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

Mohammadali Alidoost1, Jennifer L Wilson1

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Protein-protein interaction models can predict drug side effects, but often overpredict. Pathway engineering, incorporating true positive examples and omics data, improves prediction accuracy for these drug effect models.

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Area of Science:

  • Pharmacology
  • Bioinformatics
  • Systems Biology

Background:

  • Computational tools aim to predict drug side effects, but current protein-protein interaction (PPI) models exhibit limitations.
  • PPI models often overpredict drug phenotypes and require precisely defined pathway phenotypes for accurate predictions.

Purpose of the Study:

  • To evaluate and enhance the performance of PPI models, specifically PathFX, in predicting drug side effects.
  • To develop improved pathway phenotype definitions using pathway engineering strategies.

Main Methods:

  • Utilized PathFX, a PPI tool, to predict side effects for active ingredient-side effect pairs from drug labels.
  • Developed novel pathway phenotypes through network-based and gene expression-based approaches (pathway engineering).
  • Compared PPI model predictions against animal model data.

Main Results:

  • Initial PPI model performance was limited, showing a trade-off between sensitivity and specificity.
  • Pathway engineering strategies, including true positive examples and omics data, helped limit overprediction.
  • Predictions from PPI models showed comparable performance metrics to animal models, indicating their utility despite imperfect evaluation metrics.

Conclusions:

  • Pathway engineering is a promising strategy to improve the accuracy and utility of PPI network models for drug side effect prediction.
  • PPI models can be valuable tools for drug effect prediction even without perfect evaluation metrics, especially when enhanced with pathway engineering.