Like Goldilocks, genetics research can find the dose that’s just right

3 days ago

Image: Ifeolutembi Fashina

Bears take their porridge differently, and patients respond to drug treatments differently. At FameLab, genetics researcher Ifeolutembi Fashina explained how we can learn from a database of ‘taste tests’ to perfect drug doses for patients in need.

Ifeolutembi Fashina entered the world of human genetics during her undergraduate degree at Trinity College Dublin. Then, after a couple of years in the industry, reporting adverse events in clinical trials, she joined the McCoy Lab at Royal College of Surgeons in Ireland (RCSI) under funding by Science Foundation Ireland’s Centre for Research Training in Genomics.

In her PhD research, Fashina is exploring how changes to human microRNAs could influence multiple sclerosis. But when she entered the FameLab science communication competition earlier this year, she tapped into her earlier research interests.

It was during her final-year project at Trinity, working in the lab of one of Ireland’s leading geneticists, Prof Aoife McLysaght, that Fashina’s interest in bioinformatics was piqued.

Bioinformatics involves the use of software to understand and interrogate large, complex biological datasets. And Fashina’s three-minute presentation on how drug and enzyme databases can be used to determine accurate medication doses for patients scored her third place in the recent FameLab Ireland final.

She said, “I wanted to show that it is possible to use genetic information to solve a healthcare problem.”

‘Now that we have done more genetic studies in humans, we can see how small changes to a person’s genes can affect the way they break down pain medicine’

What inspired you to become a researcher?

I would say that it was more of a series of realisations than a spark. In secondary school, I enjoyed a broad range of subjects, but my favourites were maths, agricultural science, economics and biology. Since most of these were STEM leaning, it was expected that I would study medicine in university (in the typical Nigerian way). That was not what I wanted.

I became very invested in crime shows during this period, and read Forensic Science by Andrew and Julie Jackson. The use of genetic information to solve crime really captured my interest, and when I joined the human genetics programme in Trinity, the researchers in the department really opened my eyes to how many health problems we could address by understanding how genes work.

What made you want to compete in FameLab?

I noticed that when talking to my family and non-scientist friends, there were some ideas that I had encountered several times, but they had not come across at all. These would have been important updates in the scientific community, especially about the genetic basis of some common diseases. So I thought it would be interesting to try to talk about genes in a simple way and to hopefully give people some context when they are reading or hearing about new discoveries.

To prepare, I noted three points I wanted to make, and recorded myself talking around them. From those, I wrote multiple drafts of my talk. I had two friends and my mum (a non-scientist) listen to the talk and give feedback. Two of my lab-mates, Conor Duffy and Remsha Afzal, are FameLab alumni, so their support and that of my PhD supervisors and our lab members helped my journey too.

I’ve loved learning from the other participants, especially the way they tell their stories so differently but also effectively. I also appreciated getting feedback from the judges in the regional heats, especially Phil Smyth, and guidance from Jonathan McCrea and Malcolm Love in the masterclass.

How would you summarise your FameLab presentation?

Clinicians and scientists have known for a while now that drugs are not one-size-fits-all. On the other hand, many patients experience severe pain, so they need adequate pain relief.

Now that we have done more genetic studies in humans, we can see how small changes to a person’s genes can affect the way they break down pain medicine. That means that we can use those genetic changes to predict which dose or painkillers will work for them best.

What is the biggest challenge you’ve encountered in science communication?

Explaining concepts without introducing too many technical terms is challenging, so I try to use analogies and to focus on one concept at a time.

What common misconceptions about science would you like to correct?

I think the process of updating ideas that the public has already accepted might be a bit tricky. I would like for people to think of scientific facts as being the consensus as we understand so far, pending better, well-replicated evidence. So that means that when we have better evidence, the story could change, but it does not mean that the first story was wrong. It’s just more like a building block.

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