NARSAD Grantees Develop Computational Approach to Identify New Medications for Addiction

Jingchun Sun, Ph.D., expert on addiction
Jingchun Sun, Ph.D.

Two recipients of NARSAD Young Investigator Grants have published results of a novel study in which computers and a sophisticated mathematical approach were used to help identify potential new medications for addiction. The researchers employed a strategy of analyzing data from recent public databases of addictive drugs and their “targets” (the biological mechanism the drug interacts with to produce its effect).

Jingchun Sun, Ph.D., a 2010 and 2013 NARSAD Young Investigator Grantee now at The University of Texas Health Science Center at Houston, was lead author on the paper published February 9th in BioMed Research International. The research team was led by Zhongming Zhao, Ph.D., of Vanderbilt University Medical School, who received NARSAD Young Investigator Grants in 2005 and 2008.

The team hypothesized that some of the “addiction-related drugs” (having similar targets to addictive substances) not previously thought to be addictive might have the potential to treat addiction, while others might cause addiction. Also, with the inclusion of addiction-related drugs, they thought they might identify current medications that could be repurposed to treat addiction.

The team started with data on 44 known addictive drugs and their targets and then expanded their analysis to include substances with at least one common target with these addictive compounds (addiction-related drugs). Through a sophisticated computational analysis, they were able to generate a list of 91 substances currently considered non-addictive that may have associations with addiction. Some of these could become future treatments for addiction; others could prove to have addictive potential when given in combination with other substances.

Dr. Sun and colleagues consider the study a proof of concept for this approach to discovering new purposes for currently known substances. They note that “the strategy employed for building the basic and the expanded networks in this study is effective and straightforward, offering a promising computational method to predict potential drugs for a given disease. Furthermore, this study proves the concept that such a network approach can be implemented in predicting drug-target relationships and uncovering novel drugs/targets for both basic and clinical research.”

Read the research paper.