Harnessing machine learning for development of microbiome therapeutics.

Journal: Gut microbes
Published Date:

Abstract

The last twenty years of seminal microbiome research has uncovered microbiota's intrinsic relationship with human health. Studies elucidating the relationship between an unbalanced microbiome and disease are currently published daily. As such, microbiome big data have become a reality that provide a mine of information for the development of new therapeutics. Machine learning (ML), a branch of artificial intelligence, offers powerful techniques for big data analysis and prediction-making, that are out of reach of human intellect alone. This review will explore how ML can be applied for the development of microbiome-targeted therapeutics. A background on ML will be given, followed by a guide on where to find reliable microbiome big data. Existing applications and opportunities will be discussed, including the use of ML to discover, design, and characterize microbiome therapeutics. The use of ML to optimize advanced processes, such as 3D printing and prediction of drug-microbiome interactions, will also be highlighted. Finally, barriers to adoption of ML in academic and industrial settings will be examined, concluded by a future outlook for the field.

Authors

  • Laura E McCoubrey
    UCL School of Pharmacy, University College London , London, UK.
  • Moe Elbadawi
    UCL School of Pharmacy, University College London, 29-39 Brunswick Square, London WC1N 1AX, UK; School of Biological and Behavioural Sciences, Queen Mary University of London, Mile End Road, London E1 4DQ, UK. Electronic address: m.elbadawi@qmul.ac.uk.
  • Mine Orlu
    UCL School of Pharmacy, University College London , London, UK.
  • Simon Gaisford
    UCL School of Pharmacy, University College London, 29-39 Brunswick Square, London WC1N 1AX, UK. Electronic address: s.gaisford@ucl.ac.uk.
  • 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.