IUPHAR review - Data-driven computational drug repurposing approaches for opioid use disorder

Area of Science:

• Pharmacology
• Computational Biology
• Drug Discovery

Background:

• Opioid Use Disorder (OUD) presents a significant public health crisis in the US, marked by high morbidity and mortality.
• Current pharmacological treatments for OUD are insufficient, necessitating novel therapeutic strategies.
• Drug repurposing, especially using computational methods, provides an efficient avenue for identifying new indications for existing drugs.

Purpose of the Study:

• To review state-of-the-art data-driven computational drug repurposing approaches for Opioid Use Disorder (OUD).
• To discuss the advantages and challenges associated with these computational methods.
• To identify and highlight promising repurposed drug candidates for OUD.

Main Methods:

• Systematic review of data-driven computational drug repurposing techniques applied to OUD.
• Analysis of studies utilizing computational approaches to identify potential OUD treatments.
• Evaluation of the evidence supporting the mechanisms of action for candidate repurposed drugs.

Main Results:

• Several data-driven computational drug repurposing strategies show promise for OUD treatment discovery.
• Identified challenges include data integration, model validation, and clinical translation.
• A list of promising repurposed drug candidates for OUD has been compiled based on computational predictions.

Conclusions:

• Computational drug repurposing is a viable and efficient strategy for accelerating the discovery of novel OUD treatments.
• Further research is needed to validate the efficacy and safety of identified repurposed candidates.
• These computational approaches can significantly contribute to addressing the unmet medical needs in OUD management.