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Pharmaceutics
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In Silico Pharmacokinetic and Pharmacodynamic (PK-PD) Modeling
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In Silico Pharmacokinetic and Pharmacodynamic (PK-PD) Modeling

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

Deadline for manuscript submissions: 31 March 2026 | Viewed by 3583

Special Issue Editors

Prof. Dr. Neal M. Davies

E-Mail Website
Guest Editor
Faculty of Pharmacy and Pharmaceutical Sciences, Katz Group-Rexall Centre for Pharmacy & Health Research, University of Alberta, 11315–87 Avenue, Edmonton, AB T6G 2E1, Canada
Interests: pharmacokinetics; chirality; drug interactions; formulation and drug delivery
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Raimar Löbenberg

E-Mail Website
Guest Editor
Faculty of Pharmacy & Pharmaceutical Sciences, Katz Group-Rexall Centre for Pharmacy & Health Research, University of Alberta, 11361–87 Avenue, Edmonton, AB T6G 2E1, Canada
Interests: biopharmaceutics; inhalable nanopartilces; traditional medicines
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Pharmacokinetic and pharmacodynamic (PK-PD) modeling is an essential tool in modern drug development and therapeutic optimization. Leveraging recent advancements in computational methods, including physiologically based pharmacokinetic (PBPK) modeling and machine learning, researchers can simulate complex biological systems, predict therapeutic outcomes, and refine dosing strategies with unprecedented precision.

This Special Issue aims to focus on the transformative integration of in silico approaches into PK-PD modeling, showcasing advancements in methodology, computational tools, and their translational applications. By addressing key challenges in precision medicine, regulatory science, and clinical decision-making, we aim to foster interdisciplinary innovation and broaden the impact of in silico modeling on healthcare outcomes.

Key Topics of Interest:

  • Physiologically Based Pharmacokinetic (PBPK) and Toxicokinetic (PBTK) Modeling: Predicting ADME processes across populations and physiological conditions; regulatory applications.
  • Population Pharmacokinetics (PopPKs): Mixed-effects modeling of inter-individual variability in drug exposure and response.
  • Quantitative Systems Pharmacology (QSP): Integrating systems biology with PK-PD to evaluate drug interactions within complex networks.
  • Bayesian Pharmacometric Modeling: Utilizing Bayesian methods to incorporate prior knowledge for personalized therapy optimization.
  • Artificial Intelligence and Machine Learning in PK-PD: Applications in drug interaction prediction, dose optimization, and precision medicine.
  • In Silico Bioequivalence: Reducing clinical trial reliance through computational approaches.
  • Multi-Objective Optimization: Balancing efficacy and toxicity in therapeutic regimens using advanced algorithms.
  • Regulatory Science and Quality Assessment: Computational tools in regulatory decision-making and pharmaceutical product evaluation.
  • Translational and Predictive Modeling: Real-world case studies demonstrating successful clinical implementation of in silico predictions.

Benefits of Publishing in This Special Issue:

  • Enhanced visibility and citation potential through focused dissemination.
  • Opportunities for interdisciplinary collaboration across computational biology, clinical pharmacology, and pharmaceutical sciences.
  • Contribution to the advancement of cutting-edge in silico modeling techniques and applications.

Submit your manuscript today and contribute to the future of precision pharmacology and computational innovation!

Prof. Dr. Neal M. Davies
Prof. Dr. Raimar Löbenberg
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 250 words) can be sent to the Editorial Office for assessment.

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

  • physiologically based pharmacokinetics (PBPKs)
  • population pharmacokinetics (PopPKs)
  • quantitative systems pharmacology (QSP)
  • Bayesian modeling
  • artificial intelligence in PK-PD
  • in silico bioequivalence
  • multi-objective optimization
  • translational pharmacokinetics

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
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  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (3 papers)

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Research

16 pages, 1042 KB  
Article
In Silico Hypothesis Testing in Drug Discovery: Using Quantitative Systems Pharmacology Modeling to Evaluate the Therapeutic Value of Proinsulin Conversion to Insulin Therapy for Type 2 Diabetes Mellitus
by Maria E. Trujillo, Yue Han, Rebecca A. Baillie, Michael C. Weis, Douglas Chung, Sean Hayes, Paul E. Carrington and Michael Reed
Pharmaceutics 2025, 17(12), 1522; https://doi.org/10.3390/pharmaceutics17121522 - 26 Nov 2025
Viewed by 377
Abstract
Background/Objectives: Proinsulin, the precursor to insulin, has limited activity on the insulin receptor. Proinsulin levels increase with increasing insulin resistance in type 2 diabetes due to incomplete processing by the β-cell. To assess whether the development of peptides that could convert circulating [...] Read more.
Background/Objectives: Proinsulin, the precursor to insulin, has limited activity on the insulin receptor. Proinsulin levels increase with increasing insulin resistance in type 2 diabetes due to incomplete processing by the β-cell. To assess whether the development of peptides that could convert circulating proinsulin to insulin in the blood would provide therapeutic value, we used a quantitative systems pharmacology (QSP) model of glucose homeostasis. In silico hypothesis testing such as this is an example of how modeling can inform decisions in drug discovery. Methods: In silico hypothesis testing involved (1) the addition and qualification of proinsulin biology into a preexisting QSP model, (2) the creation and validation of virtual patients (VPs) for subpopulations of type 2 diabetics based on phenotypic traits, and (3) the simulation of clinical trials evaluating the therapeutic value of the conversion of circulating proinsulin to insulin in the VPs created. Results: Proinsulin conversion led to a ~0.2% reduction in HbA1c in VPs at varying stages of diabetes, a decrease that does not hold meaningful therapeutic value. The lack of significant impact on HbA1c was likely a result of the surprisingly small effect on plasma insulin levels from proinsulin, which has a significantly slower secretion and clearance rate. Although patients with higher proinsulin/insulin ratios showed the largest reductions, clinically significant ≥ 0.5% reduction in HbA1c required ratios of proinsulin/insulin above the reported physiological range. Conclusions: This effort demonstrates how in silico hypothesis testing using QSP modeling can provide insights on the probability of success of novel interventions with minimal time and resources. These efficiencies are a means of overcoming the pressures on the pharmaceutical industry to do more with less in providing therapies that improve the lives of patients. Full article
(This article belongs to the Special Issue In Silico Pharmacokinetic and Pharmacodynamic (PK-PD) Modeling)
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41 pages, 3917 KB  
Article
Physiologically Based Pharmacokinetic Modeling and Simulations in Lieu of Clinical Pharmacology Studies to Support the New Drug Application of Asciminib
by Ioannis Loisios-Konstantinidis, Felix Huth, Matthias Hoch and Heidi J. Einolf
Pharmaceutics 2025, 17(10), 1266; https://doi.org/10.3390/pharmaceutics17101266 - 26 Sep 2025
Viewed by 1507
Abstract
Background: Asciminib (Scemblix®) is approved for the first-line treatment of adult patients with chronic myeloid leukemia in the chronic phase at 40 mg twice daily (BID) and 80 mg once daily (QD) or 200 mg BID for patients harboring the [...] Read more.
Background: Asciminib (Scemblix®) is approved for the first-line treatment of adult patients with chronic myeloid leukemia in the chronic phase at 40 mg twice daily (BID) and 80 mg once daily (QD) or 200 mg BID for patients harboring the T315I mutation. Objectives: (1) Extrapolate the DDI magnitude as the perpetrator or victim of other drugs and the effect of organ impairment to untested doses; (2) Predict clinically untested DDI scenarios. Methods: Asciminib is primarily cleared by cytochrome P450 (CYP)3A4, UDP-glucuronosyltransferases (UGT)2B7, UGT2B17, UGT1A3/4, and the breast-cancer-resistance protein (BCRP). In vitro asciminib is an inhibitor of several CYP, UGT enzymes, and transporters and is an inducer of CYP1A2 and CYP3A4. Clinical DDI studies assessed asciminib 40 mg BID as a perpetrator on CYP-sensitive substrates. Additional studies evaluated the impact of strong CYP3A4 perpetrators and imatinib on a single 40 mg dose of asciminib. Hepatic and renal impairment studies were also conducted at the 40 mg dose. A nonlinear whole-body physiologically based pharmacokinetic (PBPK) model was developed and verified for asciminib as a CYP3A4, UGT, and BCRP substrate and a perpetrator of several CYP and UGT enzymes. Results: This PBPK model was applied in lieu of clinical pharmacology studies to support the new drug application of Scemblix® and to bridge data from 40 mg BID to the 80 mg QD and 200 mg BID dose regimens. Conclusions: The PBPK predictions informed the drug product label and are estimated to have replaced at least 10 clinical studies. Full article
(This article belongs to the Special Issue In Silico Pharmacokinetic and Pharmacodynamic (PK-PD) Modeling)
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17 pages, 1231 KB  
Article
Indirect Modeling of Post-Prandial Intestinal Lymphatic Uptake of Halofantrine Using PBPK Approaches: Limitations and Implications
by Malaz Yousef, Farag E. S. Mosa, Khaled H. Barakat, Neal M. Davies and Raimar Löbenberg
Pharmaceutics 2025, 17(9), 1228; https://doi.org/10.3390/pharmaceutics17091228 - 22 Sep 2025
Viewed by 663
Abstract
Background/Objectives: Despite the recognized importance and distinctive characteristics of the intestinal lymphatic pathway in drug absorption, its pharmacokinetic modeling remains largely unexplored. This study aimed to address this gap by developing a physiologically based pharmacokinetic model (PBPK) to represent the oral lymphatic uptake [...] Read more.
Background/Objectives: Despite the recognized importance and distinctive characteristics of the intestinal lymphatic pathway in drug absorption, its pharmacokinetic modeling remains largely unexplored. This study aimed to address this gap by developing a physiologically based pharmacokinetic model (PBPK) to represent the oral lymphatic uptake of halofantrine following a fatty meal. Methods: Using GastroPlus™ 9.8.3 and published literature data, halofantrine absorption, distribution, metabolism, and elimination in both fasting and fed states were modeled. As the used software does not directly simulate intestinal lymphatic transport, lymphatic involvement in the fed state was examined indirectly through parameter adjustments such as first-pass metabolism, pKa-driven solubility changes, and bile-salt-mediated solubilization, with the aid of molecular dynamics simulations under post-prandial pH. Results: The pharmacokinetic models revealed a reduction in the first-pass effect of halofantrine in the fed state compared to that in the fasting state. While adjustments in metabolism kinetics sufficed for constructing a representative PBPK model in the fasting state, capturing the fed-state profile required both modifications to metabolism kinetics and other parameters related to the structural rearrangements of halofantrine driven by the changes in intestinal pH following food intake. These changes were confirmed using molecular dynamics simulations of halofantrine in pHs reflecting the post-prandial conditions. Conclusions: This study underscores the need for further exploration and direct modeling of intestinal lymphatic uptake via PBPK models, highlighting its underexplored status in simulation algorithms. Moreover, the importance of integrating representative physicochemical factors for drugs, particularly in post-prandial conditions or lipid formulations, is evident. Overall, these findings contribute to advancing predictive regulatory and developmental considerations in drug development using post hoc analyses. Full article
(This article belongs to the Special Issue In Silico Pharmacokinetic and Pharmacodynamic (PK-PD) Modeling)
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