Empowered Patient Podcast How Genetic Testing and Real-World Evidence Based Research Shed Light on Ultra-Rare Disease FOXG1 with Nasha Fitter FOXG1 Research Foundation
Nasha Fitter, CEO and Co-Founder of the FOXG1 Research Foundation and Vice President of Rare and Neurological Diseases at Ciitizen. Nasha describes the characteristics of the ultra-rare disease FOXG1 Syndrome and the global network that has been built to gain a better understanding of this neurological condition. Using real-world evidence, machine learning, AI, and genetic testing are showing a path to finding a treatment.
Nasha explains, "My daughter, Amara, was diagnosed at nine months of age. She began having hundreds of seizures a day. And at that point, there was information known about the FOXG1 gene. It's a really important gene for brain development. But no one was studying the syndrome and how to find a cure for it. So, I and a group of other parents, like-minded parents who, refused to just take that this is the way it is. Our children have this condition, and that's it. We started this foundation."
"We've done a lot of work to get the entire world of FOXG1 to work together and then to collect patient data. That leads to my work at Ciitizen, to have all patients join the same platform, take the same surveys, and collect electronic medical records. We extract their data, so it's all on one platform. Again, that helps us understand this disease, which is critical when the population's so small."
"In-person studies, which is how we collect data today, as the medical community, don't work for ultra-rare diseases. So we have to find other ways. And that's where real-world evidence comes in."
"We use machine learning and AI at Ciitizen when we're looking at something as simple as extracting data from an unstructured medical record. When you look in medical records, the part that's really interesting is the clinic notes, and they're totally unstructured. They're paragraphs. So can we start using machine learning and AI to go through these paragraphs and actually extract relevant data points to scale up our ability to understand how the disease has progressed?"
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