Enkelejda Miho on Curiosity, Courage, and Advancing Research With Adisciplinary Approaches

October 27, 2023

By Allison Proffitt

October 27, 2023 | In the latest episode of The Chain podcast, host Ben Hackel, Professor of Chemical Engineering & Materials Science at the University of Minnesota, speaks with Enkelejda “Ledi” Miho, Professor of Digital Life Sciences at the University of Applied Sciences Northwestern Switzerland FHNW, about the data challenges currently facing drug discovery, and how an adisciplinary approach would allow a more fruitful flow of ideas between indication silos, fueling discovery.  

Opportunities in drug discovery are expanding at a “speed we haven't witnessed before,” Miho says, crediting technological advances including quantum computing, more shared public data than we’ve had before, a wealth of digital biomarker data, and AI tools with which to interrogate these new datasets. “We can do so much!” Miho says.  

That’s not to say that there are not challenges, of course. Drug discovery is no longer limited by infrastructure, Miho says. We have great technical capacity for data analytics, but a lack of data standards and quality control limits the analytics’ usefulness. “One of the biggest challenges is setting standards that are actually respected and that are across fields.”  

She also highlighted a pressing need for interoperation between datasets and research groups.   

“Everyone does their own silo research. So, I will do cardiovascular, you will do neurodiseases drug discovery, someone else will do drug discovery to immune diseases,” elaborates Miho. “This artificial fragmentation of the diseases [is] still hindering progress, and it's hindering also standardization because all of these different research fields will develop their own standards.”   

An Adisciplinary Approach  

As a solution, Miho urges adisciplinary thinking and education. She doesn’t just mean cross-disciplinary working groups and research hubs. “We need to move away from trying to put together things that already exist,” she says. “It just doesn't seem to work. We need to kind of get out of our comfort zone, starting from the researchers, from the innovators, from the ones that have the responsibility to innovate and not just to incrementally progress their own fields.” 

Miho designed a master’s degree in medical informatics course in Basel to lay an adisciplinary foundation. “Then we wouldn't need any more of the translational work that we still need between teams, between diseases, between molecular data, between any kind of fields,” she says. She knows the practical difficulty. “Of course it’s challenging, otherwise it would have been already done.” But she still sees a hopeful vision. “IT and life sciences, if they come together from early on in undergraduate or graduate studies, then it would make it almost intrinsic, the development of skills.”  

The key criteria, she says, are curiosity and motivation. 

Miho’s own career has had an adisciplinary course. Starting off as a chemist, she worked with small molecules, then antibodies, then digital therapeutics, sequencing technology, and AI. She’s worked in both academia and industry discovering anti-dengue antibodies and developing clinical decision support software for clinicians diagnosing autoimmune diseases.  

Along the way, Miho recounts many times when curiosity and a comfort with risk-taking compelled her forward. “I was actually—deep inside me— always thinking, ‘Oh my God, how is this going to work?... Does this even work?’” But the risks paid off.  

Now, she and her team are looking at how personalized immunity could be used for early, precision diagnostics. It’s another siloed problem that Miho believes will benefit from curiosity, courage, and an adisciplinary approach. “I have no idea how to do this with integrated intelligence,” she says. “I was just imagining and dreaming… When are we starting?”