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Biology
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Computational Modeling of Drug Delivery
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Computational Modeling of Drug Delivery

A special issue of Biology (ISSN 2079-7737). This special issue belongs to the section "Biophysics".

Deadline for manuscript submissions: 25 December 2025 | Viewed by 1623

Special Issue Editors

Dr. Dingding Han

E-Mail Website
Guest Editor
George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
Interests: quantitative biotransport; computational modeling of drug delivery; computational fluid dynamics; computational biophysics; biofluid mechanics
Dr. Arezoo M. Ardekani

E-Mail Website
Guest Editor
School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907, USA
Interests: biofluids; complex fluids; computational modeling of drug delivery; fluid mechanics; biotransport

Special Issue Information

Dear Colleagues,

Computational modeling has emerged to be a powerful tool in drug delivery, offering quantitative insights into the mechanisms of the delivery process, identifying novel drug targets, and elucidating complex biological pathways, thereby accelerating new treatment discoveries. By simulating and analyzing the interactions between drugs and biological systems, computational models enable researchers to predict drug behavior, optimize delivery methods, and develop more effective and targeted therapies, which is crucial for improving patient outcomes and enhancing the efficiency of therapeutic interventions.

The aim of this Special Issue is to present cutting-edge advancements in computational modeling as applied to drug delivery, highlighting innovative methodologies and their applications across multiple scales, from cellular to systemic levels. This Special Issue seeks to bring together interdisciplinary research that leverages computational modeling approaches, including physics-based simulations, kinetic models, machine learning, and statistical models, to enhance our understanding of drug delivery mechanisms, optimize therapeutic outcomes, and develop more efficient and targeted drug delivery systems.

We are pleased to invite you to contribute to this interdisciplinary research area by advancing the understanding of the biological mechanisms underlying drug delivery and the controlled release of biotherapeutics through computational modeling. We welcome the submission of original research articles and reviews that explore innovative computational approaches and their applications in drug transport and delivery. Research areas may include (but are not limited to) the following:

  • Advanced and innovative computational models for delivering various drug formulations (e.g., monoclonal antibodies, lipid nanoparticles, peptides) through various administration methods, such as subcutaneous and intravenous injections, spanning from cellular to tissue and system scales.
  • Modeling the interaction between drug delivery systems and biological barriers (e.g., interstitium matrix, blood–brain barrier, gastrointestinal tract).
  • Development of predictive models for pharmacokinetics (PK) and pharmacodynamics (PD) of drug formulations.
  • Integration of computational models, such as computational fluid dynamics simulation, with experimental data to improve predictive accuracy of drug delivery.
  • Machine learning and statistical models that can be coupled with biophysics-based computational models for personalized medicine and patient-specific drug delivery optimization.

We look forward to receiving your contributions. 

Dr. Dingding Han
Dr. Arezoo M. Ardekani
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. Biology 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 2700 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

  • computational modeling
  • drug delivery
  • computational fluid dynamics
  • biofluids
  • quantitative biotransport
  • mathematical models
  • artificial intelligence

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Published Papers (2 papers)

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Research

12 pages, 1116 KiB  
Article
Physics-Informed Neural Network-Based Inverse Framework for Time-Fractional Differential Equations for Rheology
by Sukirt Thakur, Harsa Mitra and Arezoo M. Ardekani
Biology 2025, 14(7), 779; https://doi.org/10.3390/biology14070779 - 27 Jun 2025
Viewed by 219
Abstract
Inverse problems involving time-fractional differential equations have become increasingly important for modeling systems with memory-dependent dynamics, particularly in biotransport and viscoelastic materials. Despite their potential, these problems remain challenging due to issues of stability, non-uniqueness, and limited data availability. Recent advancements in Physics-Informed [...] Read more.
Inverse problems involving time-fractional differential equations have become increasingly important for modeling systems with memory-dependent dynamics, particularly in biotransport and viscoelastic materials. Despite their potential, these problems remain challenging due to issues of stability, non-uniqueness, and limited data availability. Recent advancements in Physics-Informed Neural Networks (PINNs) offer a data-efficient framework for solving such inverse problems, yet most implementations are restricted to integer-order derivatives. In this work, we develop a PINN-based framework tailored for inverse problems involving time-fractional derivatives. We consider two representative applications: anomalous diffusion and fractional viscoelasticity. Using both synthetic and experimental datasets, we infer key physical parameters including the generalized diffusion coefficient and the fractional derivative order in the diffusion model and the relaxation parameters in a fractional Maxwell model. Our approach incorporates a customized residual loss function scaled by the standard deviation of observed data to enhance robustness. Even under 25% Gaussian noise, our method recovers model parameters with relative errors below 10%. Additionally, the framework accurately predicts relaxation moduli in porcine tissue experiments, achieving similar error margins. These results demonstrate the framework’s effectiveness in learning fractional dynamics from noisy and sparse data, paving the way for broader applications in complex biological and mechanical systems. Full article
(This article belongs to the Special Issue Computational Modeling of Drug Delivery)
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28 pages, 8154 KiB  
Article
Overcoming Clusterin-Induced Chemoresistance in Cancer: A Computational Study Using a Fragment-Based Drug Discovery Approach
by Engelo John Gabriel V. Caro, Marineil C. Gomez, Po-Wei Tsai and Lemmuel L. Tayo
Biology 2025, 14(6), 639; https://doi.org/10.3390/biology14060639 - 30 May 2025
Viewed by 691
Abstract
Clusterin is one of the many known proteins implicated in cancer chemoresistance, which hinders the effectiveness of chemotherapy. This study aimed to design novel inhibitors targeting clusterin using fragment-based drug discovery (FBDD). This approach aims to develop new medicines by identifying small, simple [...] Read more.
Clusterin is one of the many known proteins implicated in cancer chemoresistance, which hinders the effectiveness of chemotherapy. This study aimed to design novel inhibitors targeting clusterin using fragment-based drug discovery (FBDD). This approach aims to develop new medicines by identifying small, simple molecules known as “fragments” that can bind to a specific target, such as a disease-causing protein. In this study, a primary ligand-binding site and an allosteric site on the clusterin molecule were identified through hotspot analysis. We screened commercially available fragment libraries for anti-cancer activity and applied the “rule of three” to ensure drug-like properties. The highest-affinity fragment underwent “fragment-growing” to develop potential drug candidates. After docking and toxicity screening, 194 candidate drugs were identified. Quantitative structure-activity relationship (QSAR) analysis revealed that the chemical size and complexity of the fragments significantly contributed to their binding affinity. Pharmacokinetic analyses of candidate drugs from FBDD followed by molecular dynamics simulation of the top 1 final candidate drug precursor demonstrated comparatively better affinity (average = −34.01 kcal/mol) than the reference compound (average = −6.15 kcal/mol) and significant ligand flexibility. This study offers a potential strategy to identify fragments or molecules that may serve as drugs against clusterin-related chemoresistance. Full article
(This article belongs to the Special Issue Computational Modeling of Drug Delivery)
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