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Tools & Techniques Drug discovery, Informatics

Old Drugs, New Computational Tools

There are about 7,000 rare or orphan diseases. But, perhaps counterintuitively, around 1 in 10 people are affected by one – so although the diseases themselves are rare, they are certainly not rare in terms of their collective impact! But as each of the individual conditions affects a small population, each represents just a small market. Unfortunately, this is what leads to these disease areas being neglected, as this small market doesn’t justify the billion-dollar drug discovery process that big pharma needs to go through to find a new drug. But of course, the human impact is huge, and the psychological and financial burden of these diseases on society, patients and their family members is immense.

The pharmaceutical industry relies on basic scientific research that is performed by universities to provide leads, and we realized that we could help find a solution to the problem. It is well known that approved drugs can bind to multiple proteins – on average they can bind to as many as six targets – which is the cause of unwanted side effects; on the other hand, it also means that one drug has the potential to affect multiple targets, and therefore to treat multiple diseases.

One drug has the potential to affect multiple targets, and therefore to treat multiple diseases.

This potential inspired us to devise a rational way to find the possible protein targets for drugs that have already been developed, using computational tools (1). Our Computational Systems Biology Group strategically bridges Biological Sciences and the Center for Computation and Technology at Louisiana State University, which allows us to use very powerful super computers to investigate biological questions on a large scale. With eThread, eFindSite and eMatchSite, three software tools developed in-house, we can predict the structure of proteins, annotate within the protein the drug-binding sites, and match those with known pockets that available drugs bind to – and subsequently figure out if the pockets match or not. The obvious usage for such a pipeline is drug repurposing, and applying this strategy to rare diseases to help with rational drug repositioning for such a vulnerable and underserved population was a no-brainer.

Structure-based drug discovery from scratch might not always be the most effective path, but in the case of rare diseases it is a solid approach to finding a solution to a problem that has limited options. Keep in mind that we are not prescribing these drugs to patients; we are identifying proteins that are involved in a rare disease that can possibly be an ancillary target for a known drug. At best, our prediction can identify a drug that can be repurposed directly to alleviate the symptoms or treat a disease, but most probably it would be used as the first lead for drug discovery. We are hopeful about a few cases in the database that we have already published (2), and there are a few drug candidates that we are currently working on. We are also collaborating with experts in structural biology and biochemistry to test these drugs in vitro and provide the initial results needed to start the repurposing process. However, the project is an ongoing effort – as new information on protein structures is deposited into our database, and new proteins and pathways in rare diseases are discovered every day, we will continuously have new and better predictions.

Personally, I am excited to complete my PhD in Biochemistry and Master’s degree in Virology and Veterinary Medical Sciences in May 2018, and I am looking forward to joining the biotech and pharma industry, as I hope to continue to be part of work that aims to improve human health. Governments incentivize drug discovery for orphan diseases, and the process of repositioning a drug is less cumbersome than gaining approval for a new one. The US FDA provides a fast-track process for treatment of conditions to fill unmet medical needs, such as orphan diseases. Research such as ours coupled with fewer complications in the approval process makes it less expensive to develop a product, which means profitability even with a small market size. Our work has the potential to streamline the repositioning process, and hopefully attract more pharma companies to the area of rare and orphan disease.

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  1. RG Govindaraj et al., “Large-scale computational drug repositioning to find treatments for rare diseases”, NPJ Syst Biol Appl, 4, 13 (2018). PMID: 29560273.
  2. M Brylinski et al., “eRepo-ORP: Exploring the Opportunity Space to Combat Orphan Diseases with Existing Drugs”, J Mol Biol, [Epub ahead of print], (2017). PMID: 29237557.
About the Author
Misagh Naderi

Misagh Naderi is a PhD Graduand and Computational Biochemistry Research Assistant, Department of Biological Sciences, Louisiana State University, USA.

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