Changing Trends in Computational Drug Repositioning

Abstract

Maximizing the indications potential and revenue from drugs that are already marketed offers a new take on the famous mantra of the Nobel Prize-winning pharmacologist, Sir James Black, “The most fruitful basis for the discovery of a new drug is to start with an old drug”. However, rational design of drug mixtures poses formidable challenges because of the lack of or limited information about in vivo cell regulation, mechanisms of genetic pathway activation, and in vivo pathway interactions. Most of the repositioned drugs therefore are the result of “serendipity” - based on late phase clinical studies of unexpected findings. One of the reasons that the connection between drug candidates and their potential adverse drug reactions or new applications could not be identified earlier is that the underlying mechanism associating them is either very intricate and unknown or dispersed and buried in a sea of information. Discovery of such multi-domain pharmacomodules - pharmacologically relevant sub-networks of biomolecules and/or pathways - from collection of databases by independent/simultaneous mining of multiple datasets is an active area of research. Here, while presenting some of the promising bioinformatics approaches and pipelines, we summarize and discuss the current and evolving landscape of computational drug repositioning.

Predicting new drug indications for prostate cancer: The integration of an in silico proteochemometric network pharmacology platform with patient‐derived primary prostate cells

“…Despite the potential for cost-effectiveness, drug repurposing by computational and chemical screening approaches remains complex and can be time, labor, and resource intensive. 34 To address the aforementioned shortcomings, we used an integrated predictive network pharmacology platform to accurately and rapidly predict new drug-target interactions and their new uses from the available transcriptomic data. 22 While other attempts at drug repurposing for CRPC have been attempted, we believe this is a first of its type study using treatment-naive primary prostate cells in culture to identify and subsequently verify drug efficacy.…”

NEDD: a network embedding based method for predicting drug-disease associations

“…The goal of drug repositioning is to find potential new target diseases for an existing drug and apply the newly identified drug to the treatment of diseases other than the drug’s originally intended ones [ 4 ]. Historically, the discovery of finding new indications for existing drugs is mostly the result of a better understanding of a drug [ 5 ] or serendipity [ 6 ]. Then as the omics data accumulate, new bioinformatics methods emerge and play an increasingly important role.…”

cando.py: Open source software for predictive bioanalytics of large scale drug-protein-disease data

“…Therefore, interpreting the totality of protein interactions for drugs provides greater insight into their therapeutic functions, with the potential of more fruitful drug discovery. In addition, drug repurposing has emerged as a valuable alternative to traditional drug discovery pipelines, potentially easing the burden associated with clinical trial failures due to adverse events and lack of efficacy [20,13,12,11].…”