Sean Parker knows a thing or two about sharing.
In 1999, he cofounded Napster, the notorious file-sharing service that became a household name as people discovered online music piracy for the first time. Parker later became the first president of Facebook, where, of course, more than a billion people regularly share links, media and status updates.
But sharing means something different to the 36-year-old entrepreneur now. He's focused on creating a new way for the top minds fighting cancer to coordinate their efforts.
Parker is funding rigorous research into immunotherapy, a type of treatment that helps the body's own immune system combat cancer. An alternative to radiation or chemo, immunotherapy has been shown to be effective on certain cancers, though criticisms have surfaced over the treatment's cost and potential side effects.
Parker's new initiative also provides a set of tools to researchers at several different centers to create a standardized exchange of information. In short, the new initiative is about creating data and distributing it, something Parker has always been interested in.
Of course, he's changed quite a bit from his Napster days.
"There are a lot of people like myself who are frustrated that we've been working in consumer Internet for a long time, and that's of dubious long-term significance to humanity," Parker told The Huffington Post in an interview last week. "Maybe it makes sense to focus on things that are a bit more meaningful rather than doing the easy commercial stuff around building products for teenagers."
On May 2, Parker was awarded the 2016 Pontifical Key Philanthropy Award at the Vatican for his contributions to immunotherapy research. HuffPost followed up with Parker to hear more about his latest initiative.
First, congratulations on the award. Would you ever have expected anything like this?
I don't think you necessarily expect or even go into any of these things imagining that anyone's going to recognize them -- certainly not the Vatican. It's really incredible.
It's more important from the perspective of the scientists, researchers and the field of immunotherapy than it is for me personally. The field of immunotherapy has the potential to cure patients. Not even the potential -- it's already providing curative treatments for patients who otherwise weren't treatable.
Anything that can be done to draw attention to the power of this specific approach is incredibly helpful to the field. And it's also helpful for patients who need to be able to make an informed choice. They need to be able to go out there and say they want to receive immunotherapy.
Why did you become particularly interested in life sciences?
There's a general movement amongst Silicon Valley entrepreneurs toward life sciences. There's this feeling that computations and big data are finally at a point where they're converging with the work that's being done in the life sciences, and that convergence moment is driving a lot of enthusiasm.
One would expect to have a lot of interest in life science. We're presumably biological beings. And we're all faced with our own mortality. This is something that we should be really interested in.
I think people coming from a computer science or an engineering background in Silicon Valley may not feel that their skills were applicable to the field a decade ago, and maybe that applicability is more obvious now. There are a lot of people like myself who are frustrated that we've been working in consumer Internet for a long time, and that's of dubious long-term significance to humanity. Maybe it makes sense to focus on things that are a bit more meaningful rather than doing the easy commercial stuff around building products for teenagers.
So much of your mission seems to be based on connecting scientists and fostering collaboration. It strikes me as a little ironic in this era of 24/7 connectivity and communication.
I think it's widely understood within the life sciences world that collaboration doesn't take place between centers, or even between researchers as efficiently as it should. One piece of that is data sharing. It's not as straightforward as getting scientists to talk to one another. They do that. They go to conferences, they email one another, they ask questions.
But the problems definitely run deeper than just getting them communicating. If you take the problem of data sharing for instance, data sharing -- in order to share data, you need to first be, let's say, measuring the same thing. You need to be recording the same data in multiple clinical trials, in multiple sites, across patients. You need to have standardized ways of measuring the data.
We're all faced with our own mortality. This is something that we should be really interested in." Sean Parker
If you're not measuring the same thing, then it's not really shareable. That gets at systemic problems with the life sciences world that an outsider coming from another field would initially have a hard time understanding, as I certainly did when I started working in this industry. It's only logical that researchers working on the same problem across many different institutions would be collaborating and taking part in data sharing. In theory that should take place. But practically it's difficult.
When you have a consortium, you make sure people have the same tools available, so they can measure the same things. One of the advantages of having a consortium is that you have six centers effectively operating under the same framework. You're able to collect samples in the same way, then you're able to interrogate those samples or conduct tests at different points of time.
There's been so much talk about how machine learning and artificial intelligence could improve health care. IBM Watson, for example, says it can connect doctors to research like never before and help develop innovative treatments. What do you think of that model?
It's hard to speak specifically to IBM Watson because it feels a little bit like a marketing effort. In life sciences, we're dealing with systems that ultimately, if we had a perfect description of them that was perfectly defined or characterized, then we could build a system and we could do more of the research in a computer system. But we're not at that point yet. There are so many holes in the model of what's in the immune system. It's just incredible how little is known about things where it seems like we already know so much.
I think that's everyone's dream -- if we had systems that were really good models of what's happening in the human body in all of its complexity. It would be a heck of a lot easier to interrogate those systems than to do this work in humans. Talk to biologists, they'll tell you we're a lot farther away from that than the computer scientists will tell you.
I think personally I was shocked by the complexity or the number of variables that are currently unknown, or interactions that are unknown. It's always a tradeoff between doing the research that you know will be valuable in humans immediately versus doing the long-term discovery efforts that will speed up the process of learning in the future.
This interview has been edited and condensed for clarity.