Preclinical side effect prediction through pathway engineering of protein interaction network models

Abstract

Modeling tools aim to predict potential drug side effects, although they suffer from imperfect performance. Specifically, protein–protein interaction models predict drug effects from proteins surrounding drug targets, but they tend to overpredict drug phenotypes and require well-defined pathway phenotypes. In this study, we used PathFX, a protein–protein interaction tool, to predict side effects for active ingredient-side effect pairs extracted from drug labels. We observed limited performance and defined new pathway phenotypes using pathway engineering strategies. We defined new pathway phenotypes using a network-based and gene expression-based approach. Overall, we discovered a trade-off between sensitivity and specificity values and demonstrated a way to limit overprediction for side effects with sufficient true positive examples. We compared our predictions to animal models and demonstrated similar performance metrics, suggesting that protein–protein interaction models do not need perfect evaluation metrics to be useful. Pathway engineering, through the inclusion of true positive examples and omics measurements, emerges as a promising approach to enhance the utility of protein interaction network models for drug effect prediction.