Target Fishing by Cross-Docking to Explain Polypharmacological Effects

Area of Science:

• Pharmacology
• Computational Chemistry
• Drug Discovery

Background:

• Drugs often interact with unintended targets, leading to polypharmacology, side effects, or novel therapeutic applications.
• Understanding these off-target interactions is crucial for drug safety and development.
• Computational methods offer a powerful approach to predict and analyze drug-protein interactions.

Purpose of the Study:

• To present and validate an in silico cross-docking approach for target fishing and identifying new drug-protein interactions.
• To predict potential mechanisms behind drug side effects and uncover drug repositioning opportunities.
• To demonstrate the utility of the method using ethacrynic acid as a case study.

Main Methods:

• Utilized an in silico cross-docking strategy for large-scale drug-target interaction prediction.
• Employed transient transactivation assays in human HEK293 cells to validate predicted interactions.
• Applied computational modeling to explore mechanisms of drug side effects and identify repositioning candidates.

Main Results:

• The cross-docking method successfully predicted peroxisome proliferator-activated receptor (PPAR)-γ as a target of ethacrynic acid.
• Validated the antagonistic effect of ethacrynic acid on PPAR-γ in cellular assays, explaining its hyperglycemic effects.
• Identified numerous other potential drug-target interactions, suggesting mechanisms for side effects and drug repositioning.

Conclusions:

• In silico cross-docking is an effective method for identifying novel drug-protein interactions and predicting polypharmacology.
• This approach facilitates the understanding of drug side effects and opens avenues for drug repositioning.
• The predicted interactions provide a valuable resource for future drug discovery and development efforts.