Advancing oral delivery of biologics: Machine learning predicts peptide stability in the gastrointestinal tract

• Published in: International journal of pharmaceutics Vol. 634; p. 122643
• Main Authors: Wang, Fanjin, Sangfuang, Nannapat, McCoubrey, Laura E., Yadav, Vipul, Elbadawi, Moe, Orlu, Mine, Gaisford, Simon, Basit, Abdul W.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 05.03.2023
• Subjects: Administration, Oral; Artificial intelligence; Biological Products - metabolism; Biopharmaceuticals; Degradation of peptides and proteins; Drug development and industry 4.0; Formulation and delivery of macromolecules; Gastrointestinal absorption and bioavailability; Gastrointestinal Tract - metabolism; Humans; Machine Learning; Peptides; GI; Gastrointestinal absorption and bioavailability; KNN; SMILES; SVM; Biopharmaceuticals; TPSA; PCA; Formulation and delivery of macromolecules; DT; RFE; SIF; CV; SGF; USP; RF; Drug development and industry 4.0; Degradation of peptides and proteins; Artificial intelligence; ML
• ISSN: 0378-5173; 1873-3476
• DOI: 10.1016/j.ijpharm.2023.122643

[Display omitted] •109 therapeutic peptide stabilities in SGF/SIF were extracted from the literature.•Various machine learning techniques were utilised to predict the stability of untested peptides.•The best models were K-nearest neighbors (SGF, F1: 84.5 %) and XGBoost (SIF, F1: 73.4 %).•Peptide lipophilicity, rigidity, and size were the most important determinants of stability. 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.