WEBVTT - Vas Narasimhan 0:00:02.840 --> 0:00:05.920 Vos Narasimon is the CEO of Novartist, a major Swiss 0:00:05.960 --> 0:00:09.080 based pharmaceutical company. Born in the United States the Indian 0:00:09.119 --> 0:00:11.960 immigrant parents, he was educated at Harvard Medical School, but 0:00:12.080 --> 0:00:14.840 chose not to practice medicine. Instead, he chose to go 0:00:14.880 --> 0:00:18.000 into the pharmaceutical industry and is now leading the transformation 0:00:18.040 --> 0:00:21.160 of Novartists to one of the most important pharmaceutical companies 0:00:21.160 --> 0:00:23.040 in the world. Had a chance to sit down with 0:00:23.160 --> 0:00:25.960 Vos in New York recently to talk about this transformation. 0:00:26.520 --> 0:00:29.120 So many people know the names of the drugs that 0:00:29.160 --> 0:00:31.600 they use, but they don't know the names of the 0:00:31.680 --> 0:00:35.400 drug companies or pharmaceutical companies that produce them. So for 0:00:35.479 --> 0:00:38.159 people who may not know much about Nevardists, tell us 0:00:38.200 --> 0:00:41.760 what drugs or pharmaceutical products as are produced that people 0:00:41.800 --> 0:00:42.360 would have heard of. 0:00:42.800 --> 0:00:44.720 There's a couple of major drugs we have right now, 0:00:44.760 --> 0:00:48.000 and Trusto is a medicine for heart failure. Another drug 0:00:48.080 --> 0:00:51.600 Couthentics for a whole range of immunology indications. We have 0:00:51.600 --> 0:00:54.440 a drug called Kiskali for breast cancer. As you know, 0:00:54.560 --> 0:00:57.160 these names are not always tying to what the drugs 0:00:57.200 --> 0:01:00.520 actually do. But yeah, we have a pretty broad portfolio 0:01:00.560 --> 0:01:03.640 of medicines over fifteen medicines, over a billion dollars of sales. 0:01:03.720 --> 0:01:06.160 By the way, speaking of their brand names, who comes 0:01:06.240 --> 0:01:08.440 up with these brand names? Because they there are names 0:01:08.440 --> 0:01:10.640 that I can't pronounce. Sometimes you don't know what they mean. 0:01:10.800 --> 0:01:11.920 Do you have a team of people. 0:01:11.680 --> 0:01:12.160 That came up. 0:01:12.200 --> 0:01:13.200 It's a whole industry. 0:01:13.319 --> 0:01:15.800 Maybe private equity even interested to get into it, but 0:01:15.840 --> 0:01:19.600 there's a whole industry of people who basically try to 0:01:19.640 --> 0:01:24.520 find word permutations that might tie to the drug, and 0:01:24.560 --> 0:01:26.520 then you have to send it to the regulators and 0:01:26.560 --> 0:01:28.520 they have to be sure that this word will not 0:01:28.600 --> 0:01:31.920 be confused in any number of different languages for some 0:01:32.080 --> 0:01:33.440 other medicine or something else. 0:01:33.560 --> 0:01:37.440 People who don't follow this industry carefully may not recognize it. 0:01:37.480 --> 0:01:39.679 But the way the industry works, as I understand it 0:01:39.720 --> 0:01:44.480 is that large pharmaceutical companies either develop internally or through 0:01:44.600 --> 0:01:49.000 an acquisition a drug or pharmaceutical product that they can sell, 0:01:49.320 --> 0:01:52.960 and it's patented, and the patents last for seventeen years, 0:01:53.000 --> 0:01:56.840 seventeen years, seventeen years, and after seventeen years it becomes 0:01:56.920 --> 0:01:59.720 what's known as a generic, which means that it's not patent. 0:02:00.040 --> 0:02:03.040 Can get it for presumably a lower price. So is 0:02:03.080 --> 0:02:05.360 that a good way to make a business? You have 0:02:05.440 --> 0:02:08.880 you build products in seventeen years they're kind of gone. 0:02:08.960 --> 0:02:10.960 It's a tough way to make a business, for sure. 0:02:11.200 --> 0:02:13.120 It means you're on a constant treadmill and you have 0:02:13.200 --> 0:02:16.600 to constantly have the innovation capacity to have what we 0:02:16.639 --> 0:02:19.480 call replacement power. You have to replace your sales with 0:02:19.560 --> 0:02:22.600 the next medicines, the next innovation. So the only winners 0:02:22.639 --> 0:02:25.160 in the long run are companies that have the R 0:02:25.200 --> 0:02:28.480 and D firepower, R and D capacity to keep inventing 0:02:28.520 --> 0:02:32.359 medicines to rejuvenate almost their entire portfolio. So you think 0:02:32.400 --> 0:02:34.480 of a company like US forty five billion dollars in 0:02:34.560 --> 0:02:37.880 sales every year. We can have anywhere from two to 0:02:37.960 --> 0:02:40.840 eight billion dollars plus going off patent, and we have 0:02:40.880 --> 0:02:45.080 to generate enough sales to replace that and grow on top. 0:02:45.520 --> 0:02:47.600 And that's why you have a relatively limited number of 0:02:47.760 --> 0:02:49.359 very large by pharma companies. 0:02:49.400 --> 0:02:52.560 When somebody develops a drug or pharmaceutical product in a 0:02:52.600 --> 0:02:54.320 company like you, do you have a big lab. I 0:02:54.320 --> 0:02:56.160 assume is it all over the world or you have 0:02:56.200 --> 0:02:56.920 it in Switzerland. 0:02:57.240 --> 0:02:59.600 We invest about nine and a half billion dollars a 0:02:59.720 --> 0:03:03.800 year in our and DA research headquarters are in Cambridge, Massachusetts, 0:03:03.800 --> 0:03:04.680 and bossles it so on. 0:03:05.080 --> 0:03:07.400 So when you get the product and what do you do? 0:03:07.440 --> 0:03:10.160 You test it first on animals, and then you test 0:03:10.200 --> 0:03:12.639 it on small subset of humans and then a large 0:03:12.680 --> 0:03:13.480 subset of humans. 0:03:13.520 --> 0:03:14.200 Is that how it works? 0:03:14.400 --> 0:03:16.359 Roughly, that's how it works, and it's a long journey. 0:03:16.440 --> 0:03:18.080 First thing we have to do is we have to 0:03:18.120 --> 0:03:20.600 discover or find what we call a target in the 0:03:20.680 --> 0:03:23.000 human body that we want a drug. We think this 0:03:23.160 --> 0:03:25.840 target has some ability to impact human health and. 0:03:25.760 --> 0:03:27.000 A disease we're interested in. 0:03:27.320 --> 0:03:30.040 Then we have to design a drug to either inhibit 0:03:30.600 --> 0:03:34.000 or promote that target, and that takes some time. AI 0:03:34.120 --> 0:03:36.480 might help us do that faster, we'll see. And then 0:03:36.480 --> 0:03:39.560 once we have that target drug optimized, we take it 0:03:39.560 --> 0:03:43.080 into animals, make sure it doesn't cause any preclinical safety signals, 0:03:43.320 --> 0:03:46.080 and then we finally move into humans. And from the 0:03:46.160 --> 0:03:48.080 time we move into humans to when we get it 0:03:48.200 --> 0:03:50.680 to people, it's usually around nine years. 0:03:51.080 --> 0:03:54.280 Last year twenty twenty three, we saw an enormous increase 0:03:54.400 --> 0:03:57.280 in drugs being taken that reduce your weight, and they 0:03:57.280 --> 0:03:59.400 seem to work quite well. There are two drug companies 0:04:00.040 --> 0:04:02.760 United States and one in Europe that seem to dominate 0:04:02.800 --> 0:04:03.360 that business. 0:04:03.720 --> 0:04:05.680 Are you going to get into that business? And why not? 0:04:05.760 --> 0:04:07.440 It seems to be extremely profitable. 0:04:07.760 --> 0:04:09.160 It is a business we're interested in. 0:04:09.240 --> 0:04:11.760 We're working on really the next wave of medicines because 0:04:11.760 --> 0:04:14.760 I think these GLP one medicines that you're talking about 0:04:14.960 --> 0:04:17.719 are very well serviced by the current companies. It's a 0:04:17.720 --> 0:04:21.799 fascinating story goes back actually to the early nineteen nineties. 0:04:21.800 --> 0:04:23.960 It took us almost twenty five years to realize the 0:04:24.000 --> 0:04:27.280 potent effect that these types of drugs would have on obesity. 0:04:27.480 --> 0:04:30.200 But we think there's opportunity to improve on them, and 0:04:30.240 --> 0:04:33.040 we hope it'll be It's very early stage within nov Artists, 0:04:33.040 --> 0:04:35.360 but can we come up with drugs that better preserve muscle, 0:04:35.760 --> 0:04:39.000 are maybe easier to take and more infrequently taken. One 0:04:39.040 --> 0:04:42.200 area of where Novartis is one of the leaders is 0:04:42.279 --> 0:04:45.000 in what are called small interfering RNA therapies. 0:04:45.080 --> 0:04:46.840 It sounds like a fancy. 0:04:46.480 --> 0:04:48.719 Word, but really what it allows you to do is 0:04:48.839 --> 0:04:51.440 take drugs that you normally would take every single day 0:04:51.760 --> 0:04:53.559 and make them drugs you only have to take twice 0:04:53.600 --> 0:04:56.239 a year. So in cholesterol lowering, we have a medicine 0:04:56.240 --> 0:04:57.880 that you only have to take twice a year to 0:04:58.160 --> 0:05:02.479 lower cholesterol sixty percent. We're working on similar medicines for hypertensions, 0:05:02.520 --> 0:05:06.520 high blood pressure, other risk factors for cardiovascular disease. And 0:05:06.560 --> 0:05:09.279 the idea is can you get to very infrequently dosed 0:05:09.320 --> 0:05:12.200 medicines because most people don't stay on the drugs. 0:05:12.080 --> 0:05:14.520 As I get older, the drug that I'm most interested 0:05:14.839 --> 0:05:18.240 in is one that deals with Alzheimer's or dementia, because 0:05:18.240 --> 0:05:21.440 I'm always wondering am I going to be getting this disease? 0:05:21.920 --> 0:05:23.480 What are you doing in that area? 0:05:23.600 --> 0:05:26.240 So we're active and I think what we're really interested 0:05:26.279 --> 0:05:28.760 in is again the next wave of what might be 0:05:28.839 --> 0:05:32.560 effective therapies for Alzheimer's disease. Today there are two one 0:05:32.640 --> 0:05:35.760 drug license, another drug coming targeting the plaques in the 0:05:35.760 --> 0:05:38.119 brain anti amyloid that drugs as they are called. 0:05:38.279 --> 0:05:40.159 But we think the next generation. 0:05:39.839 --> 0:05:42.400 Opportunities are going to be to target other elements that 0:05:42.480 --> 0:05:45.440 accumulate in the brain. One is called TAO. There are 0:05:45.480 --> 0:05:48.200 other targets as well. But I would say Alzheimer's is 0:05:48.240 --> 0:05:49.840 a really tough space. I mean, one of the things 0:05:49.880 --> 0:05:52.599 that's very hard is you need to intervene very early 0:05:52.960 --> 0:05:57.600 because it's a very slowly progressing disease, and identifying which 0:05:57.600 --> 0:06:00.159 patients to intervene on and then figuring out what to 0:06:00.200 --> 0:06:03.120 treat them the patients with is really really difficult. 0:06:03.279 --> 0:06:06.159 On cancer related drugs, there are some cancer related drugs, 0:06:06.160 --> 0:06:08.000 and I guess you have some as well, but. 0:06:08.000 --> 0:06:09.760 They tend to deal with cancer. 0:06:09.800 --> 0:06:12.960 Once you have cancer, they try to ameliorate the side 0:06:12.960 --> 0:06:15.000 effects of it or the effects of it. What about 0:06:15.040 --> 0:06:18.000 something that prevents cancer? Is that realistic in my lifetime? 0:06:18.240 --> 0:06:20.719 That is something that's a heavy interest of the field, 0:06:20.800 --> 0:06:23.279 and also at our company, we actually just got great 0:06:23.320 --> 0:06:25.839 data on a breast cancer drug which can be given 0:06:25.880 --> 0:06:28.600 to women who have had their breast cancer but resected, 0:06:28.600 --> 0:06:31.320 but then to prevent it recurring. Now where there's a 0:06:31.320 --> 0:06:34.280 lot of interest right now is can you identify things 0:06:34.279 --> 0:06:37.080 in the blood and things are circulating so circulating tumor 0:06:37.160 --> 0:06:40.000 DNA that would show that the cancer is starting to 0:06:40.400 --> 0:06:42.440 happen in the body, but well before it would be 0:06:42.480 --> 0:06:45.520 detectable in any kind of scan. If we can get 0:06:45.520 --> 0:06:48.200 those tests up to an adequate level of precision and 0:06:48.240 --> 0:06:51.279 start to treat patients well before the cancer shows up, 0:06:51.560 --> 0:06:53.839 that would be the big opportunity, but that's still some 0:06:53.920 --> 0:06:54.440 time away. 0:06:54.520 --> 0:06:57.720 So you produce medicines that presumably help people with their 0:06:57.760 --> 0:07:00.279 lives the better and live longer and so forth. Why 0:07:00.279 --> 0:07:03.119 aren't you a more popular industry? You know, the private 0:07:03.120 --> 0:07:05.880 equ industry has its attractors, have no doubt, but the 0:07:05.880 --> 0:07:08.760 pharmaceutical industry is not right too far behind us. 0:07:08.880 --> 0:07:11.400 Why do people not love you for the drugs you're producing. 0:07:11.680 --> 0:07:14.520 Yeah, it's a long story in terms of the history 0:07:14.560 --> 0:07:16.040 of this industry. Even if you go back to the 0:07:16.040 --> 0:07:18.960 seventies or eighties, we were a much more popular sector. 0:07:19.040 --> 0:07:20.600 I think one of the things that happened is as 0:07:20.600 --> 0:07:23.560 we brought more and more medicines forward and patients got 0:07:23.600 --> 0:07:26.160 on more and more therapies. The one thing we weren't 0:07:26.160 --> 0:07:29.640 watching carefully enough as an industry was what was happening 0:07:29.640 --> 0:07:32.360 at the pharmacy counter. There's a lot of focus in 0:07:32.440 --> 0:07:35.640 congressional hearings and other places on list prices, but list 0:07:35.640 --> 0:07:38.240 prices mean very little in the sector because you have 0:07:38.280 --> 0:07:41.720 pharmacy benefit managers, you have retail pharmacies, you have wholesalers. 0:07:42.000 --> 0:07:44.160 But what really matters is at the end of the day, 0:07:44.160 --> 0:07:46.520 when somebody comes to the pharmacy counter. Can they afford 0:07:46.600 --> 0:07:49.520 their medicines? And we're starting to address that. Some recent 0:07:49.600 --> 0:07:52.440 legislation addresses that. We're thinking a lot about it. But 0:07:52.480 --> 0:07:54.200 that's what we've got to figure out how to solve. 0:07:54.280 --> 0:07:57.720 Let's talk about your background. So your parents came from where. 0:07:57.480 --> 0:07:59.920 They came from Tumbil Nadu in southern India. 0:08:00.240 --> 0:08:01.880 And when did they come to the United States. 0:08:02.000 --> 0:08:04.640 They came in the late sixties and early seventies. 0:08:04.920 --> 0:08:06.120 And were they educated? 0:08:07.040 --> 0:08:10.400 They were they actually, you know, my father was alongside 0:08:10.400 --> 0:08:12.160 his brothers were some of the first people in our 0:08:12.200 --> 0:08:15.240 family to go to college in India. Father did his 0:08:15.280 --> 0:08:18.240 PhD in India. I was able to work his way 0:08:18.280 --> 0:08:19.679 up and then it was one of the first people 0:08:19.680 --> 0:08:20.880 to come to the United States. 0:08:21.200 --> 0:08:23.480 My mother came to Carnegie Mellon. She did a. 0:08:23.440 --> 0:08:28.520 Degree in nuclear and becoming a nuclear engineer. And yeah, 0:08:28.560 --> 0:08:30.520 it was I think a pretty extraordinary story and that 0:08:30.600 --> 0:08:33.440 they were able to come from relatively modest beginnings, so 0:08:33.559 --> 0:08:37.199 beginning with my grandparents in relatively small places in India 0:08:37.280 --> 0:08:39.679 and ultimately find a way to the United States. 0:08:39.800 --> 0:08:41.439 So you grew up in the Pittsburgh area. 0:08:41.559 --> 0:08:43.120 I grew up a Pittsburgh Steelers fan. 0:08:43.160 --> 0:08:45.280 Indeed, And I assume you did well in high. 0:08:45.160 --> 0:08:46.880 School, did well, did well? 0:08:47.000 --> 0:08:49.560 So you went to univer Chicago, went to the University 0:08:49.600 --> 0:08:51.720 of chicag And why did you go there as opposed 0:08:51.720 --> 0:08:52.600 to other good schools. 0:08:52.920 --> 0:08:54.000 Well, it's an interesting story. 0:08:54.040 --> 0:08:56.880 I actually applied to all of the ivs and applied 0:08:56.920 --> 0:08:59.240 to a number of schools, and I didn't get in anywhere. Actually, 0:08:59.280 --> 0:09:01.920 the one school that I did admit me was a 0:09:02.160 --> 0:09:04.600 University of Chicago, and it ended up being such an 0:09:04.640 --> 0:09:07.320 incredible gift in the end, because the University of Chicago, 0:09:07.360 --> 0:09:10.800 I would still credit, perhaps more than any other educational experience, 0:09:10.840 --> 0:09:13.280 taught me how to think, how to synthesize, how to 0:09:13.280 --> 0:09:14.560 be relentlessly curious. 0:09:14.679 --> 0:09:15.600 It was really positive. 0:09:15.640 --> 0:09:18.680 Okay, So Harvard recognized and gets a mistake from before, 0:09:18.760 --> 0:09:21.319 admitted view to the medical school, right, they did. And 0:09:21.480 --> 0:09:23.480 so you went to Harvard Medical School. I assume your 0:09:23.520 --> 0:09:25.559 immigrant parents were saying, great, my son is going to 0:09:25.559 --> 0:09:28.160 be a doctor. Yes, So when you told them you 0:09:28.200 --> 0:09:30.400 weren't going to be a practicing doctor, what did they say? 0:09:30.520 --> 0:09:33.079 They were very confused. They were very very confused. I 0:09:33.400 --> 0:09:35.040 you know, I had this idea in my mind, I 0:09:35.120 --> 0:09:36.640 did a lot of work in public health. I had 0:09:36.720 --> 0:09:39.839 some great mentors at Harvard, and I really wanted to 0:09:39.920 --> 0:09:42.720 see how could I have a bigger impact beyond an 0:09:42.880 --> 0:09:46.040 individual patient care, and so I wanted to I first 0:09:46.080 --> 0:09:48.679 went to the World Health Organization, then I went to McKinsey. 0:09:48.960 --> 0:09:51.079 But all of this time, I certainly think my parents 0:09:51.160 --> 0:09:52.959 were assuming I was going to go back and do 0:09:53.080 --> 0:09:56.560 a residency and become a cardiologist and kind of finish 0:09:56.640 --> 0:09:59.320 the journey, and it never actually worked out that well. 0:09:59.440 --> 0:10:02.120 For a while, you were, I guess, a protege of 0:10:02.280 --> 0:10:06.120 Paul Farmer, the famous doctor who was at Harvard for 0:10:06.160 --> 0:10:10.000 a while, among other places. And you then did other 0:10:10.080 --> 0:10:12.800 things to help people in India. You went back working 0:10:12.840 --> 0:10:14.040 with street children in India. 0:10:14.280 --> 0:10:14.679 That's right. 0:10:15.120 --> 0:10:17.720 So did your parents say, you have a Harvard Medical 0:10:17.760 --> 0:10:21.040 School degree and you're making no money in India is 0:10:21.080 --> 0:10:23.480 a great country, but why don't you go practice medicine? 0:10:23.880 --> 0:10:26.680 Say that my father would ask, I worked so hard 0:10:26.720 --> 0:10:28.920 to get out of these places, why are you working 0:10:29.000 --> 0:10:30.800 so hard to go back to them? Was certainly a 0:10:31.160 --> 0:10:34.480 confusion he had in his mind. But overall my parents 0:10:34.559 --> 0:10:37.000 were very supportive of this public health goal. 0:10:37.040 --> 0:10:40.360 I had okay, So you ultimately came to McKenzie after 0:10:40.480 --> 0:10:43.720 some public health work and why did you not stay 0:10:43.720 --> 0:10:44.320 at McKenzie. 0:10:44.760 --> 0:10:47.360 Yeah, McKenzie was a great training ground for a physician 0:10:47.400 --> 0:10:49.360 who didn't know anything about a P and L, a 0:10:49.480 --> 0:10:52.040 balance sheet, knew nothing about M and A or evaluation. 0:10:52.200 --> 0:10:54.680 I learned a lot very quickly, but I just felt 0:10:54.679 --> 0:10:57.360 like it was a little bit detached from the real action. 0:10:57.720 --> 0:11:00.560 And I had an opportunity presented to me to join 0:11:00.640 --> 0:11:03.400 Novartists Pharmaceuticals to really try to look at our R 0:11:03.480 --> 0:11:06.400 and D strategy at Novartists at the time, and it 0:11:06.520 --> 0:11:08.559 was a little bit of a whim sort of a 0:11:08.679 --> 0:11:10.839 gamble to try to see how that would work out. 0:11:10.960 --> 0:11:12.880 I would have never imagined it would have unfolded the 0:11:12.920 --> 0:11:14.640 way it did, but it was really great. 0:11:14.720 --> 0:11:16.280 So you work your way up and then at the 0:11:16.360 --> 0:11:19.559 age of forty, somebody at Nevarda says, we have a 0:11:19.640 --> 0:11:23.760 forty year old who didn't go to business school, is 0:11:23.880 --> 0:11:27.160 not from Switzerland, and he's a medical doctor who's never 0:11:27.240 --> 0:11:28.040 practiced medicine. 0:11:28.200 --> 0:11:31.920 Let's make him the CEO. It's right, So were you're surprised? 0:11:32.120 --> 0:11:32.920 I was shocked. 0:11:32.960 --> 0:11:35.000 I was shocked when they first asked me that to 0:11:35.080 --> 0:11:36.480 even be part of it. I mean I had been 0:11:36.520 --> 0:11:38.800 a head of drug development. I had the opportunity to 0:11:38.880 --> 0:11:42.880 do so many different roles at Novartists in developing vaccines. 0:11:42.880 --> 0:11:44.320 I worked in our vaccines. 0:11:43.960 --> 0:11:47.080 Division, leading a little bit of our generics unit, working 0:11:47.160 --> 0:11:49.880 in drug development for many, many years. And I think 0:11:50.600 --> 0:11:52.599 part of the reason they were interested is because I 0:11:52.679 --> 0:11:55.760 had gotten a background in R and D. But I 0:11:55.840 --> 0:11:58.640 think it was also not only my age, but also 0:11:58.720 --> 0:12:01.280 the fact that I think no other major pharmaceutical company 0:12:01.360 --> 0:12:04.920 had an R and D head, our development head as 0:12:04.960 --> 0:12:05.439 their CEO. 0:12:05.720 --> 0:12:08.600 Your strategy has been different than the strategy you inherited, 0:12:08.640 --> 0:12:11.040 and the artist was in many different areas. They had 0:12:11.080 --> 0:12:14.280 a generic drug business and you sold that. Yes, so 0:12:14.400 --> 0:12:15.880 why did you get out of that business? It seems 0:12:15.880 --> 0:12:17.280 to be a recently profitable business. 0:12:17.480 --> 0:12:17.640 Yeah. 0:12:18.520 --> 0:12:21.079 As you know, when we have these conglomerates, you have 0:12:21.160 --> 0:12:24.040 to ask is there value in the conglomerate? Is their 0:12:24.120 --> 0:12:27.080 value in building all these different businesses together? And then 0:12:27.120 --> 0:12:29.120 the question is what are we really great at? And 0:12:29.200 --> 0:12:30.840 when I came into the role as CEO, I looked 0:12:30.880 --> 0:12:33.319 we were in consumer health, we were in I care devices, 0:12:33.720 --> 0:12:37.199 we were in generics. We had a broad range, broad portfolio, 0:12:37.679 --> 0:12:39.839 and I think what our company's really great at is 0:12:39.960 --> 0:12:43.439 discovering these novel breakthroughs and then getting access to over 0:12:43.480 --> 0:12:45.880 two hundred and eighty million patients, which is our reach today, 0:12:46.280 --> 0:12:49.199 largest we believe of any pharma company to all of 0:12:49.240 --> 0:12:52.080 these patients. And so in order to do that well, 0:12:52.240 --> 0:12:55.320 I didn't think we could allocate capital successfully across all 0:12:55.360 --> 0:12:58.640 of these different businesses. So we spun al Con, the 0:12:58.720 --> 0:13:02.120 largest public market spin in recent memory in Europe. We 0:13:02.240 --> 0:13:05.480 spun out Sandos, We sold our consumer health business. It's 0:13:05.520 --> 0:13:08.000 about one hundred and thirty billion dollars of transactions. Later, 0:13:08.480 --> 0:13:10.720 we come out as a pure play medicines company, which 0:13:10.760 --> 0:13:12.640 I think we're a best positioned for the long run. 0:13:12.720 --> 0:13:14.480 Soh you're the CEO and you go to the board 0:13:14.520 --> 0:13:16.200 and say, you know, we've been in the generics business, 0:13:16.200 --> 0:13:17.920 we're in the consumer drug business, and we're going to 0:13:17.960 --> 0:13:19.240 get out of all these except we're going to be 0:13:19.280 --> 0:13:20.960 in the one business that we were now in. Did 0:13:21.040 --> 0:13:23.400 they say, you know, you weren't qualified to make that 0:13:23.520 --> 0:13:24.559 judgment or what did they say? 0:13:25.000 --> 0:13:25.160 Well? 0:13:25.240 --> 0:13:28.000 I think they went along, and I think they generally 0:13:28.080 --> 0:13:29.760 were aligned. But I think it also was a step 0:13:29.800 --> 0:13:32.160 wise journey. I mean, first we did consumer health and 0:13:32.200 --> 0:13:34.719 we saw how that went, and then two years a 0:13:34.800 --> 0:13:37.320 year later we did al Con, Then the pandemic came, 0:13:37.840 --> 0:13:40.000 then we did did Santos. Because I think the fear 0:13:40.080 --> 0:13:42.520 comes back from what we discussed earlier. When you have 0:13:42.679 --> 0:13:45.600 a business in innovative medicines and you have these patent cliffs, 0:13:46.080 --> 0:13:48.800 if you have these other businesses which are much more stable, 0:13:48.880 --> 0:13:51.559 there's a feeling like you can offset your risk. You 0:13:51.679 --> 0:13:54.040 have stable businesses and you have this one business where 0:13:54.040 --> 0:13:57.400 you face these cliffs every fifteen to seventeen years. 0:13:58.120 --> 0:13:59.840 I take the view that the only way you're going 0:13:59.920 --> 0:14:00.760 to generate. 0:14:00.600 --> 0:14:03.360 Enough medicines to keep growing as you focus your capital 0:14:03.400 --> 0:14:03.959 on innovation. 0:14:04.240 --> 0:14:06.559 So last week, as we're talking now, last week you 0:14:06.679 --> 0:14:09.520 made another acquisition, I think for roughly five billion dollars. 0:14:09.840 --> 0:14:12.480 You bought a company that is dealing with blood cancer. 0:14:12.640 --> 0:14:15.439 So why was that a good acquisition? And how does 0:14:15.480 --> 0:14:17.000 it as long does it take you to make an 0:14:17.040 --> 0:14:18.920 assessment of whether an acquisition is good or not? And 0:14:19.000 --> 0:14:20.840 do you come up with the ideas yourself, or does 0:14:20.880 --> 0:14:22.880 some investment banker come along and tell you it's a 0:14:22.960 --> 0:14:23.400 great idea. 0:14:23.480 --> 0:14:26.120 We limit our use of investment bankers. In general. 0:14:26.480 --> 0:14:28.680 What we try to look at is in the therapeutic 0:14:28.760 --> 0:14:30.800 areas we're in. So we're in blood cancers. We've been 0:14:30.880 --> 0:14:32.960 in blood cancers for over twenty years. Some of the 0:14:33.360 --> 0:14:35.600 most pioneering drugs and blood cancers have come from our 0:14:35.680 --> 0:14:37.800 research labs, and we say, if this is an area 0:14:38.000 --> 0:14:41.280 we're in we have deep understanding, therefore this makes sense 0:14:41.360 --> 0:14:45.120 to actually add another medicine to that portfolio, and so we. 0:14:45.280 --> 0:14:46.000 Make that assessment. 0:14:46.040 --> 0:14:48.240 If it's a good strategic fit, in this case a 0:14:48.320 --> 0:14:51.920 good financial fit, we'll make the bet. One of the 0:14:51.960 --> 0:14:54.400 things we've tried to be much more disciplined on is 0:14:54.480 --> 0:14:56.480 to try to go at the lower end of the 0:14:56.600 --> 0:14:59.840 range to under five billion dollars for acquisitions and really 0:15:00.120 --> 0:15:02.680 ensure that they're in areas we have deep understanding. Generally, 0:15:02.720 --> 0:15:05.400 when we've gone too far away from our core, the 0:15:05.520 --> 0:15:06.880 value creation hasn't been there. 0:15:07.120 --> 0:15:10.760 In twenty twenty three, AI burst onto the scene. Everybody 0:15:10.840 --> 0:15:14.320 is now excited about AI. How is AI affecting your 0:15:14.400 --> 0:15:16.400 company and do you think it's going to help your company? 0:15:17.080 --> 0:15:19.040 I think a it's going to have a big impact, 0:15:19.080 --> 0:15:20.960 but I think it's going to take more time than 0:15:21.000 --> 0:15:24.840 most people expect. I think first in productivity areas, AI 0:15:25.000 --> 0:15:28.560 has media applications, and I feel like in productivity we 0:15:28.640 --> 0:15:31.680 can use AI for document management, document generations, not really 0:15:32.040 --> 0:15:35.600 sexy areas, but things that really tractable in our sector. 0:15:35.760 --> 0:15:38.360 Most uniquely, the big question is can you speed up 0:15:38.480 --> 0:15:40.920 or increase the efficiency of drug R and D? And 0:15:41.000 --> 0:15:43.360 there we're doing a couple of things, working on clinical 0:15:43.400 --> 0:15:46.160 trials and optimizing our clinical trials. But the big bets 0:15:46.200 --> 0:15:51.320 we've made are with Palenteer, Microsoft Research Labs, Google deep Mind, 0:15:52.000 --> 0:15:54.200 and a few other partners to say, could you really 0:15:54.280 --> 0:15:58.600 discover novel drugs that maybe weren't discoverable without AI? Can 0:15:58.680 --> 0:16:01.560 you optimize drugs much more quickly? And this is all 0:16:01.600 --> 0:16:04.280 building on the pioneering work that Google Deep Minded on 0:16:04.640 --> 0:16:08.040 a technology called Alpha fold. There's a huge opportunity here, 0:16:08.240 --> 0:16:09.840 But I think we also have to acknowledge that we 0:16:09.920 --> 0:16:12.200 only understand about five to ten percent of what is 0:16:12.240 --> 0:16:14.800 going on in the human body. So to expect AI 0:16:15.040 --> 0:16:17.480 to use that five to ten percent to extrapolate and 0:16:17.560 --> 0:16:19.880 come up with big discoveries is going to take time. 0:16:20.040 --> 0:16:23.120 So other than AI, what medical technologies do you think 0:16:23.200 --> 0:16:25.840 are coming along that in the next one, two or 0:16:25.880 --> 0:16:27.880 three years are likely to change what you do or 0:16:28.000 --> 0:16:28.920 change the medical world. 0:16:29.240 --> 0:16:30.160 I think a couple of things. 0:16:30.320 --> 0:16:33.960 One RNA therapeutics as I mentioned earlier in the broadcast, 0:16:34.120 --> 0:16:36.160 or RNA therapeutics sRNAs. 0:16:36.360 --> 0:16:37.800 These are really coming to life now. 0:16:37.880 --> 0:16:40.400 What this allows you to do is treat diseases that 0:16:40.480 --> 0:16:43.760 were not treatable or treat them with very infrequent drug dosing. 0:16:44.080 --> 0:16:45.680 And if you could imagine if we could take on 0:16:45.840 --> 0:16:49.520 blood pressure and cholesterol with once year dosing, hugely transformative. 0:16:49.800 --> 0:16:52.840 Another one is sell therapy for immune diseases. 0:16:53.080 --> 0:16:56.080 We've seen extraordinary results in early phase studies that if 0:16:56.120 --> 0:16:59.640 you can reset somebody's immune system there are autoimmune disease 0:16:59.680 --> 0:17:03.200 actually goes away, and we'll see if this is sustains. 0:17:03.320 --> 0:17:05.760 But these are some of the most impressive results that 0:17:05.800 --> 0:17:09.199 we've ever seen in early stage clinical studies. And then 0:17:09.240 --> 0:17:11.240 the last is we're very big in an area of 0:17:11.320 --> 0:17:14.639 cancer called radio ligend therapy. This is the idea that 0:17:14.720 --> 0:17:17.760 can you bring nuclear particles right next to a cancer 0:17:17.880 --> 0:17:21.359 in the body and deliver the radiation very locally, and 0:17:21.440 --> 0:17:23.600 this has the opportunity to treat a whole range of 0:17:23.680 --> 0:17:25.720 solid tumors in ways that we couldn't afford. 0:17:25.800 --> 0:17:27.639 What do you do to kind of lower your global 0:17:27.720 --> 0:17:29.520 footprint carbon footprint? What do you do? 0:17:29.920 --> 0:17:33.000 Our big topic on sustainability is much more on access 0:17:33.000 --> 0:17:35.720