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Machine learning framework for investigating nano- and micro-scale particle diffusion in colonic mucus
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Disciplinary Domain of Medicine and Pharmacy, research centers etc., Uppsala Antibiotic Center.ORCID iD: 0000-0003-2266-4011
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. SciLifeLab, Sci Life Lab, BioImage Informat Facil, Solna, Sweden..
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division Vi3. Uppsala University, Science for Life Laboratory, SciLifeLab. SciLifeLab, Sci Life Lab, BioImage Informat Facil, Solna, Sweden..ORCID iD: 0000-0002-1835-921X
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Disciplinary Domain of Medicine and Pharmacy, research centers etc., Uppsala Antibiotic Center.ORCID iD: 0000-0002-8917-2612
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2025 (English)In: Journal of Nanobiotechnology, E-ISSN 1477-3155, Vol. 23, no 1, article id 583Article in journal (Refereed) Published
Abstract [en]

Biosimilar artificial mucus models that mimic native mucus facilitate efficient, lab-based drug diffusion studies, addressing the costly and challenging preclinical phase of drug development, especially for nano- and micro-scale particle-based colonic drug delivery. This study presents a machine-learning-driven framework that integrates microrheological features into diffusional fingerprinting to characterize nano- and micro-scale particle diffusion patterns in mucus and assess the effect of mucus microrheology on such movements. We investigated the diffusion of fluorescent-labeled polystyrene particles in native pig mucus and two artificial mucus models. Particles (100, 200, and 1000 nm in diameter) with carboxylate- or amine-modified surfaces were tracked during passive diffusion. From each particle trajectory, 20 features -including microrheology-based parameters- were extracted. Based on these features, seven supervised machine learning models were applied to classify or identify similarities among mucus hydrogels. Of these, gradient boosting achieved the highest accuracy. SHapley Additive exPlanations analysis identified creep compliance as the most influential feature in distinguishing the mucus models. In native mucus, smaller negatively charged nanoparticles exhibited the highest mobility, with fewer particles being in the immobile and subdiffusive states. Microrheology data further indicated that larger particles experienced greater restriction owing to the elastic properties of native mucus. In contrast, smaller particles interacted more with the viscous liquid phase. A comprehensive feature-wide analysis revealed that hydroxyethyl cellulose (HEC)-based artificial mucus more closely resembled native pig mucus than the polyacrylic acid-based model. In conclusion, the machine-learning-driven fingerprinting approach, incorporating microrheological features, successfully differentiated the microstructural characteristics and rheological properties of the three mucus models. It also supported the selection of HEC-based artificial mucus as a viable substitute for native colonic mucus.

Place, publisher, year, edition, pages
BioMed Central (BMC), 2025. Vol. 23, no 1, article id 583
Keywords [en]
Mucus, Machine learning, Diffusion, Nanoparticles, Rheology
National Category
Physical Chemistry
Identifiers
URN: urn:nbn:se:uu:diva-566516DOI: 10.1186/s12951-025-03659-6ISI: 001556604700002PubMedID: 40847404Scopus ID: 2-s2.0-105013851519OAI: oai:DiVA.org:uu-566516DiVA, id: diva2:1997540
Funder
Vinnova, 2022-06725Swedish Research CouncilAvailable from: 2025-09-12 Created: 2025-09-12 Last updated: 2025-10-20Bibliographically approved
In thesis
1. Artificial colonic mucus for studies of the mucus absorption barrier
Open this publication in new window or tab >>Artificial colonic mucus for studies of the mucus absorption barrier
2025 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Colonic diseases affect more than 10 million people in Europe, and by 2045, more than 1% of the global population is expected to be affected by inflammatory bowel diseases. Drug development targeting colonic diseases is urgently needed. However, translating in vitro research into in vivo clinical relevance remains a challenge, with significant time and effort required in drug discovery and pre-clinical stages. Therefore, the development of efficient drug study platforms is necessary, in accordance with the ethical 3Rs principles and the UN SDGs 2030. In addition, the FDA Modernization Act 2.0 encourages the improvement of in vitro models with in vivo relevance.

Colonic mucus is the first interface in contact with drugs targeting colonic diseases. Mucus is a hydrogel composed of complex macromolecular crosslinks and acts as a structural barrier. In healthy conditions, colonic mucus consists of a stratified layer, with the outer layer hosting bacteria. Previous studies have reported changes in mucus in 85% of patients with various colonic diseases. However, characterization of the viscoelastic and barrier properties of colonic mucus in diseased states is still underexplored.

This PhD project focuses on studying the characteristics of the mucus barrier that may influence drug diffusion. Properties of native colonic mucus from human patients are characterized. Combined with studies of particle diffusion and viscoelastic properties in colonic mucus, general parameters influencing drug diffusion in the mucus are identified. Characteristics dependent on pH, surface charge, viscosity, and macromolecular composition of mucus have been investigated. Studies of drug interaction with mucus models from pig, dog, and artificial colonic mucus were performed to observe drug diffusion, drug binding, and drug permeability. Various methods to improve experimental (3D printing) and analytical (machine learning classifiers and physiology-based pharmacokinetic models) approaches were incorporated to enhance reproducibility and provide in-depth data analysis.

From this study, the macrorheology and microrheology profiles of the mucus were compared, and artificial colonic mucus was shown to capture the properties of native colonic mucus. By formulating a biosimilar artificial colonic mucus based on the native form, computational studies connecting to in vivo settings allow for better prediction and improved clinical relevance.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2025. p. 107
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Pharmacy, ISSN 1651-6192 ; 391
Keywords
hydrogel; mucus; drug diffusion; drug absorption; interspecies; colon drug delivery
National Category
Pharmaceutical Sciences
Research subject
Pharmaceutical Science
Identifiers
urn:nbn:se:uu:diva-569263 (URN)978-91-513-2649-8 (ISBN)
Public defence
2025-12-12, A1:107a, Biomedical Centre (BMC), Uppsala, 13:15 (English)
Opponent
Supervisors
Funder
EU, Horizon 2020, 956851
Available from: 2025-11-19 Created: 2025-10-20 Last updated: 2025-11-19

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Tjakra, MarcoLidayová, KristínaAvenel, ChristopheBergström, ChristelHossain, Shakhawath

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Tjakra, MarcoLidayová, KristínaAvenel, ChristopheBergström, ChristelHossain, Shakhawath
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Department of PharmacyUppsala Antibiotic CenterComputerized Image Analysis and Human-Computer InteractionDivision Vi3Science for Life Laboratory, SciLifeLab
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