Wang, Fanjin;
Sangfuang, Nannapat;
McCoubrey, Laura E;
Yadav, Vipul;
Elbadawi, Moe;
Orlu, Mine;
Gaisford, Simon;
(2023)
Advancing oral delivery of biologics: machine learning predicts peptide stability in the gastrointestinal tract.
International Journal of Pharmaceutics
, 634
, Article 122643. 10.1016/j.ijpharm.2023.122643.
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Abstract
The oral delivery of peptide therapeutics could facilitate precision treatment of numerous gastrointestinal (GI) and systemic diseases with simple administration for patients. However, the vast majority of licensed peptide drugs are currently administered parenterally due to prohibitive peptide instability in the GI tract. As such, the development of GI-stable peptides is receiving considerable investment. This study provides researchers with the first tool to predict the GI stability of peptide therapeutics based solely on the amino acid sequence. Both unsupervised and supervised machine learning techniques were trained on literature-extracted data describing peptide stability in simulated gastric and small intestinal fluid (SGF and SIF). Based on 109 peptide incubations, classification models for SGF and SIF were developed. The best models utilized k-Nearest Neighbor (for SGF) and XGBoost (for SIF) algorithms, with accuracies of 75.1% (SGF) and 69.3% (SIF), and f1 scores of 84.5% (SGF) and 73.4% (SIF) under 5-fold cross-validation. Feature importance analysis demonstrated that peptides’ lipophilicity, rigidity, and size were key determinants of stability. These models are now available to those working on the development of oral peptide therapeutics.
| Type: | Article |
|---|---|
| Title: | Advancing oral delivery of biologics: machine learning predicts peptide stability in the gastrointestinal tract |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.ijpharm.2023.122643 |
| Publisher version: | https://doi.org/10.1016/j.ijpharm.2023.122643 |
| Language: | English |
| Additional information: | This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third-party material in this article are included in the Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
| Keywords: | Artificial intelligence, Biopharmaceuticals, Degradation of peptides and proteins, Formulation and delivery of macromolecules, Gastrointestinal absorption and bioavailability, Drug development and industry 4.0 |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences 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 UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > UCL School of Pharmacy > Pharmaceutics |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10164073 |
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