WEBVTT 01:00.000 --> 01:12.000 We may need to have a conversation soon, Liam. You're showing up here an awful lot lately. 01:15.000 --> 01:17.000 It's good to see you, my friend. 02:00.000 --> 02:04.000 I hope you can feel that. 02:04.000 --> 02:10.000 I'm going to start to have basketball practice at 7.30, and so I'm going to need to stream before that. 02:10.000 --> 02:15.000 Let's not tell November, but we're trying to already shift. 02:15.000 --> 02:21.000 Ladies and gentlemen, this is number 42, number 42 in a row, just in case you're keeping track. 02:21.000 --> 02:25.000 42 in a row. 02:46.000 --> 02:50.000 We scheduled for 60 minutes next. 02:50.000 --> 02:56.000 It's going on French, British, Italian, Japanese television. 02:56.000 --> 03:02.000 We've only got 15 people watching. Maybe somebody should tweet this out. 03:02.000 --> 03:06.000 I always forget to do that. I'm not very good at this. 03:06.000 --> 03:11.000 Kids deserve a lot of credit. This town's been flooded with phony 20s for weeks. 03:11.000 --> 03:19.000 Oh, it was nothing, really. But old Mr. Pietro posing as the doorman sure had us fooled for a while. 03:19.000 --> 03:23.000 He really gave himself away when he put on his little puppet show for us. 03:23.000 --> 03:28.000 The real hero was Scooby-Doo. And by the way, where is he? 03:28.000 --> 03:31.000 Oh, no. Look at him. 03:31.000 --> 03:36.000 Like I said before, what a ham. 03:36.000 --> 03:39.000 Whoa, whoa, whoa, whoa. 04:07.000 --> 04:10.000 Good evening, ladies and gentlemen. 04:13.000 --> 04:17.000 Oh, God, and my voice still hurts like hell, but you know what? 04:17.000 --> 04:23.000 It's the 17th of October, and this is, as I said earlier, 42 in a row. 04:23.000 --> 04:26.000 I'm going to break that streak for a little sore throat. 04:36.000 --> 04:46.000 We're not going to get all the way through this. 04:46.000 --> 04:51.000 I forgot that this is a little slow. 04:51.000 --> 04:56.000 Let's see if I can fix this. 04:57.000 --> 05:06.000 Oh, there we go. That fixed it. 05:06.000 --> 05:11.000 That fixed it. 05:11.000 --> 05:15.000 Ladies and gentlemen, there was no spread in New York City. 05:15.000 --> 05:19.000 Infectious clones are the only real threat as far as R&A goes. 05:19.000 --> 05:23.000 The SIBO batches were likely distributed to make things probable, 05:23.000 --> 05:32.000 and transfection and healthy animals is dumb. 05:32.000 --> 05:35.000 Protocols were murdered, gain of function is a mythology. 05:35.000 --> 05:39.000 The Scooby-Doo mystery that you think you're solving is real. 05:39.000 --> 05:45.000 We've got to save our family and friends from it, because these players are committed to lies. 05:45.000 --> 05:49.000 And that'll keep everybody solving this mystery and the mysteries of the future 05:49.000 --> 05:53.000 if we don't expose their lies. 06:15.000 --> 06:40.000 Good evening, ladies and gentlemen. 06:40.000 --> 06:44.000 This is Giga Home Biological, a high resistance low noise information brief 06:44.000 --> 06:52.000 brought to you by a biologist. 06:52.000 --> 06:57.000 It is the 17th of October, 2023. My name is Jonathan Cooley. 06:57.000 --> 07:03.000 I'm coming to you live from Pittsburgh, Pennsylvania, in the great state of Pennsylvania 07:03.000 --> 07:09.000 in the United States of America. 07:09.000 --> 07:14.000 It often sounds like I'm on a starship because, in fact, I am. 07:14.000 --> 07:19.000 In the back of my garage, I also have a mock-up starship enterprise 07:19.000 --> 07:24.000 which I can shift to with a click of my mouse. 07:24.000 --> 07:27.000 Oh, wait, I have to also push this button. 07:27.000 --> 07:32.000 See, I'm not very good at this. I told you that a million times. 07:32.000 --> 07:38.000 The starship enterprise that I'm trapped on is a starship enterprise 07:38.000 --> 07:43.000 which has fallen out of orbit, so to speak, because of a rupture in time. 07:43.000 --> 07:51.000 And I'm going to try and explain that on Halloween over the course of a whole day on YouTube. 07:51.000 --> 07:58.000 So mark that on your calendar that once the workday is done on Halloween, 07:58.000 --> 08:07.000 JC on a bike is going to start streaming live and it's going to be a Halloween spectacular. 08:07.000 --> 08:11.000 And that's why I've been using this Star Trek background for a while 08:11.000 --> 08:17.000 because I've had this idea in my head like burning a hole in my brain for almost two years now. 08:17.000 --> 08:23.000 And it's never quite felt like the right time. 08:23.000 --> 08:33.000 But now it's definitely the right time. 08:33.000 --> 08:37.000 We don't need you in here. You can go back. 08:37.000 --> 08:41.000 So that's where we are. 08:41.000 --> 08:44.000 We don't want to be taking the bait on social media. 08:44.000 --> 08:49.000 We don't want to be taking the bait on TV, but I understand that it's hard not to turn it on. 08:49.000 --> 08:52.000 It's hard not to pay attention. 08:52.000 --> 08:55.000 But the show will go on. 08:55.000 --> 08:59.000 And the Scooby-Doo mystery is still being shoved down our throats. 08:59.000 --> 09:03.000 I showed you yesterday that there's a new book coming up by Rand Paul. 09:03.000 --> 09:11.000 And it is called the great COVID cover up. There's even a masked bad guy on the front cover. 09:11.000 --> 09:18.000 So if you think that the Scooby-Doo mystery is a joke, I hate to say it, but 09:18.000 --> 09:24.000 a few people have had a more apt analogy for what has been done to us. 09:24.000 --> 09:30.000 Because in order to describe what has been done to us, you need to adequately 09:30.000 --> 09:35.000 codify or describe what it is that happened. 09:35.000 --> 09:43.000 And you and me and all the members of drastic and everybody that wrote blog posts and medium posts and 09:43.000 --> 09:53.000 subsets about the potential that this was a lab leak in 2020 were all fooled into that behavior. 09:53.000 --> 09:56.000 We're all enchanted. 09:56.000 --> 10:01.000 Not by what was said on TV, but what was not said. 10:01.000 --> 10:04.000 What was said on social media, but was not said. 10:04.000 --> 10:09.000 What was not reported, but was not reported. 10:09.000 --> 10:15.000 In other words, it was a combination of what was said on TV and what didn't make sense. 10:15.000 --> 10:20.000 And also what wasn't said on TV and what made sense. 10:20.000 --> 10:27.000 And having enough prerequisite knowledge and enough 10:27.000 --> 10:33.000 skills with regard to learning new things when it needed to be learned, 10:33.000 --> 10:39.000 especially in the context of biology, there were very few people in a position to do that. 10:39.000 --> 10:50.000 A few hundred thousand on every continent all around the world that were currently engaged in the study of biology for a career. 10:50.000 --> 10:56.000 Reading every day, asking questions and finding the answers every day. 10:56.000 --> 11:01.000 Gee, I wonder how that receptor reacts or interacts with that receptor. 11:01.000 --> 11:04.000 I'm going to go and look it up. 11:04.000 --> 11:09.000 I'm going to see if anyone's asked this question or question related to it. 11:09.000 --> 11:15.000 And in order to be clever enough to answer those questions and to find those answers, 11:15.000 --> 11:19.000 you have to understand how those questions are asked, how they're formulated, 11:19.000 --> 11:24.000 how to use search engines, how to follow bibliographies, 11:24.000 --> 11:31.000 and you need to hold a whole host of terminology should be second nature to you 11:31.000 --> 11:36.000 so that you can read into these different fields of biology and bring together 11:36.000 --> 11:41.000 a cohesive concept of what's happening. 11:41.000 --> 11:46.000 And so not everybody that has their own business as a plumber is capable of doing that. 11:46.000 --> 11:49.000 Not everybody who teaches for a living is capable of doing that. 11:49.000 --> 11:56.000 Everybody who's a lawyer is capable of doing that in the context of biology. 11:56.000 --> 12:03.000 But it should have done, Ben, everybody working in a biology degree, academic setting, 12:03.000 --> 12:11.000 be it as a faculty member or a postdoc or a PhD student or a master's student 12:11.000 --> 12:14.000 or a senior in undergraduate biology. 12:14.000 --> 12:22.000 Anybody that had immunology 101 and had learned it, anybody that had a few years of biology 12:22.000 --> 12:29.000 so that the context of viral replication, the context of DNA and RNA, 12:29.000 --> 12:35.000 transcription and translation wouldn't be something that you also needed to learn. 12:35.000 --> 12:43.000 And at that stage, you had all of the tools necessary to be enchanted by the idea that 12:43.000 --> 12:51.000 you were covering up a lab leak and it would make sense. 12:51.000 --> 12:58.000 And so with the help of Jordan Peterson and Ben Shapiro and the Weinstein brothers 12:58.000 --> 13:03.000 and all the other people on this and elsewhere, 13:03.000 --> 13:08.000 a consensus that the mystery to be solved was where did this virus come from 13:08.000 --> 13:13.000 to be a solution according to Sam Harris, is it was an accidental lab leak 13:13.000 --> 13:17.000 or something from Mother Nature? 13:17.000 --> 13:25.000 Obvious solution for Brett Weinstein, it was, well, it was a lab leak or a gain of function virus. 13:25.000 --> 13:30.000 The obvious solution to everyone, whether they watched Brock's News or CNN, 13:30.000 --> 13:38.000 is that it's probably a lab leak and even if it wasn't a lab leak, they sure covered it up like it was. 13:38.000 --> 13:46.000 And so the beauty of this is not who they bamboozled but how we bamboozled each other. 13:46.000 --> 13:53.000 By their obvious reaction, by our obvious reaction, by those two positions being taken 13:53.000 --> 14:05.000 and so vehemently defended, it's not so different than the way one defends or doesn't defend abortion, 14:05.000 --> 14:10.000 the way that one defends or doesn't defend Israel, 14:10.000 --> 14:16.000 the way that one does defend or doesn't defend religion or Islam or the freedom of religion 14:16.000 --> 14:25.000 or the freedom of speech, the way that one does or doesn't use racism as a way of dividing people, 14:25.000 --> 14:30.000 a way of identifying a way that the world works. 14:30.000 --> 14:39.000 These divisions are all on the same line, they are dividing all the same people from one another 14:39.000 --> 14:48.000 and it's becoming increasingly clear that that is the goal, increasingly clear over decades that that's the goal. 14:48.000 --> 14:55.000 And these people have been put in place to control that narrative early on about a dangerous novel virus 14:55.000 --> 15:01.000 that we had to do something like lock down masks and make a vaccine for, 15:01.000 --> 15:11.000 that natural immunity might not work, that masks and bandanas and bushmeat, 15:11.000 --> 15:17.000 these people are responsible for it and I'm on this page right here. 15:17.000 --> 15:25.000 All of us are responsible for it, but at some moment in 2020 some people started to wake up. 15:25.000 --> 15:36.000 And so we can find some of those people, we can eliminate them from this illusion of consensus, 15:36.000 --> 15:40.000 we can see that they weren't part of it and not intentionally. 15:40.000 --> 15:47.000 More importantly, we can see that none of the people that are currently and listen carefully to this, 15:47.000 --> 16:00.000 please, none of the people that are currently out in front of the dissonant movement 16:00.000 --> 16:08.000 were out in front of the dissonant movement in 2020, none of them, 16:08.000 --> 16:17.000 not Robert Malone, not Steve Kirsch, not here at funded bush, no, not none. 16:17.000 --> 16:22.000 Michael Eden's not in front of us anymore, but you could call he was very out early 16:22.000 --> 16:32.000 and then canceled, canoe with Kowski, gone, Wolfgang Wodach, gone. 16:32.000 --> 16:41.000 The people that are currently leading the dissonant movement didn't even speak up until 2021, 16:41.000 --> 16:47.000 and meaningfully until the end of 2021 and that concludes Brett Weinstein and Steve Kirsch 16:47.000 --> 16:51.000 and Robert Malone and here at funded bush. 16:51.000 --> 17:00.000 All of these big names, none of them were speaking in Germany like Bobby was in 2020, 17:00.000 --> 17:09.000 none of them were talking about natural immunity like Ryan Cole was in 2020, 17:09.000 --> 17:19.000 none of them were coming out about hydroxychloroquine, not increasing the size of hearts and being ridiculous, 17:19.000 --> 17:27.000 Peter McCullough was, nobody was complaining about ventilating people that didn't need to be ventilated, 17:27.000 --> 17:33.000 none of these people, what Pierre Corey was, 17:33.000 --> 17:40.000 and so some of these people are real dissidents that saw the animal in the early stages, 17:40.000 --> 17:44.000 and when you see the animal in the early stages you get co-opted, 17:44.000 --> 17:50.000 because the animal in the early stages was a natural security priority, 17:50.000 --> 17:56.000 it involved special phone calls, it involved dudes with suits, 17:56.000 --> 18:06.000 it involved a national security protocol that was enacted by DHS employees, 18:06.000 --> 18:14.000 people from the military, people from homeland security, people from health and human services were all around the United States, 18:14.000 --> 18:22.000 and so when Pierre Corey went from Wisconsin to New York, he didn't just meet other doctors in New York City, 18:22.000 --> 18:27.000 he met the government, he met the military, 18:33.000 --> 18:41.000 this illusion of consensus has been created on purpose for us to solve this mystery of lab leak or natural virus, 18:42.000 --> 18:48.000 and in accepting the challenge, we have also accepted the premises of the challenge, 18:48.000 --> 18:52.000 which is that there is indeed a novel virus, 18:52.000 --> 18:59.000 and then we're always going to be trapped, our kids will always be trapped, 18:59.000 --> 19:02.000 and so as we move these people around the board, 19:02.000 --> 19:06.000 and people like Peter McCullough come out against the vaccine schedule, 19:06.000 --> 19:13.000 and Nick Hudson makes a nice presentation that Thomas Binder keeps crushing it, 19:13.000 --> 19:17.000 Denny is still doing amazing work, 19:17.000 --> 19:20.000 Jessica Hockett is still doing amazing work, 19:20.000 --> 19:25.000 Professor Martin Neal, and of course my friend Mark Yousatonic, 19:25.000 --> 19:31.000 Mark Yousatonic has been single-handedly 19:32.000 --> 19:37.000 I mean, the kinds of information that he's bringing together, 19:37.000 --> 19:44.000 the kind of backstory that he's put together is not some kind of piecemeal house of cards. 19:48.000 --> 19:52.000 It becomes very clear that this has been a long-standing plan. 19:52.000 --> 19:57.000 It becomes very clear that some of these people have been involved in this long-standing plan for a long time, 19:58.000 --> 20:03.000 Thursday, 5 o'clock Eastern, please be there. 20:05.000 --> 20:08.000 And so we need to rally around these people, 20:08.000 --> 20:14.000 rally around these people to help them, rally around them to get their message out, 20:18.000 --> 20:20.000 and to share it. 20:20.000 --> 20:22.000 This is the main message. 20:27.000 --> 20:35.000 The main message is the intramuscular injection of any combination of substances with the intent of augmenting the immune system is likely very dumb, 20:35.000 --> 20:39.000 and the transfection is not immunization. 20:39.000 --> 20:45.000 So you know the show must go on and the narrative must be kept alive, 20:45.000 --> 20:52.000 and in this quest to understand why we have been listening a lot to the words of Robert Malone, 20:53.000 --> 21:02.000 and in his rather recent objection, or let me say critique of the awarding of the Nobel Prize to Caracao and Weismann, 21:02.000 --> 21:12.000 as I recall, on the Vay John Health interview, which we covered a few weeks ago, which I highly recommend, 21:13.000 --> 21:21.000 he mentioned a name, Peter Colis, as being integral to the development of the COVID vaccines, 21:21.000 --> 21:25.000 and particularly the lipid nanoparticle that is being used for them. 21:26.000 --> 21:32.000 His intellectual property is held by Canada and in Germany, I think, 21:32.000 --> 21:37.000 and I thought it would be really cool to find a lecture by him, and it turns out, 21:37.000 --> 21:47.000 it turns out that Peter Colis actually got awarded the, I think it's the Gardener Award, 21:47.000 --> 21:49.000 which is one of these awards. 21:49.000 --> 21:54.000 It usually comes like a year or two before the Nobel Prize for some people, 21:54.000 --> 21:57.000 not all the people that get this award get a Nobel Prize, 21:57.000 --> 22:03.000 but a lot of the Nobel Prize winners have gotten, most of them have gotten this award before they get the Nobel Prize. 22:04.000 --> 22:11.000 And so luckily for us, it looks like Canada in an attempt to, you know, 22:11.000 --> 22:19.000 make sure that the Nobel Prize committee knew that it was these three awarded Weismann, Caracao, and Colis, 22:19.000 --> 22:25.000 the Gardener Award in 2022. 22:25.000 --> 22:30.000 And so in so doing, then Peter Colis has given some talks around Canada, 22:31.000 --> 22:35.000 and I thought we'd listen to one of those if you don't mind. 22:37.000 --> 22:41.000 Big head move, play. 22:42.000 --> 22:45.000 So I think we'll get going, everyone. 22:46.000 --> 22:53.000 It's a pleasure today that we're going to be welcoming Dr. Peter Colis to the University of Manitoba as our Gardener Lecture. 22:53.000 --> 22:57.000 Before I start, I just want to acknowledge that the University of Manitoba campuses are located on the original lands 22:57.000 --> 23:01.000 in Avis, and on the new land of the Métis Nation. 23:01.000 --> 23:04.000 We respect the treaties that were made on these territories and acknowledge the harms and mistakes of the past 23:04.000 --> 23:09.000 and we dedicate ourselves to move forward in partnership of Indigenous communities with a spirit of reconciliation and collaboration. 23:11.000 --> 23:13.000 That was striking. 23:13.000 --> 23:23.000 I'm not sure what to say about that, because as a multiracial American with more ethnicities in me that I care to count 23:23.000 --> 23:26.000 and I've frankly cared to do research, 23:26.000 --> 23:55.000 I don't really, I don't really know what happened in Canada, I know it must have happened in Canada, pretty similar to what happened down here in some respects and so they want to be respectful to the cultures that came before the modern Canadian culture, but it's a little striking if that's the way that they start every single 23:56.000 --> 24:10.000 I find that very impressive and I'm not sure, I'm not sure it's bad or good, I don't know what it means, it's just something that strikes me as pretty impressive 24:11.000 --> 24:22.000 Rewards Lecture celebrates the world's best biomedical and global health researchers. The Garener Foundation was established in 1957 with the main goal of recognizing and rewarding international excellence in fundamental research that impacts human health. 24:22.000 --> 24:33.000 Today there's been 402 awards that have been bestowed on laureates from over 40 countries and of those awardees, 96 have gone on to receive Nobel prizes, in fact many of the Garener Lectures, it's been seen now as almost like a pre-noble award. 24:34.000 --> 24:44.000 Today our speaker is the recipient of a Canadian Garener International Award, there are five awarded annually to outstanding biomedical scientists who have made original contributions to medicine resulting in an increased understanding of human biology and disease. 24:44.000 --> 24:50.000 Dr. Peter Collis is Director of the Nanomedicine Research Group and Professor of Biochemistry and Molecular Biology at the University of British Columbia. 24:50.000 --> 24:56.000 He and his co-workers have been responsible for fundamental advances in the development of nanomedicines employing lipid nanoparticles, technology, and he'll be talking about that today. 24:56.000 --> 25:02.000 He's co-founded 11 biotechnology companies published more than 350 scientific articles and is an inventor of over 60 patents. 25:02.000 --> 25:08.000 He's also co-founded three not-for-profit enterprises including the Center for Drug Research and Development and the Nanomedicines Innovation Network. 25:08.000 --> 25:21.000 The two drugs enabled by lipid nanopore technology, delivery systems devised by Dr. Collis and members of his UBC lab and colleagues in the companies he has co-founded have recently been approved. 25:21.000 --> 25:30.000 So with this I want to also just highlight Dr. Collis' numerous awards including the Order of Canada in 2021 and the Vin Future Prize and Prince Mojito Award in 2022. 25:30.000 --> 25:33.000 Peter, please give a warm up. 25:37.000 --> 25:40.000 Okay, thanks for that introduction, Peter. 25:40.000 --> 25:49.000 Okay, and what I'm going to talk to you about today is how accidental the way science actually is as opposed to the linear way that we often see it being portrayed. 25:49.000 --> 25:52.000 And so that's why I entitled it science and serendipity. 25:52.000 --> 25:59.000 There's a lot of good luck that's part of this and that relates to the phenomenon of particles that enable the COVID-19 vaccines. 25:59.000 --> 26:02.000 And I could also entitle it 50 years of lipids because that's pretty much what it is. 26:02.000 --> 26:06.000 So I've really been involved in this field for a while and there's been a long time. 26:06.000 --> 26:09.000 Now how do I get these to advance? That's the next thing. 26:09.000 --> 26:12.000 I'm just going to press a space bar here and maybe that'll work. There we go. 26:12.000 --> 26:21.000 So you've seen various public announcements about the analyses of the vaccine, the mRNA vaccines. 26:21.000 --> 26:26.000 And in some cases you see it's coming inside what people term as being tiny bubbles of past. 26:26.000 --> 26:29.000 This is a headline from Bloomberg. 26:29.000 --> 26:32.000 And so this is where the mRNA is coded in lipids. 26:32.000 --> 26:36.000 And so what I'm going to talk about is really these tiny bubbles of past. 26:36.000 --> 26:39.000 I'm not going to say that they're not really tiny bubbles of that, but anyway. 26:39.000 --> 26:40.000 So what are they? 26:40.000 --> 26:41.000 They're already tiny. 26:41.000 --> 26:43.000 We're talking about systems that are 100 nanometers or less in diameter. 26:43.000 --> 26:45.000 So 100 times smaller than a cell. 26:45.000 --> 26:47.000 But they're not really bubbles of past. 26:47.000 --> 26:50.000 They're nanoparticles made of membrane lipids. 26:50.000 --> 26:53.000 They've really taken my whole career 50 years to develop. 26:53.000 --> 26:59.000 So it goes way back to 1972 when I was involved in, I mean for 30 years I've been doing basic studies on membrane lipids. 26:59.000 --> 27:05.000 Starting about 1985 we started to apply some of that knowledge to development of lipidase systems. 27:05.000 --> 27:11.000 This dude is old because if he's been working on stuff since 1972, I was born in 1972. 27:11.000 --> 27:14.000 That's some impressive shit. 27:14.000 --> 27:15.000 Wow. 27:15.000 --> 27:18.000 And he didn't get the Nobel. 27:22.000 --> 27:25.000 These things aren't going to me. 27:25.000 --> 27:31.000 To development of systems that deliver cancer drugs, which is what we started to do. 27:31.000 --> 27:34.000 We started off trying to cure cancer and quite managed that with a few other things. 27:34.000 --> 27:40.000 And then starting in 1995, applying the knowledge, our basic knowledge, to deliver nucleic acid based drugs. 27:40.000 --> 27:47.000 Now, just going back to the beginning, this is, I got my PhD in solid state physics in using magnetic resonance in 1972. 27:47.000 --> 27:54.000 And I was looking at semiconductors at 40 degrees Kelvin, like transistors on the moon. 27:54.000 --> 28:00.000 Anyway, I decided after doing this that I really had to do something different. 28:00.000 --> 28:05.000 Most of the interesting problems that I could see were outside the field of physics, particularly in the life sciences. 28:05.000 --> 28:09.000 And so I got interested in applying NMR in the life sciences. 28:09.000 --> 28:13.000 And I got a fellowship to go to the biochemistry department in Oxford in 1973. 28:13.000 --> 28:15.000 And I knew absolutely nothing about biochemistry. 28:15.000 --> 28:20.000 So this was quite an introduction by fire. 28:20.000 --> 28:23.000 Now, I knew a bit about NMR. 28:23.000 --> 28:27.000 I published my last physics paper in 1976, consisted of about 96 equations. 28:27.000 --> 28:32.000 But anyway, that's pointing out that when you learn something as a physicist, you learn it in some depth. 28:32.000 --> 28:36.000 And there's other things that you learn as a physicist. 28:36.000 --> 28:49.000 You get the feeling that this guy is already like a Tyrannosaurus Rex compared to the salamander that Weisman was. 28:49.000 --> 28:54.000 I mean, I already know that I would not want to sit at a table with this guy and open my mouth. 28:54.000 --> 28:57.000 I would want to sit at this table and listen. 28:57.000 --> 29:03.000 And it is completely the opposite feeling that you have when listening to Drew Weisman. 29:03.000 --> 29:06.000 And I'm not trying to be a creep. 29:06.000 --> 29:11.000 This guy clearly, I mean, wow, I don't even know what to say. 29:11.000 --> 29:15.000 For a mentalist, when you need an instrument that isn't available, you build it. 29:15.000 --> 29:18.000 And of course, going after basic problems is another aspect. 29:18.000 --> 29:23.000 But what I got started in 1973, they had a mag, big mag. 29:23.000 --> 29:27.000 I guarantee you, there's no pictures like that of Drew Weisman. 29:27.000 --> 29:31.000 And so, some of them were powerful ones in the world at the time, actually. 29:31.000 --> 29:33.000 They had no electronics to go with it. 29:33.000 --> 29:37.000 So, we had to build me and a graduate student. 29:37.000 --> 29:40.000 So, we built all the electronics to go with the sent- 29:40.000 --> 29:41.000 Holy shit. 29:41.000 --> 29:42.000 So, that's why I spent my first year there. 29:42.000 --> 29:43.000 That's impressive. 29:43.000 --> 29:44.000 Otherwise, it would have been a bit tricky. 29:44.000 --> 29:47.000 But it was a bit tricky as it was. 29:47.000 --> 29:49.000 And then I used the Edamar machine to- 29:49.000 --> 29:51.000 See, it's not to be underestimated. 29:51.000 --> 29:57.000 And I'll put myself in the same shoes or the same boxes, everybody else. 29:57.000 --> 30:05.000 It's not to be underestimated how amazing it is that in the 70s and the 80s, 30:05.000 --> 30:11.000 if you were doing science, you made everything yourself. 30:11.000 --> 30:13.000 It's really very similar to this. 30:13.000 --> 30:18.000 Let me show you another example of what I think is so extraordinary about 30:18.000 --> 30:28.000 where we are in human time and how ridiculous it is. 30:28.000 --> 30:37.000 When I was a child, there was a place that we would drive past on the highway. 30:37.000 --> 30:42.000 We were about 30 minutes away from where the mall was and where all the stores were 30:42.000 --> 30:45.000 because we lived in a super-tidy town in northern Wisconsin. 30:45.000 --> 30:50.000 And so on this drive, we would go past this place where a lot of times 30:50.000 --> 30:54.000 there were dudes playing with model airplanes. 30:54.000 --> 31:00.000 And so you would drive past and then you would suddenly catch this super-tiny airplane 31:00.000 --> 31:05.000 flying really fast and like, you know, doing loops or whatever. 31:05.000 --> 31:08.000 And you immediately realized that it was a model airplane. 31:08.000 --> 31:12.000 And we also occasionally would see a helicopter there. 31:12.000 --> 31:14.000 But the helicopter didn't do anything. 31:14.000 --> 31:16.000 The helicopter just flew. 31:16.000 --> 31:19.000 But that was pretty unbelievable. 31:19.000 --> 31:27.000 Now in those days, those guys were making almost everything that they did. 31:27.000 --> 31:33.000 You know, even the servos, and they certainly had to be able to repair them. 31:33.000 --> 31:37.000 They certainly had to customize them. 31:37.000 --> 31:43.000 And so you couldn't go on Amazon and buy a kit for a helicopter. 31:43.000 --> 31:48.000 And if you did buy a kit for a helicopter, it might be $2,000. 31:48.000 --> 31:56.000 Now you can go on Amazon and you can get a helicopter that should cost $2,000 for about $200. 31:56.000 --> 32:04.000 And the crazy thing is, is that the amount of skill that I need to build this is zero 32:04.000 --> 32:06.000 because it comes built. 32:06.000 --> 32:09.000 So you get it out, you put the battery in, you put it on the ground. 32:09.000 --> 32:11.000 You can fly it. 32:11.000 --> 32:13.000 And there's no building. 32:13.000 --> 32:14.000 There's no tweaking. 32:14.000 --> 32:19.000 I don't have to adjust the pitch and the way that the swash plate interacts with. 32:19.000 --> 32:27.000 There's no, and I know how to do all that because I've been playing with helicopters for quite so many years. 32:27.000 --> 32:37.000 But what I'm suggesting to you is, is that there is a whole generation of biologists that's growing up 32:37.000 --> 32:47.000 going into the lab, going into graduate school, and rather than being required to build the helicopter 32:47.000 --> 32:53.000 that they're going to use for their experiments, they order one. 32:53.000 --> 33:00.000 And so they're not even really sure if the person who designed what they ordered knows what they're doing, 33:00.000 --> 33:09.000 or whether it meets the specs, or whether it meets the fidelity measurements of the specificity that's required. 33:09.000 --> 33:12.000 Science almost has to be done on the product now. 33:12.000 --> 33:19.000 Let's evaluate this helicopter for its attributes before we can realize if we can use it for the experiment. 33:19.000 --> 33:28.000 And so so much of science is spent looking for the product that you're going to use to do your measuring. 33:28.000 --> 33:38.000 Looking for the apparatus and then making up an experiment rather than coming up with the experiment and building an apparatus like they still do some places. 33:38.000 --> 33:44.000 The most cutting edge neuroscientists are making their own apparatus still. 33:44.000 --> 33:49.000 But what I need you to understand is molecular biology used to be like that. 33:49.000 --> 33:56.000 And it's clear that this guy cut his teeth during a time when science was real. 33:56.000 --> 34:00.000 And has worked with people who know how to do real science. 34:00.000 --> 34:04.000 From the ground up, from the dirt up. 34:04.000 --> 34:10.000 We need to measure X. Well we don't have one of those in the catalog, so I'm going to make one that can measure X. 34:10.000 --> 34:12.000 That's what he did. 34:12.000 --> 34:19.000 There are almost, I would go out to say, there are almost no scientists to do this anymore. 34:19.000 --> 34:27.000 And the worst is that there are almost no geneticists that do this anymore. 34:27.000 --> 34:31.000 No molecular biologists that do this anymore. 34:31.000 --> 34:35.000 The market is literally flooded with products that do it for you. 34:35.000 --> 34:39.000 With pre-made reagents. 34:39.000 --> 34:47.000 And it's not to say that accuracy can't be achieved with products, but it is to say that the mastery. 34:48.000 --> 34:50.000 That the understanding. 34:50.000 --> 34:53.000 That the in-depth grasp. 34:53.000 --> 34:57.000 The question that you're asking and the limitations of it. 34:57.000 --> 35:03.000 Are kind of lost if you can order the helicopter online. 35:03.000 --> 35:06.000 And it comes already set up. 35:06.000 --> 35:09.000 So that you don't have to program the electronic speed controller. 35:09.000 --> 35:11.000 You don't have to bind it to the radio. 35:11.000 --> 35:15.000 You don't have to program the radio. 35:15.000 --> 35:19.000 You don't have to program the swash angle. 35:19.000 --> 35:23.000 And so what the hell do you really know about how a helicopter works if you can order it online 35:23.000 --> 35:27.000 and put it on the ground plug in the battery and fly it away? 35:27.000 --> 35:34.000 And what do you really know about the genome if you can buy a mini prep kit, 35:34.000 --> 35:40.000 amplify your DNA, sequence it, and then you get a piece of paperback. 35:40.000 --> 35:46.000 Never having known what went into that aluminum product that you combined with your Abbott product. 35:46.000 --> 35:57.000 And you combined with your bio-rad product and then put it back in your aluminum product. 35:57.000 --> 36:00.000 Okay, I'm getting a little crazy here, but anyway. 36:00.000 --> 36:02.000 Well, it was a bit tricky as it was. 36:02.000 --> 36:06.000 And then I used the Edamar machine to study eulipins in biological memory. 36:06.000 --> 36:08.000 So really basic stuff. 36:08.000 --> 36:11.000 But it's going after basic problems. And so this is a model of biological memory, 36:11.000 --> 36:14.000 which is a synchronical model, which is still quite relevant today, 36:14.000 --> 36:17.000 showing the various components proteins and lipids that were all good. 36:17.000 --> 36:22.000 No, I'm going to toot my own horn for a little bit, though. 36:22.000 --> 36:37.000 Because I have a little invention that I invented for my work. 36:37.000 --> 36:45.000 Which is a very, maybe I can use this camera. 36:45.000 --> 36:51.000 It's a very hand sensitive air adjustment. 36:51.000 --> 36:56.000 You can't even roll it now. This is probably a little rusty, but you put an air hose on this end. 36:56.000 --> 37:00.000 And if you put an air hose on this end. 37:00.000 --> 37:04.000 When you're making electronic electrical recordings from neurons under a microscope, 37:04.000 --> 37:08.000 you need to be able to control the air pressure inside of the electrode. 37:08.000 --> 37:14.000 Because you use the subtle change in pressure to actually make contact with the cell. 37:14.000 --> 37:19.000 And in the old days, these patch climbers would use a hose in their mouth. 37:19.000 --> 37:23.000 And they would blow in it a little bit or they'd suck in it a little bit. 37:23.000 --> 37:28.000 And so when people were making these recordings for eight or nine hours a day when they were really obsessed, 37:28.000 --> 37:33.000 they would be the whole day with this tube in their mouth. 37:34.000 --> 37:38.000 And later on, people started using syringes and I didn't like it. 37:38.000 --> 37:48.000 So I made this thing, I asked this machine, machinist student at the, the Erasmus University. 37:48.000 --> 37:51.000 I hope George Webel here this. 37:51.000 --> 37:58.000 And this summer student machined me this thing that you can roll with your hand and it moves the piston down. 37:58.000 --> 38:02.000 And you can push this button and it makes it neutral. 38:02.000 --> 38:07.000 And so just like taking it out of your mouth or blowing or sucking, you can essentially roll this knob, 38:07.000 --> 38:10.000 you can blow and you can suck and you can also go neutral. 38:10.000 --> 38:17.000 And you can do it with your hand and it's really, really small amount with your, with this roll. 38:17.000 --> 38:19.000 You know, really small amount. 38:19.000 --> 38:25.000 And it made my, it made all of the quality of my recordings just go out the roof. 38:25.000 --> 38:30.000 And so I made lots of different varieties and versions of it and gave some to people. 38:30.000 --> 38:35.000 And for a while, it was a really cool thing and it was something I was really proud of. 38:35.000 --> 38:40.000 Because it really made me more efficient and it was really something that, you know, 38:40.000 --> 38:46.000 after all this time of all these people contributing all this stuff to this one methodology, 38:46.000 --> 38:54.000 it was cool after 15 years of doing it to finally make something that was really mine that actually made it better. 38:54.000 --> 38:58.000 You know, and, and nobody cares about this stuff. 38:58.000 --> 39:07.000 But I'm just saying that like, that's really where, where good, honest science is done. 39:07.000 --> 39:12.000 It's done at that level where, where you've got to invent something in order to measure. 39:12.000 --> 39:15.000 You've got to invent something in order to perform the experiment. 39:15.000 --> 39:23.000 You've got to, you've got to solve a problem via invention in order to get to your goal is a very rare thing. 39:23.000 --> 39:30.000 In academic science anymore and even more rare, it's rare in biology. 39:30.000 --> 39:37.000 Because biological science has been inundated, completely inundated by products. 39:37.000 --> 39:44.000 And even in neuroscience where you would think if you're designing a behavioral experiment for a monkey, 39:44.000 --> 39:48.000 you're going to have to build your own custom setup, but you'd be wrong. 39:49.000 --> 39:55.000 If you just take what Kevin McCarran does, for example, what he used to do when he was a neuroscientist 39:55.000 --> 40:01.000 and actively working with monkeys, although he may have built his own apparatuses, 40:01.000 --> 40:05.000 my guess is, is that if he were to start his own lab now, 40:05.000 --> 40:09.000 there would be some product out there that would be better than anything he could build. 40:09.000 --> 40:16.000 And if he had the grant money, he would choose to buy that because it's much faster and better and whatever. 40:17.000 --> 40:23.000 My point is, is that these products are everywhere all over the place. 40:23.000 --> 40:29.000 They're at every conference. Science isn't this going into the shop, 40:29.000 --> 40:35.000 going into the, you know, wood shop, invention shop, metal shop, 40:35.000 --> 40:39.000 using a lathe that's not like that anymore. 40:39.000 --> 40:44.000 And in molecular biology, I know I'm beating a dead horse, but I really want you to hear it. 40:44.000 --> 40:52.000 In molecular biology, it's like bakery where you don't have any recipes anymore. 40:52.000 --> 40:59.000 It would be really like if all the bakeries were just boxed cakes 40:59.000 --> 41:03.000 and you never went to the store and bought flour and sugar anymore, 41:03.000 --> 41:09.000 you just bought boxed cakes from all these different companies. 41:09.000 --> 41:12.000 And they came in all different sizes. You want to make a wedding cake? 41:12.000 --> 41:15.000 Well, you can order a wedding cake from thermal scientific, 41:15.000 --> 41:18.000 or you can order a wedding cake from Sigma. 41:18.000 --> 41:22.000 But if you want cupcakes, I think, I think that, you know, 41:22.000 --> 41:26.000 molyneau labs makes the best cupcakes you should order their product. 41:26.000 --> 41:30.000 And it would come in a little box and all the, all the ingredients would be separate 41:30.000 --> 41:33.000 and then there would be instructions on how to mix them. 41:33.000 --> 41:41.000 And it might just say put two one and tube two and add four liters of distilled water, 41:41.000 --> 41:50.000 stir for 30 minutes and then put it on at a temperature for an hour and there it is. 41:50.000 --> 41:54.000 And because you were completely not involved in any part of the baking, 41:54.000 --> 41:56.000 you don't understand anything. 41:56.000 --> 41:59.000 You don't even know what was in tube one and tube two. 41:59.000 --> 42:04.000 You just know that tube one and tube two couldn't be combined in transport. 42:04.000 --> 42:08.000 In fact, hostess might not even tell you what's in tube one and tube 42:08.000 --> 42:11.000 because it's proprietary information. 42:11.000 --> 42:14.000 The buffers are secret. 42:14.000 --> 42:17.000 Do you see my point? 42:17.000 --> 42:20.000 These people don't know what they're doing when they sequence a reaction. 42:20.000 --> 42:23.000 When they sequence a virus, they don't know what they're doing 42:23.000 --> 42:26.000 when they do a PCR reaction. They're using a product, 42:26.000 --> 42:32.000 reading the directions and interpreting the product's outcome based on the directions. 42:32.000 --> 42:36.000 That's it. 42:36.000 --> 42:40.000 And a lot of molecular biologists are so disconnected from the molecular biology 42:40.000 --> 42:46.000 that they purport to do that they can't even explain what parts of the products they use 42:46.000 --> 42:53.000 are responsible for what parts of the reaction they purport to be exhibiting. 42:53.000 --> 42:57.000 So I'm trying to say, with a ridiculously long rant, 42:57.000 --> 43:01.000 that this guy seems to be a PIMP. 43:01.000 --> 43:04.000 A real deal. 43:04.000 --> 43:06.000 A real deal. 43:06.000 --> 43:08.000 And I really like that. 43:08.000 --> 43:13.000 But you can see it because you could hear it from the very beginning. 43:13.000 --> 43:17.000 This guy had to build electronics in order to do stuff. 43:17.000 --> 43:20.000 He's the real deal. 43:21.000 --> 43:24.000 Now, all biological membranes rely on a bilayer. 43:24.000 --> 43:27.000 A little bit bilayer as a structural element. 43:27.000 --> 43:30.000 So this is taking out the proteins. This is what you're going to see. 43:30.000 --> 43:32.000 And the membrane lipid, of course, are amphicathic. 43:32.000 --> 43:35.000 I mean, they have a polar head. They're self-assembling, which is a wonderful quality. 43:35.000 --> 43:37.000 They have one end who likes to be in their water and the other end 43:37.000 --> 43:40.000 who likes to be sequestered away from water. 43:40.000 --> 43:45.000 So the basic questions I got interested in were two falls. 43:45.000 --> 43:48.000 One of them was, well, positive memories contain a lot of lipids 43:48.000 --> 43:51.000 that if you pull them out and then put them in water, they don't adopt bylars. 43:51.000 --> 43:53.000 So what are they doing there? And what's their roles? 43:53.000 --> 43:56.000 So what are these non-biler lipids there? It's called lipid polymorphism. 43:56.000 --> 43:58.000 Along way from vaccines, what we actually used, the knowledge that we gained 43:58.000 --> 44:03.000 studying with the polymorphism to develop more effective delivery systems for mRNA. 44:03.000 --> 44:06.000 And the other question that we looked at was lipid asymmetry. 44:06.000 --> 44:10.000 So the lipid composition on one side of a biological membrane is different than the lipid composition on the other side. 44:10.000 --> 44:13.000 And so we asked the question, well, can this be related to our ingredients? 44:13.000 --> 44:16.000 You know, they're present across membranes, whether they're sodium potassium 44:16.000 --> 44:18.000 or proton or pH gradients. 44:18.000 --> 44:23.000 And so this is, I spent a lot of time and still do spend time actually on investigating those properties. 44:23.000 --> 44:24.000 And what we discovered. 44:24.000 --> 44:29.000 One of the things I would hope, if you would pay attention to, if anybody has 44:29.000 --> 44:39.000 listened to Christine Grace, this person that we covered one time on a V John Health lecture, 44:39.000 --> 44:45.000 she seems to be quite the expert on nanoparticles and lipid nanoparticles. 44:45.000 --> 44:50.000 It would be interesting if you're listening and you notice anything that she said 44:50.000 --> 44:55.000 that's the same as what he said, or if you've noticed any contradictions 44:55.000 --> 44:57.000 between what he's saying and she's saying. 44:57.000 --> 45:03.000 What I guess is going to happen is, is you're going to hear almost no overlap in terms whatsoever. 45:03.000 --> 45:05.000 No overlap in concepts whatsoever. 45:05.000 --> 45:08.000 But maybe I'm wrong. I'm optimistic. 45:08.000 --> 45:11.000 It was that we could use NMR, phosphorus NMR. 45:11.000 --> 45:13.000 I mean, phosphorus have a phosphorus in them. 45:13.000 --> 45:17.000 So we could use a phosphorus NMR to study these different structures that lipids a dot. 45:17.000 --> 45:21.000 The misad water in these civil form bilayers are these other structures. 45:21.000 --> 45:25.000 They form a bilayer, then their main emotional characteristic is rotation around the long axis. 45:25.000 --> 45:28.000 And so this gives rise due to the phosphorus and the phosphate. 45:28.000 --> 45:31.000 So this kind of characteristic line shape with a low field shoulder and high field peak. 45:31.000 --> 45:34.000 So it's quite diagnostic of a lipid bilayer. 45:34.000 --> 45:38.000 On the other hand, if they adopt these strange structures such as exactly the H2 phase, 45:38.000 --> 45:41.000 it consists of long tubes of water surrounded by lipids on the outside. 45:41.000 --> 45:44.000 These are about, say, ten animes or so on, or less in diameter. 45:44.000 --> 45:47.000 Then they can, additional emotional averaging, they can rotate, they can go around these 45:47.000 --> 45:48.000 so it gets very rapidly. 45:48.000 --> 45:51.000 It ends up giving you a different line shape with a low field peak and high field shoulder. 45:51.000 --> 45:55.000 So really quite, and if they have total emotional averaging, then you see a very narrow line shape. 45:55.000 --> 46:02.000 And so this allowed us to study the properties of many lipids in terms of their face preferences. 46:02.000 --> 46:06.000 Are you understanding that he's using NMR to look at the structure of lipids 46:06.000 --> 46:12.000 that he uses the NMR signal to identify the organization of the lipid. 46:12.000 --> 46:20.000 And so this NMR signal spectrum across frequencies is different than this one, 46:20.000 --> 46:24.000 the reflection or the light or the energy that comes off of it. 46:24.000 --> 46:26.000 As long as you understand that much, it's okay. 46:26.000 --> 46:30.000 I don't know if I can even explain the particulars of NMR. 46:31.000 --> 46:36.000 It's magnetic resonance, which basically means how do the molecules resonate 46:36.000 --> 46:38.000 and the resonance across frequencies. 46:38.000 --> 46:40.000 So I think they do a frequency sweep. 46:40.000 --> 46:46.000 And when you see a frequency sweep and then the amount of resonance that you get, 46:46.000 --> 46:49.000 and it's different in the different structures here. 46:49.000 --> 46:54.000 So they can start to probe how lipids arrange themselves using NMR. 46:54.000 --> 46:57.000 And the NMR machine is what he had to build by scratch. 46:57.000 --> 47:00.000 And if they had total emotional averaging, then we can see a very narrow line shape. 47:00.000 --> 47:06.000 And so this allowed us to study the properties of many lipids in terms of their face preferences 47:06.000 --> 47:08.000 and have a good shift between one or the other. 47:08.000 --> 47:09.000 This has been the crowd. 47:09.000 --> 47:12.000 I worked on a lot of this with him in the late 70s, early 80s. 47:12.000 --> 47:15.000 And just pointing out that we could rationalize this by, and this will come in back in, 47:15.000 --> 47:17.000 when we talk about the vaccines. 47:17.000 --> 47:21.000 The lipids and the biler structure have more of a cylindrical shape. 47:21.000 --> 47:24.000 The Herion head group region can be more or less the same as in the isyl chain region. 47:25.000 --> 47:28.000 Whereas if it's like the structure of this, such as these hexagonal phases, 47:28.000 --> 47:32.000 have a cone structure where the area for a molecule in the head group is somewhat less 47:32.000 --> 47:34.000 than the area is attended by the isyl chains. 47:34.000 --> 47:36.000 So very simplistic thinking that them went on and saying, 47:36.000 --> 47:38.000 okay, well, what are the function roles? 47:38.000 --> 47:39.000 Can you hear that though? 47:39.000 --> 47:41.000 Again, maybe I'm playing it too fast. 47:41.000 --> 47:42.000 I can hear it, but maybe you don't. 47:42.000 --> 47:48.000 So the way that they organize seems to be the way that the head, the phosphate head, 47:48.000 --> 47:51.000 is related to the chain on the bottom. 47:51.000 --> 48:00.000 If the molecular shape is the phosphate head on the top is bigger than the tail, 48:00.000 --> 48:02.000 then you will get a micellar. 48:02.000 --> 48:09.000 My cell micellars are these things that we talked about with Robert Malone a few weeks ago. 48:09.000 --> 48:17.000 If the lipids on the bottom are related to the phosphate head are about the same size 48:17.000 --> 48:19.000 and they're cylindrical, then you get this bilayer. 48:19.000 --> 48:25.000 And if they're conical, meaning that the tails coming off the bottom, 48:25.000 --> 48:28.000 I think those are fatty acids, but I'm not sure. 48:28.000 --> 48:31.000 It's like a phospholipid head and then fatty acids on the bottom. 48:31.000 --> 48:35.000 But the fatty acids are spreading out like that in a cone shape, 48:35.000 --> 48:38.000 then they will make that hexagonal arrangement. 48:38.000 --> 48:42.000 And so they're trying to study how lipids assemble. 48:42.000 --> 48:46.000 Obviously, this will then allow them to make different assemblies of lipids 48:46.000 --> 48:48.000 and understand why they assemble the way that they do. 48:48.000 --> 48:49.000 It's really cool. 48:49.000 --> 48:52.000 You would not be opposed to it being slower. 48:52.000 --> 48:54.000 I'll put it one notch slower then. 48:58.000 --> 48:59.000 By the acyl chains. 48:59.000 --> 49:02.000 So very simplistic thinking that them went on saying, 49:02.000 --> 49:05.000 okay, well, what are the function roles of these lipids? 49:05.000 --> 49:10.000 Now, in order to investigate these roles, it's very difficult to do anything in the intact membrane. 49:10.000 --> 49:13.000 There are hundreds of different lipids species there, 49:13.000 --> 49:17.000 and trying to pick out the properties of one lipid versus others is pretty much impossible. 49:17.000 --> 49:22.000 And so you have to use very simple model membrane or less termed liposomal systems. 49:22.000 --> 49:26.000 Now, at the time, there was not really a good way of making these systems. 49:26.000 --> 49:29.000 You wanted to make these systems with one bilayer of an inside and an outside, et cetera. 49:29.000 --> 49:34.000 So we devised a high pressure extrusion technique 49:34.000 --> 49:37.000 where we basically took larger lipid bilayer systems 49:37.000 --> 49:41.000 and rammed them through a polycarbonate filter with a 100 nanometer pore size. 49:41.000 --> 49:45.000 And so out of the other end, you get these unital amount of systems just like this. 49:45.000 --> 49:50.000 So this is where building an apparatus should be formed a company on this that serves this very well. 49:50.000 --> 49:57.000 But anyway, we can use these liposomes to show that membrane fusion depends on, 49:57.000 --> 50:00.000 you know, if we have non-biodar lipids and we add them, 50:00.000 --> 50:04.000 we put a certain small proportion in with these bilayer forming vesicles. 50:04.000 --> 50:08.000 Then we'll see, we've been in dander fusion between them, and we'll see. 50:08.000 --> 50:13.000 So definitely, this is much other evidence indicates that the membrane fusion 50:13.000 --> 50:18.000 is really dependent on these lipids, of course, membrane fusion and vital to life in many ways. 50:18.000 --> 50:20.000 Now, we also use these. 50:20.000 --> 50:22.000 The subtitles are not getting that. 50:22.000 --> 50:24.000 It keeps saying membrane fusion. 50:24.000 --> 50:27.000 It's like saying remembering fusion just in case you're reading. 50:27.000 --> 50:31.000 We use liposomes to demonstrate the consequences of lipidase symmetry. 50:31.000 --> 50:35.000 And I'm going into this a bit because it points out the importance of doing quite basic research 50:35.000 --> 50:38.000 on how you can use the inside of your game, from basic research, 50:38.000 --> 50:44.000 to inform your and give you tools that you can use for more therapeutic or... 50:44.000 --> 50:48.000 I think he said he formed a company that made those little lipids. 50:48.000 --> 50:53.000 So it's forcing them through the carbon filter to make them a little tiny. 50:53.000 --> 50:55.000 That was a company all by itself. 50:55.000 --> 50:56.000 Patients. 50:56.000 --> 50:59.000 So we synthesize this ionizable cation lipid called dodap. 50:59.000 --> 51:03.000 And the thinking here was that this lipid has a property as a tertiary amine 51:03.000 --> 51:06.000 that can exist in a protonated state, which is positively charged, 51:06.000 --> 51:10.000 and that's at low pH, whereas it's a neutral pH that becomes repropenated 51:10.000 --> 51:12.000 on the other net neutral molecule. 51:12.000 --> 51:16.000 And the net neutral molecule can diffuse across the membrane really quickly. 51:16.000 --> 51:18.000 But it becomes to the inside and gets protonated. 51:18.000 --> 51:21.000 It becomes positively charged, but it can't get back out again. 51:21.000 --> 51:26.000 Because charge molecules don't get through lipid bylars very easily. 51:26.000 --> 51:30.000 And so we can drive lipidase symmetry in response to this pH gradient, 51:30.000 --> 51:33.000 and we can look at various consequences of having more lipid on one side of the membrane 51:33.000 --> 51:34.000 compared to the other. 51:34.000 --> 51:36.000 And the sheet change is in order to more flow. 51:36.000 --> 51:38.000 There's some huge changes that result from it. 51:38.000 --> 51:40.000 I mean, think about how elegant that is. 51:40.000 --> 51:44.000 Think about how basic of biology this is. 51:44.000 --> 51:46.000 That he's got a system with nothing. 51:46.000 --> 51:49.000 He's just looking at cell membrane. 51:49.000 --> 51:54.000 He's looking how charge can cause molecules to flip from one side of the membrane to the other, 51:54.000 --> 51:56.000 and that charge can keep them there. 51:56.000 --> 51:59.000 And then looking at this charge asymmetry, I think it's really interesting. 51:59.000 --> 52:03.000 And I think it's really amazing that people 52:04.000 --> 52:08.000 were able to come up with these stepwise investigations to break down 52:08.000 --> 52:11.000 and understand how these complex chemical systems work. 52:11.000 --> 52:13.000 It's extraordinary, isn't it? 52:13.000 --> 52:15.000 Again, real biology here. 52:15.000 --> 52:16.000 No. 52:16.000 --> 52:18.000 But we got distracted. 52:18.000 --> 52:25.000 As we also found, we could load cancer drugs into these liposomes, 52:25.000 --> 52:26.000 into the pH gradients. 52:26.000 --> 52:31.000 And so these are the weak-based drugs that can exist in a protonated form or a neutral form. 52:31.000 --> 52:33.000 Well, that neutral form can go across the bilayer. 52:33.000 --> 52:36.000 If you have a low pH on the inside, it gets protonated in traps. 52:36.000 --> 52:39.000 And so this shows where Dr. Ribison, which is a common anti-cancer drug, 52:39.000 --> 52:42.000 that we could get so much drug in there that we'd pass the solubility product 52:42.000 --> 52:45.000 and they would get these nanocrystals forming inside our... 52:45.000 --> 52:48.000 This is 100 nanometer bar here inside these vesicles. 52:48.000 --> 52:51.000 So we immediately thought, well, wow, this is going to be a good way 52:51.000 --> 52:54.000 of delivering cancer drugs more specifically to where you want them. 52:54.000 --> 52:55.000 This is an enormous problem. 52:55.000 --> 52:59.000 Of course, I mean, cancer drugs go everywhere in your body cause really nasty. 53:00.000 --> 53:01.000 Wait, did I miss something? 53:01.000 --> 53:02.000 I'll actually get to where you want to go. 53:02.000 --> 53:05.000 I'm distracted very easily. 53:05.000 --> 53:06.000 And so we could drive... 53:06.000 --> 53:09.000 Now, I see a little bit of critique here. 53:09.000 --> 53:11.000 And I just want to be sure that everybody understands. 53:11.000 --> 53:14.000 When I say that this is real biology, 53:14.000 --> 53:18.000 yeah, you might want to say that this is more like biophysics. 53:18.000 --> 53:24.000 But it's investigation of the basic properties of lipid bilayers, 53:24.000 --> 53:28.000 which are fundamental to most living systems on Earth. 53:29.000 --> 53:31.000 So in that sense, it's a very fundamental biology. 53:31.000 --> 53:37.000 Yes, it's still a theory or a hypothesis about how these lipid bilayers work. 53:37.000 --> 53:43.000 But I'm impressed with the fact that at least we're investigating that with the idea. 53:43.000 --> 53:45.000 But I don't know where we just... 53:45.000 --> 53:48.000 We just really crossed into delivering cancer drugs all of a sudden. 53:48.000 --> 53:50.000 So I missed some transition there. 53:50.000 --> 53:51.000 That's why I wanted to go back. 53:51.000 --> 53:53.000 Drive-lividase symmetry in response to this pH gradient. 53:53.000 --> 53:57.000 And we can look at various consequences of having more lipid on one side of the membrane 53:57.000 --> 53:58.000 compared to the other. 53:58.000 --> 54:00.000 And the shape changes in order to more... 54:00.000 --> 54:02.000 There's some huge changes that result from that. 54:02.000 --> 54:05.000 But we got distracted. 54:05.000 --> 54:11.000 So we also found that we could load cancer drugs into these liposomes, 54:11.000 --> 54:12.000 into the pH gradients. 54:12.000 --> 54:16.000 And so these are the weak-based drugs that consist of a protonated form 54:16.000 --> 54:17.000 or a net-neutral form. 54:17.000 --> 54:19.000 Well, that neutral form can go across the bilayer. 54:19.000 --> 54:22.000 If you have a low pH on the inside, it gets protonated in traps. 54:22.000 --> 54:25.000 And so this is shows for Doxaribacin, which is a polynomial cancer drug, 54:25.000 --> 54:29.000 that we could get so much drug in there that we'd pass the solubility products 54:29.000 --> 54:31.000 and they would get these nanocrystals forming inside our... 54:31.000 --> 54:34.000 This is 100 nanometer bar here inside these vesicles. 54:34.000 --> 54:37.000 So we immediately thought, well, wow, this is going to be a good way 54:37.000 --> 54:40.000 delivering cancer drugs more specifically to where you want them. 54:40.000 --> 54:41.000 This is an enormous problem. 54:41.000 --> 54:44.000 Of course, cancer drugs go everywhere in your body cause really nasty. 54:44.000 --> 54:48.000 Only 0.01% will actually get to where you want it or less. 54:48.000 --> 54:50.000 And so if you could deliver these more specifically, 54:50.000 --> 54:52.000 it'd be a huge benefit. 54:52.000 --> 54:57.000 So we started a company in 1992 called, this is me and four postdocs in the lab, 54:57.000 --> 55:03.000 called Inik's Pharmaceuticals to use these systems to improve the delivery 55:03.000 --> 55:04.000 of cancer drugs to tumors. 55:04.000 --> 55:07.000 I have to say, this is a great way of keeping a team together. 55:07.000 --> 55:11.000 We're still working together one way or another, some 40 years later. 55:11.000 --> 55:12.000 And we've had some success. 55:12.000 --> 55:16.000 Tom Haddon is now CEO of Acuitus, which supplied the banana particle 55:16.000 --> 55:19.000 for the Pfizer biotech COVID-19 vaccine as an example. 55:19.000 --> 55:22.000 But it's been quite a ride over the years. 55:22.000 --> 55:29.000 We started this for cancer drugs, but we started working on gene therapies. 55:29.000 --> 55:34.000 We hired a CEO, Jim Miller, and Monday about the middle 90s, 55:34.000 --> 55:38.000 he came to me and said, well, I can't raise money for the company 55:38.000 --> 55:40.000 putting these old drugs in liposomes. 55:40.000 --> 55:42.000 I mean, if you're doing gene therapy, it has much more sex appeal. 55:42.000 --> 55:44.000 That's exactly what he said. 55:44.000 --> 55:46.000 In other words, sex appeal for investors. 55:46.000 --> 55:52.000 And so this risk really started us off on a whole new path of reading. 55:52.000 --> 55:56.000 I got to say, I completely and totally agree when they say you got distracted. 55:56.000 --> 55:59.000 It means we got a shit ton of money. 55:59.000 --> 56:03.000 And we went for the money. 56:03.000 --> 56:05.000 Wow. 56:05.000 --> 56:06.000 Wow. 56:06.000 --> 56:08.000 This is a great little talk here. 56:08.000 --> 56:12.000 And gene therapy, you know, is a much more sex appeal. 56:12.000 --> 56:13.000 Great. 56:13.000 --> 56:14.000 Great. 56:14.000 --> 56:20.000 Instead of delivering cancer drugs, I'm trying to deliver nucleic acid-based drugs. 56:20.000 --> 56:22.000 So let's get back a little bit. 56:22.000 --> 56:24.000 So what does gene therapy, what did the Miller mean? 56:24.000 --> 56:27.000 If you go to Wikipedia, you can find a very general definition. 56:27.000 --> 56:29.000 It's not, there's other definitions. 56:29.000 --> 56:33.000 But one version of gene therapy is the therapeutic delivery of nucleic acids 56:33.000 --> 56:36.000 into a patient cells as a drug to treat disease. 56:36.000 --> 56:39.000 But delivery is really the operative word. 56:40.000 --> 56:43.000 These approaches really require delivery systems. 56:43.000 --> 56:45.000 Excuse me. 56:45.000 --> 56:47.000 I'm modified nucleic acid-pombers. 56:47.000 --> 56:52.000 They're really degraded very rapidly in the, I've got to sort of got some more here. 56:52.000 --> 56:56.000 They're rapidly degraded in biological fluids. 56:56.000 --> 57:00.000 But they don't go to where you want them to go if you inject them. 57:00.000 --> 57:03.000 And if they get there, they can't do anything because they can't get across cell membranes. 57:03.000 --> 57:07.000 So, you know, delivery systems are kind of important. 57:07.000 --> 57:13.000 Now what we focused on was the delivery of RNA-based molecules to either inhibit gene expression 57:13.000 --> 57:15.000 or promote gene expression. 57:15.000 --> 57:21.000 So as you're all aware, DNA goes to mRNA, goes to protein. 57:21.000 --> 57:28.000 So the sRNA interferes with translation by degrading mRNA for a very specific protein 57:28.000 --> 57:30.000 according to the sequence in the sRNA. 57:30.000 --> 57:34.000 A messenger RNA, of course, if you get that inside a cell, then it'll be taken up into the ribosomes 57:34.000 --> 57:36.000 to produce any proteins we want. 57:36.000 --> 57:38.000 And I think this is really important. 57:38.000 --> 57:49.000 This is something that I mentioned yesterday as part of my little rant about what the RNA can and can't do. 57:49.000 --> 57:53.000 I understand that there's DNA in the shots and that's real bad. 57:53.000 --> 57:57.000 But there was RNA in the shots and RNA can be really bad too. 57:57.000 --> 58:05.000 And one aspect of RNA and the insertion of foreign RNA into our system, 58:05.000 --> 58:10.000 that hasn't been discussed at all, is this short interfering RNA potential. 58:10.000 --> 58:16.000 The short interfering RNA potential is there from a nefarious perspective. 58:16.000 --> 58:19.000 It's there from an erroneous perspective. 58:19.000 --> 58:23.000 It's there from a quality control perspective. 58:23.000 --> 58:30.000 And it's something that we haven't really at all talked about, even with the greatest molecular binds on the 58:30.000 --> 58:35.000 incidence side, none of them have ever mentioned this as a potential danger. 58:35.000 --> 58:44.000 But especially, and I say this with great trepidation, but especially with injecting mRNAs into pregnant women, 58:44.000 --> 58:50.000 those mRNAs, if they are the smear on the gel that they showed to the FDA, 58:50.000 --> 58:58.000 then there are lots of small RNAs that have this potential that may have overlap with human transcripts. 58:58.000 --> 59:08.000 If you put this in a pregnant woman and then use accidentally interfere with RNA expression in the developing embryo, 59:08.000 --> 59:12.000 that's not going to be really good. 59:12.000 --> 59:17.000 And the potential for that interference is very, very high if the RNA is in pure. 59:17.000 --> 59:23.000 If the RNA has a lot of overlap, if they haven't been adequately, 59:23.000 --> 59:32.000 and you know that they haven't, if they haven't adequately investigated the potential for interference to occur. 59:32.000 --> 59:40.000 Interference happens when this shorter RNA sequence binds to an existing transcript 59:40.000 --> 59:49.000 and that double strand of RNA then gets degraded, because the body doesn't tolerate double-stranded RNA. 59:49.000 --> 59:58.000 We know that this is a thing because we've used it in academia to stop or lower the expression of a protein temporarily 59:58.000 --> 01:00:01.000 in order to study its effects. 01:00:01.000 --> 01:00:12.000 We have already used it as a way of trying to treat diseases when it comes to the inhibition of a production of a pathogenic protein, for example. 01:00:12.000 --> 01:00:23.000 And so as he talks about the delivery of RNA, he's talking about the whole spectrum of products that could be used in academia 01:00:23.000 --> 01:00:33.000 because there's billions of dollars from the welcome trust in the NIH and all these other non-governmental organizations for academic bench research. 01:00:33.000 --> 01:00:39.000 And academic bench research uses transfection and transformation all the time. 01:00:39.000 --> 01:00:47.000 So you don't even really have to think about curing disease or changing the world of childhood diseases. 01:00:47.000 --> 01:00:56.000 You can just think about delivering RNA in the context of academic biology and look to make millions. 01:00:57.000 --> 01:01:14.000 And again, here we're talking about the throwing of money at academic biology has created a whole gigantic multinational industry of chemical production 01:01:14.000 --> 01:01:28.000 and chemical apparatus, biological apparatus, molecular biological tools, kits, methodologies, machines. 01:01:28.000 --> 01:01:41.000 And all of these industries have moved scientists, biologists, chemists, behavioral neuroscientists, everybody farther away from understanding the biology that they think they're studying 01:01:41.000 --> 01:01:51.000 because they don't understand how their measuring tool works because their measuring tool is just a product 01:01:51.000 --> 01:02:03.000 that they follow the directions for like a cake that comes out of a box with tube A and tube B and sac A and B. 01:02:03.000 --> 01:02:14.000 And you combine tube A with sac A in a bowl and then you add B and B and you stir it up and put it in a oven and then you have your cake, you don't know what you did. 01:02:14.000 --> 01:02:18.000 And you wouldn't know anything about how to tweak it. 01:02:18.000 --> 01:02:23.000 You wouldn't know how to make a variation or an adjustment to it. 01:02:24.000 --> 01:02:38.000 And so as we talk about this, understand how long this guy started in 1972 and got distracted in the early 80s to deliver RNA. 01:02:38.000 --> 01:02:41.000 It ain't a new thing. 01:02:41.000 --> 01:02:50.000 So as a remarkable statement that comes out of this, if you can do this, you can potentially treat most diseases, most human diseases, which is really a remarkable statement. 01:02:50.000 --> 01:02:56.000 If you think about it, all diseases rely on their cause by proteins either being overexpressed or not being available. 01:02:56.000 --> 01:03:02.000 So both SRNA and mRNA require delivery systems to reach the interior of target cells. 01:03:02.000 --> 01:03:12.000 So we spent the last 27 years, I guess, developing these lipid nanoparticles to deliver SRNA and mRNA into cells in vivo. 01:03:12.000 --> 01:03:19.000 So I was going to go through three aspects of that which is the design for encapsulation of these nucleic acid polymers, which is still going on today 01:03:19.000 --> 01:03:21.000 and we're still improving these systems. 01:03:21.000 --> 01:03:27.000 The patocerian or on-patriole story, which is to treat a particular disorder known as trans- thyristin-induced amylidosis. 01:03:27.000 --> 01:03:31.000 And then I'll briefly mention the vaccine application. 01:03:31.000 --> 01:03:39.000 So our aim in 95 was to develop a delivery system to try and silence a gene in the liver. 01:03:39.000 --> 01:03:44.000 We started off with any sense of the nucleotides and then switched to SRNA in the early 2000s. 01:03:44.000 --> 01:03:47.000 But this is actually one heck of a problem. 01:03:47.000 --> 01:03:49.000 When you think about it, we're challenged. 01:03:49.000 --> 01:03:53.000 Packaging it up, first of all, on lipid nanoparticle, and it has a circulator round after an IV injection. 01:03:53.000 --> 01:03:58.000 Let's say if we're targeting a gene in the liver, then it has to extrapolate in the liver. 01:03:58.000 --> 01:04:03.000 So I got a message here which says, 01:04:03.000 --> 01:04:11.000 Intradidum was a biotechnology company that develops gene therapy technology based on RNA interference. 01:04:11.000 --> 01:04:20.000 Intradidum merged with silence technologies in 2009, and the merged company is now publicly traded. 01:04:20.000 --> 01:04:29.000 Silence technologies is involved in developmental research of targeted RNAi therapeutics for the treatment of serious diseases. 01:04:29.000 --> 01:04:33.000 RNAi being RNA interference. 01:04:33.000 --> 01:04:42.000 And this is just a different way of abbreviating short interfering RNA as the same as RNAi, RNA interference. 01:04:42.000 --> 01:04:51.000 Dr. Robert Malone co-founded and helped secure the 2.3 million in venture capital funding, including monies from the Norvargis Vester Fund, 01:04:51.000 --> 01:04:56.000 ETP Venture Capital Fund, and the state of Maryland. 01:04:56.000 --> 01:05:04.000 He also performed facility setup, infrastructure setup, and intellectual property development business and technology development planning, 01:05:04.000 --> 01:05:08.000 including in-depth business and scientific plan. 01:05:08.000 --> 01:05:12.000 So we know that Robert Malone knows what SIRNA is. 01:05:12.000 --> 01:05:22.000 We know that he knows that RNAi can interfere with the regulation of endogenous RNA. 01:05:22.000 --> 01:05:35.000 And so he has to know then that the injection of active lipid nanoparticles filled with less than optimal quality controlled RNAs into a pregnant woman would be absurd. 01:05:36.000 --> 01:05:45.000 He has to know that, otherwise he couldn't have helped a company like silent technologies back in 2009. 01:05:45.000 --> 01:05:52.000 I'll give you a big guess as to who just sent me that message, but he's in the chat now and he's a beast. 01:05:53.000 --> 01:06:00.000 SIRNA has to get taken up by hepatocytes, has to then get out of the endosome and into the cytoplasm. 01:06:00.000 --> 01:06:03.000 And so really building up that capability. 01:06:03.000 --> 01:06:05.000 Am I too quiet? 01:06:05.000 --> 01:06:07.000 I feel like I'm talking a lot. 01:06:07.000 --> 01:06:11.000 And also, of course, you've got the immune system fighting you every inch of the way. 01:06:11.000 --> 01:06:16.000 This certainly doesn't want foreign genetic material getting into cells in your body. 01:06:17.000 --> 01:06:24.000 So the first problem that we hit, and this is where the serendipity, some of the serendipity aspects comes in, 01:06:24.000 --> 01:06:32.000 was that in order to encapsulate negatively charged polymers such as nucleic acid and RNA DNA. 01:06:32.000 --> 01:06:41.000 I don't know if you can see it already, but for me, what I see there is the start of an Ebola virus. 01:06:41.000 --> 01:06:43.000 That's what I see. 01:06:43.000 --> 01:06:45.000 You can see the start of an Ebola virus. 01:06:45.000 --> 01:06:51.000 It is a double-stranded molecule with a lipid nanocode around it, and so it's going to become a long snake. 01:06:51.000 --> 01:06:55.000 Into a lipid nanoparticle, we had to use the positively charged lipids. 01:06:55.000 --> 01:07:00.000 You can see that the associate with the negative charge, and we can see the potential then for getting a hydrophobic entity 01:07:00.000 --> 01:07:04.000 that we could then encapsulate as the lipid nanoparticle. 01:07:04.000 --> 01:07:08.000 Now, there's a huge problem here, and that is that there's no permanently-positive charge, 01:07:09.000 --> 01:07:10.000 and that is in nature. 01:07:10.000 --> 01:07:12.000 There are really toxic molecules. 01:07:12.000 --> 01:07:15.000 There's only net-neutral lipids or negatively-charged lipids. 01:07:15.000 --> 01:07:18.000 And so we thought, wow, well, how do we get past this one? 01:07:18.000 --> 01:07:23.000 Because the toxicity if you're developing a drug, now that's always what's going to get you in the end. 01:07:23.000 --> 01:07:28.000 And so what we did was we tried the lipid that we developed for the lipid asymmetry studies, 01:07:28.000 --> 01:07:33.000 where we knew that at pH 4, say, when we're below the pKa of this tertiary amine, 01:07:33.000 --> 01:07:36.000 it's going to become proteinated, so it's positively charged, 01:07:36.000 --> 01:07:41.000 whereas that neutral pH is not going to be charged, and so, therefore, presumably, it's going to be much less toxic. 01:07:41.000 --> 01:07:44.000 And so we asked the question then, well, can we load these polymers? 01:07:44.000 --> 01:07:53.000 Can we load antisense, or SRNA, at pH 4, where the dodap or other ionous lipids is positively charged? 01:07:53.000 --> 01:07:57.000 And is it retained when we raise the pH, 2 pH 7.4? 01:07:57.000 --> 01:08:02.000 And the answer is obviously yes, otherwise we wouldn't be giving this talk. 01:08:02.000 --> 01:08:10.000 So what we developed, a rapid mixing procedure for formulating these systems, 01:08:10.000 --> 01:08:13.000 and, again, one of the lights they're working with lipids as I mentioned is their self-assembling. 01:08:13.000 --> 01:08:18.000 And so we dissolved the lipids in ethanol, and then, through a rapid mixing process, 01:08:18.000 --> 01:08:23.000 let's say, the SRNA, or antisense, or whatever else we want to get in there. 01:08:23.000 --> 01:08:24.000 And these are all... 01:08:24.000 --> 01:08:26.000 One of the first things to fall out of the solution is the... 01:08:26.000 --> 01:08:27.000 This is most... 01:08:27.000 --> 01:08:31.000 You take acid polymer, it's surrounded by the positively charged lipid. 01:08:31.000 --> 01:08:33.000 This is most certainly patentable. 01:08:33.000 --> 01:08:35.000 Oh, I didn't know I had that on. 01:08:35.000 --> 01:08:37.000 This is most certainly patentable. 01:08:37.000 --> 01:08:40.000 The whole process is patentable. 01:08:40.000 --> 01:08:43.000 All of these things, because lipids are self-assembling, 01:08:43.000 --> 01:08:45.000 all these processes are patentable. 01:08:45.000 --> 01:08:46.000 That's why I have so many patents. 01:08:46.000 --> 01:08:48.000 That's clear now to me. 01:08:48.000 --> 01:08:52.000 Now, if you do this fast enough, then other things will fall out of solution, such as this peg lipid here. 01:08:52.000 --> 01:08:56.000 And you can trap these systems in what we turn on limit-sized vesicles, in other words, 01:08:56.000 --> 01:09:00.000 the smallest size that's compatible with the molecular components. 01:09:00.000 --> 01:09:03.000 And that when we took the pH back up to pH 7.4, 01:09:03.000 --> 01:09:06.000 the contents were retained in these systems. 01:09:06.000 --> 01:09:09.000 And so this really got us past the toxicity issue. 01:09:09.000 --> 01:09:12.000 And also, you can see intuitively how it is that we could get, you know, 01:09:12.000 --> 01:09:15.000 encapsulation efficiencies that might approach 100%. 01:09:15.000 --> 01:09:18.000 So it turns out to be a pretty useful technique. 01:09:18.000 --> 01:09:24.000 But it's basically dependent on that basic research where we were playing around with the ionizable cationic lipid, 01:09:24.000 --> 01:09:27.000 and then we had a tool that we could use in the... 01:09:27.000 --> 01:09:29.000 You know, for making these nanoparticle systems, 01:09:29.000 --> 01:09:30.000 these are relatively non-toxic. 01:09:30.000 --> 01:09:31.000 They have a hydrophobic core. 01:09:31.000 --> 01:09:33.000 This is a cryo-TM picture. 01:09:33.000 --> 01:09:40.000 Certainly, any negatively charged macromolecule, we can encapsulate it 100% shopping efficiency scalable. 01:09:40.000 --> 01:09:44.000 We can change the size to all the size just by changing the surface lipid to core lipid ratio. 01:09:44.000 --> 01:09:49.000 All kinds of advantages to this process. 01:09:49.000 --> 01:09:54.000 So we know we had things that were formulated in lipid nanoparticles. 01:09:55.000 --> 01:09:57.000 You know, could they actually do anything? 01:09:57.000 --> 01:10:00.000 At least we had something that was basically not too toxic, 01:10:00.000 --> 01:10:04.000 and that was a prime criterion before we even went near animals. 01:10:04.000 --> 01:10:08.000 So after a conference that I attended in London, 01:10:08.000 --> 01:10:11.000 this was 2004, I was pursued by... 01:10:11.000 --> 01:10:13.000 Well, this term is a mad Russian, because it kind of is. 01:10:13.000 --> 01:10:18.000 The guy named Victor Katellianski, who at that point was the vice president research. 01:10:18.000 --> 01:10:21.000 He had a company in Boston called online on pharmaceuticals. 01:10:21.000 --> 01:10:27.000 Now, an island was founded in 2002 to develop the small interfering RNA as a therapeutic, 01:10:27.000 --> 01:10:32.000 and when it's silenced to particular genes in the liver, 01:10:32.000 --> 01:10:35.000 to inhibit production of proteins or causing problems. 01:10:35.000 --> 01:10:39.000 And he had a statement, was we have a delivery problem? 01:10:39.000 --> 01:10:43.000 How did we get our S RNA into the liver, into hepatocytes in vivo? 01:10:43.000 --> 01:10:50.000 And so this stimulated a seven-year collaboration that went on from 2005 to 2012. 01:10:50.000 --> 01:10:56.000 We had teams in Vancouver and in Boston, and we synthesized many different lipid nanoparticle formulations. 01:10:56.000 --> 01:11:01.000 But it resulted in a formulation that we could silence a gene in the liver 01:11:01.000 --> 01:11:03.000 with a therapeutic index approaching 1,000. 01:11:03.000 --> 01:11:06.000 In other words, give 1,000 times higher dose than that, 01:11:06.000 --> 01:11:10.000 which we received some biological effect before we saw toxic side effects. 01:11:10.000 --> 01:11:12.000 So we started off with a question. 01:11:12.000 --> 01:11:18.000 Can these lipid nanoparticle systems, silence genes in the liver following an IV injection? 01:11:18.000 --> 01:11:22.000 So there's really no targeting information on the outside here. 01:11:22.000 --> 01:11:24.000 I could go into other forms of serendipity. 01:11:24.000 --> 01:11:29.000 They actually turned out they do target quite well for reasons maybe we can discuss the question period. 01:11:29.000 --> 01:11:34.000 But so they have a very simple, I mean, it's complicated in many drug terms, 01:11:34.000 --> 01:11:41.000 but we've encapsulated, say, 1,000 of these oligonucleotides in 100 nanometer or 89 nanoparticle, 01:11:41.000 --> 01:11:43.000 and we were injecting them IV. 01:11:43.000 --> 01:11:51.000 So what's going to be the process whereby they actually have some biological effect? 01:11:51.000 --> 01:11:54.000 Now, lipid nanoparticles get into cells by a process. 01:11:54.000 --> 01:11:58.000 You're all familiar with the endocytosis of cell-eating process. 01:11:58.000 --> 01:12:03.000 And so this means that we don't want things to go on to the lysosomes. 01:12:03.000 --> 01:12:05.000 We're saying, you know, nucleic acids would get degraded. 01:12:05.000 --> 01:12:12.000 We have to break out of the end zone at some point so that the oligonucleotide is really delivered 01:12:12.000 --> 01:12:14.000 into the cytoplasm. 01:12:14.000 --> 01:12:18.000 And so they just hand-wave that. 01:12:18.000 --> 01:12:21.000 It's a little bit like the infectious cycle, right? 01:12:21.000 --> 01:12:23.000 That's it. 01:12:23.000 --> 01:12:25.000 That's what you're just going to say. 01:12:25.000 --> 01:12:29.000 It just opens up and they leak out and that's all good or what? 01:12:29.000 --> 01:12:31.000 This is really wrong. 01:12:31.000 --> 01:12:36.000 And we shouldn't let him get away with this, but they're letting him get away with it. 01:12:36.000 --> 01:12:41.000 I think that this is a lot of hand-waving here, and I don't think that they know exactly what happens. 01:12:41.000 --> 01:12:45.000 They just assume a lot like the infectious cycle of viruses. 01:12:45.000 --> 01:12:50.000 The oligonucleotide is really delivered into the cytoplasm. 01:12:50.000 --> 01:12:54.000 And so how can these ionizable lipids destabilize an end zone? 01:12:54.000 --> 01:13:00.000 What we found was that if we took positively charged lipids and added them to negatively charged lipids, 01:13:00.000 --> 01:13:03.000 just those lipids you find in endosomes and other biological membranes, 01:13:03.000 --> 01:13:06.000 it will flip the lipid from a bio-organization. 01:13:06.000 --> 01:13:09.000 This is the phosphorous NMR signal that I showed at the beginning. 01:13:09.000 --> 01:13:12.000 They'll flip it straight over into these non-biodar structures. 01:13:12.000 --> 01:13:17.000 Let me describe this to an electrostatic interaction changing the shape as it were of this molecule 01:13:17.000 --> 01:13:19.000 and flipping it from a bio-organization. 01:13:19.000 --> 01:13:22.000 But anyway, certainly very membrane destabilizing. 01:13:22.000 --> 01:13:24.000 And so this took us back to the polymorphism. 01:13:24.000 --> 01:13:30.000 They really basically, again, basic studies, but it gave us a way forward for designing better and better ionizable 01:13:30.000 --> 01:13:32.000 and counting on lipids. 01:13:32.000 --> 01:13:37.000 And so we needed to design them so they interact with the components of the endosome 01:13:37.000 --> 01:13:41.000 at some vulnerable part of the endosome maturation process. 01:13:41.000 --> 01:13:46.000 And so this is our model for saying, okay, what we're trying to do here is to destabilize it 01:13:46.000 --> 01:13:49.000 by introducing these non-biodar structures. 01:13:49.000 --> 01:13:56.000 Now we assessed the invivial potency of the systems using factor seven mouse model. 01:13:56.000 --> 01:14:01.000 This is going to point out some of the amazing advantages of gene therapy approaches. 01:14:01.000 --> 01:14:05.000 If we can get it to work for factor seven, which is the clotting protein as you're all aware, 01:14:05.000 --> 01:14:09.000 then if we can silence that gene, we can silence any other gene in the hepatocytes. 01:14:09.000 --> 01:14:11.000 And so we're used to this as a model. 01:14:11.000 --> 01:14:17.000 We had factor seven, that's SRNA to silence factor seven. 01:14:17.000 --> 01:14:21.000 Packaged that in a lipid nanoparticle, introduce that into the intravenously, 01:14:21.000 --> 01:14:24.000 and then assay for factor seven in the blood 24 hours later. 01:14:24.000 --> 01:14:27.000 So we know that factor seven is made in the hepatocytes, 01:14:27.000 --> 01:14:30.000 and so if we are inhibiting the production of factor seven, 01:14:30.000 --> 01:14:32.000 then we're going to see less factor seven in the blood subsequently. 01:14:32.000 --> 01:14:35.000 So I could do two experiments a week, it wasn't high throughput, 01:14:35.000 --> 01:14:38.000 but we could certainly run through quite a number of formulations. 01:14:38.000 --> 01:14:42.000 I should say for nanomedicines in general, screening in vitro doesn't really tell you much, 01:14:42.000 --> 01:14:45.000 you really have to get to the invivial circumstance. 01:14:45.000 --> 01:14:50.000 So we found that the potency of these systems was really sensitive to the species 01:14:50.000 --> 01:14:54.000 of the cationic lipid, the ionizable cationic lipid that we employed. 01:14:54.000 --> 01:14:59.000 And so this is just going through a sequence of, you know, we started off with DODAP and KDM-A, 01:14:59.000 --> 01:15:02.000 KC2, MC3, et cetera. They're all pretty similar. 01:15:02.000 --> 01:15:07.000 I mean, linoleic acid chains, we've got tertiary amine. 01:15:07.000 --> 01:15:11.000 So why, but, you know, there's a huge difference in terms of their potency. 01:15:11.000 --> 01:15:17.000 And we found that the potency was really very, very sensitive to the PKA, 01:15:17.000 --> 01:15:21.000 a point at which these ionizable lipids became positively charged. 01:15:21.000 --> 01:15:24.000 So this is a graph of the potency, now potency is one over the effect of those 01:15:24.000 --> 01:15:28.000 where we could basically knock down factor seven by 50%. 01:15:28.000 --> 01:15:31.000 And it's a function of the PKA. This is a log plot here. 01:15:31.000 --> 01:15:35.000 So you can see, I mean, a PKA was as little as half a unit away from some optimum. 01:15:35.000 --> 01:15:38.000 We could decrease the activity by two orders of magnitude. 01:15:38.000 --> 01:15:44.000 So that's really quite a, quite a pronounced dependence on the PKA of the ionizable lipids. 01:15:44.000 --> 01:15:48.000 And so as a result of this, we were able to take effective dose from 10 migs per gig 01:15:48.000 --> 01:15:52.000 where we would see some deans silencing down to five micrograms without increasing the toxicity. 01:15:52.000 --> 01:15:54.000 If anything, we decreased the toxicity. 01:15:54.000 --> 01:15:57.000 So this is where this therapeutic index of a thousand came from. 01:15:57.000 --> 01:16:01.000 Anyway, so at this point, the clinicians, or at least we said, 01:16:01.000 --> 01:16:03.000 we think this is ready to go into the clinic. 01:16:03.000 --> 01:16:06.000 And so the clinicians at Onalam said, okay. 01:16:06.000 --> 01:16:09.000 I'm kind of annoyed because he keeps using this therapeutic index, 01:16:09.000 --> 01:16:14.000 which he says is a thousand, 01:16:14.000 --> 01:16:19.000 and the dose that they're using is a thousand times higher than what they need 01:16:19.000 --> 01:16:23.000 to create an effect, and they still don't see toxicity, 01:16:23.000 --> 01:16:26.000 or the toxicity occurs only when they increase from 01:16:26.000 --> 01:16:29.000 an effective dose a thousand times. 01:16:29.000 --> 01:16:32.000 What is the toxicity that he measures? 01:16:32.000 --> 01:16:36.000 What is this, that, it's sort of hand waving here, 01:16:36.000 --> 01:16:40.000 but again, I get that he's given this general talk, so he can't explain everything, 01:16:40.000 --> 01:16:44.000 but it's a little, yeah, anyway. 01:16:44.000 --> 01:16:46.000 We think this is ready to go into the clinic. 01:16:46.000 --> 01:16:52.000 And so the clinicians at Onalam said, okay, what disease should we go after, 01:16:52.000 --> 01:16:54.000 and the disease that was chosen, 01:16:54.000 --> 01:16:56.000 and that was the disease that I'd never heard of before, 01:16:56.000 --> 01:17:00.000 called trans-theritin-induced amylodosis, a hereditary disorder. 01:17:00.000 --> 01:17:03.000 As I mentioned at the beginning, by science and factor 7, 01:17:03.000 --> 01:17:05.000 we knew we could start as any gene in the parasite, 01:17:05.000 --> 01:17:08.000 so all we had to do was put in a different SRNA 01:17:08.000 --> 01:17:10.000 that was going to silence our particular target. 01:17:10.000 --> 01:17:14.000 So this was work that then went on at Onalam. 01:17:14.000 --> 01:17:17.000 HATTR is actually a pretty nasty disorder. 01:17:17.000 --> 01:17:22.000 So the trans-theritin, it's a protein that's made primarily in the liver. 01:17:22.000 --> 01:17:25.000 If there's a mutation, and there are ways of number of mutations, 01:17:25.000 --> 01:17:27.000 it's a big protein, can cause a formation of fibrils, 01:17:27.000 --> 01:17:30.000 which presumably deposit everywhere in your body, 01:17:30.000 --> 01:17:34.000 but they have really nasty effects in nervous tissue and in cardiac tissue, 01:17:34.000 --> 01:17:36.000 so it's no effective therapy. 01:17:36.000 --> 01:17:39.000 They usually fail a little bit in five years of diagnosis. 01:17:39.000 --> 01:17:41.000 It's kind of a nasty disease, which is indicated here. 01:17:41.000 --> 01:17:45.000 It's a wasting disorder that goes through the stages. 01:17:45.000 --> 01:17:47.000 It's indicated here. 01:17:47.000 --> 01:17:50.000 Kind of particularly nasty because it's hereditary, 01:17:50.000 --> 01:17:53.000 and so if you find you're stumbling in your thirties, 01:17:53.000 --> 01:17:55.000 you sort of know what's in store for you, 01:17:55.000 --> 01:17:58.000 because you're likely to see in a relative go through the similar process. 01:17:58.000 --> 01:18:02.000 So this is where the simplicity of gene therapy approaches comes in. 01:18:02.000 --> 01:18:05.000 So, okay, well, this is caused by the protein trans-theritin. 01:18:05.000 --> 01:18:07.000 Well, this has stopped it being produced. 01:18:07.000 --> 01:18:11.000 And so this is taking a, I've been at a particle now, 01:18:11.000 --> 01:18:14.000 containing SRNA to silence trans-theritin, 01:18:14.000 --> 01:18:16.000 and the idea is, if we shut that down, 01:18:16.000 --> 01:18:18.000 then we're going to reduce the circulating tetramers, 01:18:18.000 --> 01:18:20.000 the deposition of the fibrils, 01:18:20.000 --> 01:18:22.000 and perhaps we might even get some clearance 01:18:22.000 --> 01:18:24.000 if there's very little in the blood. 01:18:24.000 --> 01:18:28.000 So potentially a simple solution to what is really a devastating disease. 01:18:28.000 --> 01:18:32.000 This is the results of the phase three study, 01:18:32.000 --> 01:18:35.000 which came out in 2017. 01:18:35.000 --> 01:18:40.000 So there was 148 patients who got to the drug 01:18:40.000 --> 01:18:45.000 in the dose of .3 mics per gig, IV every three weeks, 01:18:45.000 --> 01:18:47.000 or they got sterile saline as a placebo. 01:18:47.000 --> 01:18:49.000 And the neural impairment score was a primary endpoint, 01:18:49.000 --> 01:18:51.000 quality of life, weakness, et cetera, 01:18:51.000 --> 01:18:54.000 over secondary endpoints, ability to walk, and so on. 01:18:54.000 --> 01:18:56.000 Now, so these were some of the top line results 01:18:56.000 --> 01:18:58.000 for the save for the neural impairment score. 01:18:58.000 --> 01:19:00.000 So over the course of the 18 months, 01:19:00.000 --> 01:19:03.000 the patients that were on the sterile saline 01:19:03.000 --> 01:19:06.000 or the control were getting steadily worse. 01:19:06.000 --> 01:19:09.000 Their neural impairment score was getting higher, 01:19:09.000 --> 01:19:12.000 whereas those patients that were being treated with this. 01:19:12.000 --> 01:19:15.000 Okay, so we're treating a hereditary disease that I'll go back 01:19:15.000 --> 01:19:18.000 so you can see what it was called. 01:19:18.000 --> 01:19:21.000 Hatter amyloidosis 01:19:21.000 --> 01:19:24.000 that is going to kill people in a pretty awful way, 01:19:24.000 --> 01:19:26.000 and it's hereditary. 01:19:26.000 --> 01:19:29.000 Usually fatal within five years of being diagnosed. 01:19:29.000 --> 01:19:32.000 It looks like it hits you when you're an adult. 01:19:32.000 --> 01:19:39.000 But giving mRNA in a lipid nanoparticle 01:19:39.000 --> 01:19:42.000 to a person who's going to go through this in five years 01:19:42.000 --> 01:19:46.000 is very different than giving it to your four-year-old. 01:19:46.000 --> 01:19:50.000 It's so different, it shouldn't even be discussed. 01:19:50.000 --> 01:19:53.000 But vaccines are given to kids. 01:19:53.000 --> 01:19:55.000 Vaccines are given to healthy adults. 01:19:55.000 --> 01:19:58.000 This is not a healthy adult. 01:19:58.000 --> 01:20:03.000 This is a guy who has a five-year death ahead of him 01:20:03.000 --> 01:20:06.000 after diagnosis, and so it's fine to give him mRNA 01:20:06.000 --> 01:20:09.000 in a lipid nanoparticle. Why not? 01:20:09.000 --> 01:20:12.000 And it doesn't even work that well as you see. 01:20:12.000 --> 01:20:15.000 It does stop it, but I mean, it's not curing him. 01:20:15.000 --> 01:20:17.000 Since they were being treated with a drug 01:20:17.000 --> 01:20:19.000 to silence a transliteration in the liver, 01:20:19.000 --> 01:20:21.000 if anything, we're getting better. 01:20:21.000 --> 01:20:24.000 So, really, you're improving the status of these people 01:20:24.000 --> 01:20:27.000 suffering from a hereditary disease. 01:20:27.000 --> 01:20:30.000 So, it's quite a remarkable thing. 01:20:30.000 --> 01:20:33.000 So, the Phase III trial results were announced in 2017. 01:20:33.000 --> 01:20:36.000 As I said, I also refer to the p-value for the 01:20:36.000 --> 01:20:38.000 neural impairment story. 01:20:38.000 --> 01:20:40.000 This is 10th of the minus 24, 01:20:40.000 --> 01:20:42.000 one over Avogadro's number. 01:20:42.000 --> 01:20:44.000 They're absolutely sure that this drug works, 01:20:44.000 --> 01:20:47.000 but it's equally convincing for self-reported quality of life, 01:20:47.000 --> 01:20:51.000 muscle strength, ability to walk, nutritional status, etc., etc. 01:20:51.000 --> 01:20:53.000 So, it's certainly one of the most, 01:20:53.000 --> 01:20:55.000 why that's the most impressive clinical trial result 01:20:55.000 --> 01:20:56.000 I've ever seen. 01:20:56.000 --> 01:20:59.000 It really points out how these genetic drugs, you know, are... 01:20:59.000 --> 01:21:03.000 So, if you talk, you start with a dude who's really dying. 01:21:03.000 --> 01:21:05.000 You can use mRNA to briefly fix him. 01:21:05.000 --> 01:21:08.000 I don't think this returns them to normal. 01:21:08.000 --> 01:21:11.000 I don't think it means that he's going to live for 50 more years. 01:21:14.000 --> 01:21:16.000 And I bet if he's really honest about it, 01:21:16.000 --> 01:21:18.000 the guy probably still died two years later, 01:21:18.000 --> 01:21:20.000 but at least he didn't feel bad while he died. 01:21:20.000 --> 01:21:22.000 At least he didn't deteriorate anymore. 01:21:24.000 --> 01:21:27.000 Because my guess is that therapy won't work forever. 01:21:27.000 --> 01:21:29.000 That's the tricky part. 01:21:29.000 --> 01:21:32.000 That's the part he doesn't have to tell you right now. 01:21:33.000 --> 01:21:34.000 They really work. 01:21:34.000 --> 01:21:37.000 Because we're going right after the root cause of things. 01:21:37.000 --> 01:21:41.000 So, this was approved by the FDA, the trade name is on Petro, 01:21:41.000 --> 01:21:44.000 and by the FDA in 2018, for treatments, 01:21:44.000 --> 01:21:47.000 the first FDA approval of an SRNA-based drug, 01:21:47.000 --> 01:21:50.000 and a clinical validation, obviously, 01:21:50.000 --> 01:21:53.000 for the, depending on a particle delivery approach, 01:21:53.000 --> 01:21:58.000 really dramatically demonstrates the power of the gene therapy approaches. 01:21:59.000 --> 01:22:04.000 Okay, I'm just going to go on now to the vaccine story, 01:22:04.000 --> 01:22:08.000 which really took place between 2012 and 2020 01:22:08.000 --> 01:22:10.000 for the COVID-19 vaccine. 01:22:10.000 --> 01:22:18.000 Now, this is, when the, when on Patrick went into the clinic in 2012, 01:22:18.000 --> 01:22:21.000 there was nothing you can do as a scientist that has that point. 01:22:21.000 --> 01:22:23.000 I mean, it's all the clinical trial. 01:22:23.000 --> 01:22:25.000 You can't change things in the middle of a clinical trial. 01:22:25.000 --> 01:22:29.000 And so, we said, well, okay, if we can deliver SRNA 01:22:29.000 --> 01:22:31.000 and get that to silence the gene in the liver, 01:22:31.000 --> 01:22:32.000 it's a pretty big molecule. 01:22:32.000 --> 01:22:33.000 It's not as big as an mRNA, 01:22:33.000 --> 01:22:35.000 but maybe if we package mRNA in the same way, 01:22:35.000 --> 01:22:37.000 we'll get some gene expression. 01:22:37.000 --> 01:22:39.000 And so, that's what we did. 01:22:39.000 --> 01:22:43.000 And the, what we found was that we could use the, 01:22:43.000 --> 01:22:47.000 depending on a particle approach to use the liver as a biorat 01:22:47.000 --> 01:22:49.000 to produce any protein we want. 01:22:49.000 --> 01:22:50.000 So, think about that. 01:22:50.000 --> 01:22:52.000 If you want to win the Tour de France, this is the... 01:22:52.000 --> 01:22:55.000 So, is that where we're making spike protein in our liver? 01:22:55.000 --> 01:22:57.000 Probably some of it. 01:22:57.000 --> 01:22:59.000 Injecting mRNA, including for erythropolietin, 01:22:59.000 --> 01:23:01.000 and you get, this is a big model. 01:23:01.000 --> 01:23:05.000 Now, you get super physiological levels of EPO 01:23:05.000 --> 01:23:08.000 and the corresponding increase in the metacritic cetera in these, 01:23:08.000 --> 01:23:09.000 in these animals. 01:23:09.000 --> 01:23:12.000 So, this points out to the, there's, so this, this got, 01:23:12.000 --> 01:23:15.000 this, this was our whole focus was, why? 01:23:15.000 --> 01:23:17.000 There's, there's just an enormous number of diseases, 01:23:17.000 --> 01:23:21.000 rare diseases, et cetera, that one can go after using this, 01:23:21.000 --> 01:23:23.000 using this, this approach. 01:23:23.000 --> 01:23:26.000 But we, again, had another serendipitous event 01:23:26.000 --> 01:23:31.000 because we were approached in 2014 by Drew Weissman 01:23:31.000 --> 01:23:34.000 of the University of Pennsylvania. 01:23:34.000 --> 01:23:38.000 He'd been working with Katie Currico and Drew and, 01:23:38.000 --> 01:23:41.000 Drew Weissman and Katie Currico are co-winners with me of the 01:23:41.000 --> 01:23:43.000 Geradner Award. 01:23:43.000 --> 01:23:45.000 So, they've been working for many years to try and get, 01:23:45.000 --> 01:23:48.000 reduce the toxicity of messenger RNA, the immunotoxicity, 01:23:48.000 --> 01:23:52.000 and that, so they come up with, you know, they show them by 01:23:52.000 --> 01:23:54.000 modifying mRNA, they could, that problem. 01:23:54.000 --> 01:23:57.000 So, in a way, right, this is exactly what I was talking about 01:23:57.000 --> 01:23:59.000 earlier with too many words. 01:23:59.000 --> 01:24:03.000 Currico and Weissman couldn't have done anything that they did 01:24:03.000 --> 01:24:09.000 without the products and patents and proprietary 01:24:09.000 --> 01:24:14.000 preparation methodologies for the lipid nanoparticles that this 01:24:14.000 --> 01:24:16.000 guy came up with. 01:24:17.000 --> 01:24:20.000 And they just decided not to give them the Nobel, him the Nobel 01:24:20.000 --> 01:24:23.000 Prize for that, instead gave them the Nobel Prize for the 01:24:23.000 --> 01:24:27.000 adjustment to the mRNA that they made. 01:24:27.000 --> 01:24:31.000 Seems pretty random, also seems pretty useless because they 01:24:31.000 --> 01:24:33.000 don't even know what they're doing because they don't understand 01:24:33.000 --> 01:24:36.000 the chemistry of the lipid nanoparticles this guy does. 01:24:36.000 --> 01:24:38.000 They just bought him. 01:24:39.000 --> 01:24:44.000 They just added him to the team, right? 01:24:44.000 --> 01:24:52.000 His 30 years of biochemistry and NMR research on how lipids 01:24:52.000 --> 01:24:56.000 stick together. 01:24:56.000 --> 01:24:59.000 But still, they had a delivery problem. 01:24:59.000 --> 01:25:02.000 How do we get mRNA coding for viral proteins into muscle 01:25:02.000 --> 01:25:05.000 and immune cells in vivo? 01:25:06.000 --> 01:25:10.000 And so, they needed a lipid nanoparticle, obviously, enough 01:25:10.000 --> 01:25:16.000 to get the mRNA coding for vaccine into the cytoplasm of both, 01:25:16.000 --> 01:25:19.000 say, muscle cells and antigen presenting cells to get a 01:25:19.000 --> 01:25:24.000 vigorous MHC1 or MHC2 class immune response. 01:25:24.000 --> 01:25:27.000 So, the first thing that we looked at was, or the drew looked 01:25:27.000 --> 01:25:29.000 at, was Zika virus. 01:25:29.000 --> 01:25:34.000 So, the lipid nanoparticle in this case contained messenger 01:25:34.000 --> 01:25:38.000 RNA coding for a surface protein on the supreme membrane 01:25:38.000 --> 01:25:41.000 and envelope biker protein on Zika virus. 01:25:41.000 --> 01:25:47.000 And so, this was in the mouse model, and just showing that this 01:25:47.000 --> 01:25:51.000 is interdermally injected with nanoparticles containing mRNA. 01:25:51.000 --> 01:25:56.000 I didn't see the question, but amyloidosis is the process 01:25:56.000 --> 01:25:58.000 that leads to amyloid plaques. 01:25:58.000 --> 01:26:03.000 Amyloid plaques is just a consequence of amyloidosis. 01:26:03.000 --> 01:26:06.000 So, here we are at Zika. 01:26:06.000 --> 01:26:08.000 Here we are at Zika. 01:26:08.000 --> 01:26:14.000 And I don't know what to say other than here we are at Zika. 01:26:14.000 --> 01:26:15.000 It's all the same. 01:26:15.000 --> 01:26:17.000 It's all the same stunts. 01:26:17.000 --> 01:26:18.000 It's all the same theaters. 01:26:18.000 --> 01:26:20.000 It's all the same targets. 01:26:20.000 --> 01:26:22.000 It's all the same nonsense. 01:26:22.000 --> 01:26:25.000 Are they going to talk about antibodies to the envelope 01:26:25.000 --> 01:26:30.000 glycoprotein and antibodies to the premembrane? 01:26:30.000 --> 01:26:32.000 I would be willing to bet they are. 01:26:32.000 --> 01:26:36.000 For the Zika virus, the premembrane and envelope biker protein, 01:26:36.000 --> 01:26:40.000 a bit of the mouthful, and then challenging it two weeks 01:26:40.000 --> 01:26:43.000 or 20 weeks with Zika virus itself. 01:26:43.000 --> 01:26:45.000 Did they show? 01:26:45.000 --> 01:26:49.000 Oh my gosh, they showed microencephaly. 01:26:49.000 --> 01:26:55.000 We had Randall Bach on our show, and he's also been on Mark's 01:26:55.000 --> 01:27:01.000 useatonic live. 01:27:01.000 --> 01:27:07.000 And in his book, he describes in detail how it's pretty sure 01:27:07.000 --> 01:27:12.000 that microencephaly is actually not really associated with Zika virus. 01:27:12.000 --> 01:27:14.000 That was kind of baloney. 01:27:14.000 --> 01:27:16.000 So, that's really interesting. 01:27:16.000 --> 01:27:20.000 We are in A-quoting for the Zika virus, the premembrane and envelope 01:27:20.000 --> 01:27:23.000 lycoprotein, a bit of the mouthful. 01:27:23.000 --> 01:27:28.000 And then challenging it two weeks or 20 weeks with the Zika virus itself. 01:27:28.000 --> 01:27:33.000 And the quite remarkable results, and as much as was published in Nature 01:27:33.000 --> 01:27:37.000 in 2017, complete protection both at two weeks and 20 weeks 01:27:37.000 --> 01:27:40.000 against the Zika virus challenge. 01:27:40.000 --> 01:27:44.000 So, this is looking at the viral copy numbers per milliliter. 01:27:44.000 --> 01:27:45.000 Nature paper. 01:27:45.000 --> 01:27:46.000 Nature paper. 01:27:46.000 --> 01:27:47.000 Wow. 01:27:47.000 --> 01:27:50.000 This is not a direction we were going in at all. 01:27:50.000 --> 01:27:53.000 It was only, we were trying to say, express proteins in the liver, 01:27:53.000 --> 01:27:56.000 and you got a phone call, and then things change. 01:27:56.000 --> 01:28:02.000 Now, in this cause, cutus, it causes to start working with the biointech 01:28:02.000 --> 01:28:04.000 to develop an influenza vaccine. 01:28:04.000 --> 01:28:08.000 Obviously, that's from a commercial point of view of huge interest. 01:28:08.000 --> 01:28:11.000 Now, it turned out that biointech had also been working Pfizer on this. 01:28:11.000 --> 01:28:14.000 From a commercial point of view is of a huge interest. 01:28:14.000 --> 01:28:18.000 Not from a public health perspective, because we don't need flu vaccines. 01:28:18.000 --> 01:28:19.000 This is so stupid. 01:28:19.000 --> 01:28:20.000 The vaccine. 01:28:20.000 --> 01:28:25.000 And so, in January, February of 2020, all those efforts were switched, 01:28:25.000 --> 01:28:33.000 using the liver nanoparticle that we've been supplying to developing a COVID-19 vaccine. 01:28:33.000 --> 01:28:39.000 And you all know the results of this, that in November of 2020, 01:28:39.000 --> 01:28:44.000 the vaccine was demonstrated to be 95% effective against COVID-19, 01:28:44.000 --> 01:28:46.000 independent of age or gender, race, et cetera, 01:28:46.000 --> 01:28:49.000 and was reasonably well tolerated across all populations. 01:28:49.000 --> 01:28:51.000 This is 43,000 participants. 01:28:51.000 --> 01:28:53.000 So, this is certainly one of the larger clinical trials you're ever going to see 01:28:53.000 --> 01:28:56.000 from a vaccine, remarkably convincing results. 01:28:56.000 --> 01:29:00.000 They expected to produce 1.3 billion doses by the end of 2021. 01:29:00.000 --> 01:29:02.000 They were much closer to 3 billion, I think, 01:29:02.000 --> 01:29:05.000 and obviously, has been approved in many jurisdictions. 01:29:05.000 --> 01:29:08.000 And so, that's really what the, as to say, 01:29:08.000 --> 01:29:12.000 we spent 27 years developing these systems. 01:29:12.000 --> 01:29:13.000 It's been a pretty amazing journey. 01:29:13.000 --> 01:29:18.000 I hope I pointed out how that journey has really depended on some very basic research 01:29:18.000 --> 01:29:20.000 and also some good luck, the serendipity. 01:29:20.000 --> 01:29:22.000 It's really quite remarkable. 01:29:22.000 --> 01:29:24.000 But the point is, it's just beginning. 01:29:24.000 --> 01:29:29.000 I often refer to mRNA systems as really being, you know, 01:29:29.000 --> 01:29:32.000 the third generation of pharmaceuticals where the first generation, 01:29:32.000 --> 01:29:39.000 small molecule drugs, second generation, biologics, monoclonal antibodies, et cetera. 01:29:39.000 --> 01:29:43.000 And then third generation, these gene therapies for protein replacement, 01:29:43.000 --> 01:29:45.000 you know, you don't have a protein being made, okay, well, let's make it. 01:29:45.000 --> 01:29:47.000 Those vaccines, this is obvious. 01:29:47.000 --> 01:29:52.000 Gene editing is another approach that we can use to alter gene expression. 01:29:52.000 --> 01:29:57.000 So, quite remarkable how these are the broad applicability. 01:29:57.000 --> 01:30:00.000 And the other thing that's kind of mind-boggling is we're used to, you know, 01:30:00.000 --> 01:30:03.000 developing a drug that might take 15 years and a billion dollars. 01:30:03.000 --> 01:30:08.000 Once we've identified the protein that we want to express or silence 01:30:08.000 --> 01:30:11.000 or edit or whatever, we can reduce the S RNA or the mRNA 01:30:11.000 --> 01:30:13.000 and it's time on the order of say a month or so. 01:30:13.000 --> 01:30:15.000 And we can package it in a day or so. 01:30:15.000 --> 01:30:19.000 And so you have this really highly targeted personalized medicine available 01:30:19.000 --> 01:30:21.000 and it's time on the order of weeks. 01:30:21.000 --> 01:30:24.000 So this changes the paradigm of medicine as far as I'm concerned. 01:30:24.000 --> 01:30:27.000 It just makes a huge, it's a huge leap. 01:30:27.000 --> 01:30:31.000 He's making the same argument that they've been making for a long time 01:30:31.000 --> 01:30:34.000 because all you have to do is change the RNA 01:30:34.000 --> 01:30:38.000 that you don't need to do any therapeutic trials on it anymore 01:30:38.000 --> 01:30:41.000 because it's just like a cassette. 01:30:41.000 --> 01:30:44.000 It doesn't matter what cassette you put in the cassette player. 01:30:44.000 --> 01:30:46.000 It's not going to hurt the cassette player. 01:30:46.000 --> 01:30:52.000 Put Van Halen in there or if you put Miles Davis in there, it doesn't matter. 01:30:52.000 --> 01:30:58.000 And that is the dumbest thing that you could ever possibly hear out of these people's heads. 01:30:58.000 --> 01:31:02.000 But this is, maybe this guy's more of a chemist. 01:31:03.000 --> 01:31:07.000 He believes that the clinical trials are the most remarkable thing ever. 01:31:09.000 --> 01:31:15.000 And now we're going to kill Alzheimer's disease and Huntington's and cystic fibrosis, 01:31:15.000 --> 01:31:18.000 cancer, heart disease, Alzheimer's. 01:31:18.000 --> 01:31:20.000 It's all going away. 01:31:21.000 --> 01:31:27.000 All going away thanks to this guy's many patents on how to make small soap bubbles. 01:31:27.000 --> 01:31:29.000 Holy crap. 01:31:33.000 --> 01:31:36.000 What happened here? 01:31:36.000 --> 01:31:38.000 I muted it somehow. 01:31:38.000 --> 01:31:40.000 They do that. 01:31:40.000 --> 01:31:44.000 So the next few years, obviously other vaccines are certainly in the cards. 01:31:44.000 --> 01:31:49.000 Universal flu vaccine, for example, HIV is looking promising. 01:31:49.000 --> 01:31:53.000 The chronic diseases, for example, heart diseases. 01:31:53.000 --> 01:31:57.000 There's just a paper published basically for heart failure. 01:31:57.000 --> 01:32:02.000 I'm sure we can eliminate fibrosis fibrotic lesions in the liver and the heart gets better 01:32:02.000 --> 01:32:04.000 using a protein cell approach actually. 01:32:04.000 --> 01:32:08.000 Inherited diseases, the obviously replacing a protein that's not being made. 01:32:08.000 --> 01:32:10.000 Anyway, the list goes on and on. 01:32:10.000 --> 01:32:14.000 And so it's a very exciting time to be in medicine when we're suddenly enabling all of our 01:32:14.000 --> 01:32:18.000 understanding of microbiology in a very direct therapeutic manner. 01:32:18.000 --> 01:32:20.000 So it's changing the game. 01:32:20.000 --> 01:32:23.000 Clearly he doesn't deserve a Nobel Prize for this work. 01:32:23.000 --> 01:32:27.000 It might be very creative from a chemical perspective and a right might be quite creative 01:32:27.000 --> 01:32:33.000 from a product development perspective, from a methodological development perspective, 01:32:33.000 --> 01:32:42.000 from a mathematical therapeutic perspective, from a biological insight perspective, 01:32:42.000 --> 01:32:47.000 and I mean if there's anything that you can take from this, the idea that Robert Malone 01:32:47.000 --> 01:32:52.000 said this guy should have gotten the prize before them is really 01:32:53.000 --> 01:32:58.000 That to me makes Robert Malone sound like a cloud. 01:32:58.000 --> 01:33:04.000 Because I was ranting at the beginning, this guy's a real scientist, a real chemist. 01:33:04.000 --> 01:33:06.000 He did this, these experiments. 01:33:06.000 --> 01:33:09.000 He made these measurements. 01:33:09.000 --> 01:33:15.000 The insights that he's gained in terms of how to put these things inside of stuff, 01:33:15.000 --> 01:33:21.000 to put RNA and DNA inside of these things and then make changes in animals. 01:33:21.000 --> 01:33:24.000 It all works great. 01:33:24.000 --> 01:33:26.000 But that is so very different. 01:33:26.000 --> 01:33:33.000 Augmenting a pattern integrity that's about to self-destruct, like a man with 01:33:33.000 --> 01:33:39.000 head or amyloidosis, is not the same as augmenting a pattern integrity that is currently 01:33:39.000 --> 01:33:47.000 operating perfectly with exquisite precision and has the momentum to operate with that 01:33:47.000 --> 01:33:52.000 exquisite precision for another 90 years. 01:33:52.000 --> 01:33:53.000 Let me explain that again. 01:33:53.000 --> 01:33:57.000 It's very different to augment the system. 01:33:57.000 --> 01:34:03.000 A pattern integrity that is within five years of self-destruction. 01:34:03.000 --> 01:34:09.000 A man with head or amyloidosis, who has less than five years to live and is a 01:34:09.000 --> 01:34:14.000 wasting disease. 01:34:14.000 --> 01:34:20.000 Augmenting that pattern integrity for it to last longer or to become more stable is 01:34:20.000 --> 01:34:33.000 not the same ethical, biological, chemical or temporal problem of augmentation that 01:34:33.000 --> 01:34:45.000 augmenting a four-year-old perfect child's immune system is. 01:34:45.000 --> 01:34:49.000 It's not the same. 01:34:49.000 --> 01:34:51.000 It's not even the same ballgame. 01:34:51.000 --> 01:34:56.000 It's not even the same sport. 01:34:56.000 --> 01:35:05.000 And this kind of representation that the anecdotal Jesse Gelsinger that didn't 01:35:05.000 --> 01:35:11.000 die is evidence that we can use this technology to augment the perfect children 01:35:11.000 --> 01:35:14.000 of the world. 01:35:14.000 --> 01:35:23.000 It's just so wrong and so ridiculous, arrogant and naive. 01:35:23.000 --> 01:35:27.000 As somebody in the chat said, this guy really likes to be bought. 01:35:27.000 --> 01:35:31.000 He loves selling his stuff. 01:35:31.000 --> 01:35:35.000 He's very proud of it. 01:35:35.000 --> 01:35:44.000 But he's no, he's no, I guess I would say he's no genius. 01:35:44.000 --> 01:35:50.000 The talk started out amazing and it ended really weak. 01:35:50.000 --> 01:35:57.000 Because after all that, all that pomp and circumstance in the beginning there was 01:35:57.000 --> 01:35:58.000 really nothing left. 01:35:58.000 --> 01:36:05.000 It's just a bunch of soap bubbles and he got distracted by therapies and products and 01:36:05.000 --> 01:36:11.000 processes that he could patent and charge people money for. 01:36:11.000 --> 01:36:15.000 And his understanding of lipids and his understanding of how lipids are used in 01:36:15.000 --> 01:36:20.000 biology, how they're used in cells, how they're used in exosomes, how they're used in 01:36:20.000 --> 01:36:29.000 endosomes became secondary even tertiary to the objective of delivering nucleic 01:36:29.000 --> 01:36:35.000 acids to cells in animals and people. 01:36:35.000 --> 01:36:39.000 That's what I see. 01:36:39.000 --> 01:36:40.000 And I agree with the person in the chat. 01:36:40.000 --> 01:36:45.000 I think it was Jason M. who said that he likes to be bought. 01:36:45.000 --> 01:36:50.000 That's what's very obvious here. 01:36:50.000 --> 01:36:52.000 In a fundamental way. 01:36:52.000 --> 01:36:57.000 Now, I can't stop without acknowledging all of the people I've worked with for literally 40 years 01:36:57.000 --> 01:37:00.000 as I pointed out, as well as many as Mick Hope Tom Madden. 01:37:00.000 --> 01:37:05.000 As many such as Steve Ansel, Ying Tam, Barbooie, Paul Lynn, 20 years or more. 01:37:05.000 --> 01:37:07.000 And the people that are beautists. 01:37:07.000 --> 01:37:09.000 Ian McLaughlin, James Hayes, O'Mileham. 01:37:09.000 --> 01:37:12.000 This is a hugely successful collaboration. 01:37:12.000 --> 01:37:15.000 Mark Tracy, akin to King Martin Mayor, Madam Madam Heron. 01:37:15.000 --> 01:37:20.000 Marcus Ifloni in the chemistry department at UBC who really played a big role in breaking 01:37:20.000 --> 01:37:22.000 the whole problem of the ionized lipids. 01:37:22.000 --> 01:37:24.000 Now, I think these things work in the brain. 01:37:24.000 --> 01:37:26.000 I haven't discussed that, but it's in my own group. 01:37:26.000 --> 01:37:28.000 And of course, rewisement at the University of Pennsylvania. 01:37:28.000 --> 01:37:32.000 So with that, I'll close and take any questions that's rewisement at the University of 01:37:32.000 --> 01:37:33.000 Pennsylvania. 01:37:33.000 --> 01:37:37.000 So he didn't thank Carrico at all? 01:37:37.000 --> 01:37:41.000 Just Weismann? 01:37:41.000 --> 01:37:42.000 That's kind of shit. 01:37:42.000 --> 01:37:43.000 He isn't it? 01:37:43.000 --> 01:37:44.000 I don't know. 01:37:44.000 --> 01:37:48.000 I mean, I know she was on the slide earlier, but I mean Weismann and not her. 01:37:48.000 --> 01:37:50.000 Maybe she's not at Penn anymore. 01:37:50.000 --> 01:37:53.000 She's at BioNTech, so he doesn't have Doug in here. 01:37:53.000 --> 01:37:56.000 Maybe, but he's got other people here. 01:37:56.000 --> 01:38:00.000 So with that, I'll close and take any questions that people may have. 01:38:00.000 --> 01:38:07.000 Thanks, Peter. 01:38:07.000 --> 01:38:08.000 That was a fantastic talk. 01:38:08.000 --> 01:38:10.000 And if there's anybody who has any questions, please raise your hand. 01:38:10.000 --> 01:38:12.000 But maybe I'll start with. 01:38:12.000 --> 01:38:15.000 So silencing a protein is one thing, putting one in that's going to be upregulated. 01:38:15.000 --> 01:38:17.000 How do you control that regulation? 01:38:17.000 --> 01:38:19.000 How do you know you don't over-expressing a protein? 01:38:19.000 --> 01:38:21.000 Have you even talked to that? 01:38:21.000 --> 01:38:22.000 Yeah. 01:38:22.000 --> 01:38:24.000 I mean, one of the things about this approach course is titradable. 01:38:24.000 --> 01:38:26.000 So you can start low and work it up. 01:38:26.000 --> 01:38:29.000 The other is that it's not like you're inducing production for protein forever. 01:38:29.000 --> 01:38:32.000 You know, you're going to get it going from maybe a week or two. 01:38:32.000 --> 01:38:33.000 But that's about it. 01:38:33.000 --> 01:38:35.000 And so it's self-limiting in that extent too. 01:38:35.000 --> 01:38:39.000 So if you're causing nasty side effects, then no case stop giving it and then things will. 01:38:39.000 --> 01:38:40.000 So that would do. 01:38:40.000 --> 01:38:42.000 Things will go back to normal. 01:38:42.000 --> 01:38:46.000 If it's not autoimmunity, you see how naive they are? 01:38:46.000 --> 01:38:49.000 If you're getting bad side effects, just don't give anymore. 01:38:49.000 --> 01:38:50.000 And it'll go back to normal. 01:38:50.000 --> 01:38:54.000 That's not how... 01:38:54.000 --> 01:38:55.000 It's not all of this works. 01:38:55.000 --> 01:39:00.000 I mean, it might work that way in a mouse if you don't look very hard. 01:39:00.000 --> 01:39:05.000 But that's not how it works in a four-year-old girl. 01:39:05.000 --> 01:39:10.000 That's not how it worked in Jesse Gelsinger. 01:39:10.000 --> 01:39:13.000 Holy crap. 01:39:13.000 --> 01:39:17.000 I mean, holy crap. 01:39:17.000 --> 01:39:18.000 That is extraordinary. 01:39:18.000 --> 01:39:19.000 My next question. 01:39:19.000 --> 01:39:21.000 How often do you think you'd have to give something if you were dosing? 01:39:21.000 --> 01:39:23.000 It depends on the length of time that you want. 01:39:23.000 --> 01:39:26.000 And also the stability of the protein that you're producing. 01:39:26.000 --> 01:39:29.000 Like antibodies, we can make those in the liver. 01:39:29.000 --> 01:39:31.000 Say if you put the heavy-like gene mRNA in. 01:39:31.000 --> 01:39:34.000 And they'll circulate around for, say, a week or so because they're fairly stable. 01:39:34.000 --> 01:39:37.000 Other molecules, you know, might be a matter of a night. 01:39:37.000 --> 01:39:41.000 He just said they can make antibodies in the liver. 01:39:41.000 --> 01:39:46.000 Have you ever thought about that possibility that they can give you mRNA that would just make antibodies? 01:39:47.000 --> 01:39:52.000 Then you're cells that express antibodies in the liver, I guess. 01:39:52.000 --> 01:39:54.000 Do they just excrete them then or what? 01:39:54.000 --> 01:39:56.000 That's incredible. 01:39:56.000 --> 01:40:01.000 I mean, I'm sure it's fine if you make other cells other than the immune system cells make antibodies, right? 01:40:01.000 --> 01:40:02.000 I mean, what difference does it make? 01:40:02.000 --> 01:40:04.000 B cells, liver cells, who cares? 01:40:04.000 --> 01:40:05.000 Anybody's or anybody's, right? 01:40:05.000 --> 01:40:07.000 An hour or so. 01:40:07.000 --> 01:40:13.000 So it's going to, this is where you're going to see improvements in the technology itself. 01:40:13.000 --> 01:40:16.000 You know, a more of a depot effect to have things play out over a longer time. 01:40:16.000 --> 01:40:19.000 There's a long way to go in the ways in which we can manipulate things. 01:40:19.000 --> 01:40:22.000 And in your delivery system, do you think there's going to be a way to actually target it to different tissues? 01:40:22.000 --> 01:40:27.000 So right now you've been talking about getting it into the liver, but how do you, can we actually restrict which tissues? 01:40:27.000 --> 01:40:28.000 It's going to be tricky. 01:40:28.000 --> 01:40:29.000 That's going to be tricky. 01:40:29.000 --> 01:40:33.000 People have been trying to target these lipid or many nanomedicine actually. 01:40:33.000 --> 01:40:35.000 That's going to be tricky. 01:40:35.000 --> 01:40:37.000 I thought it stayed in the muscle right now. 01:40:38.000 --> 01:40:41.000 I thought the COVID-19 vaccine stayed in the muscle. 01:40:41.000 --> 01:40:44.000 Is he actually admitting that it doesn't? 01:40:44.000 --> 01:40:50.000 In 2022 at a University of Manitoba Gardener Lecture. 01:40:50.000 --> 01:40:55.000 He's admitting it's tricky to target it to a tissue. 01:40:55.000 --> 01:40:58.000 I thought it just stayed in the muscle. 01:40:58.000 --> 01:41:01.000 Oh, wait, it goes all over the body. 01:41:01.000 --> 01:41:03.000 And they're admitting it now. 01:41:03.000 --> 01:41:05.000 Do you hear it? 01:41:05.000 --> 01:41:07.000 It's kind of funny. 01:41:07.000 --> 01:41:09.000 It's like they don't even care. 01:41:09.000 --> 01:41:11.000 Holy shit. 01:41:11.000 --> 01:41:16.000 Being into the liver, but how do you, can we actually restrict which tissues? 01:41:16.000 --> 01:41:17.000 It's going to be tricky. 01:41:17.000 --> 01:41:18.000 That's going to be tricky. 01:41:18.000 --> 01:41:22.000 People have been trying to target these lipid or many nanomedicine actually. 01:41:22.000 --> 01:41:24.000 More specifically to where you want it from than to cancer cells. 01:41:24.000 --> 01:41:28.000 It's an obvious choice without any success over 40 years of trying. 01:41:28.000 --> 01:41:30.000 And that put points out how difficult it is. 01:41:30.000 --> 01:41:33.000 You tend to raise an immune response or you put it on the outside. 01:41:33.000 --> 01:41:35.000 And you get aggregation and the difficulty of the manning. 01:41:35.000 --> 01:41:36.000 But the list goes on and on. 01:41:36.000 --> 01:41:38.000 I've worn out five graduate students. 01:41:38.000 --> 01:41:41.000 You get aggregation. 01:41:41.000 --> 01:41:45.000 I mean, he wore out five grad students. 01:41:45.000 --> 01:41:46.000 Holy crap. 01:41:46.000 --> 01:41:49.000 And that put points out how difficult it is. 01:41:49.000 --> 01:41:55.000 40 years of trying and they still haven't gotten into work. 01:41:55.000 --> 01:41:58.000 You tend to raise an immune response or any problem on the outside. 01:41:58.000 --> 01:42:00.000 And you get aggregation and difficulty of the manning. 01:42:00.000 --> 01:42:01.000 But the list goes on and on. 01:42:01.000 --> 01:42:04.000 I've worn out five graduate students on that project. 01:42:04.000 --> 01:42:05.000 The last one refused to go on. 01:42:05.000 --> 01:42:06.000 That's why I changed your projects. 01:42:06.000 --> 01:42:07.000 Okay. 01:42:07.000 --> 01:42:08.000 I did that. 01:42:08.000 --> 01:42:10.000 But the points. 01:42:10.000 --> 01:42:12.000 But there are other ways to go. 01:42:12.000 --> 01:42:16.000 The microbiology, you can start to say, okay, one of the expressed in a particular cell. 01:42:16.000 --> 01:42:18.000 That requires very sophisticated microbiology. 01:42:18.000 --> 01:42:20.000 But I think it's doable. 01:42:20.000 --> 01:42:24.000 Oh, there he's talking about cree recombinase or something like that where it's independent. 01:42:24.000 --> 01:42:25.000 The mRNA is dependent. 01:42:25.000 --> 01:42:30.000 It's expression won't happen unless there's a gene present or a protein. 01:42:31.000 --> 01:42:33.000 You could use that too. 01:42:33.000 --> 01:42:34.000 That could work. 01:42:34.000 --> 01:42:42.000 Then the mRNA'd go a bunch of places and just sit there and you never know what it would do until... 01:42:42.000 --> 01:42:44.000 He just admitted it. 01:42:44.000 --> 01:42:46.000 He just admitted they don't have a clue where it goes. 01:42:46.000 --> 01:42:48.000 They can't control where it goes. 01:42:48.000 --> 01:42:50.000 He wasted five grad students on it. 01:42:50.000 --> 01:42:55.000 The last one said, if you don't change my project, I'm going to quit. 01:42:55.000 --> 01:43:00.000 And his solution is that we can make the molecular biology of the molecule that we're carrying 01:43:00.000 --> 01:43:06.000 in the lipid nanoparticle dependent on the cell type. 01:43:06.000 --> 01:43:13.000 But that's some pretty sophisticated biology, but I think it's doable. 01:43:13.000 --> 01:43:18.000 I'm hurting my tongue now. 01:43:18.000 --> 01:43:20.000 He completely admitted it. 01:43:20.000 --> 01:43:24.000 The other approach is that things are activated by external stimulation. 01:43:24.000 --> 01:43:28.000 One of the nice things you can do with the nanoparticles, we can incorporate things like iron oxide 01:43:28.000 --> 01:43:30.000 nanoparticles or golden up. 01:43:30.000 --> 01:43:33.000 And then laser or RF or whatever other stimulation you can see. 01:43:33.000 --> 01:43:38.000 Oh, we can use radio frequencies or electricity to open them. 01:43:38.000 --> 01:43:41.000 Oh, walk in, Robert Malone, please. 01:43:41.000 --> 01:43:47.000 Why don't we just bring in Robert Malone from the right side of the stage with his electroporation gun? 01:43:48.000 --> 01:43:51.000 They say, OK, turn something on here, but not here. 01:43:51.000 --> 01:43:54.000 So there's going to be approaches that will achieve that. 01:43:54.000 --> 01:44:00.000 There's going to be approaches that will achieve specificity in the targeting of lipid nanoparticles, 01:44:00.000 --> 01:44:05.000 but we don't have it yet. 01:44:05.000 --> 01:44:09.000 We're sorry, but all you people that took the lipid nanoparticle and thought we had it. 01:44:09.000 --> 01:44:12.000 Sorry, we didn't really have it yet. 01:44:12.000 --> 01:44:13.000 But we're going to get there. 01:44:13.000 --> 01:44:16.000 It's coming just over the next hill. 01:44:17.000 --> 01:44:20.000 Any questions from the audience? 01:44:20.000 --> 01:44:23.000 A question here and then a question way at the back and up there. 01:44:23.000 --> 01:44:24.000 So we'll start here. 01:44:24.000 --> 01:44:25.000 I'll go up there. 01:44:25.000 --> 01:44:26.000 So please, loudly. 01:44:26.000 --> 01:44:27.000 All right. 01:44:27.000 --> 01:44:28.000 Very nice computer. 01:44:28.000 --> 01:44:34.000 So I mean, it's sort of alluded to that open, but with mRNA, our ability is obviously a kind of limited expression 01:44:34.000 --> 01:44:38.000 so that works well for an acute disease or a vaccine. 01:44:38.000 --> 01:44:43.000 But any work on DNA, the research then says it's going to come back 01:44:43.000 --> 01:44:49.000 and we'll be able to see if it's an integration or say an internal coming to be where you don't have that. 01:44:49.000 --> 01:44:52.000 Yeah, we worked on it without any success. 01:44:52.000 --> 01:44:55.000 I mean, it's commonly used in cell culture for context. 01:44:55.000 --> 01:44:56.000 Absolutely. 01:44:56.000 --> 01:44:58.000 I mean, in cell culture, you've got rapidly dividing cells. 01:44:58.000 --> 01:45:02.000 He said it's got to be used in cell culture for transfection. 01:45:02.000 --> 01:45:06.000 But the problem is that for cells that are not rapidly dividing, 01:45:06.000 --> 01:45:10.000 and so therefore the nucleus isn't, you know, you can't get things into the nucleus as readily, 01:45:10.000 --> 01:45:12.000 then these systems don't work. 01:45:12.000 --> 01:45:16.000 And so that's a, which is, you know, we certainly haven't beaten that one. 01:45:16.000 --> 01:45:20.000 Jason M, you're totally correct, but we had already busted that. 01:45:20.000 --> 01:45:25.000 There's no frickin' way that Robert Malone didn't know that they hadn't solved this problem. 01:45:25.000 --> 01:45:28.000 There's no frickin' way that he didn't know that. 01:45:28.000 --> 01:45:34.000 And him saying that is just a bald-faced lie, but they're liars. 01:45:34.000 --> 01:45:39.000 They are liars. 01:45:39.000 --> 01:45:44.000 Remember, Robert Malone gave a talk in front of the who about flu vaccines 01:45:44.000 --> 01:45:48.000 and flu vaccine fill-and-finish technologies. 01:45:48.000 --> 01:45:55.000 And after he finished that talk in 2011, he was thanked by Rick Bright. 01:45:55.000 --> 01:45:58.000 Robert Malone has been in this space. 01:45:58.000 --> 01:46:01.000 A vaccine technology and brokering of vaccine technologies 01:46:01.000 --> 01:46:06.000 between non-governmental organizations and governmental organizations 01:46:06.000 --> 01:46:09.000 for more than three decades. 01:46:09.000 --> 01:46:13.000 Robert Malone has been in this space of gene delivery 01:46:13.000 --> 01:46:17.000 and RNA delivery for three decades. 01:46:17.000 --> 01:46:20.000 And there's no way on God's green earth that he didn't know 01:46:20.000 --> 01:46:25.000 that lipid nanoparticles had no means of mechanistically targeting a tissue. 01:46:25.000 --> 01:46:28.000 Never mind that it could stay where you put it. 01:46:28.000 --> 01:46:35.000 He knew it was exactly the opposite, and he's a liar every time he said that he thought that they had solved it. 01:46:36.000 --> 01:46:41.000 And he and other people are responsible for all of the damage 01:46:41.000 --> 01:46:47.000 that has been done by this horrible, poor excuse for a medical product. 01:46:47.000 --> 01:46:52.000 They're all responsible because they all knew. 01:46:52.000 --> 01:46:56.000 He also knew. 01:46:56.000 --> 01:46:59.000 He knows that they can't make it stay in the shoulder. 01:46:59.000 --> 01:47:04.000 He knows that they lied about it. 01:47:05.000 --> 01:47:10.000 Every time he's heard someone talk about it, he knows they're lying. 01:47:13.000 --> 01:47:16.000 Yeah, we're feeling pretty good about having God's power if he's gotten, 01:47:16.000 --> 01:47:19.000 but yes, there's other barriers to get through for sure. 01:47:23.000 --> 01:47:26.000 Yeah, yeah, and there's no doubt we can get DNA into the cytoplasm 01:47:26.000 --> 01:47:29.000 in the same way that we can, or that messenger RNA, 01:47:29.000 --> 01:47:32.000 but to get it into the nucleus. 01:47:33.000 --> 01:47:36.000 At least for non-dividing cells, we haven't achieved that. 01:47:44.000 --> 01:47:47.000 Oh, yeah, no, we're fiddling around with every possible... 01:47:47.000 --> 01:47:49.000 Well, we really haven't figured out. 01:47:49.000 --> 01:47:51.000 I mean, it's coming to light now a little bit, 01:47:51.000 --> 01:47:55.000 but are the structure-activity relationships between the structure of the lipid nanoparticle 01:47:55.000 --> 01:47:59.000 and the actual activity in vitro and in vivo. 01:47:59.000 --> 01:48:02.000 Well, those things are starting to become elucidated. 01:48:02.000 --> 01:48:03.000 It's quite remarkable. 01:48:03.000 --> 01:48:04.000 You have a very successful medicine, 01:48:04.000 --> 01:48:07.000 and we still don't understand some of the basics of how it actually works. 01:48:09.000 --> 01:48:11.000 We have a very successful medicine, 01:48:11.000 --> 01:48:13.000 but we still don't understand how it works. 01:48:13.000 --> 01:48:16.000 Actually, I think you're unaware that it doesn't work. 01:48:16.000 --> 01:48:20.000 I think you have been misled to believe that it works, but it don't work. 01:48:21.000 --> 01:48:29.000 Holy shit, I can't believe it. 01:48:29.000 --> 01:48:32.000 So, you know, those things are becoming pure. 01:48:32.000 --> 01:48:37.000 A lot of it has to do with manipulating the various components that we have in there. 01:48:50.000 --> 01:48:57.000 I'm sorry, I can't hear the question anymore. 01:48:57.000 --> 01:49:05.000 I don't know if it's worth listening anymore. 01:49:05.000 --> 01:49:08.000 We've already caught him on the worst one. 01:49:08.000 --> 01:49:12.000 We've already caught him on the worst possible thing he could have said. 01:49:12.000 --> 01:49:16.000 And that is that we don't know how to target places. 01:49:16.000 --> 01:49:18.000 Yeah, we can't make it go anywhere. 01:49:18.000 --> 01:49:20.000 We don't know how to do that. 01:49:20.000 --> 01:49:25.000 So, it goes to the liver because it goes to the liver because that's what the liver does. 01:49:25.000 --> 01:49:27.000 It cleans stuff out. 01:49:27.000 --> 01:49:29.000 So, a lot of it will go to the liver. 01:49:29.000 --> 01:49:30.000 It could go to the bone marrow. 01:49:30.000 --> 01:49:32.000 It goes wherever we said it goes. 01:49:35.000 --> 01:49:38.000 And it goes random because he doesn't know how to control it at all. 01:49:38.000 --> 01:49:41.000 He burnt five post-docs on that project. 01:49:44.000 --> 01:49:46.000 The last one threatened to quit. 01:49:46.000 --> 01:49:48.000 We're trying all kinds of stuff. 01:49:50.000 --> 01:49:51.000 But it's crazy. 01:49:51.000 --> 01:49:52.000 We have a working medicine. 01:49:52.000 --> 01:49:54.000 We don't know how it works. 01:49:58.000 --> 01:50:00.000 We have been completely and totally bamboozled. 01:50:00.000 --> 01:50:03.000 Even this guy has been completely and totally bamboozled. 01:50:03.000 --> 01:50:05.000 That his bullshit works. 01:50:05.000 --> 01:50:12.000 It's extraordinary. 01:50:12.000 --> 01:50:17.000 You want to link to this video? 01:50:17.000 --> 01:50:20.000 It's just extraordinary. 01:50:20.000 --> 01:50:23.000 I just can't believe it. 01:50:23.000 --> 01:50:25.000 I thought it was going to be kind of a fun video at the start. 01:50:25.000 --> 01:50:29.000 I thought we were going to hear some really intelligent guy. 01:50:29.000 --> 01:50:32.000 Real smart dude. 01:50:33.000 --> 01:50:40.000 Tell us all about the limitations of his work. 01:50:40.000 --> 01:50:48.000 It turns out it's just, you know, serendipitous that they were able to make something that ended up saving the world. 01:50:48.000 --> 01:50:51.000 That's how science works. 01:50:51.000 --> 01:50:54.000 I guess it's just serendipity. 01:50:54.000 --> 01:50:57.000 Although we still don't know how it works. 01:50:57.000 --> 01:50:59.000 We still don't know why it works. 01:51:00.000 --> 01:51:06.000 We're still not really, I mean, wow, I'm just, I'm a loss for words. 01:51:06.000 --> 01:51:09.000 I'm fumbling really bad here. 01:51:09.000 --> 01:51:16.000 But obviously these guys believe in pandemic potential because they think their technology saved us from it. 01:51:16.000 --> 01:51:18.000 And they're Canadian. 01:51:18.000 --> 01:51:25.000 So it sounds like they're pretty down with the inversion of individual rights to community privileges. 01:51:26.000 --> 01:51:31.000 If you don't take these highly effective. 01:51:31.000 --> 01:51:36.000 What an illusion of consensus we have to fight my friends. 01:51:36.000 --> 01:51:40.000 What a terrible place we live right now. 01:51:40.000 --> 01:51:51.000 Where the who can invert our understanding of respiratory disease can invert the way that we treat respiratory disease to lead to a higher cause, all cause mortality. 01:51:51.000 --> 01:51:58.000 But the TV and social media could lead us to believe that an RNA molecule did it. 01:51:58.000 --> 01:52:03.000 And RNA molecule did it. 01:52:03.000 --> 01:52:13.000 And it was a RNA molecule that's been pretty consistent all around the world for like four years now, except for one part of it. 01:52:13.000 --> 01:52:20.000 And this lie is going to, it's going to deceive our children and our grandchildren. 01:52:20.000 --> 01:52:28.000 And if we don't dispel this lie, our grandchildren will be enslaved by it. 01:52:28.000 --> 01:52:34.000 This illusion of consensus will enslave our children if we don't break it now. 01:52:34.000 --> 01:52:36.000 And we don't have that much time. 01:52:36.000 --> 01:52:39.000 We got about a year. 01:52:39.000 --> 01:52:44.000 Bobby's book is coming out in December 5th. 01:52:44.000 --> 01:52:52.000 And from that point on, we have about a year to break it and break the illusion we must. 01:52:52.000 --> 01:52:57.000 Because people are still solving the mystery. 01:52:57.000 --> 01:53:03.000 People are still believing that we had to change our mind about these things because it was a novel virus. 01:53:03.000 --> 01:53:12.000 And people have still not taken into account how many people were killed by these protocols. 01:53:12.000 --> 01:53:19.000 And how many people were killed by some novel thing, RNA. 01:53:19.000 --> 01:53:23.000 People are not talking about this stuff anymore, ladies and gentlemen. 01:53:23.000 --> 01:53:31.000 It's me and Mark and crickets. 01:53:31.000 --> 01:53:32.000 And it shouldn't be this way. 01:53:32.000 --> 01:53:39.000 This illusion of consensus about a laboratory leak needs to be broken. 01:53:39.000 --> 01:53:49.000 We need people to understand that they have taken a natural phenomenon, a natural noise signal and turned it into a consensus about a laboratory leak. 01:53:49.000 --> 01:53:55.000 By fooling us into believing that something was covered up. 01:53:55.000 --> 01:53:59.000 I'll tell you what was covered up, that the PCR test is bogus. 01:53:59.000 --> 01:54:07.000 I'll tell you what was covered up, that there are coronavirus signals everywhere that we can't really track and follow with any fidelity. 01:54:07.000 --> 01:54:15.000 And so we make shit up and we use infectious clones to try and approximate them in a laboratory. 01:54:15.000 --> 01:54:18.000 Nobody's talking about that. 01:54:18.000 --> 01:54:21.000 Protocols were murder and transfection is not medicine. 01:54:21.000 --> 01:54:25.000 It might have been an infectious clone. It could have been a transfection agent. 01:54:25.000 --> 01:54:31.000 I don't care what you call it, it wasn't no viruses. 01:54:31.000 --> 01:54:38.000 Because we can make infectious clones and we can make exosomes and we can make self replicating RNA. 01:54:38.000 --> 01:54:45.000 And we can package that in little tiny, little tiny vesicles. 01:54:45.000 --> 01:54:50.000 You don't want to call them viruses, don't call them viruses, but transfection works. 01:54:50.000 --> 01:54:52.000 Transformation works. 01:54:52.000 --> 01:54:56.000 And these things can be sprayed. 01:54:56.000 --> 01:54:59.000 That's it. 01:54:59.000 --> 01:55:04.000 The protocols were murder and transfection is not medicine. 01:55:04.000 --> 01:55:09.000 The players will continue not to address the PCR as fraud. 01:55:09.000 --> 01:55:16.000 These players will continue to address the variants as real and unquestionable. 01:55:16.000 --> 01:55:21.000 They're not going to talk about death certificate fraud or whether the RNA is pure or not. 01:55:21.000 --> 01:55:27.000 They're going to focus exclusively on our DNA contamination. 01:55:27.000 --> 01:55:40.000 They're not going to talk about protein folding and how that impacts the potential epitopes that are generated by a codon optimized pseudo-uridine substituted version of a viral protein. 01:55:40.000 --> 01:55:53.000 Not going to talk about transfection in general and the fact that it doesn't really work except for in limited circumstances where a biological pattern integrity is about to be destroyed. 01:55:54.000 --> 01:55:59.000 By cancer or had or amyloidosis. 01:55:59.000 --> 01:56:05.000 And all of these same people are not talking about natural immunity at all. 01:56:05.000 --> 01:56:07.000 They never have. 01:56:07.000 --> 01:56:12.000 They've never given you an immuno 101 lecture. 01:56:12.000 --> 01:56:15.000 Never mind a six hour one. 01:56:15.000 --> 01:56:18.000 That's how I see through all these liars. 01:56:18.000 --> 01:56:39.000 If somebody says they're an immunologist by training and they've never done an immunology lecture online in three years of being a dissident, they're a liar. 01:56:39.000 --> 01:56:46.000 They are trying to invert our sovereignty to permissions because they want our data. 01:56:46.000 --> 01:56:49.000 More importantly, they want our children's data. 01:56:49.000 --> 01:56:53.000 Most importantly, they want our children's children's data. 01:56:53.000 --> 01:57:03.000 This is a long game for all the marbles. 01:57:03.000 --> 01:57:10.000 And it starts here, intramuscular injection of any combination of substances with the intent of augmenting the immune system is dumb. 01:57:10.000 --> 01:57:15.000 And transfection is not immunization. 01:57:15.000 --> 01:57:21.000 We need to teach our grandparents this, our parents this, and our children this. 01:57:21.000 --> 01:57:26.000 Or they will be enslaved by this mythology for the rest of their lives. 01:57:26.000 --> 01:57:41.000 Ladies and gentlemen, stop all transfections in humans because they are trying to eliminate the control group by any means necessary. 01:57:41.000 --> 01:57:44.000 I'm just going to keep pushing through. 01:57:44.000 --> 01:57:53.000 Either I'm going to completely lose my voice or it's coming back soon. 01:57:53.000 --> 01:57:56.000 I like my new hat. 01:57:56.000 --> 01:58:03.000 Bessel Park Black Hawks. 01:58:03.000 --> 01:58:09.000 Pittsburgh. 01:58:09.000 --> 01:58:12.000 Thanks for joining me, guys. I'll see you tomorrow. 01:58:12.000 --> 01:58:17.000 42, 42, 42 in a row, baby. 01:58:40.000 --> 01:58:45.000 Thanks for being here, guys. Good to see you, Jeff from Earth. 01:58:45.000 --> 01:58:49.000 Good to see you again, Christie. 01:58:49.000 --> 01:58:56.000 Grace Southwick. I haven't seen you here in a while, but thank you for being here tonight, Andy Rock. 01:58:56.000 --> 01:59:05.000 And Madarina, thank you very much, sir, for being here and being the doubter in the group or the critic in the group. 01:59:05.000 --> 01:59:09.000 I really appreciate you being here and still keeping it real. 01:59:09.000 --> 01:59:11.000 Lisa Simon says, good night. 01:59:11.000 --> 01:59:13.000 Sue Spider. 01:59:13.000 --> 01:59:15.000 Welcome to Eagle. 01:59:15.000 --> 01:59:17.000 You want to be a guest on the show? 01:59:17.000 --> 01:59:21.000 You want to be a guest on the show sometime? Welcome to Eagle. 01:59:21.000 --> 01:59:27.000 There's a lot of people at the Defender that are interested in hearing about your bears reporting. 01:59:27.000 --> 01:59:34.000 And they would like you to be on the show, so maybe you need to send me an email. 01:59:34.000 --> 01:59:36.000 See you, guys.