ksRepo: a generalized platform for computational drug repositioning

Area of Science:

• Computational biology
• Pharmacology
• Bioinformatics

Background:

• Drug repositioning is crucial for the pharmaceutical industry, but current computational methods often require specific data inputs, limiting their applicability.
• There's a need for generalized methodologies that can integrate various gene expression data types and database schemas for drug repositioning.

Purpose of the Study:

• To develop a generalized computational methodology for drug repositioning that accommodates diverse data inputs.
• To present ksRepo, a novel tool designed to overcome the limitations of existing drug repositioning techniques.

Main Methods:

• ksRepo was developed as a series of four functions in the R statistical environment.
• The tool was tested using five prostate cancer datasets and the Comparative Toxicogenomics Database (CTD) for gene-compound interactions.

Main Results:

• ksRepo successfully identified five frontline prostate cancer therapies from over 7000 compounds in the CTD.
• The method demonstrated significant enrichment in predictions and achieved specificity comparable to existing repositioning approaches.
• ksRepo eliminates the need for a single microarray platform and supports various drug and chemical exposure databases.

Conclusions:

• ksRepo provides a flexible and generalized approach to computational drug repositioning, enabling the use of any data inputs.
• The source code and a user guide (vignette) are freely available, facilitating broader adoption and application of the tool.