1 00:00:02,360 --> 00:00:06,720 Speaker 1: Bloomberg Audio Studios, podcasts, radio news. 2 00:00:06,880 --> 00:00:10,680 Speaker 2: Now biotech company Flagship Pioneering has raised three point six 3 00:00:10,960 --> 00:00:15,720 Speaker 2: billion dollars for ventures in health, sustainability and AI. With 4 00:00:15,880 --> 00:00:19,000 Speaker 2: fourteen billion dollars of assets under management, it ranks amongst 5 00:00:19,040 --> 00:00:22,320 Speaker 2: the top biotech investors globally. Now the firm has launched 6 00:00:22,560 --> 00:00:25,920 Speaker 2: and invested in dozens of companies, including Moderna. Well, I'm 7 00:00:25,920 --> 00:00:28,440 Speaker 2: delighted to be joined as always by newber Afam, the 8 00:00:28,440 --> 00:00:31,360 Speaker 2: Flagship chief executive and founder, who's also of course co 9 00:00:31,440 --> 00:00:34,280 Speaker 2: founder of Moderna. Newberg, thank you so much for joining 10 00:00:34,360 --> 00:00:38,800 Speaker 2: us today. Congratulation on this big round of fundraising. As 11 00:00:38,800 --> 00:00:42,920 Speaker 2: a mainstream biotech VC, how do you see AI actually 12 00:00:43,000 --> 00:00:47,199 Speaker 2: impacting drug discovery but also you know, development and healthcare 13 00:00:47,200 --> 00:00:48,920 Speaker 2: in general, of which you want to be part of. 14 00:00:50,600 --> 00:00:52,839 Speaker 1: Well, Francy Chris, thanks for having me this morning. 15 00:00:53,880 --> 00:00:57,240 Speaker 3: It is indeed an important milestone because you know, in 16 00:00:57,280 --> 00:00:59,560 Speaker 3: our type of business, where we're creating new companies and 17 00:00:59,600 --> 00:01:03,400 Speaker 3: helping them grow, kind of every ending is a beginning. 18 00:01:03,440 --> 00:01:05,960 Speaker 3: So this is a beginning of a new wave of 19 00:01:06,120 --> 00:01:11,160 Speaker 3: innovation and potential to impact healthcare and sustainability. And in fact, 20 00:01:11,160 --> 00:01:13,880 Speaker 3: we are living in a time where over the past 21 00:01:13,920 --> 00:01:17,160 Speaker 3: twenty five years the flagship's operated. We've never seen a 22 00:01:17,280 --> 00:01:22,040 Speaker 3: technology such as AI that is so suitable for kind 23 00:01:22,040 --> 00:01:26,320 Speaker 3: of reducing the uncertainty involved in drug discovery and drug development. 24 00:01:26,680 --> 00:01:29,160 Speaker 1: Here we're dealing with complex biological systems. 25 00:01:29,640 --> 00:01:33,360 Speaker 3: Historically, we've not really understood the details and the mechanisms 26 00:01:33,360 --> 00:01:36,160 Speaker 3: of what actually goes wrong in disease and how we 27 00:01:36,240 --> 00:01:39,760 Speaker 3: can actually make some specific changes to be able to 28 00:01:39,800 --> 00:01:41,520 Speaker 3: help heal. 29 00:01:41,800 --> 00:01:43,319 Speaker 1: Or improve the state of a patient. 30 00:01:43,640 --> 00:01:47,440 Speaker 3: And we're beginning to measure, handle the data and create 31 00:01:47,520 --> 00:01:50,560 Speaker 3: models that will help guide us, and we're seeing acceleration 32 00:01:51,160 --> 00:01:53,880 Speaker 3: in the time it takes to come up with early 33 00:01:53,920 --> 00:01:57,240 Speaker 3: candidates and drugs and then very much so helping us 34 00:01:57,560 --> 00:02:01,400 Speaker 3: identify the patients to be able to target in trials. 35 00:02:01,440 --> 00:02:06,520 Speaker 3: So everywhere you look, this technology has applications, and we 36 00:02:06,560 --> 00:02:08,480 Speaker 3: think it's very early in its development. 37 00:02:10,000 --> 00:02:12,440 Speaker 2: So Newberg, what do you think will be disrupted the 38 00:02:12,440 --> 00:02:14,519 Speaker 2: most in this drug discovery? Is it? 39 00:02:14,639 --> 00:02:14,880 Speaker 3: You know? 40 00:02:15,280 --> 00:02:18,880 Speaker 2: I guess an assets of medicine that can benefit more 41 00:02:18,960 --> 00:02:19,880 Speaker 2: quickly than others. 42 00:02:21,880 --> 00:02:24,280 Speaker 3: Look, I think that if you look at what medicines 43 00:02:24,320 --> 00:02:29,280 Speaker 3: generally do, they're intended to very precisely and safely alter 44 00:02:29,800 --> 00:02:33,320 Speaker 3: the state of a particular cell or particular target protein. 45 00:02:33,720 --> 00:02:35,480 Speaker 3: And to do that you need to be able to 46 00:02:36,040 --> 00:02:38,200 Speaker 3: know enough about the system to be able to do that, 47 00:02:38,240 --> 00:02:41,359 Speaker 3: and historically we've relied heavily on lots of trial and 48 00:02:41,440 --> 00:02:45,320 Speaker 3: there lots of experimentation, very low probabilities and success, and 49 00:02:45,400 --> 00:02:48,280 Speaker 3: this industry has succeeded despite those odds. 50 00:02:48,440 --> 00:02:50,120 Speaker 1: Those odds are increasing steadily. 51 00:02:50,120 --> 00:02:53,280 Speaker 3: If you look, for example, at Maderna, that technology, the 52 00:02:53,400 --> 00:02:55,760 Speaker 3: mr and a technology that we developed now over a 53 00:02:55,800 --> 00:02:59,560 Speaker 3: decade ago now is being applied to over fifty different 54 00:02:59,560 --> 00:03:04,760 Speaker 3: clinical programs. Why because we're able to program programmably deliver 55 00:03:04,840 --> 00:03:08,560 Speaker 3: a medicine for a particular purpose. Historically we've not been 56 00:03:08,560 --> 00:03:10,799 Speaker 3: able to do that in the pharmacool industry. We think 57 00:03:10,840 --> 00:03:14,560 Speaker 3: that's going to spread beyond just one instance to many 58 00:03:14,639 --> 00:03:18,760 Speaker 3: many different approaches, and the benefit will be shorter times, 59 00:03:18,840 --> 00:03:22,560 Speaker 3: higher predictability, reduction in cost of development, we expect. 60 00:03:22,720 --> 00:03:24,760 Speaker 1: But also in the clinical development. 61 00:03:24,280 --> 00:03:26,959 Speaker 3: Stage where you're actually testing this, do you have the 62 00:03:27,040 --> 00:03:28,600 Speaker 3: testings in thousands of people. 63 00:03:28,800 --> 00:03:29,560 Speaker 1: It's a very. 64 00:03:29,360 --> 00:03:32,440 Speaker 3: Different activity than if you can identify those who are 65 00:03:32,480 --> 00:03:34,560 Speaker 3: likely going to benefit so you can actually make them 66 00:03:34,600 --> 00:03:35,880 Speaker 3: more precise medicine. 67 00:03:37,000 --> 00:03:39,520 Speaker 2: So a number. You've raised three point six billion dollars 68 00:03:39,520 --> 00:03:41,160 Speaker 2: for these new ventures. Of course, what you do is 69 00:03:41,200 --> 00:03:44,160 Speaker 2: bankroll some of the biotech startups, but you also generate 70 00:03:44,240 --> 00:03:46,400 Speaker 2: in house ideas and build them from the ground up. 71 00:03:46,440 --> 00:03:51,680 Speaker 2: Do you know how you'll deploy those three point six billion, Well, this. 72 00:03:51,800 --> 00:03:54,040 Speaker 3: Is the second time in about three years that we've 73 00:03:54,080 --> 00:03:56,840 Speaker 3: raised roughly a similar sum of money. Of course, the 74 00:03:56,920 --> 00:04:01,840 Speaker 3: underlying technologies shift, but generally we fran frensceine our creating 75 00:04:02,120 --> 00:04:04,640 Speaker 3: about twenty five companies in the span of three years 76 00:04:04,880 --> 00:04:08,200 Speaker 3: through our labs. That's after roughly about one hundred attempts 77 00:04:08,440 --> 00:04:11,440 Speaker 3: that we do around different approaches to science, and these 78 00:04:11,440 --> 00:04:15,000 Speaker 3: will be roughly three quarters in the broad therapeutics arena, 79 00:04:15,040 --> 00:04:19,520 Speaker 3: in about a quarter in sustainability that includes agriculture, carbon capture, 80 00:04:19,680 --> 00:04:23,440 Speaker 3: flu regeneritive agriculture, and areas that use life science in 81 00:04:23,480 --> 00:04:26,719 Speaker 3: the planet itself, not just human health. Within human health, 82 00:04:26,839 --> 00:04:29,400 Speaker 3: there's a number of diseases which are more and more 83 00:04:29,480 --> 00:04:33,400 Speaker 3: lending themselves to more effective therapeutics. We've seen about vaccines, 84 00:04:33,680 --> 00:04:35,960 Speaker 3: We're seeing in cancer some major breakthroughs. 85 00:04:36,200 --> 00:04:39,560 Speaker 1: Also neurodegenitive diseases. Obesity has opened up. 86 00:04:39,600 --> 00:04:41,719 Speaker 3: If we were talking three years ago, it would not 87 00:04:41,800 --> 00:04:44,479 Speaker 3: be a big area of focus. Now we and others 88 00:04:44,600 --> 00:04:47,839 Speaker 3: have dozens of approaches going after that, so you know, 89 00:04:47,920 --> 00:04:51,279 Speaker 3: we'll go where it looks like there are more clear 90 00:04:51,320 --> 00:04:54,520 Speaker 3: opportunities to have impact. And of course we keep in 91 00:04:54,560 --> 00:04:57,080 Speaker 3: mind the cost effectiveness of the drugs that we can 92 00:04:57,120 --> 00:05:00,520 Speaker 3: develop through the innovations, not just the fact that we 93 00:05:00,520 --> 00:05:03,440 Speaker 3: can find small niche markets to be able to fulfill. 94 00:05:03,720 --> 00:05:06,839 Speaker 3: So this is the dynamic that the VIA technology industry 95 00:05:07,600 --> 00:05:10,360 Speaker 3: finds itself in and we feel privileged to be able 96 00:05:10,360 --> 00:05:13,919 Speaker 3: to have the resources, the human capital, scientific capital, and 97 00:05:13,920 --> 00:05:16,440 Speaker 3: financial capital to be able to continue our work. 98 00:05:17,800 --> 00:05:21,200 Speaker 2: So is this mainly also to develop individual drugs or 99 00:05:21,240 --> 00:05:24,080 Speaker 2: is it to revolutionize a part of the industry like 100 00:05:24,120 --> 00:05:28,159 Speaker 2: you did with the mRNA COVID vaccine at Maderna. 101 00:05:29,080 --> 00:05:32,640 Speaker 3: Well, that's an excellent question. So we create traditionally what 102 00:05:32,680 --> 00:05:37,080 Speaker 3: we call platform companies. These all involve major advances that 103 00:05:37,080 --> 00:05:40,000 Speaker 3: can lead to many drugs per company. To be able 104 00:05:40,040 --> 00:05:43,520 Speaker 3: to advance those drugs, we forge partnerships with many, many 105 00:05:43,600 --> 00:05:48,039 Speaker 3: large pharmacucual companies. So our style of company creation is 106 00:05:48,160 --> 00:05:51,200 Speaker 3: very much about platforms that lead to many products. We 107 00:05:51,279 --> 00:05:54,159 Speaker 3: have the ability to develop products in house ourselves and 108 00:05:54,240 --> 00:05:57,480 Speaker 3: increasingly in Flagship we have a team called Pioneering Medicines 109 00:05:57,800 --> 00:06:00,600 Speaker 3: doing that as well. So it'll be a bound between 110 00:06:00,680 --> 00:06:04,359 Speaker 3: platforms that lead to products in our companies, but also 111 00:06:04,440 --> 00:06:07,520 Speaker 3: selectively a few products that will develop as well. 112 00:06:07,560 --> 00:06:08,240 Speaker 1: It'll be a mix. 113 00:06:10,320 --> 00:06:13,760 Speaker 2: And where are you deploying regionally that capital is? You know, 114 00:06:13,760 --> 00:06:16,880 Speaker 2: how is the IRA actually impacting your investment decision in 115 00:06:16,920 --> 00:06:17,440 Speaker 2: the US. 116 00:06:19,040 --> 00:06:21,080 Speaker 3: Look at this point we look carefully at the IRA 117 00:06:21,680 --> 00:06:23,919 Speaker 3: and how it manifests it, so of course going after 118 00:06:24,320 --> 00:06:26,600 Speaker 3: more mature drugs in the first instance, but it does 119 00:06:26,680 --> 00:06:30,280 Speaker 3: affect how we think about disease areas and approaches small 120 00:06:30,320 --> 00:06:33,800 Speaker 3: molecules versus large ones that we need to be thoughtful 121 00:06:33,839 --> 00:06:37,200 Speaker 3: of as we innovate. But in terms of geography are 122 00:06:37,720 --> 00:06:41,080 Speaker 3: the essence of our operations are in Enrichment Boston, Massachusetts. 123 00:06:41,279 --> 00:06:43,360 Speaker 3: But over the last few years we've begun to set 124 00:06:43,440 --> 00:06:47,640 Speaker 3: up satellite operations, one in London, one in Singapore to 125 00:06:47,720 --> 00:06:50,279 Speaker 3: serve the IMPAC region and there what we're trying to 126 00:06:50,320 --> 00:06:54,159 Speaker 3: do is to create linkages between our ecosystem of roughly 127 00:06:54,240 --> 00:06:56,800 Speaker 3: forty eight distinct companies that have come out of our 128 00:06:56,920 --> 00:07:01,520 Speaker 3: lands and those regional ecosystems, trying to forge alliances in science, 129 00:07:01,800 --> 00:07:06,560 Speaker 3: in clinical trials, in partnerships with manufacturing, financial partnerships. So 130 00:07:06,880 --> 00:07:10,920 Speaker 3: we're beginning to create linkages between our proprietary ecosystem of 131 00:07:11,000 --> 00:07:15,560 Speaker 3: startups with a global presence in these key markets whereby 132 00:07:15,680 --> 00:07:18,400 Speaker 3: technology is also flourish. 133 00:07:18,480 --> 00:07:20,320 Speaker 2: I mean, do you have some things. I know, you're 134 00:07:20,720 --> 00:07:22,760 Speaker 2: very knowledgeable in some of the technologies that you're using 135 00:07:22,800 --> 00:07:24,760 Speaker 2: some of you know the mentionine that could come up. 136 00:07:24,800 --> 00:07:26,480 Speaker 2: Is there something that you think could be on the 137 00:07:26,520 --> 00:07:28,960 Speaker 2: cusp of really being a breakthrough. 138 00:07:30,240 --> 00:07:32,120 Speaker 3: Look, there's two things that I'll just mention, but of 139 00:07:32,120 --> 00:07:34,200 Speaker 3: course we have a lot, so anytime I mentioned too, 140 00:07:34,520 --> 00:07:37,280 Speaker 3: all the rest kind of feel like they're not not 141 00:07:37,320 --> 00:07:39,800 Speaker 3: as important. In fact they are, but just very briefly mentioned. 142 00:07:40,080 --> 00:07:42,920 Speaker 3: There's two companies I'll mentioned one called Generate by Medicines 143 00:07:43,200 --> 00:07:46,560 Speaker 3: that's truly cracked the ability to be able to computationally 144 00:07:46,640 --> 00:07:50,360 Speaker 3: generate antibodies, monop well and antibodies. These are the mainstay 145 00:07:50,440 --> 00:07:53,640 Speaker 3: drugs over the last few decades by technology, but we 146 00:07:53,680 --> 00:07:57,480 Speaker 3: can computationally now generate them to go after any target. 147 00:07:57,600 --> 00:07:59,360 Speaker 1: Very very specifically, very quickly. 148 00:07:59,600 --> 00:08:02,040 Speaker 3: That means that we have dozens and dozens of programs 149 00:08:02,320 --> 00:08:05,400 Speaker 3: now accelerating. This is using generative AI. It's a six 150 00:08:05,440 --> 00:08:08,680 Speaker 3: year old effort, quite large, about a dozen programs advancing. 151 00:08:08,960 --> 00:08:13,040 Speaker 3: So that's one breakthrough approach that will be a systemic 152 00:08:13,160 --> 00:08:15,560 Speaker 3: change in the way proteins are developed. The other is 153 00:08:15,560 --> 00:08:19,280 Speaker 3: a technology called gene writing that our company tes Therapeutics 154 00:08:19,360 --> 00:08:22,960 Speaker 3: has pioneered. And here instead of editing and making very 155 00:08:23,040 --> 00:08:25,560 Speaker 3: tiny changes to it, you know, we're able to insert 156 00:08:25,560 --> 00:08:27,960 Speaker 3: the correct gene that can take care of any and 157 00:08:28,040 --> 00:08:31,840 Speaker 3: all variants of a particular rare disease or in cancer, 158 00:08:32,160 --> 00:08:34,760 Speaker 3: have some very meaningful interventions. 159 00:08:34,800 --> 00:08:37,840 Speaker 1: So we're looking at gene writing. We're looking at these things. 160 00:08:38,040 --> 00:08:40,080 Speaker 3: And these are both platforms that are four or five 161 00:08:40,120 --> 00:08:42,440 Speaker 3: six years old, with hundreds of millions that have gone 162 00:08:42,440 --> 00:08:45,280 Speaker 3: into establishing the science and now really beginning to see 163 00:08:45,280 --> 00:08:46,000 Speaker 3: the fruits. 164 00:08:47,400 --> 00:08:50,040 Speaker 2: Nimber, what do you make of China and the Biosecures Act. 165 00:08:50,120 --> 00:08:54,079 Speaker 2: Would you, for example, consider investing in Chinese biotechs? 166 00:08:55,360 --> 00:08:58,040 Speaker 3: Well, for see, because our focus is so much on 167 00:08:58,080 --> 00:09:02,679 Speaker 3: our own internal science, investing per se in science that's 168 00:09:02,720 --> 00:09:06,280 Speaker 3: generated elsewhere, but we're rather financing the companies that we create. 169 00:09:06,600 --> 00:09:10,480 Speaker 3: But those companies in turn, have historically had occasionally ties 170 00:09:10,520 --> 00:09:14,120 Speaker 3: with Chinese companies, whether it was to do contract research, 171 00:09:14,160 --> 00:09:17,280 Speaker 3: to do manufacturing. Even in some cases we've explored commercial 172 00:09:17,280 --> 00:09:21,000 Speaker 3: relationships and those have been affected by the recent climate 173 00:09:21,080 --> 00:09:24,480 Speaker 3: that's been created, and so we, like others, are carefully 174 00:09:24,480 --> 00:09:27,880 Speaker 3: assessing where we might find headwinds when it comes to 175 00:09:27,920 --> 00:09:30,960 Speaker 3: those types of partnership and where we must continue to 176 00:09:30,960 --> 00:09:33,640 Speaker 3: be involved. Because health is a global challenge, it's not 177 00:09:33,679 --> 00:09:37,040 Speaker 3: a regional challenge, and if our technologies can address health 178 00:09:37,120 --> 00:09:40,480 Speaker 3: challenges in China, we need to find ways by which 179 00:09:40,480 --> 00:09:43,720 Speaker 3: we can actually apply ourselves there. But we're doing so 180 00:09:43,960 --> 00:09:48,439 Speaker 3: through partnerships, sometimes to regional partnerships. For example, in Singapore, 181 00:09:48,559 --> 00:09:51,400 Speaker 3: we have some discussions which may in turn make us 182 00:09:52,280 --> 00:09:54,559 Speaker 3: better partners to be present in China. 183 00:09:54,840 --> 00:09:56,760 Speaker 1: These are the things that are more refinements. 184 00:09:56,760 --> 00:09:59,440 Speaker 3: But we and others are looking at this and saying, okay, 185 00:09:59,480 --> 00:10:01,840 Speaker 3: where's this and how come we continue to bring our 186 00:10:01,880 --> 00:10:04,200 Speaker 3: science to bear on important problems there? 187 00:10:05,679 --> 00:10:07,240 Speaker 2: No bar thank you so much for joining us today. 188 00:10:07,240 --> 00:10:09,400 Speaker 2: There was No ber Afayan, the co founder of Maderna 189 00:10:09,440 --> 00:10:13,240 Speaker 2: and of course chief executive of Flagship Pioneering Pioneering after 190 00:10:13,880 --> 00:10:17,720 Speaker 2: fundraising some three point six billion for new ventures,