, Adupa P. Rao and Paul M. Beringer
Pharmaceutics 2025, 17(3), 318; https://doi.org/10.3390/pharmaceutics17030318 - 1 Mar 2025
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Abstract
Background/Objectives: The increasing use of statins in people with cystic fibrosis (CF) necessitates the investigation of potential drug–drug interactions (DDI) of statins with cystic fibrosis transmembrane conductance regulator (CFTR) modulators, including elexacaftor, tezacaftor, and ivacaftor (ETI). The interactions may involve the potential inhibition [...] Read more.
Background/Objectives: The increasing use of statins in people with cystic fibrosis (CF) necessitates the investigation of potential drug–drug interactions (DDI) of statins with cystic fibrosis transmembrane conductance regulator (CFTR) modulators, including elexacaftor, tezacaftor, and ivacaftor (ETI). The interactions may involve the potential inhibition of cytochrome P450 isoenzymes (CYPs), organic anion-transporting polypeptides (OATPs), and Breast Cancer Resistance Protein (BCRP) by ETI. This presents a therapeutic challenge in CF due to the potential for elevated statin levels, consequently heightening the risk of myopathy. This study aimed to predict potential DDIs between statins and ETI using a physiologically based pharmacokinetic (PBPK) modeling approach. Methods: We performed in vitro assays to measure the inhibitory potency of ETI against OATPs and CYP2C9 and incorporated these data into our PBPK models alongside published inhibitory parameters for BCRP and CYP3A4. Results: The PBPK simulation showed that atorvastatin had the highest predicted AUC ratio (3.27), followed by pravastatin (2.27), pitavastatin (2.24), and rosuvastatin (1.83). Conclusions: Based on these findings, rosuvastatin appears to exhibit a weak interaction with ETI, whereas other statins exhibited a moderate interaction, potentially requiring appropriate dose reductions. These data indicate potential clinically significant DDIs between ETI and certain statins, which warrants a clinical study to validate these findings. Full article
(This article belongs to the Special Issue In Vitro–In Vivo Correlation and Mechanistic Pharmacokinetic Models: Bridging Drug In Vitro Characteristics and In Vivo Behaviors)
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