Considerations and challenges for sex-aware drug repurposing

Area of Science:

• Pharmacology and Toxicology
• Genetics and Genomics
• Computational Biology

Background:

• Sex differences are critical in disease etiology and manifestation, influencing conditions like cardiovascular disease, cancer, and neurodegeneration.
• Policies like NIH's Sex as a Biological Variable (SABV) encourage incorporating sex as a biological variable in research.
• Current drug repurposing strategies often overlook sex-specific drug responses and potential sex-biased adverse events (SBAEs).

Purpose of the Study:

• To review the influence of sex on drug response and the current landscape of drug repurposing.
• To highlight the need for and potential of sex-aware computational drug repurposing methods.
• To explore how model organism study design impacts drug repurposing validation.

Main Methods:

• Review of existing literature on sex differences in disease and drug response.
• Analysis of current computational drug repurposing techniques and their limitations regarding sex.
• Exploration of emerging computational approaches, including data mining, ligand binding, molecular associations, and network analysis for sex-aware methods.

Main Results:

• Drug repurposing currently lacks robust sex-aware methodologies, potentially leading to suboptimal efficacy or SBAEs.
• Computational approaches show promise for sex-aware drug repurposing, offering cost- and time-efficient prioritization of sex-specific drug candidates or SBAEs.
• Expansion of sex-aware methods to data types like DNA variation and improved performance with balanced sample sizes are areas for development.

Conclusions:

• Integrating sex as a biological variable into drug repurposing is essential for advancing personalized medicine.
• Low-dimensional representations of molecular associations and network approaches are promising for future sex-aware drug repurposing.
• Further research is needed to develop and validate sex-aware computational methods across diverse data types and model systems.