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Pharmaceutics
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Advances in AI-Driven Drug Delivery Systems
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Advances in AI-Driven Drug Delivery Systems

A special issue of Pharmaceutics (ISSN 1999-4923). This special issue belongs to the section "Drug Delivery and Controlled Release".

Deadline for manuscript submissions: 30 June 2026 | Viewed by 3623

Special Issue Editors

Dr. Junhuang Jiang

E-Mail Website
Guest Editor
College of Pharmacy, Jinan University, Guangzhou 511436, China
Interests: artificial intelligence; machine learning; pulmonary drug delivery; amorphous solid dispersion
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Xin Pan

E-Mail Website
Guest Editor
School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, China
Interests: transdermal drug delivery; pulmonary drug delivery; smart gel for drug delivery
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recently, artificial intelligence (AI) has emerged as a transformative technique in the field of pharmaceutics, particularly in relation to the development of drug delivery systems. With the integration of AI and machine learning (ML), the development of drug delivery systems can be greatly accelerated, specifically in the preformulation, formulation design and optimization, process control, manufacturing, and drug delivery processes. Numerous AI algorithms and models have successfully been developed to improve the efficiency and enhance the understanding of these aspects.

This Special Issue aims to focus on recent advances in AI-driven drug delivery systems, including but not limited to, the following: (1) emerging AI applications in formulation design and optimization; (2) ML-enabled drug formulation analytics and characterization; (3) AI-driven methodologies to predict in vitro drug delivery processes; (4) data-driven drug formulation manufacturing and quality control; (5) AI-driven 3D and 4D printing of drug delivery systems; and (6) regulatory sciences of AI-driven drug delivery system development. These topics highlight the importance of AI in the field of pharmaceutics. Original research and review papers that reflect this topic are warmly welcomed.

Dr. Junhuang Jiang
Prof. Dr. Xin Pan
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

  • artificial intelligence
  • machine learning
  • drug delivery system
  • formulation development
  • pulmonary drug delivery system
  • microneedles
  • three-dimensional printing

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Published Papers (3 papers)

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Research

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19 pages, 4508 KB  
Article
Machine Learning-Guided Development of Anti-Tuberculosis Dry Powder for Inhalation Prepared by Co-Spray Drying
by Xiaoyun Hu, Xian Chen, Ziling Zhou, Aichao Wang, Xin Pan, Chuanbin Wu and Junhuang Jiang
Pharmaceutics 2026, 18(2), 191; https://doi.org/10.3390/pharmaceutics18020191 - 1 Feb 2026
Viewed by 758
Abstract
Background/Objectives: Tuberculosis (TB) remains a major global health threat. Current administration methods for anti-TB drugs, including oral or intravenous, suffer from systemic side effects, low lung distribution, and poor patient compliance. Dry powder inhalers (DPIs) offer a promising alternative. This study investigates the [...] Read more.
Background/Objectives: Tuberculosis (TB) remains a major global health threat. Current administration methods for anti-TB drugs, including oral or intravenous, suffer from systemic side effects, low lung distribution, and poor patient compliance. Dry powder inhalers (DPIs) offer a promising alternative. This study investigates the aerodynamic performance of co-spray-dried DPIs containing rifampin or pyrazinamide and amino acids by using machine learning. Methods: Firstly, 72 formulations were prepared by varying drug-amino acid combinations, molar ratios, and spray-drying parameters. Subsequently, the aerodynamic performance of all 72 formulations was evaluated using a Next Generation Impactor, and the solid-state characterizations of optimal DPIs were carried out. Finally, four machine learning (ML) models were successfully developed and were utilized to predict the fine particle dose (FPD), FPF, MMAD, and geometric standard deviation (GSD) of DPIs based on the high-quality in-house data above. Results: Key results showed that the aerodynamic performance of DPIs was highly dependent on the specific drug-amino acid combination, with rifampin-L-lysine acetate and pyrazinamide-L-leucine formulations achieving the highest fine particle fraction (FPF, 73.37%, 87.74%) and optimal mass median aerodynamic diameter (MMAD, 2.59 µm, 1.88 µm). Notably, XGBoost (v3.1.3) exhibited the best predictive performance, with R2 values ranging from 0.894 to 0.991 in the testing set for the four prediction tasks. Meanwhile, SHapley Additive exPlanations (v0.50.0) was used for model interpretability analysis. The molecular weights and LogP of the drug and amino acid were identified as two of the most important features affecting the prediction of FPD, FPF, MMAD, and GSD. Conclusions: This work demonstrates the feasibility of ML in accelerating the development of inhalable spray-dried anti-TB drugs by enabling the prediction of DPI formulations. Full article
(This article belongs to the Special Issue Advances in AI-Driven Drug Delivery Systems)
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14 pages, 2414 KB  
Article
Building Artificial Neural Networks for the Optimization of Sustained-Release Kinetics of Metronidazole from Colonic Hydrophilic Matrices
by Cristina Maderuelo, Roberto Arévalo-Pérez and José M. Lanao
Pharmaceutics 2025, 17(11), 1451; https://doi.org/10.3390/pharmaceutics17111451 - 10 Nov 2025
Viewed by 913
Abstract
Introduction: Drug development has traditionally used mathematical models to predict formulation behavior. Objective: Building artificial neural networks for the drug release evaluation of drug delivery systems using sustained-release metronidazole-coated colonic hydrophilic matrices as a model. Methods: The technological factors associated [...] Read more.
Introduction: Drug development has traditionally used mathematical models to predict formulation behavior. Objective: Building artificial neural networks for the drug release evaluation of drug delivery systems using sustained-release metronidazole-coated colonic hydrophilic matrices as a model. Methods: The technological factors associated with the biopharmaceutical performance of hydrophilic metronidazole matrices were evaluated using a quality by design approach (QbD). The developed neural network includes variables related to the technological process for producing the matrices. These are related to the materials used, such as the type and viscosity of core polymers, the type of coating agent, or the matrix production process, such as the mixing time of core materials or the percentage of the coating agent. The output variables of the neural network were the percentages of drug released in vitro at 1, 6, 12, and 24 h and the mean dissolution time of the matrix. An iterative quasi-Newton method was used to train the artificial neural network. Results: A neural network with excellent prediction capacity allows selecting the technological variables with the greatest influence on the % of drug dissolved: the type of coating agent used and the percentage of the total weight increase after coating for 1 h and 6 h of drug release and also the viscosity of the HPMC for 12 and 24 h. Conclusions: The optimized neural network demonstrated an excellent predictive capacity for in vitro drug dissolution profiles, allowing the use of this type of methodology based on artificial intelligence methods in the optimization of drug delivery systems. Full article
(This article belongs to the Special Issue Advances in AI-Driven Drug Delivery Systems)
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Review

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31 pages, 3101 KB  
Review
Novel AI-Driven Precision Strategies in Diabetic Wound Healing: Immunomodulation and Advances in Smart Composite Nanocarriers
by Yibin Zheng, Junshan Lan, Qian Huang, Qi Li, Yuting Liu, Bing Li, Xuan Wu, Qianxi Wang, Yongqi Liao, Xing Zhou, Zhipeng Teng and Jie Lou
Pharmaceutics 2026, 18(2), 252; https://doi.org/10.3390/pharmaceutics18020252 - 18 Feb 2026
Cited by 1 | Viewed by 1027
Abstract
Diabetic chronic wounds (CWs) represent a recalcitrant, difficult-to-heal pathological condition characterized by an imbalance of the immune microenvironment. Smart composite nanocarriers for immune regulation enable multi-targeted, spatiotemporally controllable synergistic interventions by responding to pathological signals such as reactive oxygen species (ROS), pH, and [...] Read more.
Diabetic chronic wounds (CWs) represent a recalcitrant, difficult-to-heal pathological condition characterized by an imbalance of the immune microenvironment. Smart composite nanocarriers for immune regulation enable multi-targeted, spatiotemporally controllable synergistic interventions by responding to pathological signals such as reactive oxygen species (ROS), pH, and abnormal enzyme activity, thereby offering a novel pharmaceutical strategy to overcome the limitations of traditional single-target therapies. Artificial intelligence (AI) integrates clinical and biological data to predict healing risks, optimize treatment plans and nanocarrier design, and dynamically adjust strategies based on patient conditions, ensuring precision and personalized therapies. This paper systematically reviews the immunopathological basis of CWs, summarizes the design rationale and functional evolution of immune-modulating smart composite nanocarriers, and discusses an AI-enabled precision therapy framework from an interdisciplinary perspective. It aims to establish a theoretical foundation and research paradigm for constructing programmable drug delivery systems tailored to complex disease microenvironments, facilitating the transition of smart nanopharmacy from material-oriented to system-regulation-oriented approaches, and accelerating the clinically predictable translation of diabetic wound therapies. Full article
(This article belongs to the Special Issue Advances in AI-Driven Drug Delivery Systems)
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