Latest / Get Real with Jim Weiss / Sean Khozin, MD: From Revolution to Precision Oncology

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Sean Khozin, MD: From Revolution to Precision Oncology · Get Real with Jim Weiss

0:00
Speaker 1: Sean was a great conversation. He is CEO of the
0:04
Speaker 1: CEO Roundtable.
0:05
Speaker 2: On King King Sorry, yeah.
0:07
Speaker 1: And I'm curious, Jim, you obviously knew him already. You
0:12
Speaker 1: talked about music and healing and what else did you
0:14
Speaker 1: feel was like a strong takeaway from that episode or
0:17
Speaker 1: did you learn about him or about his work that
0:19
Speaker 1: was important for you.
0:20
Speaker 2: Well, I think one of the stories that we tell
0:23
Speaker 2: a lot is how and we had it with Ilias Orhoney,
0:27
Speaker 2: And I think it's a common thing more and more
0:30
Speaker 2: as people are coming from other countries and coming into
0:34
Speaker 2: this world, the US world, you know, and making their way,
0:39
Speaker 2: and I think, you know, those immigrant stories are fun
0:41
Speaker 2: to listen to and when people are so productive. I
0:46
Speaker 2: also thought it's interesting, you know, he's worked mostly in
0:50
Speaker 2: the public sector, not the private sector, so he's trying
0:52
Speaker 2: to leverage the private sector to help what is a
0:57
Speaker 2: public sector problem, right, it's an epidemic and cancer and diagnosis.
1:02
Speaker 2: So I think, you know, a lot of what he's
1:06
Speaker 2: focused on is around data and using data to help,
1:11
Speaker 2: you know, detect earlier, identify new targets. So I think
1:16
Speaker 2: CEO around tables on target Yeah, you know, I think
1:20
Speaker 2: he's an interesting guy to do it, given his regulatory background,
1:26
Speaker 2: and that I think, you know, was for me a
1:29
Speaker 2: bit of the revelation because he kind of knows he's
1:32
Speaker 2: kind of thinking with the end in mind. Yeah, which
1:34
Speaker 2: I don't think we always get with people who run
1:37
Speaker 2: those sorts of organizations because as you know, that's mostly
1:41
Speaker 2: heads of development oncology development at companies that make up
1:46
Speaker 2: the vast majority. So I think he's galvanizing a lot
1:50
Speaker 2: of people. There was a ton of energy at the
1:52
Speaker 2: meeting I was at, so but you know, it's always
1:56
Speaker 2: fun to hear about how they are personally. He makes music,
2:00
Speaker 2: he does, Yeah, so obviously I'm interested in music as
2:05
Speaker 2: you know, a healer. But I also think that is
2:10
Speaker 2: there something about people who are musically inclined that helps
2:15
Speaker 2: you know herd the cats that he needs to heard?
2:18
Speaker 2: Is that is there something about that some is a
2:21
Speaker 2: composition or something you asked you to.
2:24
Speaker 1: Do that You asked him about how being a scientist
2:28
Speaker 1: relates to being a musician and if there are a
2:29
Speaker 1: lot of like synergy there, and he said yes.
2:32
Speaker 2: I think it's very common. Yeah, very common. Now you
2:37
Speaker 2: hear about a lot, but you know, like jam Allison
2:39
Speaker 2: has a band all all kinds of guys have been
2:42
Speaker 2: being able to get Jim on the podcast, Actually, do you.
2:45
Speaker 1: Think we're using the data to create better systems and
2:50
Speaker 1: better equipment and like to harness AI more efficiently or
2:53
Speaker 1: do you think there's like a white space there?
2:56
Speaker 2: Well, I think the data is what informs the ability
3:01
Speaker 2: for AI to function. Right. If without good data, right,
3:04
Speaker 2: they're not going to be able to Uh, those technologies
3:07
Speaker 2: aren't going to be able to give you much and too.
3:10
Speaker 1: Right, data is the key.
3:13
Speaker 2: Data is the key. Query is the key, and the
3:15
Speaker 2: asking is key. But data is the under that's going
3:18
Speaker 2: to be you know that, and I guess you know
3:21
Speaker 2: storage and things like that. Those are the keys to
3:24
Speaker 2: the future. But yeah, we need to accelerate all these
3:29
Speaker 2: technologies so we can actually really finally get to both
3:33
Speaker 2: prevention and curing. Essureach, I always like to start with
3:40
Speaker 2: origin stories. And what a story you have. You escape
3:44
Speaker 2: the Iranian Revolution for the US so so topical. Right now,
3:48
Speaker 2: walk me through moments, right, your teachers, your mentors, programs
3:51
Speaker 2: and jobs that change your trajectory. How the hell did
3:54
Speaker 2: you end up here? And then we'll talk a little
3:57
Speaker 2: bit about the current sitch y. Well, it's to be here, Jim.
4:01
Speaker 3: You're right, I escaped Iran after the revolution. You know,
4:05
Speaker 3: a lot of us, anyone who could leave, left the country.
4:08
Speaker 3: So I left as a young boy after revolution. And frankly, Jim,
4:12
Speaker 3: what I remember most vividly is not necessarily the chaos,
4:16
Speaker 3: although there was plenty of it. What I remember is
4:19
Speaker 3: the silence afterwards, disorientation of arriving somewhere entirely new and
4:25
Speaker 3: realizing that everything that you understood about life and the
4:28
Speaker 3: world around you no longer applies. Iran was a very
4:33
Speaker 3: liberal society, and to someone my age, it looks like
4:38
Speaker 3: almost overnight everything changed. So we had to change the
4:41
Speaker 3: way we spoke, what we wore, what we listened to.
4:44
Speaker 3: So every aspect of life became micromanaged. So it was
4:49
Speaker 3: certainly a very informative experience and it really shaped everything
4:54
Speaker 3: that I do in a way even to this day.
4:56
Speaker 3: And you're right, it has become a topical theme again. Fortunately.
5:00
Speaker 2: Well, what are your thoughts about the current situation.
5:03
Speaker 3: This is one of the most complex situations. The Iranian
5:08
Speaker 3: people are quite different than the Runyan region. Yeah, and
5:12
Speaker 3: it's quite strange because we've had dictatorships throughout history. The
5:16
Speaker 3: gap between the people that we want and the regime
5:19
Speaker 3: is so wide that I can't compare it to any
5:23
Speaker 3: other country, either today or throughout history. It's quite an anomaly.
5:29
Speaker 3: So every three four years since the so called revolution,
5:33
Speaker 3: after the Islamists took hold of various factions of society
5:38
Speaker 3: and government, which took them a few years where revolution
5:42
Speaker 3: was technically in nineteen seventy nine, but it wasn't just
5:44
Speaker 3: the Islamists. There were socialists and communists, there were, in
5:47
Speaker 3: fact the architects of the revolution.
5:49
Speaker 2: They all were.
5:50
Speaker 3: Executed following the takeover of the Islamist and since then,
5:54
Speaker 3: every three to four years there are mass upraisings that
5:57
Speaker 3: sometimes we hear about that are met with thematic and
6:01
Speaker 3: very violent repression and crackdowns. Most recently, more than forty
6:07
Speaker 3: thousand on armed civilians protesting against the regime just a
6:11
Speaker 3: couple months ago were killed by the IERGC.
6:14
Speaker 2: Did you say forty thousand or four thousand?
6:17
Speaker 3: Forty thousand, So it's there are different numbers coming out
6:20
Speaker 3: anywhere from ten thousand to more than forty thousand. The
6:23
Speaker 3: regime itself puts a number around about ten thousand and
6:26
Speaker 3: this happened in about three days, so a massacre of
6:30
Speaker 3: historic proportions. The real numbers are around forty according to
6:33
Speaker 3: human rights groups. So it tells you that the people
6:36
Speaker 3: have been on this path to liberate and essentially reclaim
6:40
Speaker 3: their land for a number of years, and right now
6:43
Speaker 3: what they've been lacking is an architecture and an even
6:47
Speaker 3: playing field. They're unarmed, and the IERGC, which is actually
6:51
Speaker 3: the perimilitary group that is only tasked not with the
6:55
Speaker 3: territorial integrity of you want, but their only task is
6:59
Speaker 3: to protect the from enemies within and abroad. That's exactly
7:04
Speaker 3: what they say. So they have declared in the sense
7:06
Speaker 3: that people that Iran as their enemy, and right now
7:10
Speaker 3: they could class half full side of the equation is
7:13
Speaker 3: that hopefully, with the pressure exerted on the regime by
7:18
Speaker 3: the US and Israel, that people will hopefully have a
7:21
Speaker 3: chance to reclaim their land. And again, this is a
7:25
Speaker 3: fight that they've been on for a number of years,
7:27
Speaker 3: and this may be the architecture of freedom that they've
7:30
Speaker 3: been longing for such a long time.
7:32
Speaker 2: Well, it's cool to hear that perspective. I don't think
7:35
Speaker 2: we get that a lot. Do you have family there
7:37
Speaker 2: that's impacted.
7:38
Speaker 3: Still my immediate family, we're all in the US. I
7:42
Speaker 3: have a lot of relatives in the US, but I
7:44
Speaker 3: do have cousins and uncles in Iran. It's very hard
7:47
Speaker 3: to get in touch with them. What the regime does
7:49
Speaker 3: usually during uprisings they completely shut down the Internet, all
7:54
Speaker 3: forms of communication, and they've continued to do so even
7:58
Speaker 3: now that they're under attack, which makes it very hard
8:00
Speaker 3: actually for average Iranians to communicate with each other as
8:04
Speaker 3: the strikes happen, to find safety and to check on
8:08
Speaker 3: their loved ones. So there's a complete blackout and it's
8:12
Speaker 3: very hard to get in touch with folks. But thanks
8:14
Speaker 3: to Elon Musk, actually they've been managed to send some
8:18
Speaker 3: star Link satellites.
8:20
Speaker 2: To the area. Yeah, he's done that in a few situations.
8:23
Speaker 3: Yeah, it's remarkable. So the only form of communication is
8:26
Speaker 3: that not everyone has access to it. In fact, very
8:28
Speaker 3: few people do. And one of the reasons that it's
8:31
Speaker 3: very hard to understand what's going on is that unless
8:33
Speaker 3: you know someone Iran that you manage to get in
8:35
Speaker 3: touch with, you only get a regime propaganda and unfortunately,
8:41
Speaker 3: what we hear and what we're seeing Western media for
8:44
Speaker 3: the most part is the regime's propaganda. But I've managed
8:48
Speaker 3: to We've managed to talk to family members. Okay, you know,
8:51
Speaker 3: they have a strange sense of hope and anxiety. It's
8:55
Speaker 3: quite a complicated situation.
8:57
Speaker 2: Tell us about you get here, you settle, and where
9:01
Speaker 2: did your family settle? And then tell us your trajectory,
9:05
Speaker 2: how did you overcome that, How was it to adapt
9:08
Speaker 2: here and how did you adapt and how did you
9:12
Speaker 2: get to where you.
9:12
Speaker 3: Got Sure, Well, you know, it's for lack of a
9:16
Speaker 3: better phrase, and I don't want to sound cliche, but
9:19
Speaker 3: it is a cliche immigrant story that the United States
9:22
Speaker 3: is the beacon of freedom. Still to this day, it's
9:25
Speaker 3: the greatest country in the world and the best really
9:29
Speaker 3: experiment in the history of human civilization. So that was
9:34
Speaker 3: really what kept us going in Iran that one day
9:38
Speaker 3: will manage to come to the United States. It was
9:40
Speaker 3: really help me, my family and you know a lot
9:44
Speaker 3: of my peers to remain hopeful and to weather the storm.
9:49
Speaker 3: We ended up coming to the US. I came here
9:52
Speaker 3: in nineteen ninety when I was in high school, and
9:56
Speaker 3: it was basically a path of trying to figure out
10:01
Speaker 3: how to fit in, trying to explain Iran to my
10:06
Speaker 3: friends and my peers in school, because again, everything that
10:09
Speaker 3: we hear from Iran is quite distorted since it's so
10:12
Speaker 3: hard to get information, and the view that most people
10:16
Speaker 3: have is the view that's been projected by the regime,
10:19
Speaker 3: not the actual fabric of who Iranians are. But for
10:23
Speaker 3: the most part, I think I found a welcoming country,
10:27
Speaker 3: and my family certainly felt a lot safer in the
10:31
Speaker 3: US than in the Iran. What does your dad do
10:35
Speaker 3: inm What did they not do? You know, odd jobs
10:38
Speaker 3: and anything that they could get their hands on.
10:41
Speaker 2: And they were.
10:42
Speaker 3: So motivated to make things work in the US that
10:48
Speaker 3: they were willing to do whatever it takes, and so
10:51
Speaker 3: they had a tremendous amount of energy and enthusiasm. And
10:56
Speaker 3: you know, eventually my dad was able to work with
11:00
Speaker 3: some colleagues who start a business that more or less
11:03
Speaker 3: did well well enough. But I myself had to work
11:08
Speaker 3: to put myself through college. The good thing was that
11:10
Speaker 3: I knew a little bit of music, so I taught music,
11:14
Speaker 3: but I also did other things, waited on tables and
11:16
Speaker 3: so forth. What's interesting is that reflecting back on those days,
11:20
Speaker 3: we were so excited and happy to finally have freedom.
11:24
Speaker 3: And that sense of freedom was so strong that whether
11:29
Speaker 3: I was teaching music or waiting on tables, it didn't matter.
11:33
Speaker 3: We were all filled with optimism and a sense of
11:37
Speaker 3: hope that kept us going. So it was really the
11:40
Speaker 3: fuel that propelled all of us. And you know other
11:43
Speaker 3: immigrants that we from Iran that we engaged with, and
11:47
Speaker 3: probably one of the reasons that the Iranian population in
11:51
Speaker 3: the US and Europe tend to be quite successful because
11:55
Speaker 3: they saw the worst of the worst and it just
11:58
Speaker 3: changed your perspective on hardship.
12:01
Speaker 2: Yeah, I think that integrant experience is really important and
12:04
Speaker 2: very common to many of the people I've had on
12:07
Speaker 2: the podcast, like Elias Dahoney and others that came and
12:12
Speaker 2: felt fortunate, pulled themselves up by the bootstraps and followed
12:16
Speaker 2: the American dream as it were. Where'd you go to school?
12:19
Speaker 3: So I went to the University of Maryland. When we
12:22
Speaker 3: came to the US, we settled in the DC area, Yeah,
12:25
Speaker 3: because we had some family in DC. And so for
12:28
Speaker 3: undergrad I went to Maryland for met school, also the
12:31
Speaker 3: University of Maryland.
12:33
Speaker 2: Nice And then how'd you end up in government? What
12:36
Speaker 2: was the road there? Did you treat patients first and
12:39
Speaker 2: then end up at NCI because I want to ask
12:41
Speaker 2: about that.
12:43
Speaker 3: Sure also with NCI because that was my first step
12:46
Speaker 3: into the federal government. My time at the NCAI, actually,
12:49
Speaker 3: Jim came in two chapters. The first one was when
12:53
Speaker 3: I was a preductor fellow, so this was after college,
12:56
Speaker 3: before med school. I applied. He had a competitive portrom
13:00
Speaker 3: at the time where it would give you a grant
13:03
Speaker 3: to spend a year or two at the NIH to
13:06
Speaker 3: do research. So I was lucky enough to get a
13:09
Speaker 3: grant from the NCI, and I ended up working as
13:13
Speaker 3: a productor of fellow in the late Anita Roberts lab.
13:17
Speaker 3: This is the lab that discovered TGF beta, which is
13:20
Speaker 3: one of the most fascinating molecules in cancer biology because
13:23
Speaker 3: it plays a dual role. It can suppress tumors in
13:27
Speaker 3: some contexts and it also promotes tumors in other situations.
13:31
Speaker 3: Anita at the time was the most cited female scientist
13:35
Speaker 3: in the world, so it was a very intellectually fulfilling environment,
13:41
Speaker 3: and she was eventually unfortunately diagnosed with gastric cancer herself,
13:45
Speaker 3: the very disease that she had spent her career studying
13:48
Speaker 3: basically and for those interests that she actually wrote about
13:52
Speaker 3: her experience to start the blog. If you google her name,
13:55
Speaker 3: you can read her experience until her eventual death in
13:59
Speaker 3: two thousand and six. So working with her taught me
14:02
Speaker 3: very early about the urgency of the war and about
14:05
Speaker 3: cancer and its impact not just in the lab, but
14:08
Speaker 3: also on actual humans and the human side of things.
14:12
Speaker 3: And I essentially realized that cancer is always personal. It
14:15
Speaker 3: started my cancer journey as a scientist again at the NCI,
14:20
Speaker 3: but Anita's death was really highlight that personal dimension. So
14:25
Speaker 3: the second chapter was clinical. I came back to NCI
14:30
Speaker 3: for fellowship training in thorastic oncology and then rare tumors,
14:35
Speaker 3: and then stayed on as a clinical investigator the NCI.
14:38
Speaker 3: The clinical center is very unique. It's part of the
14:41
Speaker 3: NIH's intramural program. So basically there's a building on the
14:45
Speaker 3: nahe campus, Building ten, and all they do is clinical trials.
14:50
Speaker 3: It's fascinating because you can be quite bold and experimental.
14:56
Speaker 3: There are no insurance companies, no third party players, government
15:00
Speaker 3: can't regulate itself, so we have a lot of freedom
15:02
Speaker 3: to operate and to really exercise your imagination and push
15:06
Speaker 3: the boundaries. So that culture really allowed me to stretch
15:12
Speaker 3: my imagination. And so that was my second chapter at NCI,
15:17
Speaker 3: and then later I went to the FDA. That's a
15:19
Speaker 3: little more complicated story, but happy to get into it.
15:22
Speaker 2: Oh, we'll get into that. Look at NCI, the things
15:25
Speaker 2: you did in clinical trials, molecular profiling, biomarker development, thoracic
15:31
Speaker 2: malignancies like you mentioned, and lung cancer. What did you
15:35
Speaker 2: learn and accomplish from that work that we actually see
15:39
Speaker 2: in the world today. Are you surprised at what's emerged
15:42
Speaker 2: and hasn't and it's still taking too long to get there?
15:45
Speaker 2: Like you said, there was an urgency.
15:47
Speaker 3: Yeah, that's a great question. The experience was again quite informative.
15:51
Speaker 3: It really gave me a visceral understanding of how slowly
15:56
Speaker 3: actually evidence moves from the bench to the bedside. The
16:00
Speaker 3: very unique soundbox you could actually take things from the
16:03
Speaker 3: bench to bet sides, all on the same campus more
16:06
Speaker 3: or less. So I essentially build a conviction that the
16:10
Speaker 3: tools that we need to accelerate bench to bedside drug
16:13
Speaker 3: discovery and development or within reach, but we need a
16:17
Speaker 3: new architecture, we need new rails that the system as
16:21
Speaker 3: a whole is not designed to accommodate the type of
16:23
Speaker 3: precision that we're able to advance today. And you know,
16:27
Speaker 3: I was caring for patients that had really no options.
16:30
Speaker 3: We didn't do any standard of care at the NCAI
16:33
Speaker 3: for the most part, so almost everyone was on a protocol.
16:36
Speaker 3: And again, these are patients who are quite refractory and
16:40
Speaker 3: didn't have any other options, So you really gain a
16:43
Speaker 3: visceral understanding of the urgency of the situation. But also
16:48
Speaker 3: another thing that I learned was that sandbox in this
16:51
Speaker 3: case being the clinical center that's conducive to experimentation. Bold
16:54
Speaker 3: experimentation can be quite efficient and effective. You know, we
16:58
Speaker 3: handled a large number of studies with a very small staff.
17:02
Speaker 3: Why because it was very ironically not much bureaucracy and
17:06
Speaker 3: was essentially how I thought medicine was like before going
17:09
Speaker 3: to med school. When people do residency and then clinical practice,
17:13
Speaker 3: they are faced with the realities of practicing medicine, which
17:16
Speaker 3: is very different than what do you think practicing medicine
17:18
Speaker 3: would be like. But the NCI the intramural branch, was
17:21
Speaker 3: quite pure that you just have to focus on your patient.
17:23
Speaker 2: There were no.
17:24
Speaker 3: Electronic health records except for a health record that was
17:28
Speaker 3: actually designed for clinicians by clinicians, because again we didn't
17:32
Speaker 3: do any billing, so there was no billing jargon, and
17:35
Speaker 3: everything that we did was focused on advancing the needs
17:38
Speaker 3: of the patient was very efficient, and it really taught
17:41
Speaker 3: me that so much of what we do outside of
17:44
Speaker 3: in the real world, let's say, at the point of
17:46
Speaker 3: routine care, is really just checking boxes that don't advance
17:51
Speaker 3: the interests of the patient. And that NCI experiment really
17:55
Speaker 3: showed me what practicing real medicine can be like. It
17:59
Speaker 3: can be very efficient, can spend a lot of time
18:01
Speaker 3: with patients, and technology that's designed around the needs of
18:05
Speaker 3: the patient and the clinician can actually be quite good.
18:09
Speaker 3: And you know, I had a chance to also exercise
18:11
Speaker 3: my quantitative urges, if you will. And since the clinical
18:14
Speaker 3: center is all about experimentation, as an example, we had
18:18
Speaker 3: a number of protocols where we start to incorporate AI
18:21
Speaker 3: into our workflows and research specifically. For example, we had
18:26
Speaker 3: a study that be offered as a substudy to every
18:29
Speaker 3: patient enrolled at the clinical center where we use facial
18:33
Speaker 3: recognition to estimate the patient's level of pain and mood.
18:37
Speaker 3: And this was before llms. But even with image recognition
18:41
Speaker 3: modality and technologies back then, we were able to beat
18:45
Speaker 3: the eleven point pain scale, you know, the happy face
18:47
Speaker 3: sat face, which is so subjective that for those that
18:51
Speaker 3: have been to the er, they ask you to point
18:53
Speaker 3: toward towards where your level of pain is. But we
18:56
Speaker 3: were able to beat that and quantify it, and so
18:59
Speaker 3: that was encouraging and I got to satisfy my quantitative
19:04
Speaker 3: versions if you will.
19:05
Speaker 2: There. I mean, how is AI changing molecular profiling or
19:09
Speaker 2: even the business of doing it. It sounds like half
19:13
Speaker 2: the battle was just not having to do all the
19:15
Speaker 2: administ trivia, which was a big complaint of everyone you
19:19
Speaker 2: ever talked to who's a doctor. Yeah.
19:21
Speaker 3: Absolutely, I think if we play our cards right, we
19:24
Speaker 3: can automate the mechanics of the enterprise. And here specifically,
19:28
Speaker 3: I'm talking about care delivery versus uncle research, because physicians
19:32
Speaker 3: taking care of patients today are spending the majority of
19:35
Speaker 3: their time checking boxes and documentation versus sitting face to
19:39
Speaker 3: face with a patient. The patient's complaining that the doctor
19:43
Speaker 3: doesn't look at them anymore, you know, they're just looking
19:45
Speaker 3: at a computer monitor. They're in and out five minutes,
19:48
Speaker 3: ten minute visits. Well, the physician is for every ten
19:52
Speaker 3: minute that they spend with a patient, they're spending a
19:54
Speaker 3: half hour on documentation and really just again chicking boxes.
19:57
Speaker 3: So if we automate all of that, which is a
20:00
Speaker 3: low hanging fruit for AI, we can in fact we
20:03
Speaker 3: store the doctor patient therapeutic bond, which to this day,
20:07
Speaker 3: I think it's one of the most important determinants of
20:11
Speaker 3: health and well being. How much access do you have
20:13
Speaker 3: to a clinician that knows everything about you? So we
20:16
Speaker 3: can in a way go back to the future when
20:18
Speaker 3: physicians knew everything about you and your family and we
20:22
Speaker 3: let AI and automation take care of the rest. We're
20:25
Speaker 3: already seeing signs of that, for example ambient scribes that
20:28
Speaker 3: are recording the conversation between the doctor and the patient
20:31
Speaker 3: and then structuring that into the EHR so that the
20:34
Speaker 3: clinician can just focus on the patient. So that's care
20:39
Speaker 3: delivery by Kinco research obviously and something different and happy
20:44
Speaker 3: to delve into that too. When it comes to AI.
20:46
Speaker 2: In AI and incology, doctors are moving away from legacy
20:51
Speaker 2: measurement standards right to kind of more of a real
20:54
Speaker 2: world signal world. And I talk about the phrase prevent
20:58
Speaker 2: the event, How does that apply? How will you apply
21:01
Speaker 2: AI in that setting? Or we should? And how are
21:04
Speaker 2: things going to change treatment?
21:06
Speaker 3: I love that the way you describe that prevent the event.
21:09
Speaker 3: I'd like to put a pin in that and get
21:11
Speaker 3: back to that.
21:11
Speaker 2: We use the treating the disease, not really getting ahead
21:14
Speaker 2: of it.
21:14
Speaker 3: Right, Absolutely, yeah, and really going to an ance of
21:18
Speaker 3: prevention is worth more than a tound of cure. It's
21:21
Speaker 3: very true. Obviously cancer is quite complex. Yeah, but if
21:24
Speaker 3: you think about let's start with molecular profiling that you
21:28
Speaker 3: brought up. You know, when I was at the NCI,
21:30
Speaker 3: I remember we were doing that. Wasn't that long ago.
21:33
Speaker 3: By the way, we're doing feasibility studies to see even
21:36
Speaker 3: if it's possible to do molecular profiling on patients with
21:39
Speaker 3: thoracic malignancies. And things have come a long way, And
21:43
Speaker 3: even while I was at the NCI, we started to
21:46
Speaker 3: not only do molecul profiling but work on several target
21:49
Speaker 3: the therapies. But the shift that I find most exciting
21:53
Speaker 3: when it comes to molecular profiling in oncology is to
21:56
Speaker 3: essentially move from interrogation to characterization. Basically, historically molecular profiling
22:03
Speaker 3: has been panel based. You test for a specific what
22:06
Speaker 3: we call driver mutation egfr app, b raph and so on.
22:11
Speaker 3: That's enormously valuable, but it's also inherently reductive. You're basically
22:17
Speaker 3: asking the tumor a very narrow question and getting back
22:21
Speaker 3: a very narrow answer. It's not really telling you much
22:24
Speaker 3: about the underlying biology of the disease. That's why, unfortunately,
22:28
Speaker 3: even with our most potent targeted therapies, the disease always
22:32
Speaker 3: comes back, usually within a year or two. But AI
22:36
Speaker 3: is enabling something fundamentally different. For example, foundation models trained
22:41
Speaker 3: on multimodal data sets, if the data is available, different
22:45
Speaker 3: challenge can now essentially generate a comprehensive biological signature of
22:50
Speaker 3: a tumor. So instead of asking does this tumor have
22:54
Speaker 3: mutation X, you could be asking what is the biology
22:58
Speaker 3: of this tumor, which is a much more important question
23:01
Speaker 3: if we are serious about precision oncology and really preventing
23:06
Speaker 3: catastrophic events, whether it be tumor progression or metastasis. So
23:12
Speaker 3: the answer that you get back when you are asking
23:15
Speaker 3: those questions from these holistic if you will, and multimodal
23:20
Speaker 3: data sets are much more dynamic than getting a readout
23:25
Speaker 3: on a single driver mutation. So in a way, the
23:29
Speaker 3: way I think about it is that we're moving from
23:31
Speaker 3: testing to understanding, and that distinction, I believe is what
23:37
Speaker 3: is starting to fundamentally reshape how we do biomedical research
23:42
Speaker 3: and direct discovery and development.
23:44
Speaker 2: So you stated or use the phrase making biomedical data
23:48
Speaker 2: more liquid, What does that mean? Is that liquid biopsy
23:52
Speaker 2: or what are you referring to that? Right?
23:56
Speaker 3: That's more or less what I've been referring to data liquidity.
23:59
Speaker 3: If we think about what liquidity means in finance, it
24:02
Speaker 3: means essentially that an asset can move freely without friction.
24:08
Speaker 3: It can be exchanged, it can be valued, it can
24:10
Speaker 3: be deployed, so.
24:11
Speaker 2: We can sell our own data. I know guys working
24:14
Speaker 2: on that, right.
24:15
Speaker 3: That will be one dimension of it. But really liquidity
24:18
Speaker 3: is about data fluidity, having access to the data that
24:21
Speaker 3: you need to characterize the patients, individual intrinsicain extrinsic variables.
24:28
Speaker 3: Because again, it's more than just one mutation that's driving
24:31
Speaker 3: on progenesis. It's multifactorial. So without data of liquidity, no
24:35
Speaker 3: single organization or company is really able to holistically create
24:41
Speaker 3: essentially foundation models needed to understand human diseases at the
24:46
Speaker 3: individual level, i e. Precision medicine. So that's what I
24:50
Speaker 3: mean by data liquidity. We need really data to be
24:53
Speaker 3: a utility. And obviously there are challenges with that, fragmentation
24:59
Speaker 3: and so on. But if we are smart about this,
25:02
Speaker 3: especially now that we have an amazing engine that is AI,
25:09
Speaker 3: in order to maximize our use of these amazing tools,
25:14
Speaker 3: we need to liberate the data. I need to ensure
25:16
Speaker 3: that there's data liquidity, otherwise we won't be able to
25:20
Speaker 3: take maximum advantage of our tools, and to the extent
25:25
Speaker 3: that AI will determine winners and losers at the global level.
25:31
Speaker 3: On the global stage, really thinking about our data assets
25:35
Speaker 3: in healthcare, by medicine and defense are perhaps the most
25:40
Speaker 3: critical thing that we can do in the US, beyond
25:43
Speaker 3: even the doing the right thing, which is to improve
25:46
Speaker 3: health and well being for the population, but to really
25:49
Speaker 3: maintain our position globally as being in the front lines
25:54
Speaker 3: of innovation and.
25:56
Speaker 2: Right and I think there's been a lot of talk
25:57
Speaker 2: about recent moves that might put that jeopardy. Do you
26:00
Speaker 2: agree with that or do you think the disruption is
26:03
Speaker 2: good for us?
26:04
Speaker 3: You know, I think Wow, that's that's a very nuanced question, Jim,
26:07
Speaker 3: And in very broad strokes, I think destruction is needed,
26:12
Speaker 3: not but not disruption for distruction's sake.
26:15
Speaker 2: Right, Not destruction, maybe disruption, but.
26:19
Speaker 3: Not maybe creative destruction to paraphrase Eric Topol. But we
26:24
Speaker 3: know that the world that we envision when it comes
26:28
Speaker 3: to let's say, by medical research, councer research, and care delivery.
26:32
Speaker 3: The world that we're all imagining precision oncology, you know,
26:36
Speaker 3: treating the end of one, preventing catastrophic events before they occur.
26:40
Speaker 3: They're not compatible with our existing workflows. So there needs
26:44
Speaker 3: to be some creative destruction, but it needs to be
26:47
Speaker 3: done judiciously, and we have to be adaptable. What's encouraging today?
26:53
Speaker 3: You know, when I was in med school, I as
26:55
Speaker 3: an example, I realized very quickly I was in computer
26:58
Speaker 3: science before med school and always inclined towards data science
27:02
Speaker 3: and AI. But I realized very quickly that the fastest
27:05
Speaker 3: way for me to lose all my credibility was to
27:08
Speaker 3: mention imboart AI. Well, things have changed and we're actually
27:12
Speaker 3: talking about it. So the creative destruction that we're thinking
27:16
Speaker 3: about may not be as disruptive. Today as it may
27:20
Speaker 3: have been a few years ago.
27:22
Speaker 2: Oh, it's totally happening now right all over the place
27:26
Speaker 2: on the Internet, and search has changed just what people
27:29
Speaker 2: look for and how they find it, and it's all
27:32
Speaker 2: being searched on those engines. The Web is not over,
27:36
Speaker 2: but it's not SEO optimization. It's all changed. But I
27:41
Speaker 2: wanted to ask. You were a founding member of the
27:44
Speaker 2: FDA's Oncology Center of Excellent and built Inform, the agency's
27:49
Speaker 2: first data science incubator. How'd you convince the FDA to
27:53
Speaker 2: build informed.
27:54
Speaker 3: So inform information exchange on data transformation was an anomaly.
27:59
Speaker 3: It was fascinating journey for me. I learned a lot
28:02
Speaker 3: about a lot of things. So when I went to
28:07
Speaker 3: the FDA, I started to work with Rick on the
28:09
Speaker 3: Oncologist Center of Excellence. And we you know, I was
28:12
Speaker 3: an oncologist, so I worked very closely with him. I
28:16
Speaker 3: had accidentally in my career, started a couple of companies,
28:20
Speaker 3: so you know, more or less, his thinking was, Hey, Shaan,
28:24
Speaker 3: you're oncologist, you're an entrepreneur. I'm building on college Center
28:28
Speaker 3: of Excellence. Let's team up. It was fascinating experience because
28:33
Speaker 3: we were Jim quite disruptive within a very large and
28:37
Speaker 3: static bucratic agency. But that experience gave me a little
28:42
Speaker 3: bit of credibility. I believe we created a blueprint for
28:46
Speaker 3: the rest of perhaps the centers at the FDA. And
28:50
Speaker 3: because of that credibility, and because of what was happening
28:53
Speaker 3: at HHS and some of the special authorities at HHS
28:57
Speaker 3: was creating at the time, I made a pitch to
28:59
Speaker 3: HCHS that more or less. The pitch was, we're at
29:03
Speaker 3: an inflection point right now, just like how we were
29:06
Speaker 3: at an inflection point in the nineteen forties where the
29:09
Speaker 3: Department of Defense now the Department of War launched the
29:12
Speaker 3: National Labs as a public, private enterprise and entrepreneurial sandbox
29:17
Speaker 3: and obviously the results change the world. We're at that
29:19
Speaker 3: inflection point in biomedicine. Let me use some of these
29:22
Speaker 3: special authorities to launch an incubator for the FDA. Why
29:25
Speaker 3: the FDA, because it regulates a third of the US economy,
29:28
Speaker 3: so we could have the greatest impact. So that's how
29:30
Speaker 3: Inform was born. Just as an example of what it
29:33
Speaker 3: allowed me and us to do was one of for example,
29:36
Speaker 3: the major advantages was it gave me direct hiring authority,
29:40
Speaker 3: meaning I could direct anyone I wanted from anywhere I
29:44
Speaker 3: wanted without going through USA Jobs. The traditional way of
29:48
Speaker 3: bringing talent into the FDA and writing a position description,
29:52
Speaker 3: not just the FA Any government ation can take a
29:54
Speaker 3: year or two. Just writing a position description can take
29:57
Speaker 3: a year and a half.
29:58
Speaker 2: Yeah, a lot of people die in that time.
30:01
Speaker 3: Right for I or retired that it happens a lot.
30:03
Speaker 3: You're absolutely right. So this direct hiring authority allowed me
30:07
Speaker 3: to basically just have people apply and then pay them
30:11
Speaker 3: a competitive salary to compete with folks in the private sector.
30:15
Speaker 3: So BE brought in entrepreneurs and residents on temporary assignments
30:20
Speaker 3: and these were a lot of them were successful quants
30:23
Speaker 3: that were mission driven, which was exactly what we wanted.
30:27
Speaker 3: They gave back, they learned the inner mechanics of the FDA,
30:31
Speaker 3: the regultary machinery, and some of them are quite successful today.
30:35
Speaker 3: So it was a very interesting experience because it showed
30:40
Speaker 3: that being mission driven and entrepreneurial and quote unquote disruptive
30:45
Speaker 3: can go hand in hand even within a large and
30:49
Speaker 3: complex ecosystem such as the FDA.
30:53
Speaker 2: Yeah, all right, So that got us moving into what
30:57
Speaker 2: is a new era right, All the rest of it
31:00
Speaker 2: is accelerating drug development timelines. Are there any examples jumping
31:05
Speaker 2: out at you that you pioneered and inform that are
31:08
Speaker 2: nearer on the market today. Yeah.
31:10
Speaker 3: We published two reports in Nature Reviews Drug Discovery on
31:14
Speaker 3: inform that I encourage folks that are interested in the
31:17
Speaker 3: portfolio to check out. But some of the highlights, you know,
31:20
Speaker 3: we started a new mechanism of doing public private collaborations
31:24
Speaker 3: and doing research on the ground in the trenches with
31:28
Speaker 3: anyone who was at the leading edge, whether they were
31:30
Speaker 3: in academia or whether they were in a startup or
31:34
Speaker 3: a large company. So we had multiple projects with various startups.
31:38
Speaker 3: For example, a number of studies with flat Iron characterizing
31:42
Speaker 3: the use of real world evidence in a meaningful way.
31:45
Speaker 3: That was important. Not only we surfaced new scientific insights
31:49
Speaker 3: for the whole community to use, actually learned a lot
31:52
Speaker 3: about the use of real world evidence in greater decision making.
31:55
Speaker 3: So that actually was one of the founding pillars of
31:59
Speaker 3: ft IS Real World Evidence Framework that later on was
32:04
Speaker 3: mandated by Congress through the twenty first Century Cures Act.
32:07
Speaker 3: We work with IBM to develop a blockchain for exchange
32:10
Speaker 3: of genomic data, and we piloted it with genomic data
32:14
Speaker 3: sets such as an example. Obviously, this is not something
32:18
Speaker 3: that we implemented, but we were really trying to see
32:25
Speaker 3: where the leading edge of technology was and not only
32:28
Speaker 3: to use these technologies, but actually to help refine and
32:32
Speaker 3: define the appropriate use cases for these tools, in this case,
32:35
Speaker 3: for example, blockchain. Now what I know right now is
32:38
Speaker 3: that blockchain is probably not the most efficient mechanism of
32:42
Speaker 3: moving genomic data if we are interested in the traditional
32:46
Speaker 3: way of doing it. Now it decentralized ecosystem as a
32:49
Speaker 3: different story. If patients, for example, are selling their own data,
32:52
Speaker 3: that makes sense, but not to exchange data with the regulators.
32:55
Speaker 3: But those lessons learned were quite quite informative. We created
32:59
Speaker 3: a safety surveillance for the first time at d FDA,
33:02
Speaker 3: and this may in the pre market space. This may
33:04
Speaker 3: sound almost scandalous, but theft is only authority at the
33:08
Speaker 3: ID stage is safety and there are much many more
33:11
Speaker 3: I in ds than inndas or blas because of value
33:13
Speaker 3: of death. Most clinical trials don't, unfortunately go on to
33:17
Speaker 3: become pivotal studies. So how the DFDA exercise and met
33:23
Speaker 3: its only statutory authority at the ID stage, which is
33:26
Speaker 3: again a safety review, well on paper MPDF files, And
33:31
Speaker 3: how do you do signal detection with paper MPDF files? Well,
33:35
Speaker 3: you don't, and you can. So it was basically number
33:40
Speaker 3: of PDF files we call them MedWatch forms that were
33:43
Speaker 3: being either faxed or sent to the FDA via essentially
33:47
Speaker 3: an FTP mechanism that the FDA has a secured portal,
33:51
Speaker 3: and reviewers were reading them talking to their peers, Hey,
33:54
Speaker 3: did you see a fatal event with this drug in
33:58
Speaker 3: your application? I noticed that. Write it on the post
34:01
Speaker 3: it and keep it to the side to see if
34:03
Speaker 3: they need to take any action. Well, we create a
34:05
Speaker 3: complete digital infrastructure and for the first time enabled the
34:09
Speaker 3: FDA to do safety signal detection. Piloted with merc Genetech,
34:13
Speaker 3: so other companies published it in Nature Reviews Drug Discovery.
34:18
Speaker 3: A few years later, FDA issued the guidance and tells
34:21
Speaker 3: you you know, when we left, we were actually informed
34:24
Speaker 3: was working on the implementation of this. We realized that
34:28
Speaker 3: this would be very disruptive to FDA's workflows because again
34:33
Speaker 3: the majority of what the FDA reviewers spend their time
34:37
Speaker 3: is safety reviews. Seven day and fifteen day reports, not
34:40
Speaker 3: NBA's on BLAS, and we were envisioning that this would
34:44
Speaker 3: require complete reorganization. That implementation is not complete yet. It's
34:49
Speaker 3: just to just tell you that when the champions leave
34:52
Speaker 3: a place, when the bureaucracy is left to its own accord,
34:56
Speaker 3: things can fall through the cracks. Right we publish actually
34:59
Speaker 3: on the is that this would save the agency at
35:02
Speaker 3: least five hundred full time equivalent hours a month conservative estimates. Again,
35:09
Speaker 3: highly disruptive. I've been talking to my FEA colleagues now
35:12
Speaker 3: and you know they have issue to guidance document and
35:15
Speaker 3: they're moving actually finally to this digital framework. But those
35:20
Speaker 3: were sort of the types of things we were doing.
35:22
Speaker 3: And it was horizontal versus vertical, meaning harmonized and connected
35:27
Speaker 3: all of FDA's data assets. So we started to do
35:29
Speaker 3: a lot of studies and meta analyzes, gave the data,
35:32
Speaker 3: and we basically made the data liquid. That's one example
35:35
Speaker 3: of data liquidity, where the data was stuck in silos
35:40
Speaker 3: for one application. We essentially liberated data inside the FDA
35:44
Speaker 3: and provided that as a resource to the FDA reviewers
35:46
Speaker 3: and they did amazing studies and to this day they're
35:50
Speaker 3: doing amazing studies with the data, publishing in high impact journals,
35:54
Speaker 3: and just as a footnote, right before I left the FDA,
35:57
Speaker 3: I was working on providing access to the dat not
36:00
Speaker 3: just to the FDA reviewers but to the greater community.
36:04
Speaker 3: And we were doing a legal assessment of the feasibility
36:07
Speaker 3: of doing that, but we didn't stay there long enough
36:10
Speaker 3: to take it across the finish line.
36:12
Speaker 2: How do you prioritize all the things you do and
36:15
Speaker 2: you have a family. Let's not forget that I.
36:17
Speaker 3: Love what I do so much that it doesn't feel
36:19
Speaker 3: like work. It's really part of what I do. And
36:22
Speaker 3: I never actually understood the idea of a work life balance,
36:27
Speaker 3: because if that is top of mind for anyone, that
36:30
Speaker 3: means that there's something wrong.
36:32
Speaker 2: Yeah, I've always thought the same thing.
36:34
Speaker 3: So I've been fortunate. I've been lucky. I've managed to
36:37
Speaker 3: be in places professionally that are very close to what
36:41
Speaker 3: I would do otherwise if weren't getting paid.
36:46
Speaker 2: So you studied music theory alongside neurobiology. This is an
36:50
Speaker 2: area I care about a lot. I'm working with the
36:52
Speaker 2: FNIH on a program with Renee Fleming called Music in
36:56
Speaker 2: the Mind. That's her book. But we're big getting to
37:01
Speaker 2: embark on granting scientists who are working in the space,
37:05
Speaker 2: which of course it has. And you're a musician with
37:07
Speaker 2: a Spotify profile. How does music inform science and vice versa?
37:14
Speaker 2: You see them together a lot, you know, you see
37:16
Speaker 2: a lot of scientists who have bands or musicians. What's
37:19
Speaker 2: the connection? Is music a science? Yeah?
37:23
Speaker 3: I think so, you know, I think music is science,
37:26
Speaker 3: and science is harmony and music. This is, by the way,
37:29
Speaker 3: a question that I never get tired of answering, even
37:33
Speaker 3: though a lot of people don't ask these questions. But
37:35
Speaker 3: I think music and biologies share the same mathematical foundation.
37:39
Speaker 3: You know, Harmony is a set of frequency ratios. Rhythm
37:45
Speaker 3: is periodic resonance is essentially the tendency of a system
37:49
Speaker 3: to oscillate at greater amplitude at certain frequencies. You know,
37:54
Speaker 3: all of these translate into the human body when you
37:57
Speaker 3: essentially find the same principles everywhere. Healthy heart is rhythmic
38:02
Speaker 3: and maintains that periodic rhythm, which is rich with information.
38:09
Speaker 3: You know, a heart rhythm is just not the heart beating,
38:12
Speaker 3: it actually is communicating important information. So there are certainly parallels.
38:19
Speaker 3: And circadium biology, for example, is oscillation and cellular signaling
38:23
Speaker 3: is frequency modulation, and homeostasis is harmony, and when homeostasis
38:29
Speaker 3: is disrupted that's dissonance. So these can all be harmonically
38:34
Speaker 3: and mathematically defined. So I always saw the parallel really
38:37
Speaker 3: at that fundamental mathematical level, which made it actually feasible
38:43
Speaker 3: for me to do music while I was in med school.
38:46
Speaker 3: Although I was kind of naive, I always saw that, hey,
38:48
Speaker 3: I could be a musician and a doctor. I'll just
38:51
Speaker 3: be a musician on the side. Obviously that is not
38:54
Speaker 3: really feasible. There are only so many hours in the day.
38:57
Speaker 2: Right Again, how do you prioritize? Yeah, if there was
39:00
Speaker 2: an album that's the soundtrack of your life, what would
39:03
Speaker 2: it be?
39:04
Speaker 3: Oh my god, that's a tough question. Birth of a
39:07
Speaker 3: Cool is amazing. There's a jazz album that many are
39:11
Speaker 3: familiar with. It's amazing because the reason that it's a
39:15
Speaker 3: timeless classic is because of not just the individual musicians,
39:21
Speaker 3: which Miles Davis and others that were essentially virtuoso's, but
39:26
Speaker 3: it was the convergence. They were so in sync and
39:32
Speaker 3: so harmonious with each other that if someone else performs
39:39
Speaker 3: that album today mechanically and note by note exactly the
39:43
Speaker 3: same way. It's never going to be the same because
39:46
Speaker 3: at that organic sort of convergence that these great musicians have,
39:50
Speaker 3: and that can only happen maybe once or twice in
39:53
Speaker 3: a musician's lifetime. So it was a special moment I
39:56
Speaker 3: think in music history that tells me at least that sure,
40:00
Speaker 3: you have to have technical expertise and you have to
40:03
Speaker 3: be good technically at what you do, but at the
40:05
Speaker 3: end of the day, to produce amazing work, great work
40:09
Speaker 3: that is timeless, it's all about the people around you
40:13
Speaker 3: and how harmonious you can be with those people.
40:17
Speaker 2: Right, It's all about the music you make. So I
40:19
Speaker 2: like to close with a rapid fire around and you're
40:22
Speaker 2: in the hot seat. I name a topic and you
40:25
Speaker 2: give your fastest take. AI models predict the best treatment protocols.
40:31
Speaker 3: Yes, they can, because that's a function of analyzing various
40:38
Speaker 3: data modalities that is beyond human cognitive capabilities.
40:43
Speaker 2: Right and the most objective maybe versus subjective or not.
40:47
Speaker 3: The most objective and reproducible. Right now, some of the
40:50
Speaker 3: unevenness that we see in health outcomes is lack of
40:54
Speaker 3: access to care and lack of access to the appropriate expertise.
40:58
Speaker 3: So this could democratize that.
41:00
Speaker 2: In fact, the future of chemotherapy.
41:03
Speaker 3: I think we've had an amazing experience in the past
41:06
Speaker 3: decade or so with targeted therapies immunotherapies. We've saturated what
41:10
Speaker 3: we can do with those tools in our toolboxes, and
41:15
Speaker 3: combining them with psychotoxic chemotherapy is underutilized, and I think
41:21
Speaker 3: we're going to see a lot of interesting combinations. And
41:24
Speaker 3: I also believe that most chemotherapeutics are actually targeted. They
41:31
Speaker 3: have a mechanism of action that targets pathways, so to
41:36
Speaker 3: really understand those targets in a meaningful way, combining them
41:39
Speaker 3: with more traditional small molecule target therapies or immune checkpoint
41:44
Speaker 3: inhibitors on other immune modulators will open up the aperture
41:49
Speaker 3: for new therapies. Future of immunotherapy, I think it will
41:54
Speaker 3: be in vivo cell therapy, meaning that you don't have
41:59
Speaker 3: to harvest the patient's elves and engineer them in the lab.
42:02
Speaker 3: You do all of that in vivo in the patient
42:06
Speaker 3: by specifics. And what that really speaks to is our
42:10
Speaker 3: ability to have antibodies that target different things versus just
42:16
Speaker 3: one target, and combining that with our increasing understanding of
42:23
Speaker 3: disease biology will open up a whole new realm of possibilities.
42:29
Speaker 2: Aim matching patients with clinical trials the right ones, you know.
42:34
Speaker 3: I think that is one of those examples of automating
42:37
Speaker 3: the mechanics of the enterprise. Right now, anyone can search
42:41
Speaker 3: clinical trials at GOV. It's a little confusing, well convoluted,
42:45
Speaker 3: it's going to take a long time, hard for patients
42:48
Speaker 3: to do. Physicians won't have the time. So AI can
42:51
Speaker 3: do that quite effectively today. Now the question is how
42:55
Speaker 3: do we deploy that. But that's a great example of
42:59
Speaker 3: automating the mechanic of the enterprise, so that we're not
43:01
Speaker 3: expecting the oncologist that has only a few minutes per
43:05
Speaker 3: patient to comb through clinical trials at GOV and try
43:09
Speaker 3: to match the best possible steady for their patient. They
43:13
Speaker 3: need support, and in this case, AI is the support
43:16
Speaker 3: that they need.
43:17
Speaker 2: I mean, I feel like it's the era and age
43:20
Speaker 2: of patient empowerment and that's the most powerful use of
43:24
Speaker 2: AI in my mind, that you really can find the
43:27
Speaker 2: best some of the best answers you're ever going to
43:30
Speaker 2: get that we just didn't have before. It's just funny
43:33
Speaker 2: how yesterday it was one way and today it's another
43:36
Speaker 2: It's like how things move really quickly. But my view
43:40
Speaker 2: is half class full. I know everybody, Look, I lost
43:43
Speaker 2: my wife to cancer and I didn't have all the answers,
43:46
Speaker 2: but chemo was effective for her for a period of time,
43:49
Speaker 2: and we did manage the cancer at high quality for
43:52
Speaker 2: a couple of years, and way more than we would
43:55
Speaker 2: have had had those tools not been available. So, you know,
43:59
Speaker 2: I look at it as we're in this era of
44:04
Speaker 2: incredible tech, genomics, molecular mapping, all the things. We can't
44:09
Speaker 2: be too far away. Maybe it's a decade where we
44:12
Speaker 2: really are. We never thought we'd solve the obesity issue.
44:15
Speaker 2: GLP ones are proliferative now. I guess one thing i'd ask,
44:20
Speaker 2: you know, what do you want to get real about
44:22
Speaker 2: in twenty twenty six with all that in mind, to
44:25
Speaker 2: end on.
44:26
Speaker 3: This, Yeah, I love that you highlighted patient empowerment and
44:30
Speaker 3: I think hopefully moving forwards, especially now that we have
44:35
Speaker 3: powerful tools at our disposal, we really continue to focus
44:39
Speaker 3: on the needs of the patient. And when we do that,
44:43
Speaker 3: a lot of what we talk about when it comes
44:46
Speaker 3: to the healthcare system as complexity starts to break down
44:49
Speaker 3: in fact, and you realize that if you look at
44:51
Speaker 3: the patient and work your way backwards, sure things may
44:55
Speaker 3: look very different than how we do things today, but
44:59
Speaker 3: there is is a direct path to serving that patient's needs,
45:03
Speaker 3: and the disruption that we need should be along the
45:08
Speaker 3: lines of getting to the individual patient. Again, we have
45:12
Speaker 3: the tools necessary and we can work our way backwards,
45:16
Speaker 3: and if we did that the right way, the complexities
45:20
Speaker 3: of the healthcare system will dissolve, because, frankly, the majority
45:25
Speaker 3: of what we do in the system is either not
45:29
Speaker 3: related to the patient, or several degrees removed from the
45:36
Speaker 3: needs of the patient, or several degrees removed from the
45:40
Speaker 3: needs of the patient.
45:41
Speaker 2: Ending on a high note, positive note, Thanks Sean. You
45:45
Speaker 2: know we could go on and on and maybe I'll
45:47
Speaker 2: come on your cool podcast and we keep talking.
45:50
Speaker 3: Would love to have you.
45:51
Speaker 2: Thanks for joining mine, Thanks for becoming a colleague. I
45:56
Speaker 2: was excited by the CEO around Table on Cancer what
45:59
Speaker 2: it can push and I think, just like you said,
46:02
Speaker 2: the fact that these things exist at all are the
46:05
Speaker 2: impetus to keep moving forward and solve the problems. The
46:10
Speaker 2: only way you're going to do it is by bringing
46:11
Speaker 2: people together. So thanks for.
46:13
Speaker 3: Doing that, and thank you for having me and thank
46:15
Speaker 3: you for your thought, partnership and your general city of spirit.
46:20
Speaker 2: Hey everyone, thanks for watching. I really appreciate that you
46:23
Speaker 2: take the time to listen to this and tune in,
46:26
Speaker 2: so please subscribe, follow I've got a lot of amazing guests.
46:31
Speaker 2: You're going to learn a lot, and you're gonna feel
46:33
Speaker 2: a lot better because you did it.