A tool to utilize adverse effect profiles to identify brain-active medications for repurposing

Area of Science:

• Pharmacology
• Drug Discovery
• Computational Chemistry

Background:

• Drug repurposing accelerates the development of new treatments by leveraging existing FDA-approved medications.
• Investigating adverse effect profiles may predict a drug's ability to penetrate the central nervous system (CNS).

Purpose of the Study:

• To evaluate the utility of adverse effect profiles in predicting CNS penetrance of drugs.
• To enhance the prioritization of drug candidates for CNS indications.

Main Methods:

• Analyzed pre- and post-marketing adverse effect data for FDA-approved drugs.
• Developed algorithms using drug similarity profiles and logistic regression to predict CNS activity.
• Compared prediction performance with and without adverse effect similarity data.

Main Results:

• Models incorporating adverse effect similarity data showed improved discrimination between brain-penetrant and non-penetrant drugs.
• A visualization tool was created to assess medication similarity to CNS-active drug panels.

Conclusions:

• Adverse effect profiles offer a valuable in silico method for prioritizing drug candidates for CNS indications.
• This approach, combined with chemical screening, can expedite the repurposing of potential CNS-active drugs.