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In this Q/A session with Fergus Chan, founder and CEO of portfolio company Epinomics, Fergus shares his perspectives and outlook on the future of frontier healthcare and precision medicine. Epinomics is decoding the programming of our genome to drive personalized medicine and impact drug development and clinical applications.

What does the future of medicine look like to you?

For me, it's accessible medicine.

How do you see the industry as being ripe for disruption today?

I think there's two sides. One side is the accessibility to much more data types like genomic data. And the other side is better computational capabilities. And I think both of these combined have the capability to accelerate the research and make diagnostics more personalized. 

What does personalized, or precision medicine mean to you? 

I think the underlying mission of personalized medicine is giving the right medical care to the right patient. For me, I think that's also an additional layer which is giving the medical care that's catered to the patient and also for his or her current health base. 

What are the biggest hurdles to precision medicine becoming mainstream?

I think one side is the data collection from the past. A lot of the current personalized medicine efforts in the industry are indirect or lack historical health records. It’s hard to target a person in general instead of his or her current health base — for example, in genomic data that is sort of targeting a person in general. 

I think the other hurdle is adoption. For data adoption, we need to have an equation of growth. It has to be more cost effective and be in alignment with healthcare insurance.

Why should people be excited about new tools or platforms for the drug developments or treatments?

I think the current process of drug development displays a very long cycle to bring a drug to market. It usually takes billions of dollars per drug. I think the new type of data that is available now enables new markets and a new understanding. 

On the other hand, the better computational capabilities can shorten the traditional drug development cycle and also reduce the cost that is associated with the drug development process. And I think the main benefit is for the patients since the drug will launch to market faster. And also diagnostics I think will also be more personalized. 

What is the current issue in the industry that you are helping to address and how are you doing so?

We are solving a problem in 3 main areas. 

On one side is the data side. As I mentioned, genetic data are stagnant — once you're born, it won’t change. But then we’re looking at the genomic data. So on this side, it is dynamic. We have the proprietary technology to generate this epigenetic data that is much more sensitive and has a faster turnover time. It produces much richer data than the current gold standard.

The second side is the data analytics. Through working in collaboration with biopharma companies and other institutes, we have a rich data pool that we use to train our data analytics platform to produce new data. 

The third part is creation. We work together with our partners in immunotherapy to create new methods for production. For all these three areas, we hope to push this personalized medicine to realization faster.