Drug repositioning prediction for psoriasis using the adverse event reporting database.

Area of Science:

• Pharmacology
• Computational Biology
• Genomics

Background:

• Drug repositioning leverages existing drug data for new therapeutic indications.
• Adverse event reporting systems provide valuable insights into drug safety profiles.
• Gene expression data offers a molecular understanding of disease and drug effects.

Purpose of the Study:

• To predict novel drug candidates for psoriasis treatment.
• To integrate drug adverse event data with gene expression profiles for drug discovery.
• To explore the utility of inverse signals from disproportional analysis in identifying drug candidates.

Main Methods:

• Utilized spontaneous adverse event reports from the US Food and Drug Administration Adverse Event Reporting System (FAERS) (2015-2020).
• Calculated inverse signals using Reporting Odds Ratio (ROR), Information Component (IC), and Empirical Bayes Geometric Mean (EBGM).
• Integrated disease-specific gene expression profiles (from Gene Expression Omnibus) and drug-induced gene expression profiles (from Library of Integrated Network-based Cellular Signatures).

Main Results:

• Identified reversal genes and candidate compounds associated with psoriasis.
• Validated correlations between reverse gene expression scores and drug efficacy metrics (IC50 from ChEMBL).
• Demonstrated the potential of inverse signals for predicting drug candidates.

Conclusions:

• Inverse signals derived from disproportional analysis of adverse event reports are effective for drug repositioning.
• This computational approach successfully predicts drug candidates for psoriasis.
• The integration of diverse data sources enhances drug discovery and repositioning strategies.