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

Journal: Journal of controlled release : official journal of the Controlled Release Society
Published Date:

Abstract

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.

Authors

  • Youssef Abdalla
    UCL School of Pharmacy, University College London, 29-39 Brunswick Square, London WC1N 1AX, UK.
  • Laura E McCoubrey
    UCL School of Pharmacy, University College London , London, UK.
  • Fabiana Ferraro
    Univ. Lille, Inserm, CHU Lille, U1008, F-59000 Lille, France.
  • Lisa Maria Sonnleitner
    Univ. Lille, Inserm, CHU Lille, U1008, F-59000 Lille, France.
  • Yannick Guinet
    Univ. Lille, CNRS, INRAE, Centrale Lille, UMR 8207 - UMET - Unité Matériaux et Transformations, F-59000 Lille, France.
  • Florence Siepmann
    Univ. Lille, Inserm, CHU Lille, U1008, F-59000 Lille, France.
  • Alain Hédoux
    Univ. Lille, CNRS, INRAE, Centrale Lille, UMR 8207 - UMET - Unité Matériaux et Transformations, F-59000 Lille, France.
  • Juergen Siepmann
    Univ. Lille, Inserm, CHU Lille, U1008, F-59000 Lille, France.
  • Abdul W Basit
    Department of Pharmaceutics, UCL School of Pharmacy, University College London, 29-39 Brunswick Square, London WC1N 1AX, UK; FabRx Ltd., 3 Romney Road, Ashford, Kent TN24 0RW, UK. Electronic address: a.basit@ucl.ac.uk.
  • Mine Orlu
    UCL School of Pharmacy, University College London , London, UK.
  • David Shorthouse
    UCL School of Pharmacy, University College London, 29-39 Brunswick Square, London WC1N 1AX, UK. Electronic address: d.shorthouse@ucl.ac.uk.