This method has proven that scientists will be able to forgo preclinical testing in biological specimens and use computational analysis instead – saving vast amounts of time. Lead author Bin Chen remarked, “I personally would like to use this approach to treat individual patients.”
How was this achieved?
The Cancer Genome Atlas is a detailed map of genomic changes found in nearly 36 different types of cancer. The team compared the gene expression signatures from 14 different cancers with the gene expression signatures from the same tissue when it is normal and cancer-free. This enabled them to see which genes were up- or down-regulated in the cancerous tissue, compared to the normal tissue.
Armed with this information, the team then searched the open-access database The Library of Integrated Network-based Cellular Signatures L1000, to ascertain which chemicals and compounds affect cancer cells. Their research identified 12,442 small molecules that are able to reverse abnormal gene expression, such as cancer.
The database also showed them information on the gene expression of the most important genes within each signature, at each concentration of the drug containing the molecule and over different treatment durations.
Then, taking this information, the researchers studied a third database – ChEMBL to learn how well certain drugs killed specific types of cancer cells. Next, Chen and his team used the database the Cancer Cell Line Encyclopedia to compare molecular profiles from more than 1,000 cancer cell lines. Four drugs were found most likely to work, of which pyrvinium pamoate was one.
The team’s own invention
“Since in many cancers, we already have lots of known drug efficacy data, we were able to perform large-scale analyses without running any biological experiments,” Chen elaborated. Developing on this, the team created a database – the Reverse Gene Expression Score (RGES).
A ranking system, it predicts how well any given drug will function to reverse the expression of a gene in a particular disease and bringing it to the level of a normal tissue.
Whilst cancer researchers usually target individual genetic mutations, Chen believes that drugs used in this way are less effective and cause drug resistance. He hopes that RGES, will lead to better drugs and help researchers find new targets. Additionally, as RGES is based on the molecular characteristics of real tumours, it may be able to better predict a drug’s efficacy than drugs tested on lab-grown tumour cells.
“As costs come down and the number of gene expression profiles in diseases continues to grow, I expect that we and others will be able to use RGES to screen for drug candidates very efficiently and cost-effectively,” Chen said.
“Our hope is that ultimately our computational approach can be broadly applied – not only to cancer – but also to other diseases where molecular data exist, and that it will speed up drug discovery in diseases with high unmet needs,” he continued.
There is still a big hurdle to overcome
For now, Chen and his team are pushing to put pyrvinium pamoate for treatment of liver cancer through clinical trials but this proving difficult due to poor funding. According to Chen, pharmaceutical companies have “very limited” interest in developing repurposed drugs, especially if the patent has already expired, as for most of the ones the team has been looking at have.
Senior co-author of the paper, and the director of the Institute for Computational Health Sciences at the university, Atul Butte said, “I definitely do believe high rewards are possible for some of these molecules, but it’s going to take commercial creativity to figure out how to regain value on some of these compounds, at least enough to justify the costs of clinical trials.” MIMS
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