Title :

Machine learning of Raman spectra predicts drug release from polysaccharide coatings for targeted colonic delivery

Author(s) :

Abdalla, Youssef [Auteur]
University College of London [London] [UCL]
McCoubrey, Laura [Auteur]
University College of London [London] [UCL]
Ferraro, Fabiana [Auteur]
106179|||Médicaments et biomatériaux à libération contrôlée: mécanismes et optimisation - Advanced Drug Delivery Systems - U 1008 [MBLC - ADDS] (VALID)
Sonnleitner, Lisa [Auteur]
106179|||Médicaments et biomatériaux à libération contrôlée: mécanismes et optimisation - Advanced Drug Delivery Systems - U 1008 [MBLC - ADDS] (VALID)
Guinet, Yannick [Auteur]
1002334|||Unité Matériaux et Transformations - UMR 8207 [UMET] (VALID)
Siepmann, Florence [Auteur]
106179|||Médicaments et biomatériaux à libération contrôlée: mécanismes et optimisation - Advanced Drug Delivery Systems - U 1008 [MBLC - ADDS] (VALID)
Hedoux, Alain [Auteur]
1002334|||Unité Matériaux et Transformations - UMR 8207 [UMET] (VALID)
Siepmann, Juergen [Auteur]
106179|||Médicaments et biomatériaux à libération contrôlée: mécanismes et optimisation - Advanced Drug Delivery Systems - U 1008 [MBLC - ADDS] (VALID)
Basit, Abdul [Auteur]
University College of London [London] [UCL]
Orlu, Mine [Auteur]
University College of London [London] [UCL]
Shorthouse, David [Auteur]
University College of London [London] [UCL]

Journal title :

Journal of Controlled Release

Volume number :

374

Pages :

103-111

Publisher :

Elsevier

Publication date :

2024-10

ISSN :

0168-3659

English keyword(s) :

Machine learning
Oral colonic drug delivery film coatings
Mesalazine
Mesalamine
Gut microbiome
Raman spectroscopy

HAL domain(s) :

Physique [physics]/Matière Condensée [cond-mat]/Science des matériaux [cond-mat.mtrl-sci]
Physique [physics]/Matière Condensée [cond-mat]/Matière Molle [cond-mat.soft]
Physique [physics]/Matière Condensée [cond-mat]/Systèmes désordonnés et réseaux de neurones [cond-mat.dis-nn]
Sciences du Vivant [q-bio]

English abstract : [en]

Colonic drug delivery offers numerous pharmaceutical opportunities, including direct access to local therapeutic targets and drug bioavailability benefits arising from the colonic epithelium's reduced abundance of cytochrome ...
Show more >Colonic drug delivery offers numerous pharmaceutical opportunities, including direct access to local therapeutic targets and drug bioavailability benefits arising from the colonic epithelium's reduced abundance of cytochrome P450 enzymes and particular efflux transporters. Current workflows for developing colonic drug delivery systems involve time-consuming, low throughput in vitro and in vivo screening methods, which hinder the identification of suitable enabling materials. Polysaccharides are useful materials for colonic targeting, as they can be utilised as dosage form coatings that are selectively digested by the colonic microbiota. However, polysaccharides are a heterogeneous family of molecules with varying suitability for this purpose. To address the need for high-throughput material selection tools for colonic drug delivery, we leveraged machine learning (ML) and publicly accessible experimental data to predict the release of the drug 5-aminosalicylic acid from polysaccharide-based coatings in simulated human, rat, and dog colonic environments. For the first time, Raman spectra alone were used to characterise polysaccharides for input as ML features. Models were validated on 8 unseen drug release profiles from new polysaccharide coatings, demonstrating the generalisability and reliability of the method. Further, model analysis facilitated an understanding of the chemical features that influence a polysaccharide's suitability for colonic drug delivery. This work represents a major step in employing spectral data for forecasting drug release from pharmaceutical formulations and marks a significant advancement in the field of colonic drug delivery. It offers a powerful tool for the efficient, sustainable, and successful development and pre-ranking of colon-targeted formulation coatings, paving the way for future more effective and targeted drug delivery strategies.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Administrative institution(s) :

Université de Lille
CNRS
INRAE
ENSCL

Collections :

• Advanced Drug Delivery Systems (ADDS) - U1008
• Unité Matériaux et Transformations (UMET) - UMR 8207

Research team(s) :

Matériaux Moléculaires et Thérapeutiques

Submission date :

2024-09-02T13:38:47Z
2024-09-02T14:03:23Z
2024-09-04T07:48:03Z