Predicting drug adverse effects using a new Gastro-Intestinal Pacemaker Activity Drug Database (GIPADD)

• Published in: Scientific reports Vol. 13; no. 1; p. 6935
• Main Authors: Liu, Julia Yuen Hang, Rudd, John A.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 28-04-2023; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114; 631/154; 631/154/1435; 631/154/1438; 631/154/555; 692/4020; Algorithms; Artificial Intelligence; Bayes Theorem; Classification; Databases, Pharmaceutical; Discriminant analysis; Drug-Related Side Effects and Adverse Reactions; Duodenum; Humanities and Social Sciences; Humans; Ileum; Intestine; Machine Learning; multidisciplinary; Pacemakers; Science; Science (multidisciplinary); Side effects; Therapeutic targets
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-023-33655-5

Electrical data could be a new source of big-data for training artificial intelligence (AI) for drug discovery. A Gastro-Intestinal Pacemaker Activity Drug Database (GIPADD) was built using a standardized methodology to test drug effects on electrical gastrointestinal (GI) pacemaker activity. The current report used data obtained from 89 drugs with 4867 datasets to evaluate the potential use of the GIPADD for predicting drug adverse effects (AEs) using a machine-learning (ML) approach and to explore correlations between AEs and GI pacemaker activity. Twenty-four “electrical” features (EFs) were extracted using an automated analytical pipeline from the electrical signals recorded before and after acute drug treatment at three concentrations (or more) on four-types of GI tissues (stomach, duodenum, ileum and colon). Extracted features were normalized and merged with an online side-effect resource (SIDER) database. Sixty-six common AEs were selected. Different algorithms of classification ML models, including Naïve Bayes, discriminant analysis, classification tree, k-nearest neighbors, support vector machine and an ensemble model were tested. Separated tissue models were also tested. Averaging experimental repeats and dose adjustment were performed to refine the prediction results. Random datasets were created for model validation. After model validation, nine AEs classification ML model were constructed with accuracy ranging from 67 to 80%. EF can be further grouped into ‘excitatory’ and ‘inhibitory’ types of AEs. This is the first time drugs are being clustered based on EF. Drugs acting on similar receptors share similar EF profile, indicating potential use of the database to predict drug targets too. GIPADD is a growing database, where prediction accuracy is expected to improve. The current approach provides novel insights on how EF may be used as new source of big-data in health and disease.