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Active Machine learning for formulation of precision probiotics

McCoubrey, LE; Seegobin, N; Elbadawi, M; Hu, Y; Orlu, M; Gaisford, S; Basit, AW; (2022) Active Machine learning for formulation of precision probiotics. International Journal of Pharmaceutics , 616 , Article 121568. 10.1016/j.ijpharm.2022.121568. Green open access

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Abstract

It is becoming clear that the human gut microbiome is critical to health and well-being, with increasing evidence demonstrating that dysbiosis can promote disease. Increasingly, precision probiotics are being investigated as investigational drug products for restoration of healthy microbiome balance. To reach the distal gut alive where the density of microbiota is highest, oral probiotics should be protected from harsh conditions during transit through the stomach and small intestines. At present, few probiotic formulations are designed with this delivery strategy in mind. This study employs an emerging machine learning (ML) technique, known as active ML, to predict how excipients at pharmaceutically relevant concentrations affect the intestinal proliferation of a common probiotic, Lactobacillus paracasei. Starting with a labelled dataset of just 6 bacteria-excipient interactions, active ML was able to predict the effects of a further 111 excipients using uncertainty sampling. The average certainty of the final model was 67.70% and experimental validation demonstrated that 3/4 excipient-probiotic interactions could be correctly predicted. The model can be used to enable superior probiotic delivery to maximise proliferation in vivo and marks the first use of active ML in microbiome science.

Type: Article
Title: Active Machine learning for formulation of precision probiotics
Location: Netherlands
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.ijpharm.2022.121568
Publisher version: https://doi.org/10.1016/j.ijpharm.2022.121568
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Artificial intelligence, Colonic delivery, Drug discovery and development, In silico prediction, Live biotherapeutic products, Next generation probiotics
UCL classification: UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > UCL School of Pharmacy > Pharmaceutics
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > UCL School of Pharmacy
URI: https://discovery.ucl.ac.uk/id/eprint/10145420
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