Department of Bioengineering, University of California, Los Angeles, California, USA.
CPT Pharmacometrics Syst Pharmacol. 2024 Jul;13(7):1180-1200. doi: 10.1002/psp4.13150. Epub 2024 May 12.
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.
建模工具旨在预测潜在的药物副作用，尽管它们的性能并不完美。具体来说，蛋白质-蛋白质相互作用模型从药物靶点周围的蛋白质预测药物作用，但它们往往会过度预测药物表型，并需要明确定义的途径表型。在这项研究中，我们使用蛋白质-蛋白质相互作用工具 PathFX，来预测从药物标签中提取的有效成分-副作用对的副作用。我们观察到性能有限，并使用途径工程策略定义了新的途径表型。我们使用基于网络和基于基因表达的方法来定义新的途径表型。总的来说，我们发现了敏感性和特异性值之间的权衡，并展示了一种在有足够的真正阳性例子的情况下限制副作用过度预测的方法。我们将我们的预测与动物模型进行了比较，并展示了类似的性能指标，这表明蛋白质-蛋白质相互作用模型不需要完美的评估指标就能发挥作用。通过纳入真正的阳性例子和组学测量，途径工程似乎是增强蛋白质相互作用网络模型在药物效应预测中的效用的一种有前途的方法。
