1 00:00:00,160 --> 00:00:03,760 Speaker 1: This is Bloomberg Business Week with Carol Masser and Jason 2 00:00:03,840 --> 00:00:08,160 Speaker 1: Kelly on Bloomberg Radio. This is a really interesting discussion 3 00:00:08,160 --> 00:00:10,639 Speaker 1: we're about to have and it actually harkens back and 4 00:00:10,720 --> 00:00:13,440 Speaker 1: thanks to Carol Masser for pointing this out on Twitter 5 00:00:13,520 --> 00:00:16,000 Speaker 1: earlier to a conversation. We actually had it in j 6 00:00:16,160 --> 00:00:18,800 Speaker 1: I t earlier this year when we were still out 7 00:00:18,840 --> 00:00:20,840 Speaker 1: about out and about in the world. It was a 8 00:00:20,840 --> 00:00:25,720 Speaker 1: really live event. Yeah, they're in Newark. We were talking 9 00:00:25,720 --> 00:00:29,280 Speaker 1: about the concept of digital twins. What is that, you ask, 10 00:00:29,520 --> 00:00:32,240 Speaker 1: Let's get into it. Charles Fisher is founder and CEO 11 00:00:32,400 --> 00:00:35,400 Speaker 1: of Unlearned AI. He joins us on the phone from 12 00:00:35,440 --> 00:00:38,159 Speaker 1: San Francisco. Dr Fisher, thank you so much for joining us. 13 00:00:39,479 --> 00:00:41,440 Speaker 1: Thank you for having me. All Right, So this is 14 00:00:41,479 --> 00:00:45,920 Speaker 1: exciting in part because we are all now many experts, 15 00:00:46,000 --> 00:00:49,080 Speaker 1: or at least we consider ourselves such on drug development. 16 00:00:49,120 --> 00:00:51,680 Speaker 1: You know, we talked with the CEO of Teva Pharmaceuticals 17 00:00:51,720 --> 00:00:54,200 Speaker 1: here on this program earlier and really got into a 18 00:00:54,240 --> 00:00:57,720 Speaker 1: lot of the issues here. How does artificial intelligence play 19 00:00:57,800 --> 00:01:00,320 Speaker 1: into this? How might it help us in an age 20 00:01:00,320 --> 00:01:06,200 Speaker 1: where we're all really interested in accelerating development? Here? Yeah, well, 21 00:01:06,240 --> 00:01:09,080 Speaker 1: I think there are a few different ways, starting all 22 00:01:09,120 --> 00:01:11,400 Speaker 1: the way from the beginning of the cycle to saying 23 00:01:11,600 --> 00:01:16,160 Speaker 1: how can we discover new compounds that might be effective therapies, 24 00:01:16,480 --> 00:01:18,679 Speaker 1: all the way to the end of the process, and 25 00:01:18,720 --> 00:01:21,760 Speaker 1: saying how can we leverage all of the data that 26 00:01:21,840 --> 00:01:26,280 Speaker 1: we have from electronic health records to make more efficient 27 00:01:26,400 --> 00:01:32,120 Speaker 1: clinical trials that can get drugs to patients faster. Well, okay, 28 00:01:32,400 --> 00:01:34,680 Speaker 1: so what's interesting is, and we're hearing more and more 29 00:01:34,680 --> 00:01:37,360 Speaker 1: about this, right, We've heard various time frames and what 30 00:01:37,480 --> 00:01:41,800 Speaker 1: it will take to develop um a new vaccine, and 31 00:01:42,200 --> 00:01:46,120 Speaker 1: the latest have been anywhere from nine months maybe the 32 00:01:46,160 --> 00:01:49,160 Speaker 1: most optimistic too, maybe a couple of years. So tell us, 33 00:01:49,920 --> 00:01:52,520 Speaker 1: you know, you shared with us some some some wisdom 34 00:01:52,560 --> 00:01:55,960 Speaker 1: about when you normally develop a new medicine, it's between 35 00:01:55,960 --> 00:02:00,360 Speaker 1: ten and fifteen years. How can digital cloning help skip 36 00:02:00,440 --> 00:02:07,120 Speaker 1: through this vaccine creation process faster and also ensure that 37 00:02:07,160 --> 00:02:12,440 Speaker 1: it's safe. Yeah, I think the biggest part of the 38 00:02:12,520 --> 00:02:15,280 Speaker 1: problem in terms of the time when it takes to 39 00:02:15,320 --> 00:02:17,120 Speaker 1: talk about how long will it take us to get 40 00:02:17,160 --> 00:02:19,840 Speaker 1: new treatments or new vaccines, whether that be for for 41 00:02:19,919 --> 00:02:23,280 Speaker 1: COVID nineteen or any disease, is the amount of time 42 00:02:23,320 --> 00:02:25,760 Speaker 1: it takes for us to tell if those are safe 43 00:02:25,800 --> 00:02:29,080 Speaker 1: and effective. So you know, one of the sort of 44 00:02:29,320 --> 00:02:32,200 Speaker 1: bad parts about drug development is that actually about nine 45 00:02:32,200 --> 00:02:36,120 Speaker 1: out of ten drugs that we try in clinical trials 46 00:02:36,200 --> 00:02:39,440 Speaker 1: end up failing. So most of the time our guesses 47 00:02:39,680 --> 00:02:43,040 Speaker 1: end up not working well. So it's what we'd like 48 00:02:43,080 --> 00:02:44,840 Speaker 1: to be able to do is to speed that up, 49 00:02:45,360 --> 00:02:48,640 Speaker 1: uh and in a variety of ways by postentially leveraging 50 00:02:49,240 --> 00:02:51,760 Speaker 1: uh So, what we call these these digital patients is 51 00:02:51,760 --> 00:02:57,480 Speaker 1: is leveraging data from electronic health records to make trials faster. Alright, 52 00:02:57,520 --> 00:03:01,639 Speaker 1: so tell us how it works. Sure, So, basically, when 53 00:03:01,639 --> 00:03:05,320 Speaker 1: a patient enrolls in the trial, we create a digital 54 00:03:05,360 --> 00:03:09,440 Speaker 1: copy of that patient that tells us what would happen 55 00:03:09,600 --> 00:03:12,040 Speaker 1: to that person if they were to receive a placebo, 56 00:03:12,120 --> 00:03:14,480 Speaker 1: so a dummy treatment, and then at the end of 57 00:03:14,480 --> 00:03:17,800 Speaker 1: the study, you give that real person the real treatment 58 00:03:18,520 --> 00:03:20,760 Speaker 1: how it affects them, and then you can compare it 59 00:03:20,800 --> 00:03:24,240 Speaker 1: to the digital twins prediction for what would have happened 60 00:03:24,520 --> 00:03:26,920 Speaker 1: if they had received the placebo, and then you can 61 00:03:27,080 --> 00:03:30,040 Speaker 1: estimate if that treatment was effective or not. And basically, 62 00:03:30,080 --> 00:03:33,960 Speaker 1: because you can do this but these predicted placebo responses, 63 00:03:34,280 --> 00:03:36,760 Speaker 1: you don't need to enroll as many subjects into a 64 00:03:36,760 --> 00:03:38,840 Speaker 1: clinical trump so you can run a trial with up 65 00:03:38,920 --> 00:03:43,680 Speaker 1: to half as many half as many subjects, So doing 66 00:03:43,720 --> 00:03:46,520 Speaker 1: this are we doing this in this? In this you know, 67 00:03:47,360 --> 00:03:54,840 Speaker 1: search um for a vaccine. I'm not aware of any 68 00:03:55,320 --> 00:04:00,000 Speaker 1: trials that are doing this in COVID nineteen right now. UM. 69 00:04:00,040 --> 00:04:03,040 Speaker 1: I think one of the difficulties there is because it's 70 00:04:03,080 --> 00:04:06,200 Speaker 1: such a new disease, we need to have a lot 71 00:04:06,240 --> 00:04:08,600 Speaker 1: of data for, you know, to say we know how 72 00:04:08,600 --> 00:04:11,160 Speaker 1: this disease will progress if you don't receive a treatment. 73 00:04:11,480 --> 00:04:13,960 Speaker 1: But since COVID nineteen is new, we really don't have 74 00:04:14,040 --> 00:04:16,840 Speaker 1: that that those data yet. But lots and lots of 75 00:04:16,920 --> 00:04:20,360 Speaker 1: data are being collected every day, So the ability to 76 00:04:20,360 --> 00:04:23,520 Speaker 1: to apply these types of technologies may that may be 77 00:04:23,640 --> 00:04:26,720 Speaker 1: something that could be done in the near future. All right, 78 00:04:26,839 --> 00:04:29,280 Speaker 1: we are all aware, and you're much more aware than 79 00:04:29,320 --> 00:04:33,479 Speaker 1: we are of the trials and tribulations as it were. 80 00:04:33,480 --> 00:04:35,760 Speaker 1: When it comes to the regulatory side of this, and 81 00:04:35,800 --> 00:04:38,640 Speaker 1: a lot of the regulatory framework exists for a reason. 82 00:04:38,720 --> 00:04:40,640 Speaker 1: We want people to be safe. We want drugs when 83 00:04:40,640 --> 00:04:43,280 Speaker 1: they get to the market to be as safe as 84 00:04:43,279 --> 00:04:47,159 Speaker 1: they can possibly be. What is the reaction, what has 85 00:04:47,200 --> 00:04:51,560 Speaker 1: been the reaction by organizations and institutions like the f 86 00:04:51,720 --> 00:04:56,560 Speaker 1: D A two plans like this mhm. The FDA is 87 00:04:56,600 --> 00:04:59,760 Speaker 1: actually doing a lot of work uh these days to 88 00:05:00,120 --> 00:05:04,240 Speaker 1: modernized clinical trials really across the board, and so in 89 00:05:04,279 --> 00:05:07,880 Speaker 1: general they're they're quite supportive of new approaches to bring 90 00:05:07,920 --> 00:05:10,520 Speaker 1: in these kinds of data to make things more efficient. 91 00:05:10,920 --> 00:05:13,560 Speaker 1: You really just have to work with them to demonstrate 92 00:05:13,640 --> 00:05:16,640 Speaker 1: with real evidence that the approach you're taking works well, 93 00:05:16,680 --> 00:05:20,040 Speaker 1: which is really how it should be anyway, right, um, So, 94 00:05:20,279 --> 00:05:22,360 Speaker 1: so I would say that they're quite open to it. Uh. 95 00:05:22,400 --> 00:05:25,440 Speaker 1: The approaches are also new, so there they have been 96 00:05:25,560 --> 00:05:29,800 Speaker 1: some I think a handful now of of drugs for 97 00:05:30,040 --> 00:05:33,880 Speaker 1: targeting different types of cancer where these types of evidence 98 00:05:33,880 --> 00:05:36,680 Speaker 1: have been have been used. Um. And then you know, 99 00:05:37,080 --> 00:05:39,599 Speaker 1: our company is applying some of these approaches in some 100 00:05:39,720 --> 00:05:44,440 Speaker 1: trials for for all disease. Now wells then talk to 101 00:05:44,520 --> 00:05:47,000 Speaker 1: us a little bit about you know, maybe it's not 102 00:05:47,080 --> 00:05:50,080 Speaker 1: something that's applicable as you said to COVID nineteen and 103 00:05:50,120 --> 00:05:54,560 Speaker 1: the hunt for a vaccine, but there are certainly some diseases, 104 00:05:54,560 --> 00:05:58,159 Speaker 1: whether it's Alzheimer's and so on, that really have plagued 105 00:05:58,240 --> 00:06:01,080 Speaker 1: us and trying to figure out some kind of cure 106 00:06:01,240 --> 00:06:03,960 Speaker 1: or some kind of you know, more significant treatment than 107 00:06:03,960 --> 00:06:07,000 Speaker 1: we currently have. Talked to us attle bit about those 108 00:06:07,040 --> 00:06:09,440 Speaker 1: types of ailments that might there might be some promise 109 00:06:09,560 --> 00:06:14,480 Speaker 1: using digital clones. Yeah, I think in you know, lots 110 00:06:14,520 --> 00:06:18,000 Speaker 1: of these diseases, whether they be you know, cancer, or 111 00:06:18,160 --> 00:06:22,720 Speaker 1: neurologic disease like Alzheimer's, or or rare genetic diseases. UM, 112 00:06:22,800 --> 00:06:25,400 Speaker 1: the ability to leverage all of the data we have 113 00:06:25,760 --> 00:06:28,680 Speaker 1: collected on those diseases to make those trials more efficient 114 00:06:29,279 --> 00:06:31,279 Speaker 1: UM with digital twins is I think going to be 115 00:06:31,400 --> 00:06:33,440 Speaker 1: very Uh. I think that's going to be the future 116 00:06:33,480 --> 00:06:36,160 Speaker 1: of how trials are run. It also, you know, one 117 00:06:36,200 --> 00:06:39,520 Speaker 1: of the sort of side effects of COVID nineteen that 118 00:06:39,560 --> 00:06:42,239 Speaker 1: since we're all sheltering in place right now, no one's 119 00:06:42,279 --> 00:06:45,880 Speaker 1: participating in clinical trial right so clinical trials for all 120 00:06:45,920 --> 00:06:49,080 Speaker 1: of these other diseases, for Alzheimers, for cancer, they're all 121 00:06:49,120 --> 00:06:52,840 Speaker 1: disruptive right now. And so figuring out ways to run 122 00:06:52,880 --> 00:06:57,400 Speaker 1: those trials with fewer patients or where so that patients 123 00:06:57,440 --> 00:07:00,640 Speaker 1: don't have to maybe go into the hospital by applying 124 00:07:00,680 --> 00:07:03,919 Speaker 1: new technologies like like digital twins will really help to 125 00:07:04,040 --> 00:07:07,200 Speaker 1: keep medical research going so that it's not all set 126 00:07:07,240 --> 00:07:11,160 Speaker 1: back because we aren't able to participate because of COVID 127 00:07:11,200 --> 00:07:15,200 Speaker 1: Night Team. All right, well, really interesting, interested to see 128 00:07:15,200 --> 00:07:17,640 Speaker 1: where this goes from. Here are thanks to Charles Fisher, 129 00:07:17,920 --> 00:07:20,960 Speaker 1: founders CEO of Unlearned AI, joining us on the phone 130 00:07:21,280 --> 00:07:24,600 Speaker 1: from San Francisco and the idea of digital twins. It 131 00:07:24,720 --> 00:07:28,360 Speaker 1: is cool, very cool. It makes sense, yes, and we 132 00:07:28,440 --> 00:07:29,840 Speaker 1: need to do more on it because I do think 133 00:07:29,840 --> 00:07:31,480 Speaker 1: this is going to be a big way for the 134 00:07:31,560 --> 00:07:34,480 Speaker 1: medical arena and their way forward to in terms of 135 00:07:34,520 --> 00:07:35,560 Speaker 1: tackling a lot of ailments.