One Drug, Three Diseases: Computational Drug Repurposing in MS, Parkinson's, and Epilepsy

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Multiple Sclerosis, Parkinson’s Disease, and epilepsy look different on the surface, but all force Canadians into a completely foreign lifestyle. 93,500 Canadians [1] live with MS – and Alberta has one of the highest rates in the world [2]. Over 103,000 live with Parkinson’s Disease [3], and approximately 139,200 live with epilepsy [4], with no effective cure. The global prevalence is even more striking (Figure 1).

None of these diseases have a single cause – genetic predispositions and environmental triggers both heighten susceptibility. I hypothesized that if one gene increases susceptibility to MS – where the immune system attacks the nervous system, impairing electrical signalling – that same gene may be driving neuron death in Parkinson’s, or uncontrolled electrical misfiring in epilepsy. If true, a drug already approved for one condition could be the solution for another, without developing, testing, and approving anything new (Figure 2).

If these diseases share broken genes, network pharmacology analysis can identify existing approved compounds targeting those shared genes – suggesting repurposing potential across all three conditions. The obstacle was never scientific. Nobody profits from proving a generic drug works for a different disease, so clinical trials go unfunded and the question goes unasked. Computational analysis solves this – it shows exactly where to look before anyone enters a lab. Sending researchers into experimentation without identifying which genes are driving disease would be like an electrician rewiring your entire house because one light flickered, without checking which breaker tripped. This study is the breaker check.

I followed a six-step computational pipeline, moving from gene extraction to literature validation (see computational methodology images). The first step was identifying genes linked to each disease using Open Targets [7], a database that compiles evidence from genetic studies, clinical data, and scientific literature, assigning each gene-disease association a score from 0 to 1. A threshold of >0.3 balanced reliability and inclusivity [8]. A lower threshold would produce thousands of low-confidence results while a higher threshold would risk excluding valid but understudied associations – especially for epilepsy, which has fewer documented gene links than MS and Parkinson’s.

After compiling three gene lists, Venny [9] identified overlap between datasets, isolating genes common to all three diseases. These shared genes form the molecular foundation of this study (Figure 3). Not all were equally important – some function as central regulators connecting to many proteins and influencing entire pathways, while others operate independently.

To understand how these genes function biologically, STRING [10] constructed a protein-protein interaction network (see fourth image). Because proteins function in interconnected systems rather than isolation, this reveals how shared genes interact within molecular pathways – each protein represented as a node, each interaction as a connecting line. To mathematically determine which proteins were most influential, the PPIN was imported into Cytoscape [11].

Degree centrality was calculated for each node (Figure 4) – the more connections a protein has, the greater its influence. Like a power grid, when hub nodes fail, entire regions go dark. Degree centrality identifies these hub genes: the most connected and impactful targets.

To connect targets to potential treatments, DGIdb [12] identified known interactions between hub genes and approved drugs, with particular attention to drugs targeting multiple hub genes simultaneously. Finally, literature validation through PubMed [13] confirmed that computational predictions align with existing experimental and clinical evidence.

Using Venny, 30 shared gene associations across MS, Parkinson’s, and epilepsy were identified based on the association data pulled from Open Targets. This confirms the first condition of the hypothesis: these diseases do share significant genetic overlap.

A minor error was identified, corrected during this step. The spreadsheet column header ‘symbol’ was initially counted as a shared gene, producing an incorrect total of 31 shared associations. This inconsistency was noticed, corrected, and documented, resulting in the final count of 30 shared genes.

The STRING protein-protein interaction network revealed that these genes were not equally connected. Some proteins formed dense clusters with multiple interactions, while others were completely isolated. Six had zero connections, indicating that they play minimal roles in the shared disease pathway and would have little impact on the disease as drug targets. These were excluded from further analysis.

The remaining network showed clear structure, with density and clustering patterns indicating that the shared genes are connected by function rather than random associations, supporting the presence of common underlying mechanisms between MS, PD, and epilepsy.

Using Cytoscape, degree centrality analysis identified six hub genes (Figure 5):

GRIK5 (degree 15)

GRIN2A (degree 14)

GRIN2B (degree 14)

SCN2A (degree 9)

GRIN1 (degree 8)

SCN8A (degree 8)

These genes are the most connected and influential nodes in the network. Notably, five of the six hub genes code for glutamate receptor subunits, which is not a coincidence. Glutamate is the brain’s primary excitatory neurotransmitter, a chemical that excites neurons to send signals. NMDA receptors, formed by subunits such as GRIN1, GRIN2A, and GRIN2B, regulate calcium influx and neural signalling between neurons. When overactivated, these glutamate receptors allow excessive calcium into neurons, triggering cellular stress and eventual cell death. This process, known as glutamate excitotoxicity (Figure 6), is strongly linked to neurodegenerative diseases and seizure activity. Its presence across the top hub genes is strongly indicative of a shared mechanism connecting MS, Parkinson’s, and Epilepsy.

DGIdb identified 69 total drug-gene interactions across the six hub genes, meaning that there were 69 documented instances where an approved drug targets one of these key proteins. Several drugs targeted multiple hub genes simultaneously. These “cross-hub” drugs are especially important because they influence multiple points in the disease network at once, increasing their potential impact.

Top cross-hub candidates included (Figure 7):

Orphenadrine (targets 4 hub genes)

Amantadine (targets 3 hub genes)

Ketamine/Esketamine (targets 3 hub genes)

Acamprosate (targets 3 hub genes)

Memantine (targets 2 hub genes)

Topiramate (targets 2 hub genes, including GRIK5)

One result is distinct from the rest: Amantadine is already approved for Parkinson’s disease, yet it was independently identified by this network purely through mathematical analysis. This is strong validation that the model and pipeline that was used is accurately identifying biologically meaningful targets.

All of the methodology and results (Figure 8) lead to one key question: why does this matter?

Current treatments for Multiple Sclerosis, Parkinson’s disease, and epilepsy manage symptoms but fail to stop or reverse the underlying neuronal damage. Developing a new drug from scratch takes over a decade and billions of dollars (Figure 9). This study addresses that challenge by using publicly available tools to identify shared molecular pathways and match them with existing approved drugs.

Literature validation confirmed that every top candidate has existing research supporting its relevance (Figure 7). Amantadine acts as an NMDA receptor blocker and has shown neuroprotective effects in PD, while also being studied for fatigue in MS. Memantine has been investigated in MS and certain forms of epilepsy. Topiramate, Felbamate, and Ketamine also target overlapping pathways across all three conditions.

Notably, some drugs show mixed clinical results. This doesn't undermine the findings of this study – it highlights an important distinction. This study answers a molecular question: do these drugs target genes and pathways involved in these diseases? The answer is yes, supported both by network analysis and existing research. Clinical trials answer a different question: do these drugs improve patient outcomes? Mixed clinical outcomes can occur for many reasons, including incorrect dosage, variation in patient population, or differences in disease stage.

Overall, this study provides a focused starting point for future research. By identifying biologically relevant targets, it reduces reliance on trial-and-error approaches and supports more efficient, targeted, and cost-effective drug development.

The pipeline identified where to look. Now someone has to look.

The immediate next step is taking Amantadine, which is already approved, into cell and animal models. The pipeline can be enhanced – different threshold, additional diseases, patient data – the same tools, run again, could surface the next candidate.

Longer term, GRIN1, GRIN2A, GRIN2B, GRIK5, SCN2A, and SCN8A deserve a drug designed specifically for them. That’s the path that could change the treatment landscape permanently.

The real obstacle has never been science – it’s been the funding. No patent, no profit, no trial. This analysis exists to break that cycle.

I want to express my gratitude toward my junior high science teacher, Kathryn Gauthier, for pushing me to do my best and enjoy the journey. A huge thanks to my biology teacher, Linda Serediak, whose comprehensive lessons sparked the curiosity that started it all – I never would have thought to ask whether MS, Parkinson's, and Epilepsy shared a link without her inspiration. Turns out they do, and that question was worth chasing.

I'd especially like to thank my mom, Lois Eagles, for her support, encouragement, and seemingly endless patience through every long day when I simply didn't have the energy to keep going. She kept me moving forward anyway.

And to my friends – thank you for believing I could pull this off even when I wasn't so sure. Your encouragement pushed this project across the finish line.

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Image citations will be included in my log book at the science fair if you'd like to take a look at them.