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Pharmaceuticals
Special Issues
Mathematical Modeling in Drug Metabolism and Pharmacokinetics
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Mathematical Modeling in Drug Metabolism and Pharmacokinetics

A special issue of Pharmaceuticals (ISSN 1424-8247). This special issue belongs to the section "Medicinal Chemistry".

Deadline for manuscript submissions: 25 December 2025 | Viewed by 374

Special Issue Editors

Dr. Ziran Li

E-Mail Website
Guest Editor
UCSF-Stanford Center of Excellence in Regulatory Science and Innovation (CERSI), University of California San Francisco, 600 16th St., San Francisco, CA 94158, USA
Interests: pharmacokinetics & pharmacodynamics modeling; machine learning; clinical pharmacology; pharmacometrics
Dr. Dongsheng Yang

E-Mail Website
Guest Editor
1. Department of Bioengineering and Therapeutic Sciences, School of Pharmacy, University of California San Francisco, 600 16th St., San Francisco, CA 94158, USA
2. Department of Clinical Pharmacy and Pharmacy Administration, School of Pharmacy, Fudan University, 220 Handan Rd, Shanghai 200437, China
Interests: quantitative systems pharmacology; population pharmacokinetics; epidemiology; physiologically based pharmacokinetic (PBPK) modeling; machine learning

Special Issue Information

Dear Colleagues,

Mathematical modeling has become an integral component at all stages of drug development, offering powerful tools to predict drug behavior in biological systems, recommend first-in-human dosage, and individualize dosing regimens to improve clinical outcomes. As pharmaceutical research becomes increasingly complex, mathematical modeling approaches are playing an increasingly important role in helping to understand the intricate processes of drug metabolism and pharmacokinetics (DMPK).

This Special Issue of Pharmaceuticals focuses on recent advances in mathematical modeling approaches applied to DMPK. We invite original and review articles on the current state of mathematical modeling in DMPK and highlight emerging trends that will shape the future of pharmaceutical research and development.

The proposed topics cover multiple mathematical modeling approaches applied in DMPK, including the following:

  • Physiologically-based pharmacokinetic (PBPK) modeling
  • Population pharmacokinetic/pharmacodynamic (PK/PD) modeling
  • Machine learning modeling
  • Quantitative systems pharmacology (QSP) approaches
  • Quantitative structure-activity relationship (QSAR) modeling
  • Integration of multi-omics data into PK/PD models
  • Advanced modeling approaches for special populations (pediatric, geriatric, pregnancy, disease states)
  • Translational modeling bridging preclinical and clinical studies

We are looking forward to your valuable contribution.

Dr. Ziran Li
Dr. Dongsheng Yang
Guest Editors

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. Pharmaceuticals 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

  • pharmacokinetics
  • mathematical modeling
  • physiologically based pharmacokinetic (PBPK)
  • pharmacokinetic/pharmacodynamic (PK/PD)
  • quantitative systems pharmacology (QSP)
  • machine learning
  • model-informed drug development
  • translational modeling
  • artificial intelligence (AI)
  • personalized medicine

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

22 pages, 3876 KiB  
Article
In Vivo PK-PD and Drug–Drug Interaction Study of Dorzagliatin for the Management of PI3Kα Inhibitor-Induced Hyperglycemia
by Guanqin Jin, Kewei Zheng, Shihuang Liu, Huan Yi, Wei Wei, Congjian Xu, Xiaoqiang Xiang and Yu Kang
Pharmaceuticals 2025, 18(6), 927; https://doi.org/10.3390/ph18060927 - 19 Jun 2025
Abstract
Objectives: The anticancer effects of PI3Kα inhibitors (PI3Ki) are constrained by their hyperglycemic side effects, while the efficacy of conventional hypoglycemic agents, such as insulin, metformin, and SGLT-2 inhibitors, in mitigating PI3Ki-induced hyperglycemia remains suboptimal. Dorzagliatin, a novel glucokinase activator, has been approved [...] Read more.
Objectives: The anticancer effects of PI3Kα inhibitors (PI3Ki) are constrained by their hyperglycemic side effects, while the efficacy of conventional hypoglycemic agents, such as insulin, metformin, and SGLT-2 inhibitors, in mitigating PI3Ki-induced hyperglycemia remains suboptimal. Dorzagliatin, a novel glucokinase activator, has been approved in China for the management of hyperglycemia, offering a promising alternative. This study aims to investigate the pharmacokinetic properties and potential mechanisms of drug interactions of dorzagliatin in the regulation of PI3K-induced hyperglycemia. Methods: Plasma concentrations of WX390, BYL719, and Dorz in mice were measured using high performance liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis. Pharmacokinetic (PK) parameters and PK/PD models were derived by using Phoenix WinNonlin 8.3.5 software. Blood glucose levels at various time points and tumor volume changes over a four-week period were assessed to explore the interactions when PI3Ki were combined with dorzagliatin. Results: The results indicated that, compared to the Dorz group, the combination groups (Dorz + BYL719, Dorz + WX390) exhibited increases in AUC0→t of dorzagliatin by 41.65% and 20.25%, and in Cmax by 33.48% and 13.32%, respectively. In contrast, co-administration of these PI3Ki with dorzagliatin resulted in minimal increase in their plasma exposure. The combination therapy group (Dorz+BYL719) exhibited superior antitumor efficacy compared to the BYL719 group. Conclusions: Our findings indicate that the drug–drug interactions (DDIs) between dorzagliatin and multiple PI3Ki (including WX390 and BYL719) may partially account for the enhanced antitumor efficacy observed in the combination therapy group compared to PI3Ki monotherapy. This interaction may be explained by the inhibition of P-glycoprotein (P-gp) and the pharmacological mechanism of dorzagliatin regarding the activation of insulin regulation. Full article
(This article belongs to the Special Issue Mathematical Modeling in Drug Metabolism and Pharmacokinetics)
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14 pages, 1690 KiB  
Article
Investigation of the ABCB1 Gene Polymorphism and Food Effects on the Avatrombopag Pharmacokinetics in Chinese Individuals: A Population Pharmacokinetic/Pharmacodynamic Analysis
by Xin Liu, Lulu Chen, Gehang Ju, Chao Li, Bijue Liu, Yunzhou Fei, Xintong Wang, Yang Gao, Qingfeng He, Xiao Zhu and Dongsheng Ouyang
Pharmaceuticals 2025, 18(6), 903; https://doi.org/10.3390/ph18060903 - 16 Jun 2025
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Abstract
Background/Objectives: Avatrombopag (AVA), a thrombopoietin receptor agonist used to treat thrombocytopenia in patients with chronic liver disease, exhibits significant pharmacokinetic (PK) variability, particularly under fasting conditions. This study investigates the combined influence of food intake and genetic polymorphisms in CYP2C9 and ABCB1 on [...] Read more.
Background/Objectives: Avatrombopag (AVA), a thrombopoietin receptor agonist used to treat thrombocytopenia in patients with chronic liver disease, exhibits significant pharmacokinetic (PK) variability, particularly under fasting conditions. This study investigates the combined influence of food intake and genetic polymorphisms in CYP2C9 and ABCB1 on the PK and pharmacodynamics (PD) of AVA, with the goal of informing individualized dosing strategies. Methods: A pharmacogenetic analysis was conducted in 92 healthy participants, who received 20 mg of AVA under both fasting and fed conditions. A population PK/PD model was developed to evaluate the covariates effects on the PK variability. Monte Carlo simulations were used to predict AVA exposure and platelet count profiles under diverse dosing scenarios. Results: Food intake significantly reduced PK variability, with approximately 50% reductions in clearance (CL/F) and volume of distribution (Vd/F) compared to fasting conditions. Under fed conditions, CYP2C9 intermediate metabolizers showed a 1.70-fold increase in exposure compared to normal metabolizers, but this difference was not observed under fasting conditions. ABCB1 polymorphisms showed minimal impact, with the exception of ABCB1 (C1236T) heterozygotes, which exhibited 1.37-fold increased exposure. Despite the observed PK variability, simulations demonstrated a consistent platelet count response across dosing regimens. Conclusions: While food intake and genetic polymorphisms in CYP2C9 and ABCB1 influenced AVA PK, these factors may not require dose adjustments, as platelet count responses remained consistent across genotypes and dosing conditions in the Chinese participants. These findings support simplified dosing strategies without the need for pharmacogenetic testing in Chinese individuals and may contribute to more individualized thrombocytopenia management. Full article
(This article belongs to the Special Issue Mathematical Modeling in Drug Metabolism and Pharmacokinetics)
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