Stream.Transcripts/twitch/1953665426 (2023-10-17) - Pieter Cullis 2022 Study Hall -- 16 Oct 2023/1953665426 (2023-10-17) - Pieter Cullis 2022 Study Hall -- 16 Oct 2023 -- Gigaohm Biological High Resistance Low Noise Information Brief.vtt

WEBVTT

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We may need to have a conversation soon, Liam. You're showing up here an awful lot lately.

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It's good to see you, my friend.

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I hope you can feel that.

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I'm going to start to have basketball practice at 7.30, and so I'm going to need to stream before that.

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Let's not tell November, but we're trying to already shift.

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Ladies and gentlemen, this is number 42, number 42 in a row, just in case you're keeping track.

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42 in a row.

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We scheduled for 60 minutes next.

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It's going on French, British, Italian, Japanese television.

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We've only got 15 people watching. Maybe somebody should tweet this out.

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I always forget to do that. I'm not very good at this.

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Kids deserve a lot of credit. This town's been flooded with phony 20s for weeks.

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Oh, it was nothing, really. But old Mr. Pietro posing as the doorman sure had us fooled for a while.

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He really gave himself away when he put on his little puppet show for us.

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The real hero was Scooby-Doo. And by the way, where is he?

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Oh, no. Look at him.

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Like I said before, what a ham.

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Whoa, whoa, whoa, whoa.

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Good evening, ladies and gentlemen.

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Oh, God, and my voice still hurts like hell, but you know what?

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It's the 17th of October, and this is, as I said earlier, 42 in a row.

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I'm going to break that streak for a little sore throat.

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We're not going to get all the way through this.

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I forgot that this is a little slow.

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Let's see if I can fix this.

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Oh, there we go. That fixed it.

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That fixed it.

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Ladies and gentlemen, there was no spread in New York City.

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Infectious clones are the only real threat as far as R&A goes.

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The SIBO batches were likely distributed to make things probable,

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and transfection and healthy animals is dumb.

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Protocols were murdered, gain of function is a mythology.

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The Scooby-Doo mystery that you think you're solving is real.

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We've got to save our family and friends from it, because these players are committed to lies.

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And that'll keep everybody solving this mystery and the mysteries of the future

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if we don't expose their lies.

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Good evening, ladies and gentlemen.

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This is Giga Home Biological, a high resistance low noise information brief

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brought to you by a biologist.

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It is the 17th of October, 2023. My name is Jonathan Cooley.

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I'm coming to you live from Pittsburgh, Pennsylvania, in the great state of Pennsylvania

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in the United States of America.

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It often sounds like I'm on a starship because, in fact, I am.

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In the back of my garage, I also have a mock-up starship enterprise

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which I can shift to with a click of my mouse.

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Oh, wait, I have to also push this button.

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See, I'm not very good at this. I told you that a million times.

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The starship enterprise that I'm trapped on is a starship enterprise

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which has fallen out of orbit, so to speak, because of a rupture in time.

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And I'm going to try and explain that on Halloween over the course of a whole day on YouTube.

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So mark that on your calendar that once the workday is done on Halloween,

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JC on a bike is going to start streaming live and it's going to be a Halloween spectacular.

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And that's why I've been using this Star Trek background for a while

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because I've had this idea in my head like burning a hole in my brain for almost two years now.

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And it's never quite felt like the right time.

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But now it's definitely the right time.

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We don't need you in here. You can go back.

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So that's where we are.

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We don't want to be taking the bait on social media.

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We don't want to be taking the bait on TV, but I understand that it's hard not to turn it on.

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It's hard not to pay attention.

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But the show will go on.

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And the Scooby-Doo mystery is still being shoved down our throats.

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I showed you yesterday that there's a new book coming up by Rand Paul.

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And it is called the great COVID cover up. There's even a masked bad guy on the front cover.

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So if you think that the Scooby-Doo mystery is a joke, I hate to say it, but

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a few people have had a more apt analogy for what has been done to us.

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Because in order to describe what has been done to us, you need to adequately

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codify or describe what it is that happened.

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And you and me and all the members of drastic and everybody that wrote blog posts and medium posts and

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subsets about the potential that this was a lab leak in 2020 were all fooled into that behavior.

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We're all enchanted.

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Not by what was said on TV, but what was not said.

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What was said on social media, but was not said.

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What was not reported, but was not reported.

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In other words, it was a combination of what was said on TV and what didn't make sense.

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And also what wasn't said on TV and what made sense.

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And having enough prerequisite knowledge and enough

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skills with regard to learning new things when it needed to be learned,

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especially in the context of biology, there were very few people in a position to do that.

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A few hundred thousand on every continent all around the world that were currently engaged in the study of biology for a career.

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Reading every day, asking questions and finding the answers every day.

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Gee, I wonder how that receptor reacts or interacts with that receptor.

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I'm going to go and look it up.

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I'm going to see if anyone's asked this question or question related to it.

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And in order to be clever enough to answer those questions and to find those answers,

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you have to understand how those questions are asked, how they're formulated,

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how to use search engines, how to follow bibliographies,

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and you need to hold a whole host of terminology should be second nature to you

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so that you can read into these different fields of biology and bring together

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a cohesive concept of what's happening.

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And so not everybody that has their own business as a plumber is capable of doing that.

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Not everybody who teaches for a living is capable of doing that.

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Everybody who's a lawyer is capable of doing that in the context of biology.

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But it should have done, Ben, everybody working in a biology degree, academic setting,

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be it as a faculty member or a postdoc or a PhD student or a master's student

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or a senior in undergraduate biology.

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Anybody that had immunology 101 and had learned it, anybody that had a few years of biology

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so that the context of viral replication, the context of DNA and RNA,

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transcription and translation wouldn't be something that you also needed to learn.

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And at that stage, you had all of the tools necessary to be enchanted by the idea that

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you were covering up a lab leak and it would make sense.

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And so with the help of Jordan Peterson and Ben Shapiro and the Weinstein brothers

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and all the other people on this and elsewhere,

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a consensus that the mystery to be solved was where did this virus come from

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to be a solution according to Sam Harris, is it was an accidental lab leak

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or something from Mother Nature?

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Obvious solution for Brett Weinstein, it was, well, it was a lab leak or a gain of function virus.

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The obvious solution to everyone, whether they watched Brock's News or CNN,

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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.

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And so the beauty of this is not who they bamboozled but how we bamboozled each other.

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By their obvious reaction, by our obvious reaction, by those two positions being taken

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and so vehemently defended, it's not so different than the way one defends or doesn't defend abortion,

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the way that one defends or doesn't defend Israel,

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the way that one does defend or doesn't defend religion or Islam or the freedom of religion

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or the freedom of speech, the way that one does or doesn't use racism as a way of dividing people,

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a way of identifying a way that the world works.

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These divisions are all on the same line, they are dividing all the same people from one another

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and it's becoming increasingly clear that that is the goal, increasingly clear over decades that that's the goal.

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And these people have been put in place to control that narrative early on about a dangerous novel virus

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that we had to do something like lock down masks and make a vaccine for,

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that natural immunity might not work, that masks and bandanas and bushmeat,

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these people are responsible for it and I'm on this page right here.

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All of us are responsible for it, but at some moment in 2020 some people started to wake up.

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And so we can find some of those people, we can eliminate them from this illusion of consensus,

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we can see that they weren't part of it and not intentionally.

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More importantly, we can see that none of the people that are currently and listen carefully to this,

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please, none of the people that are currently out in front of the dissonant movement

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were out in front of the dissonant movement in 2020, none of them,

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not Robert Malone, not Steve Kirsch, not here at funded bush, no, not none.

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Michael Eden's not in front of us anymore, but you could call he was very out early

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and then canceled, canoe with Kowski, gone, Wolfgang Wodach, gone.

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The people that are currently leading the dissonant movement didn't even speak up until 2021,

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and meaningfully until the end of 2021 and that concludes Brett Weinstein and Steve Kirsch

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and Robert Malone and here at funded bush.

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All of these big names, none of them were speaking in Germany like Bobby was in 2020,

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none of them were talking about natural immunity like Ryan Cole was in 2020,

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none of them were coming out about hydroxychloroquine, not increasing the size of hearts and being ridiculous,

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Peter McCullough was, nobody was complaining about ventilating people that didn't need to be ventilated,

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none of these people, what Pierre Corey was,

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and so some of these people are real dissidents that saw the animal in the early stages,

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and when you see the animal in the early stages you get co-opted,

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because the animal in the early stages was a natural security priority,

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it involved special phone calls, it involved dudes with suits,

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it involved a national security protocol that was enacted by DHS employees,

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people from the military, people from homeland security, people from health and human services were all around the United States,

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and so when Pierre Corey went from Wisconsin to New York, he didn't just meet other doctors in New York City,

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he met the government, he met the military,

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this illusion of consensus has been created on purpose for us to solve this mystery of lab leak or natural virus,

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and in accepting the challenge, we have also accepted the premises of the challenge,

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which is that there is indeed a novel virus,

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and then we're always going to be trapped, our kids will always be trapped,

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and so as we move these people around the board,

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and people like Peter McCullough come out against the vaccine schedule,

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and Nick Hudson makes a nice presentation that Thomas Binder keeps crushing it,

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Denny is still doing amazing work,

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Jessica Hockett is still doing amazing work,

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Professor Martin Neal, and of course my friend Mark Yousatonic,

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Mark Yousatonic has been single-handedly

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I mean, the kinds of information that he's bringing together,

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the kind of backstory that he's put together is not some kind of piecemeal house of cards.

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It becomes very clear that this has been a long-standing plan.

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It becomes very clear that some of these people have been involved in this long-standing plan for a long time,

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Thursday, 5 o'clock Eastern, please be there.

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And so we need to rally around these people,

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rally around these people to help them, rally around them to get their message out,

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and to share it.

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This is the main message.

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The main message is the intramuscular injection of any combination of substances with the intent of augmenting the immune system is likely very dumb,

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and the transfection is not immunization.

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So you know the show must go on and the narrative must be kept alive,

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and in this quest to understand why we have been listening a lot to the words of Robert Malone,

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and in his rather recent objection, or let me say critique of the awarding of the Nobel Prize to Caracao and Weismann,

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as I recall, on the Vay John Health interview, which we covered a few weeks ago, which I highly recommend,

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he mentioned a name, Peter Colis, as being integral to the development of the COVID vaccines,

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and particularly the lipid nanoparticle that is being used for them.

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His intellectual property is held by Canada and in Germany, I think,

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and I thought it would be really cool to find a lecture by him, and it turns out,

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it turns out that Peter Colis actually got awarded the, I think it's the Gardener Award,

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which is one of these awards.

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It usually comes like a year or two before the Nobel Prize for some people,

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not all the people that get this award get a Nobel Prize,

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but a lot of the Nobel Prize winners have gotten, most of them have gotten this award before they get the Nobel Prize.

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And so luckily for us, it looks like Canada in an attempt to, you know,

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make sure that the Nobel Prize committee knew that it was these three awarded Weismann, Caracao, and Colis,

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the Gardener Award in 2022.

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And so in so doing, then Peter Colis has given some talks around Canada,

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and I thought we'd listen to one of those if you don't mind.

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Big head move, play.

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So I think we'll get going, everyone.

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It's a pleasure today that we're going to be welcoming Dr. Peter Colis to the University of Manitoba as our Gardener Lecture.

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Before I start, I just want to acknowledge that the University of Manitoba campuses are located on the original lands

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in Avis, and on the new land of the Métis Nation.

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We respect the treaties that were made on these territories and acknowledge the harms and mistakes of the past

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and we dedicate ourselves to move forward in partnership of Indigenous communities with a spirit of reconciliation and collaboration.

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That was striking.

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I'm not sure what to say about that, because as a multiracial American with more ethnicities in me that I care to count

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and I've frankly cared to do research,

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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

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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

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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.

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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.

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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.

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Dr. Peter Collis is Director of the Nanomedicine Research Group and Professor of Biochemistry and Molecular Biology at the University of British Columbia.

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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.

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He's co-founded 11 biotechnology companies published more than 350 scientific articles and is an inventor of over 60 patents.

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He's also co-founded three not-for-profit enterprises including the Center for Drug Research and Development and the Nanomedicines Innovation Network.

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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.

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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.

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Peter, please give a warm up.

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Okay, thanks for that introduction, Peter.

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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.

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And so that's why I entitled it science and serendipity.

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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.

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And I could also entitle it 50 years of lipids because that's pretty much what it is.

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So I've really been involved in this field for a while and there's been a long time.

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Now how do I get these to advance? That's the next thing.

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I'm just going to press a space bar here and maybe that'll work. There we go.

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So you've seen various public announcements about the analyses of the vaccine, the mRNA vaccines.

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And in some cases you see it's coming inside what people term as being tiny bubbles of past.

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This is a headline from Bloomberg.

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And so this is where the mRNA is coded in lipids.

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And so what I'm going to talk about is really these tiny bubbles of past.

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I'm not going to say that they're not really tiny bubbles of that, but anyway.

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So what are they?

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They're already tiny.

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We're talking about systems that are 100 nanometers or less in diameter.

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So 100 times smaller than a cell.

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But they're not really bubbles of past.

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They're nanoparticles made of membrane lipids.

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They've really taken my whole career 50 years to develop.

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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.

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Starting about 1985 we started to apply some of that knowledge to development of lipidase systems.

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This dude is old because if he's been working on stuff since 1972, I was born in 1972.

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That's some impressive shit.

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Wow.

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And he didn't get the Nobel.

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These things aren't going to me.

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To development of systems that deliver cancer drugs, which is what we started to do.

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We started off trying to cure cancer and quite managed that with a few other things.

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And then starting in 1995, applying the knowledge, our basic knowledge, to deliver nucleic acid based drugs.

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Now, just going back to the beginning, this is, I got my PhD in solid state physics in using magnetic resonance in 1972.

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And I was looking at semiconductors at 40 degrees Kelvin, like transistors on the moon.

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Anyway, I decided after doing this that I really had to do something different.

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Most of the interesting problems that I could see were outside the field of physics, particularly in the life sciences.

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And so I got interested in applying NMR in the life sciences.

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And I got a fellowship to go to the biochemistry department in Oxford in 1973.

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And I knew absolutely nothing about biochemistry.

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So this was quite an introduction by fire.

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Now, I knew a bit about NMR.

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I published my last physics paper in 1976, consisted of about 96 equations.

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But anyway, that's pointing out that when you learn something as a physicist, you learn it in some depth.

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And there's other things that you learn as a physicist.

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You get the feeling that this guy is already like a Tyrannosaurus Rex compared to the salamander that Weisman was.

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I mean, I already know that I would not want to sit at a table with this guy and open my mouth.

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I would want to sit at this table and listen.

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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.