WEBVTT

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combining to form microclots, that's all I'm totally down with that. But to then

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jump to the stage where that explanation now covers all of the problems with

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vaccines, it's another problem with vaccines. And I think that's the that's

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the that's the nuance that everybody needs to constantly be vigilant of

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because all all of the people that are trying to trip us up for sure are

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gonna give us easy explanations which replace other explanations. And that is

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a temptation that you cannot jump to because it's just like the DNA

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contamination of the RNA shot is now tempting to jump to that as the

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explanation where in reality we have this whole toolbox of of attributes of

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transfection which are bad and then the quality control and then this and then

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they all add together and the trick is to not let any one of us any one of

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these these explanations start to dominate to the point where all of the

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other impacts which we know are real are diluted or lost and that's basically

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what they're trying to do with the with the protocols right they're trying to

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tell stories and stories and stories so that the protocols go farther and farther

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into the past so that the rising number of opioid deaths from 2020 to 2021 to

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2022 go farther and farther in the past that's what they want and so that's why

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we have to be very vigilant and and work constantly to to update this list and

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not let the list be changed but rather just added to and that that was

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especially true with regard to what Kevin McCurn and I believe has been

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basically up to is he's populating this list but then he's he's not reciting it

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anymore and so if he if he has all this publicity has all this chance to tell

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people how bad transfection is he's definitely not taking advantage of it

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and instead he's focused on the latest greatest thing which is this CD and A

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contamination which of course is a very different objection than what he was

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making in 2020 in 2021 in 2022 and it's really important that we pay

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attention to these people because it's over time it's it's very hard to lie

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consistently and you can be like Mark and you can have made mistakes you can be

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like Mark and have sort of followed false leads or chased things that didn't

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need to be chased anymore but if he's correcting himself and he's he's if he's

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reevaluating where he stands like I try to do and like others try to do then

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then there's nothing wrong with playing a few notes as long as you're playing

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with passion and with the idea that that you want to get better and so you know

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it feels really good but I'm gonna try and take a little easy on my throat just

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to make sure I don't go nuts because I'm really excited and and I want to just

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talk all day but we're gonna go a little slower here so the show must go on and

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we did a number of shows recently about about companies related to Canadian

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biosciences and it's become really increasingly curious to me how so much

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of this story is just crossed that border and how much of that story

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actually was already across the border at the very early parts of the pandemic

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with this with this researcher who a Chinese researcher who was working on Z

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map and had this antibody cocktail that was really good and was on the front of

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time and and then accused of being a spy and stealing and disappearing and it

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wasn't really clear what it was about this lady that was weird but Mark was

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one of the first people to really point out that this Z map was this antibody

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cocktail that might have been useful for this or or at least wasn't

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antibody cocktail that had been previously compared to remdesivir and so

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there was reason to believe that that this Z map would would somehow muddy the

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waters around remdesivir and maybe it needed to disappear anyway we have more

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recently crossed the border into Canada with regards to Peter colas and Peter

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colas brought us to Canada because number one he's the guy who apparently is

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responsible for the intellectual property of the lipid nanoparticles

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that are now used for all of these gene therapies and vaccines I shouldn't

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call them vaccines and transfections but more importantly Robert Malone was the

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one who actually pointed us to him in one of his sub-stacks which was being

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quite critical of the awarding of the Nobel Prize and said that you know if

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anybody had and he used the patent law language enabling technology it would

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be Peter colas and so in my offline discussions with my friend regular guy

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it became clear to him in his eyes that the language that Robert Malone had

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been using in that article was really intellectual property law type language

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when you say enabling technology in regards to a patent you really talking

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about patent law and in that sense it seemed almost as if the the idea of the

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intellectual property that was being you know acknowledged with the MR of the

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mRNA with the Nobel Prize that intellectual property may not be as solid

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as the award might make it seem and the reason why is because the enabling

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technology in Robert Malone's mind wasn't the chemical alterations of the

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mRNA but it was the lipid nanoparticles that allowed it so it's an interesting

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assertion because remember and I'm just breaking this down with this on screen

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I don't really know why maybe you can you can copy this video that we're gonna

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watch here but it's interesting because up until that statement it should be

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very clear to you that it was his it was his idea of using RNA as a vaccine

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and all of the he had like eight patents around this so as he first came out in

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2021 remember part of the reason why he came out was to claim in mentorship but

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now after the Nobel Prize is awarded it's almost a change in position if you

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think that well in 2021 I was the one who came up with all the relevant ideas for

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using RNA as a vaccine and then when the Nobel Prize is awarded you say well

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actually what they did isn't that impressive because the enabling

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technology is is Peter Kullis's technology so it's a very very

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interesting pivot that actually is not consistent with his his earlier three

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years of behavior and so you can only see this as a continuing evolving op or

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continuing evolving construct it's not just somebody struggling to tell the

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truth this is a person who is adjusting the narrative and adjusting what he

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says in order to keep aiming at whatever goal they're aimed at and I think that

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that's how you should start to see most of the people's changing behavior is if

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you think about what their goal was for the entire three years that I've been

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online my goal has been to free my children from this illusion into free our

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society from this illusion and that's not really the goal of a lot of other

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people that let's say compete for this this kind of airtime I mean Kevin McCarran

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has never been about stopping the thing Kevin McCarran has never been about

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breaking any illusions that are around it's been about maximum fear and

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confusion and really Kevin McCarran has never been about stopping anything he's

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never said that transfection and kids would be crazy and the list really goes

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on quite long how many people haven't said really anything useful about what

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about what should be done with regard to this technology and they certainly

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didn't say it with any in any useful length of time and so here we are this

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is the epivax presentation from January 26th 2021 so put that into your time

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frame we are about to roll out the we had already rolled out the vaccines in the

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UK so the the the J&J and the AstraZeneca are already out and we're about to roll

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out the mRNA and epivax is also trying to put together a vaccine candidate and

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and they were actually working with crazy enough the University of Seattle I

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believe or University why I don't know which one it is Mark would know where a

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guy by the name of Biesler was working and Biesler also surprisingly had a

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three-dimensional model of the spike like in in February or January of 2020 he had

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a candidate vaccine ready to go based on the spike in a nanoparticle and that's

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also where Sohomish County man just happened to be so epivax is a company

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who for whom Robert Malone was a consultant for a very long time from

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2005 to 2018 or something like this and epivax is also a a company that has

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worked with Abcellular which is this this

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which is this company in Canada that is developing antibodies or finding them

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with this new technology that we watched yesterday so I want to watch this

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video because in this video they're going to discuss the methodological

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technology that is epivax and I think it's really Mark sent me a video this

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morning from was it eight years ago or something like that where this lady is

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also talking about this technology on a news program and I thought to watch that

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one now but actually I'm gonna watch that one with Mark sometime and invite him

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to watch it because it's an older one and she makes some crazy references this

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one has a lot more biology and it's I thought it'd be more appropriate for a

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study hall and I can take some notes and then give my voice a little bit of

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rest in between but not that it really needs rest the only reason why sometimes

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sound doesn't come out I think is because there's that flap of skin or

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tissue or whatever it was that was full of blood and then it emptied so it's

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kind of still there but I mean I don't I don't hear it sometimes but it's not

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like it was I could never do that yesterday I mean it's it's crazy how

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happy I am to have my voice back so let's see if it's here yes it is great

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thank you guys for joining me tonight I know that I am the CEO and CSO heavy

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acts incorporated and I'll be talking to you today about prediction and

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validation of immunogenicity and tolerance to generic peptides and

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impurities clearly this refers to the FDA draft guidance in which the FDA

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requested sponsors to identify impurities in their generic drug products

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that were above a certain percentage of the final drug product and and their

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request and validation sorry I missed the title too

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hey everyone this is Annie DeGroote I am the CEO and CSO heavy-vax

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incorporated and I'll be talking to you today about prediction and validation of

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immunogenicity and tolerance to generic peptides and impurities clearly this

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refers to the FDA draft guidance in which the FDA requested sponsors to identify

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impurities in their generic drug products that were above a certain

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percentage of the final drug product and and their request related to the

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identification of T cell epitopes shown in this slide that could be present in

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the impurities in the drug product it turns out that we've been working in the

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field of immunogenicity risk assessment focusing on peptides and

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proteins and their T cell epitopes for the last 20 years so we developed a

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comprehensive program for assessing the immunogenicity risk of peptide

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impurities building on decades of experience with biologics this program

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is called the panda program for peptide accelerated new drug application no and

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one of the things that I want to point out here is that this T cell epitope thing

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that she's speaking about is actually very serious the reason why I think it's

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serious is because in 2020 late 2020 when people were still screaming about the

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spike protein being a designed protein and I was still playing along it really

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felt like to me that one possibility was that they lied about the virus but they

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they lied to the extent to which they actually lied about the spike and so

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the spike was a designed spike on purpose with the idea that okay we're

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gonna lie about this virus but then we're gonna say that it has this spike and

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this spike protein we're gonna use it for the vaccine and why because we've

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already altered it we've already added a couple let's say universal T cell

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epitopes so that we know that this spike protein will generate antibodies and

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more importantly we can predict with a reasonably high accuracy what antibodies

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they will produce or at least what epitopes they will target and so then

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all you really need to know is how that epitope will be evaluated and

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represented given the different HLA subtypes of a particular person and

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that's what she's talking about for 20 years they've been working on finding the

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T cell epitopes that are chosen by your immune system when exposed to

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proteins and trying to see if there's some pattern based on the HLA receptors

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of the animal or the human what is the pattern of T cell epitope selection and

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is that pattern predictable and she's been working on it for a very long time

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now the reason why I find it enticing is because before I got to clones and

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swarm and all this other stuff and I was still really playing along with the idea

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that it was a lab leak and something is spreading around which again is still a

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possibility that the idea would be to lie about the sequence so that you could

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get your guaranteed shoe in immunogen into the vaccine and so you could imagine

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this story where you everybody insists that this spike bro spike bro spike

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protein all the time and all these people are laughing because while the spike

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protein is being portrayed as this evil evil protein it's really not it's a

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design protein guaranteed to produce seroprevalence and even more importantly

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it could be guaranteed to produce seroprevalence on many serial exposures

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because it's a T cell epitope that in theory the immune system would have a

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harder time ignoring it and that's the product that's the that's the product

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design from a vaccine designer's perspective that's the product design

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you want because the NIH says that antibodies are the indication of immunity

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and the antibodies are how they're going to measure the effectiveness of your

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product and so if you have a product where you can you can administer it

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multiple times and every time you get a good robust antibody response it's the

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ideal vaccine and so what she has been working on with this company basically

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for 20 years is that this ability to hijack what the T cells are doing and

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force them to react and it's doing it by selecting these epitopes out of the

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natural proteins and looking at the responses and screening them and using

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computational approaches to narrow down see it's still there to the proteins

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that will that will produce this I'm sure it's just scar tissue that's got to

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work his way out now I mean maybe my voice will get even lower peptide

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accelerated new drug application and the methods that I'm going to describe

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today are published in this recent compendium of methods in immunogenicity

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risk assessment that was written by a group of members of the APS therapeutic

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protein immunogenicity group so please refer to this article for additional

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details on the assays and in silico tools that I'll be talking about today

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members of the team that work with me include Brian Roberts who's the associate

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scientific director and manager of the protein therapeutics program at Epivax

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Francis Terry who is the director of analysis and immuno informatics at

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epivax and Bill Martin who is co-founder of epivax and the architect of our

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in silico tools of course I would be remiss not to mention the epivax panda

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team members who work in the laboratory to do the validation of the in silico

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predictions that we make and they are listed here on this slide my talk will be

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divided in four parts first why do immunogenicity screening peptides what

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what is it about these impurities that could generate a T cell response how to

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do it in silico and in vitro and I'll be describing the special tools that we

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have that evaluate tolerance and help find T reg epitopes I will also be

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describing some case studies that we recent T regulatory epitopes boy oh boy

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they're really they're doing it all like this is exactly what before I knew

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that's company existed I would have said that you need to be working on and so now

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you just imagine just imagine remember Robert Malone has been consulting with

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all of these companies that are in a position like absolera to make

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antibodies with mRNA she is in a position to make small peptides with

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mRNA so it's all kind of the same next generation game and now I might be going

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out on a limb here when I say this but it almost seems like that's the whole game

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that was the whole game all they have to do is preserve the national security

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priority of vaccinating against covid and then all they got to do is make the

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first generation of covid vaccines kind of lame and now guess what we need new

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vaccines for covid and those new vaccines will be much better because we

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won't use this silly spike protein we'll just use a few T cell epitopes or we'll

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we'll just make the antibodies and so after four years of brainwashing

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everybody or 40 years of brainwashing everybody the antibodies are important

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they've finally gotten around to saying well we'll just find the antibody and

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make the RNA and code for it that's not gonna work there's lots of reasons why

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that's not gonna work and we'll talk about that over the next weeks but I'm

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just trying to let you know that this sounds like reasonable immunology it

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sounds like really smack on point immunology that you we should have we

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should have been hearing so now the question I'm just burping from drinking

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too much if the question is how deceptive is she gonna be about antibodies and

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T cells how deceptive is she gonna be about the order in which those memories

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are made I think she's gonna have to do some hand waving here because she can't

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be too aggressive and say that actually the whole immune system is T cells and

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antibodies are dumb she can't say that but I'm sure that when they're in their

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own offices that's what they're always thinking they're always thinking that

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well yeah but anybody's come from T cells I mean they they require T cell

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help and all this other stuff and T regulatory cells to down like it I mean

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she obviously knows that so it's gonna be a good talk it's only 30 minutes long

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I'll try not to interrupt it anymore we completed under an FDA contract on

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calcitonin and terraperitide impurities and we'll show you some of the

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interesting findings that we uncovered while doing those projects now I'm gonna

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be primarily focusing on in silico so I do want to go over how a peptide drug

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impurity can potentially be presented to a T cell so that's illustrated in this

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slide here you can see a peptide drug which could be up to 50 amino acids in

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length and a peptide impurity which contains a modification to the amino acid

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sequence notice that this impurity is shorter mainly because in the antigen

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presenting cell it will be processed down to the length that will be presented on

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the surface of the HLA binding molecule on the surface of the antigen presenting

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cell the these so I'm I gotta say I'm actually confused because they're talking

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about impurities so that's I guess you produce proteins and there's a certain

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amount of impurities and so what she's trying to figure out how these impurities

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cause T cell activation this is like bait and switch and what she's trying to

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explain and what she's trying to talk around I'm sure of it is that T cell

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epitopes are only about nine amino acids long and so these very short

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proteins are actually extremely dangerous because they have a propensity to

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easily be presented as antigens unlike the larger proteins which need to be

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brought in and then process down to smaller fragments and then that process

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and this is the trick that process is the responsibility of this mature antigen

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presenting cell it's a process that we don't understand and so the larger

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protein goes in that's this one and it gets it gets processed by the antigen

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presenting cell and it's something we don't understand what she has noticed

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and pharmacological producers of biologics have noticed is that the

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impurities the small peptides are the ones that tend to generate the immune

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response and that's because of this and the way that the processing in a in a

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mature antigen presenting cell works if you don't give it anything to break down

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then there's nothing really to do but present and I'm not speaking from knowledge

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here I'm I'm imagining based on what I understand of antigen presentation and

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the the sorting of an antigen to the epitope that you present to the T cells

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and that selection process is extremely important in what she and her epivax

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company have been doing over the last decades is trying to figure that process

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out what parts of peptides are regularly chosen as T cell epitopes and how does

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that interplay connect with HLA diversity HLA being the MHC receptor that is used

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by them and by T cells to to compare notes so to speak if you just join me I

38:22.100 --> 38:29.180
hear a look a bunch of people there just join me yes a polyp or a blister or

38:29.180 --> 38:35.980
some kind of growth in my throat exploded today on a cough it filled my mouth

38:35.980 --> 38:41.900
with blood I spit it out on the garage floor I looked at my son and I said the

38:41.900 --> 38:48.140
words I might need some help and it came out like I might need some help and my

38:48.180 --> 38:54.100
voice was back and so I actually haven't had this voice this exact voice I don't

38:54.100 --> 39:00.820
think in maybe six years it started to go away when I was on my bike and it's

39:00.820 --> 39:06.700
just slowly gone worse in the last couple years it really went awful and I think

39:06.700 --> 39:11.420
it was this thing this I don't know what it was I haven't been to the doctor I

39:11.420 --> 39:18.460
don't know all I know is I'm not feeling any real pain if I cough it's a little

39:18.460 --> 39:23.500
painful but if I'm talking it feels fine and I don't need the best thing is I

39:23.500 --> 39:27.420
don't need to force any air through my voice box anymore I'm not running out of

39:27.420 --> 39:31.900
breath you can hear me right now I still when I breathe there's this little thing

39:31.900 --> 39:38.380
in there I don't know if it's loose skin or what it is that you hear it but it

39:38.860 --> 39:43.780
doesn't it doesn't stop me that thing was waking me up at night it was blocking

39:43.780 --> 39:48.700
my breathing at night and and I was actually having sleep dapnea for the

39:48.700 --> 39:54.740
last two and a half months or something so that's all the reason why my my my

39:54.740 --> 39:58.900
health has been going downstream or downhill very rapidly and I think this

39:58.900 --> 40:02.660
is gonna be a huge turnaround because of anything I'm gonna get sleep now sorry

40:02.660 --> 40:09.620
back to T cell activation peptides can be anywhere between 8 or 9 to 15 to 20

40:09.620 --> 40:12.540
amino acids in the length when they're presented on the surface of the cell

40:12.540 --> 40:19.020
but the key sequence that binds into the HLA molecule binding groove is about

40:19.020 --> 40:24.020
9 amino acids in length the side chains of the peptide will bind into the

40:24.020 --> 40:27.580
binding pockets and you can well imagine that a peptide drug that has a

40:27.580 --> 40:32.020
modification of one of its residues may not bind with the same affinity or may

40:32.020 --> 40:37.780
bind with a different affinity than the active pharmaceutical ingredient or the

40:37.780 --> 40:43.180
API in this picture on the right-hand side I've illustrated for you in in an

40:43.180 --> 40:50.820
EM electron microscope a picture the interaction with T cell it's receptor

40:50.820 --> 40:55.340
which you can't even see on this slide and the antigen presenting cell that's

40:55.340 --> 40:59.260
presenting HLA you can see that there's probably a very close fit between those

40:59.260 --> 41:04.300
two cells and what the T cell is coming down to see is the peptide lying in a

41:04.300 --> 41:10.780
linear fashion in the HLA molecule and there is a face that faces down and binds

41:10.780 --> 41:15.820
to the HLA that's called the epitope and there's sorry the agritope and there's a

41:15.820 --> 41:19.860
face that faces up to the T cell receptor and that's called the epitope the

41:19.860 --> 41:23.860
epitope is what drives the T cell response and that can be good or bad

41:23.860 --> 41:30.700
depending on which T cell is responding so why do impurities affect the T cell

41:30.700 --> 41:35.380
response to peptide drug well let's take our API and now let's make some

41:35.380 --> 41:41.020
modification and to it the way that they would occur in a synth in a synthetic

41:41.020 --> 41:47.820
process you could have truncations that could remove peptide T cell epitopes you

41:47.820 --> 41:52.780
could have amino acid deletions that change the sequence so that the frame of

41:52.780 --> 41:57.260
the nimer is disturbed and if there is a T cell epitope there it's no longer

41:57.260 --> 42:02.500
gonna bind in the HLA molecule you could have an amino acid insertion which

42:02.500 --> 42:06.940
could change the side chain that's binding into the HLA molecule or it

42:06.940 --> 42:10.900
could actually shift everything over and I'll show show you that disturbing the

42:10.900 --> 42:16.500
binding frame you could have amino acid duplications which again shift the

42:16.500 --> 42:21.460
binding frame of T cell epitopes and you could also have incorporation of

42:21.460 --> 42:27.700
unusual amino acids such as stereoisomers or side chain modifications which also

42:27.700 --> 42:32.340
interfere with the binding of the peptide to the HLA molecule and that's

42:32.340 --> 42:36.580
what's illustrated in greater detail on this slide I've already mentioned that

42:36.580 --> 42:41.380
there's a an agritope side of the T cell epitope that might be in your peptide

42:41.380 --> 42:48.860
drug and there is an epitope side which faces up to the T cell receptor there

42:48.860 --> 42:52.780
are specific amino acids that bind to the HLA and there so this is interesting

42:52.780 --> 42:57.860
I've never seen this biology before apparently the HLA receptor interacts

42:57.860 --> 43:05.380
with certain amino acids of the epitope and the the T cell react so I don't know

43:05.380 --> 43:08.940
if this would be like their functional groups that would stick out this way or

43:08.940 --> 43:14.420
their charged groups that would stick out this way it's a very interesting way of

43:14.820 --> 43:21.500
and she's portraying that in every picture which I guess I got to read because I

43:21.500 --> 43:26.820
didn't know that they knew this much about how antibodies were interesting

43:26.820 --> 43:32.060
we're here in this slide and other ones that bind up that face the T cell

43:32.060 --> 43:37.100
receptor that is recognized by the T cell let's now introduce a duplication of

43:37.100 --> 43:42.740
an amino acid of position two and you can imagine that a peptide that once

43:42.820 --> 43:48.780
found to HLA now that you've shifted the frame over by one position no longer

43:48.780 --> 43:53.620
presents side chains that bind down into the HLA binding group so that peptide

43:53.620 --> 43:59.220
will no longer bind to the HLA molecule that may be good if there was a T cell

43:59.220 --> 44:02.700
response there you would obviously potentially lose it because the peptide

44:02.700 --> 44:06.900
will no longer bind if there was however a T cell response that could

44:06.900 --> 44:11.580
induce a suppressor immune response a regulatory T cell then you might not

44:11.620 --> 44:16.820
want to lose that signal in your peptide drug so let's look at the

44:16.820 --> 44:22.140
converse maybe you have a sequence that doesn't bind so did you hear what she

44:22.140 --> 44:28.660
said and this is really key she said that if you have a a mutation in or a

44:28.660 --> 44:32.660
deletion that gets rid of your T regulatory response you might not want

44:32.660 --> 44:37.940
that and why not because we've talked about this before the basics of the T

44:37.980 --> 44:43.540
cell response are such that the T cell response ramps up the inflammation

44:43.540 --> 44:49.100
ramps up the immune response and a corresponding T regulatory cell

44:49.100 --> 44:54.260
response that recognizes many of the same epitopes is responsible for the

44:54.260 --> 45:00.220
day crescendo and so if you only have T cell epitopes which stimulate T cells

45:00.220 --> 45:05.060
but not T regulatory cells then you'll have this imbalance induced by your

45:05.060 --> 45:08.820
biologic I think that's what she's talking about here and it's really

45:08.820 --> 45:15.740
curious to hear someone talk so focused on T cells and not realize that that

45:15.740 --> 45:20.980
all of these vaccines that are being sold distributed whatever I just don't

45:20.980 --> 45:25.660
don't even look at this it's not even on their radar I guess that's what makes

45:25.660 --> 45:30.140
their that's what makes this company so good to see you can hear my voice is

45:30.140 --> 45:34.420
not the other thing I have to tell you is that because that ball was in the back

45:34.460 --> 45:37.420
of my throat I haven't been able to get all the mucus out of my lungs like I

45:37.420 --> 45:41.060
should have so I'm probably going to need to cough a lot of stuff out over the

45:41.060 --> 45:45.180
next few days and maybe that'll give me more wind too I don't know but I can

45:45.180 --> 45:51.540
tell you that my voice is rattling around in my voice box and in my jaw in a way

45:51.540 --> 45:56.740
that it hasn't done in at least five years

45:56.860 --> 46:03.140
not want to lose that signal in your peptide drug so let's look at the

46:03.140 --> 46:09.020
converse maybe you have a sequence that doesn't bind it's a particular peptide

46:09.020 --> 46:13.620
that doesn't seem to have any T-cell epitopes in it now you introduce a

46:13.620 --> 46:18.620
duplication of the amino acid in position two you've just shifted the side chains

46:18.620 --> 46:24.380
over and now you have an agritope which will bind to HLA and present an epitope

46:24.380 --> 46:28.620
to the T cell receptor that could be a new immunogenic impurity and that's

46:28.620 --> 46:32.300
exactly what we're talking about when we're concerned about impurities and

46:32.380 --> 46:37.140
their potential to trigger a T-cell response so how do we look for these

46:37.140 --> 46:43.700
T-cell epitopes we have basically a three-step process which also involves a

46:43.700 --> 46:50.060
four-step integrating everything into one overall perspective and we start always

46:50.060 --> 46:54.420
with epitope prediction because we want to look and see if the T-cell epitope is

46:54.420 --> 47:01.500
present in the API and how far from that binding kind of threshold will the

47:01.540 --> 47:05.220
impurities travel maybe you have something that has a lot of T-cell epitopes in it

47:05.220 --> 47:10.300
impurities will therefore induce a lot of new epitopes if you have something

47:10.300 --> 47:14.220
that doesn't have any T-cell epitopes in it it's hard to imagine how immunogenic

47:14.220 --> 47:18.020
that could be with the substitution of a single amino acid because that won't

47:18.020 --> 47:22.620
really change a lot about the binding capability of that T-cell of that sorry

47:22.620 --> 47:28.460
peptide sequence so we start with epitope prediction and then we validate we like

47:28.460 --> 47:33.100
to check at least HLA binding maybe the peptide is not predicted to bind will

47:33.100 --> 47:39.020
validate that in HLA binding assay just to confirm our suspicion but we also like

47:39.020 --> 47:43.340
to do T-cell assays and we can do that with naive blood because naive donors

47:43.340 --> 47:47.740
have never seen the drug or its impurities so it's similar to testing it

47:47.740 --> 47:52.500
in a patient so the the epitope prediction is the first step and then

47:52.500 --> 47:57.540
we'll start with the API sequence and then compare it to the impurities that

47:57.580 --> 48:04.060
we can look at also in silico we use the epimatrix tool it is an algorithm that

48:04.060 --> 48:10.820
has been developed over 20 plus years developed epivax it predicts both class

48:10.820 --> 48:14.380
one and class two binding potential and here we're going to be focusing focusing

48:14.380 --> 48:21.860
on class two which facilitates cytokine release as well as antibody response so

48:21.860 --> 48:25.060
that might be the the two things that you're going to be concerned about in

48:25.100 --> 48:32.660
terms of a safety signal for peptide drugs it's an algorithm that focuses on

48:32.660 --> 48:38.060
nine HLADR supertypes for the purpose of measuring immunogenicity risk why

48:38.060 --> 48:42.780
do we do that because when we look at all of the different human HLAs we can

48:42.780 --> 48:47.460
actually find that they fall into families these families share pocket

48:47.460 --> 48:53.300
preferences which side chains bind into the HLA molecule so now that we know

48:53.300 --> 48:56.700
that there are supertypes comprising families we can kind of narrow down the

48:56.700 --> 49:00.980
search space to a manageable level and find patterns that actually give help us

49:00.980 --> 49:05.500
figure out if something's going to be immunogenic or not so those are the nine

49:05.500 --> 49:09.860
supertype families that we usually work with there are certain alleles that are

49:09.860 --> 49:14.940
more common in certain populations but such as DR9 which is an Asian a couple

49:14.940 --> 49:19.100
papers there I gotta read I guess however we find that if we include all of these

49:19.180 --> 49:24.740
HLADR molecules in our search space we can get results that are representative

49:24.740 --> 49:31.740
of 95% of the human populations worldwide that's the in silico approach of course

49:31.740 --> 49:36.380
how short these sequences are here that selection and try to get a lot and so

49:36.380 --> 49:39.620
now what I want you to think about is the idea that if they wanted to make it

49:39.620 --> 49:44.140
sure thing that they would say oh there's a coronavirus out there and it's got

49:44.140 --> 49:48.820
this spike routine and they only needed to put in a couple of those just a

49:48.820 --> 49:53.500
couple and then they would be guaranteed almost guaranteed to get the right

49:53.500 --> 50:01.260
antibody response think about that think about how easy that that that trick

50:01.260 --> 50:05.940
would be to pull off given what we now know about the sequences and rule on the

50:05.940 --> 50:14.340
sequence in Washington it would be a joke and so that's actually the kind of

50:14.420 --> 50:19.860
deception that I was thinking had occurred in 2020 and then of course had

50:19.860 --> 50:23.500
all kinds of different changes in terms of how I think about it now but it's

50:23.500 --> 50:27.980
still something to consider we don't know what happened this is a crime and we

50:27.980 --> 50:31.860
still don't know how far clones would spread if you made enough of them we

50:31.860 --> 50:35.140
don't know how many people they would jump through I don't know would they

50:35.140 --> 50:42.660
really stop with the first person that inhaled them I don't know so it's it's

50:42.700 --> 50:49.620
some really interesting immunology being sold here very carefully sold here

50:49.620 --> 50:55.820
mind you but you know it's exactly what you need to start thinking about as you

50:55.820 --> 51:06.220
as you consider how the immune system works that somehow the way that she's

51:06.300 --> 51:13.780
screening for immunogenic peptides seems to not jive with the idea that we can

51:13.780 --> 51:18.740
just throw a a transfection in somebody and they'll express a protein in your

51:18.740 --> 51:24.020
immune system will be fine it doesn't really make sense from the perspective

51:24.020 --> 51:30.900
of what she's saying because small contaminants or point mutations in a

51:30.900 --> 51:36.780
biologic can be a problem so when we know all the things we know now we have

51:36.780 --> 51:41.860
to really who the hell oh my goodness he doesn't even know I'm online Matthew

51:41.860 --> 51:47.300
Crawford is calling me on signal that is interesting I'm not gonna take that

51:47.300 --> 51:53.140
call though thank you very much I'm gonna keep this going actually well I

51:53.140 --> 51:55.780
don't know I'm gonna speed it up because I don't know how much she's really gonna

51:55.780 --> 52:00.780
do and I wanted to watch this other two-minute video of her where she's

52:00.780 --> 52:03.820
talking a little more silly because she's on the nightly news and it's only a

52:03.820 --> 52:08.620
two-minute video so anyway I'll leave it like this I think it would go a little

52:08.620 --> 52:11.060
faster.

52:11.060 --> 52:14.460
Located coverage of each HLA allele so probably 30 to 50 patients is really the

52:14.460 --> 52:19.140
ideal number of patients to be testing in an in vitro assay. Now screening peptide

52:19.140 --> 52:21.820
in silica we do look at it this way we basically parse it into nine we're

52:21.820 --> 52:24.660
overlapping frames and then we're gonna look at each overlapping frame for its

52:24.660 --> 52:28.020
predicted affinity to bind to specific HLA alleles here the nine super types

52:28.020 --> 52:31.180
that we've described. Notice in this picture that we see several nine

52:31.180 --> 52:35.220
reframes that bind promiscuously across HLA alleles and those are highlighted in

52:35.220 --> 52:37.660
yellow those are what we call epibars they're promiscuous T cell epitopes

52:37.660 --> 52:40.680
they're the primary drivers of an immune response and if we're making a

52:40.680 --> 52:43.260
vaccine those are great if they're in our peptide drug and they're foreign that's

52:43.260 --> 52:47.220
bad or or our impurity. They also give you the sense that the more T cell epitopes

52:47.220 --> 52:50.020
you pack into a single sequence whether they're for the same allele or other

52:50.020 --> 52:53.340
alleles in general higher epitope content is gonna drive a higher T cell

52:53.340 --> 52:55.820
response and that's what's we've observed in the literature as well as in

52:55.820 --> 52:58.820
our experience over the 20 years that we've been doing this. So we developed a

52:58.820 --> 53:01.140
ranking scale the ranking scale basically looks for the number of T cell

53:01.140 --> 53:05.220
epitopes per unit protein or peptide and we can look at how immunogenic a peptide

53:05.220 --> 53:10.020
might be for a population compared to others or for an individual. And as Mark

53:10.020 --> 53:14.540
said in the chat over ten years ago she came up with the idea of using

53:14.540 --> 53:21.220
Microsoft Word to look for these small epitopes in genetic sequences and the

53:21.220 --> 53:25.260
crazy thing is is that he found a video where she was talking to a

53:25.260 --> 53:29.980
newscaster and said well you know people started to contact us and asked us to

53:29.980 --> 53:33.820
analyze their protein sequence and at first we started with well how about a

53:33.820 --> 53:37.060
thousand dollars and they said fine so then we said how about five thousand

53:37.060 --> 53:39.580
dollars and then we were up to twenty thousand dollars and then we knew we

53:39.580 --> 53:46.940
really had a company for using Microsoft Word to search through their their

53:46.980 --> 53:53.020
genetic sequences to find T cell epitopes they had previously identified that's

53:53.020 --> 53:57.300
what we're dealing with here ladies and gentlemen this woman and people like

53:57.300 --> 54:02.100
her and that are looking to make products and looking to start companies

54:02.100 --> 54:07.300
and looking for things to from a business perspective catch on are very

54:07.300 --> 54:12.500
different than people are trying to save the world change the world or free

54:12.540 --> 54:18.740
humanity and so you have to see this for what it is you have to see Robert

54:18.740 --> 54:22.940
Malone for who he is you have to see Kevin McCurnan for who he is you have to

54:22.940 --> 54:29.300
see that these people have been swimming in this space for decades and they've

54:29.300 --> 54:34.820
been playing this game for decades the idea is to get a house on the harbor a

54:34.820 --> 54:40.340
seven thousand square foot house on the harbor the idea is to get really rich

54:40.340 --> 54:47.460
and to become famous the idea is to get the Bloomberg businessman award that's

54:47.460 --> 54:54.220
what they want that's the that's the kind of of prestige and fame and and

54:54.220 --> 55:00.420
and recognition that they want they aren't interested in giving they're not

55:00.420 --> 55:05.820
interested in teaching they're interested in making stuff that's worth stuff worth

55:05.900 --> 55:11.420
money and it is unfortunately a lot about money they measure it and their

55:11.420 --> 55:15.140
success based on money and that's what was so insightful about that interview

55:15.140 --> 55:21.300
from eight years ago with Annie is that it's she said it in such a way that it

55:21.300 --> 55:26.580
was almost like wow you're really an asshole like I went to this school and

55:26.580 --> 55:30.420
the school needed my help and so I said sure I can help you for a thousand

55:30.420 --> 55:33.780
dollars this week and they said great and so I was happy with that thousand

55:33.780 --> 55:39.460
dollars because a thousand dollars gets me through the week but then another

55:39.460 --> 55:44.820
school called me and I decided to say two thousand and they said fine and so I

55:44.820 --> 55:49.260
just did the job for two thousand and then when the next person called me I

55:49.260 --> 55:52.860
just scratched my eye wonder if they'll do it for five how about five thousand

55:52.860 --> 55:58.860
dollars and they said yes and suddenly I talked to my friend and I realized wow

55:58.860 --> 56:02.700
you know people will pay twenty thousand dollars for us to use Microsoft Word to

56:02.700 --> 56:07.980
search for nine letters in their protein sequence and we can charge five

56:07.980 --> 56:15.540
grand I wonder if we can charge twenty that's literally how this worked that's

56:15.540 --> 56:24.060
literally where epivacs came from it's no more complicated than that and yes it

56:24.060 --> 56:27.140
became more complicated of course because people threw millions of

56:27.140 --> 56:32.380
dollars at them the Bill Gates Foundation threw almost a million dollars at them

56:32.380 --> 56:39.300
many years ago already it's a really really interesting thing look now they

56:39.300 --> 56:45.260
have a pandemic sorry a peptide immunogenic scale it's kind of like the

56:45.260 --> 56:51.300
scoval rating of peppers I guess and here you go a 20 mer that's 20 amino

56:51.300 --> 56:58.860
acids theoretically minimum right and then all these guys have many many many

56:59.300 --> 57:06.580
potential T cell activating or HLA binding short sequences and so they're

57:06.580 --> 57:12.180
more immunogenic now if they if they have this idea already in their head for 10

57:12.180 --> 57:17.580
years then it's very easy to put the let's say where where where is where does

57:17.580 --> 57:24.620
the coronavirus spike protein fit in here not this one but the other ones it's

57:24.660 --> 57:29.660
not on here is it pretty convenient you see why we're this is exactly the story

57:29.660 --> 57:36.220
this is exactly where immunology should be where I expected it to be and now

57:36.220 --> 57:41.460
that we find it we have to understand why it's here what were these people doing

57:41.460 --> 57:47.100
before and after the pandemic and what does it mean going forward knowing that

57:47.100 --> 57:53.380
all of these Canadian companies were connected to American companies and

57:53.420 --> 58:00.300
connected to DARPA and a lot of these companies had a red thread of Robert

58:00.300 --> 58:07.940
Malone as an advisor it's just not possible for this to be random we have

58:07.940 --> 58:14.540
finally stumbled upon the joke the first generation of mRNA vaccines and the

58:14.540 --> 58:20.180
second generation of mRNA vaccines and that's probably been their long game

58:20.180 --> 58:24.900
plan all along whether or not Robert Malone was supposed to come out and

58:24.900 --> 58:29.180
and play shell games with everybody I don't really know but that's what that's

58:29.180 --> 58:35.460
where it had to go and to what extent we forced it to go there I don't know but

58:35.460 --> 58:41.380
we are definitely pushing the apple cart now we are definitely pushing the

58:41.380 --> 58:45.340
apple cart now we know genicity can be ranked here I'm analyzing the overall

58:45.340 --> 58:48.660
immunogenicity of generic peptide drugs compared to some well-known immunogenic

58:48.660 --> 58:51.260
peptides which are on the left-hand side of my scale from tennis talks and

58:51.260 --> 58:54.780
influenza EVV and you can see that the generic peptides which are generally

58:54.780 --> 58:58.300
derived from self peptides tend to be lower on that scale the scale is set to

58:58.300 --> 59:01.940
be zero for the average or median of a set of more than a hundred thousand

59:01.940 --> 59:04.860
random peptide sequences so you can see that there's a distribution things that

59:04.860 --> 59:08.180
are high on this scale could be expected to be more immunogenic than a random

59:08.180 --> 59:10.980
peptide by random chance and things that are lower on the scale are predicted to

59:10.980 --> 59:14.420
be less immunogenic than a random peptide found by random chance so okay

59:14.420 --> 59:18.180
maybe I'm just gonna stop this and I'm gonna look for the other video because I

59:18.460 --> 59:25.100
hold on let me just I'm gonna copy this because I think I watched it right

59:25.100 --> 59:34.420
before this video and so I should be able to go back here as efforts are

59:34.420 --> 59:37.260
underway worldwide trying find a way to stop this virus a local company is

59:37.260 --> 59:40.620
helping in the efforts working to create a vaccine okay hold on Logan

59:40.620 --> 59:45.740
Wilbur joins us now from Providence tonight with the story this is great so

59:45.740 --> 59:51.740
this is a an interview with her right at the start of the pandemic and now

59:51.740 --> 59:55.100
there's too much that goes on in this two minutes so I'm gonna have to stop it

59:55.100 --> 01:00:01.460
a lot but you're just not gonna believe it now you try your best and bring your

01:00:01.460 --> 01:00:09.140
most trained critical ears to this interview and bang on your own table

01:00:09.140 --> 01:00:15.180
every time you hear something you you simply can't believe she says from the

01:00:15.180 --> 01:00:19.020
outside this brick building in Onlyville may not look like much but people

01:00:19.020 --> 01:00:24.100
inside are working to find a possible solution to the coronavirus

01:00:24.100 --> 01:00:29.540
Providence based epivacs is working on two coronavirus vaccines with one focus

01:00:29.540 --> 01:00:33.420
on protecting health care workers they're the first line of defense against the

01:00:33.420 --> 01:00:37.540
virus and we want to give them something that will actually generate what we

01:00:37.540 --> 01:00:41.660
would call immune system body armor will give them a protection we asked Dr.

01:00:41.660 --> 01:00:46.100
Andy Groot to explain how vaccines work using a virus most people are

01:00:46.100 --> 01:00:50.900
familiar with the flu you have pre-existing immunity that allows your

01:00:50.900 --> 01:00:55.980
immune systems to go oh I see flu here I see a word that says flu I'm gonna help

01:00:55.980 --> 01:01:00.020
my immune system respond and make a lot of antibody which is exactly what the

01:01:00.020 --> 01:01:05.100
epivacs vaccine does it generates that immune memory you need that immune

01:01:05.100 --> 01:01:08.620
memory in order to generate an effective immune response currently without a

01:01:08.620 --> 01:01:13.540
vaccine Dr. DeGroot calls the coronavirus very dangerous this virus is

01:01:13.540 --> 01:01:23.140
killing somewhere between two to four people per 100 two to four people per

01:01:23.140 --> 01:01:32.540
100 three to four percent are dying of this virus is that not exactly the worst

01:01:32.540 --> 01:01:38.540
case scenario is that not exactly the bullshit that we're trying to fight

01:01:38.540 --> 01:01:44.540
right now it's not it was the protocols it was then do not resuscitate orders

01:01:44.540 --> 01:01:50.020
and she's a hundred percent on narrative it's killing between three and four

01:01:50.020 --> 01:01:55.140
people out of every 100 and no everyone is vulnerable listen that's very

01:01:55.140 --> 01:02:01.620
different from influenza where it's it's 10 to 50 fold higher so while there is a

01:02:01.620 --> 01:02:05.860
comparison to the flu as to why vaccines are important to solving this pandemic

01:02:05.860 --> 01:02:10.300
as the mortality rate shows the two are not alike I think there's a tendency to

01:02:10.300 --> 01:02:15.380
kind of not take this seriously and that's because people have said it's like

01:02:15.380 --> 01:02:19.420
the flu so we you know maybe we're somewhat immune to the flu but in

01:02:19.420 --> 01:02:24.020
actuality it's not like flu in that way we need to build the immunity through

01:02:24.020 --> 01:02:33.060
vaccines and vaccines are extremely important Wow I mean wow that's just

01:02:33.060 --> 01:02:39.780
complete hogwash it is nonsense it's ridiculous but she's saying it and it's

01:02:39.780 --> 01:02:45.300
not it's not unusual you just have to understand that as we look backwards and

01:02:45.300 --> 01:02:51.620
we actually take stock of what was happening at this time in 2020 it's

01:02:51.620 --> 01:02:57.060
going to become more and more painfully obvious that some of these people she's

01:02:57.060 --> 01:03:01.460
smiling in this damn interview the whole time because she's excited about the

01:03:01.540 --> 01:03:06.380
possibility that she's about to make a billion dollars there's a possibility

01:03:06.380 --> 01:03:11.980
that she still will make a billion dollars with the next one they're all

01:03:11.980 --> 01:03:16.300
excited about the possibility that they're going to go from zero to a billion in

01:03:16.300 --> 01:03:25.020
a year it's exactly what the video of the absolera CEO was titled you don't

01:03:25.020 --> 01:03:30.140
think all these people start dreaming about that the moment that it becomes

01:03:30.140 --> 01:03:34.780
possible the moment that somebody whispers in their ear this might be an

01:03:34.780 --> 01:03:44.140
IPO you don't think that happens I'm sure it does and I'm sure that this woman

01:03:44.140 --> 01:03:49.060
has been trying to get a hold of one of those hot air balloons for some time

01:03:49.060 --> 01:03:54.340
venture capital hot air balloon I mean I'd be happy to have someone invest a

01:03:54.340 --> 01:04:00.460
million dollars in gigo and biological to make sure that all of this you know

01:04:00.460 --> 01:04:09.140
live up-to-date coverage of pandemic mythology continues and I'm sure I would

01:04:09.140 --> 01:04:15.940
accept it greatly but it's not the reason why I'm doing it it's not the

01:04:15.940 --> 01:04:21.460
actual goal which is the actual goal of a company like this and was the actual

01:04:21.500 --> 01:04:25.580
goal of her spin-out company and the exact goal of all of these companies is

01:04:25.580 --> 01:04:31.780
to eventually go public and become worth five billion dollars that's the dream

01:04:31.780 --> 01:04:41.100
of every biologist at their bench or at least the ones that think like her

01:04:41.100 --> 01:04:45.340
you're not thinking about saving the world you're not thinking about making a

01:04:45.340 --> 01:04:50.980
therapeutic that everybody can afford you're thinking about making yourself

01:04:50.980 --> 01:04:57.580
rich and famous and Dr. DeGroote tells me we could see a vaccine and as

01:04:57.580 --> 01:05:02.100
little as five to six months but that all depends on funding she says she's

01:05:02.100 --> 01:05:05.580
going to ask the federal government to help with that more than three hundred

01:05:05.580 --> 01:05:10.340
million dollars needed to ensure both the safety and efficiency of the vaccine

01:05:10.340 --> 01:05:15.660
in Providence Logan Wilbur Eyewitness News she's gonna ask for three million

01:05:15.660 --> 01:05:24.380
dollars holy shit three hundred million dollars holy cow excuse me they do have

01:05:24.380 --> 01:05:30.020
the identical T-cell receptor-facing residues and bind to the same HLA the

01:05:30.020 --> 01:05:34.140
T-cell probably wouldn't be able to differentiate between that human GPI

01:05:34.140 --> 01:05:37.140
I'm gonna go back here and speed it up they can actually they can go up so if

01:05:37.140 --> 01:05:40.460
they're in the red zone we would be concerned if they're in the green zone

01:05:40.460 --> 01:05:44.100
we probably wouldn't even do a T-cell assay or an in vitro binding assay because

01:05:44.100 --> 01:05:48.260
we would imagine that peptide would be very low risk so furthermore we have

01:05:48.260 --> 01:05:51.020
other tools that allow us to screen for those happy bars of customers one such

01:05:51.020 --> 01:05:55.140
tool that finds regions of high epitope density in a protein or peptide sequence

01:05:55.140 --> 01:05:57.940
it's very important to find these okay now make sure that you understand what

01:05:57.940 --> 01:06:03.540
we're watching here she is assembling a software stack so there was a software

01:06:03.540 --> 01:06:07.860
that she was using earlier and now that was called panda or something like that

01:06:07.860 --> 01:06:13.980
now there's customer and you cannot underestimate how this is the business

01:06:13.980 --> 01:06:19.820
model it is very very similar to abcellular which is basically a stack of

01:06:19.820 --> 01:06:27.900
IA machine learning programs which can use the data that's gathered in their

01:06:27.900 --> 01:06:32.500
single-cell machines to do what it is they say they're doing which is

01:06:32.500 --> 01:06:35.980
identifying antibodies that are important and finding the B cells that

01:06:35.980 --> 01:06:42.140
are producing in them this is this is the same kind of screening it's screening

01:06:42.140 --> 01:06:46.540
the genetic sequence and they're they they're doing the same thing with

01:06:46.540 --> 01:06:55.340
antibodies it's a sequence and and they're using software to do it and so they

01:06:55.340 --> 01:07:02.860
make this patented software stack through which your biologic gets washed and

01:07:02.860 --> 01:07:09.460
what comes out is a patentable biologic because you washed it through this

01:07:09.460 --> 01:07:17.180
software stack and I think that that there are people who are right when they

01:07:17.180 --> 01:07:20.500
say that they hope that the mRNA would go better I think there are people that

01:07:20.500 --> 01:07:25.180
there were probably people that didn't think that it would go as bad as it has

01:07:25.180 --> 01:07:31.820
both from the perspective of coverage and the side effects I actually think

01:07:31.820 --> 01:07:36.500
they thought it would go better and I think Mark is also on this page at this

01:07:36.500 --> 01:07:40.380
stage where he thinks that sort of the cancers and all this other stuff it

01:07:40.380 --> 01:07:45.260
doesn't seem like very many people were anticipating this one of the ways that

01:07:45.260 --> 01:07:51.020
Mark has shown me of looking at this is going to the stock prices of cancer

01:07:51.020 --> 01:07:56.300
companies and you can see that they didn't rise ahead of this they certainly

01:07:56.300 --> 01:08:01.540
didn't rise before the pandemic like some other companies did and so it's

01:08:01.620 --> 01:08:08.260
possible that we are so off-script right now that we have a chance to actually

01:08:08.260 --> 01:08:13.620
change the direction of the cruise ship it's possible and I think that if you

01:08:13.620 --> 01:08:18.780
listen to this session where she talks so confidently about this technology and

01:08:18.780 --> 01:08:23.100
then we realize that this technology didn't even make it it didn't even make

01:08:23.100 --> 01:08:29.020
it to production what does that mean does that mean it's still waiting does

01:08:29.060 --> 01:08:33.500
that mean this is gonna be the next generation and if we think about the

01:08:33.500 --> 01:08:36.620
fact that a lot of these companies are all united by an interaction with

01:08:36.620 --> 01:08:42.220
Robert Malone and Robert Malone came out and controlled the narrative about how

01:08:42.220 --> 01:08:48.580
the mRNA vaccine was successful or not or who was successful for you can start

01:08:48.580 --> 01:08:53.780
to see a scenario here I think you can start to see a scenario here where you

01:08:53.780 --> 01:09:00.980
can understand what these people are clowning about now there are a couple

01:09:00.980 --> 01:09:05.100
other pieces to this puzzle one is that I've heard Robert Malone state several

01:09:05.100 --> 01:09:10.700
times that him and his wife have a patent on mucosal and inhaled vaccine

01:09:10.700 --> 01:09:17.220
vaccines and all technologies that do that if that's a possibility then I

01:09:17.220 --> 01:09:23.140
assure you this company having its long ties with Robert Malone will

01:09:23.140 --> 01:09:30.580
certainly be on this shortlist as we'll probably absolera and maybe a cutis or

01:09:30.580 --> 01:09:35.980
whoever else is that that he has ongoing relationships with but it's it's really

01:09:35.980 --> 01:09:45.740
hard not to see a scenario where the wrong question has been asked for several

01:09:45.740 --> 01:09:52.100
years now wrong question that has been propelled by George Webb and Veritas and

01:09:52.100 --> 01:09:59.020
Robert Malone it is a it is a wrong question about who is Boston

01:09:59.020 --> 01:10:04.620
consulting and what happened in this video and the fact that Robert Malone

01:10:04.620 --> 01:10:12.020
promoted that video and the fancy org chart of George Webb is exactly that

01:10:12.020 --> 01:10:19.820
video with exactly Jordan Walker and so we're all distracted by Boston

01:10:19.820 --> 01:10:26.700
consulting group and Pfizer and we really have never seen any of the

01:10:26.700 --> 01:10:31.660
companies that Robert Malone is actually working for and consulting with except

01:10:31.660 --> 01:10:38.060
for on Mark Street and we've never known really how they're connected until we

01:10:38.060 --> 01:10:43.500
watch this video with Colis and he revealed all the things that he revealed

01:10:43.820 --> 01:10:51.540
and that Mark kind of got pushed in just the right direction where he where he

01:10:51.540 --> 01:10:57.340
found what he we needed to find to really see that absolera is the the trick

01:10:57.340 --> 01:11:06.660
Peter Teal is there the antibody paradox anybody patent paradox is there and so

01:11:06.660 --> 01:11:10.260
now we need to really understand what the real immunology is going forward and I

01:11:10.300 --> 01:11:14.820
believe that although this company has been in the background for a long time

01:11:14.820 --> 01:11:20.380
and not really doesn't seem taken very seriously I actually have always believed

01:11:20.380 --> 01:11:24.500
you hear a lot of people talk about this the T cells are the cells that we

01:11:24.500 --> 01:11:32.220
should be targeting and we who were trying to actually augment and so at least in

01:11:32.220 --> 01:11:36.820
principle this company is thinking the right way and so I think has potential

01:11:36.820 --> 01:11:42.740
to be the part of the next generation of mRNA therapeutics which I assure you

01:11:42.740 --> 01:11:48.100
are coming they have no I have no doubt in my mind that that's the plan that

01:11:48.100 --> 01:11:53.980
that even if it becomes common knowledge that some of the batches of of the

01:11:53.980 --> 01:12:00.260
mRNA were contaminated it's not going to be enough to penetrate the the psyche of

01:12:00.260 --> 01:12:04.300
the average skilled TV watcher for them to understand that newer

01:12:04.380 --> 01:12:10.700
transfections or newer versions of this kind of of therapeutic are going to be

01:12:10.700 --> 01:12:14.620
just as dangerous they're going to think that the next generation is better just

01:12:14.620 --> 01:12:18.820
like Robert Malone says that he took the mRNA shot because he assumed they

01:12:18.820 --> 01:12:24.940
solved the trafficking problem or the targeting problem and of course yesterday

01:12:24.940 --> 01:12:29.140
we heard from or day before we heard from Peter Cullis that they haven't they

01:12:29.140 --> 01:12:32.500
tried for 40 years to solve the trafficking problem and they've never done

01:12:32.500 --> 01:12:39.780
it he burnt five postdocs on it we have been lied to thoroughly and now we're

01:12:39.780 --> 01:12:44.180
just getting to the point where we can start to see why I think and it's again

01:12:44.180 --> 01:12:50.820
it is exactly as Mark has said it the best the job of these people is only to

01:12:50.820 --> 01:12:55.180
make sure that you're asking the wrong question it doesn't even matter what

01:12:55.180 --> 01:13:01.700
question it is ask a question about bioweapons fine you're lost ask a question

01:13:01.700 --> 01:13:06.380
about amyloidosis and prion disease okay you're not on the story ask a

01:13:06.380 --> 01:13:13.020
question about hydroxychloroquine or Ivermectin and their effectiveness okay

01:13:13.020 --> 01:13:19.580
you're not on the story and so as long as you keep fighting about all these

01:13:19.580 --> 01:13:26.180
nonsense things and you keep chasing down all this nonsense biology he won't

01:13:26.180 --> 01:13:30.740
ever actually see the breadth and the depth of which you can you can see

01:13:30.740 --> 01:13:34.220
right through them they're just right here they tell you the truth over and

01:13:34.220 --> 01:13:40.540
over again they believe in things that don't work they believe in technology

01:13:40.540 --> 01:13:46.300
with a little bit of hand waving they believe in the future of the technologies

01:13:46.300 --> 01:13:51.860
that they're working on now and they believe lies are justified because they're

01:13:51.860 --> 01:13:55.740
getting rich because you can lie in business there's nothing wrong with

01:13:55.740 --> 01:14:00.300
lying in business to get rich it's all spare in love and war and business I

01:14:00.300 --> 01:14:07.340
guess so they have to sell this as the best technology it's their technology

01:14:07.340 --> 01:14:15.860
but they're revealing in my mind how again shallow their understanding of the

01:14:15.860 --> 01:14:20.700
immune response is in shallow in terms of their outlook on how to augment it

01:14:20.700 --> 01:14:24.300
they're really still just looking for antibody responses again those are the

01:14:24.300 --> 01:14:28.580
immunodominant signals in outbred populations here's an example this is a

01:14:28.580 --> 01:14:31.900
peptide that is found in GAD 65 it has an epi bar and it's a well-known

01:14:31.900 --> 01:14:36.140
autoimmune disease epitope found in patients who have type 1 diabetes now

01:14:36.140 --> 01:14:39.180
what happens when we have an impurity that contains a natural or artificial

01:14:39.180 --> 01:14:43.020
amino acid and what we do there is we basically use a best proxy approach we

01:14:43.020 --> 01:14:45.700
take three steps we replace with a neutral placeholder just to kind of see

01:14:45.700 --> 01:14:48.940
what the potential for immunogenicity is in that peptide on its own without the

01:14:48.940 --> 01:14:52.020
unnatural amino acid then we do a replacement analysis with all 20

01:14:52.020 --> 01:14:55.980
natural amino acids and look at the best case sorry the best case low immunogenicity

01:14:55.980 --> 01:15:00.220
or worst case high immunogenicity amino acid replacement that gives us a

01:15:00.220 --> 01:15:03.540
range and that tells us how immunogenic could this peptide be in the worst case

01:15:03.540 --> 01:15:07.300
scenario if that third position was replaced by a peptide that found

01:15:07.300 --> 01:15:10.100
promiscuously across all each allele so that tells you the worst case scenario

01:15:10.100 --> 01:15:13.620
since you don't or can't predict in silica what the unnatural would actually

01:15:13.620 --> 01:15:16.540
do the third thing we do is we pick a proxy and in this case for this peptide

01:15:16.540 --> 01:15:20.140
we actually picked the tryptophan proxy which allowed us to make an

01:15:20.140 --> 01:15:23.740
insilico prediction for an unnatural that was not a tryptophan so that's what we

01:15:23.780 --> 01:15:27.020
do in silico for un-naturals now once we've identified the potential immunogenicity

01:15:27.020 --> 01:15:31.140
of our API and our impurities we also want to evaluate if those APIs or

01:15:31.140 --> 01:15:35.020
impurities have the potential to induce tolerance in in vitro and in vivo how

01:15:35.020 --> 01:15:38.260
do peptides induce tolerance they engage t-reg cells that suppress immune

01:15:38.260 --> 01:15:41.940
response it turns out that the peptides that trigger t-regs bind to HLA

01:15:41.940 --> 01:15:45.380
molecules similarly to peptides that trigger a t-effector immune response so

01:15:45.380 --> 01:15:49.060
you can't differentiate them by their HLA binding group files you can as we

01:15:49.060 --> 01:15:52.460
discovered however determine if a peptide might be a t-reg epitope by

01:15:52.500 --> 01:15:55.700
looking at its T cell receptor facing surface and so we actually use a tool

01:15:55.700 --> 01:15:58.820
called Janus matrix to do that let me explain further here what I'm showing

01:15:58.820 --> 01:16:02.580
is the peptide from your another software to purity binding the binding

01:16:02.580 --> 01:16:05.540
group the agritobus facing down the abitopus facing up to the T cell

01:16:05.540 --> 01:16:08.780
receptor and what we're doing with this Janus matrix tool is basically dividing

01:16:08.780 --> 01:16:11.540
the peptide into two different components the amino acids that bind to

01:16:11.540 --> 01:16:15.180
the HLA and the amino acids that bond that face up to the T cell receptor you

01:16:15.180 --> 01:16:18.020
can imagine when the T cell receptor comes down on top of that peptide that is

01:16:18.020 --> 01:16:20.900
really only seeing the amino acid side chains that face up it can't see the

01:16:20.940 --> 01:16:24.740
ones that are bound down into the HLA binding group so we could actually take

01:16:24.740 --> 01:16:28.660
those amino acids and look in the human genome for other peptides it may not be

01:16:28.660 --> 01:16:32.260
identical in sequence that do have the identical T cell receptor facing

01:16:32.260 --> 01:16:35.460
residues and bind to the same HLA the T cell probably wouldn't be able to

01:16:35.460 --> 01:16:38.780
differentiate between that human genome peptide and say the impurity peptide

01:16:38.780 --> 01:16:41.460
that we're showing it in an HLA molecule as long as the HLA molecule is

01:16:41.460 --> 01:16:44.700
the same and the T cell receptor facing residues are the same that's the

01:16:44.700 --> 01:16:47.940
hypothesis turns out that when we do have extensive cross conservation at the TCR

01:16:47.940 --> 01:16:51.340
phase that we're able to show that those peptides which have say this peptide

01:16:51.340 --> 01:16:55.540
which is an IMER has six different peptides to which it can be related in

01:16:55.540 --> 01:16:58.300
the human genome by conservation of the TCR phase and restriction by the same

01:16:58.300 --> 01:17:01.780
HLA allele that peptide is going to either be tolerated because we've been

01:17:01.780 --> 01:17:05.340
trained to tolerate it we've seen that peptide before in our own genome or it

01:17:05.340 --> 01:17:08.140
will be actively regulatory and that's what's really interesting about some of

01:17:08.140 --> 01:17:12.860
the peptides that we're dealing with as generic peptides today so what she

01:17:12.860 --> 01:17:17.900
seems to imply here is that if it if a peptide sequence has a high homology

01:17:17.900 --> 01:17:23.540
with self proteins it will tend to be a T regulatory epitope and if it has an

01:17:23.540 --> 01:17:29.060
a foreign one then it will tend to be a a immunogenic epitope which is

01:17:29.060 --> 01:17:35.460
interesting because in another talk of hers you can hear her say very plainly

01:17:35.460 --> 01:17:41.660
that coronaviruses and RNA viruses have adopted a tremendous amount of

01:17:41.660 --> 01:17:51.340
homology between human proteins to avoid detection to cause tolerance which is

01:17:51.340 --> 01:17:55.980
exactly what I've said from the very beginning is essentially the response to

01:17:55.980 --> 01:18:02.020
a coronavirus RNA if they exist as they are portrayed in cartoons our bodies

01:18:02.020 --> 01:18:09.380
have learned to respond to these over millennia with a lack of immune response

01:18:09.460 --> 01:18:17.300
and a lack of inflammation and so one of the reasons why the common cold is in

01:18:17.300 --> 01:18:20.380
particularly dangerous except for in people who are already dying is that

01:18:20.380 --> 01:18:26.700
reason because our immune system isn't really triggered by them and one of the

01:18:26.700 --> 01:18:30.980
reasons may be what she has explained in one of her many lectures is that a lot

01:18:30.980 --> 01:18:36.700
of the coronavirus proteins are homologous through large stretches with

01:18:36.700 --> 01:18:42.220
human proteins which could be mimicry or it could be a sign that they're just

01:18:42.220 --> 01:18:46.940
exosomes right I mean this is part of what's so frustrating about this this

01:18:46.940 --> 01:18:54.620
idea is that all of these people when they talk they are discussing their ideas in

01:18:54.620 --> 01:18:58.700
the context of this bad biology and this bad understanding of of the immune

01:18:58.700 --> 01:19:03.820
system and so it's hard to talk around them in some ways because they're limited

01:19:03.820 --> 01:19:08.420
by this this huge assumption that you know there's just a few knobs in the

01:19:08.420 --> 01:19:14.340
immune system to tweak and one goes up and one goes down and it's it's like

01:19:14.340 --> 01:19:20.220
that but it's not it's not as simple as you know symbols in the band and symbols

01:19:20.220 --> 01:19:23.740
not in the band it's not like that other aspects of that peptide might bind

01:19:23.740 --> 01:19:26.920
similarly to an MHC molecule or HLA molecule but would have no cross

01:19:26.920 --> 01:19:29.660
conservation in the human genome and those could potentially be immunogenic

01:19:29.660 --> 01:19:32.980
now let's just pretend this API is now has a modification and it is an

01:19:32.980 --> 01:19:36.180
impurity this part of the impurity that binds to a specific HLA allele could

01:19:36.180 --> 01:19:40.140
actually be tolerogenic this part of the impurity could actually bind to a MHC or

01:19:40.140 --> 01:19:44.020
HLA alleles and drive an unexpected and unwanted immune response so that's why we

01:19:44.020 --> 01:19:47.260
do this analysis with the Janus matrix tool the method is published it's in the

01:19:47.260 --> 01:19:49.940
literature and there are actually quite a few papers which I'm showing you here

01:19:49.940 --> 01:19:53.100
and I invite you to look at later what's an example of a T-reg epitope that is

01:19:53.100 --> 01:19:56.100
confirmed with Janus matrix one is found in immunoglobulin actually there are

01:19:56.100 --> 01:19:59.820
six to eight to ten that are found in immunoglobulin G and these are promiscuous

01:19:59.820 --> 01:20:02.580
T cell epitopes that are highly cross conserved not only in immunoglobulin G

01:20:02.580 --> 01:20:06.820
but across a whole set of other human genome peptides they are tolerogenic they

01:20:06.820 --> 01:20:11.420
do induce T-regs in vitro and they induce adaptive tolerance to other parts of

01:20:11.420 --> 01:20:14.900
proteins that contain them so T-regitopes are an interesting discovery that we can

01:20:14.900 --> 01:20:18.260
make with the Janus matrix tool and when we're looking at contain them so T-regics

01:20:18.260 --> 01:20:21.940
in vitro and they induce adaptive talk I'm not really sure how this works she's

01:20:21.940 --> 01:20:29.260
saying IgG this is a antibody year and I'm not really sure how this arrow works

01:20:29.260 --> 01:20:35.660
with regard to now that antigen presenting cell is gonna present a

01:20:35.660 --> 01:20:38.580
portion of the antibody this is something that I have never heard of

01:20:38.580 --> 01:20:43.140
before so I guess I'm gonna have to read some of those papers I might need to

01:20:43.140 --> 01:20:48.660
catch up on this this is a this is a new thing for me but it's interesting to

01:20:48.660 --> 01:20:55.780
think that the T-regulatory cell epitope might be on IgG antibodies I don't know

01:20:55.780 --> 01:20:59.540
what I have to draw pictures I'm not gonna be able to follow this anymore I

01:20:59.540 --> 01:21:03.340
don't think but I'm gonna listen to it but I'm gonna get lost to other parts

01:21:03.340 --> 01:21:06.540
of proteins that contain them so T-regitopes are an interesting discovery

01:21:06.540 --> 01:21:09.420
that we can make with the Janus matrix tool now when we're looking at peptides

01:21:09.420 --> 01:21:12.380
and their impurities we're gonna try to assess their risk by looking at these

01:21:12.380 --> 01:21:16.300
two aspects of the peptide itself let's look at for example on the y-axis how

01:21:16.300 --> 01:21:19.660
many T-cell epitopes per unit length that peptide or impurity might have you

01:21:19.660 --> 01:21:22.820
could have epitode dense peptides or epitopes sparse peptides and then let's

01:21:22.820 --> 01:21:26.020
look at the humanists of that peptide you could have peptides that are more

01:21:26.020 --> 01:21:29.220
that are have TCR faces that are more common in human proteins and peptides

01:21:29.220 --> 01:21:32.780
that have that look less human and so you can obviously divide peptides and

01:21:32.780 --> 01:21:35.300
their impurities into four different quadrants with the least immunogenic

01:21:35.300 --> 01:21:37.580
potential down here and the most immunogenic potential up there where

01:21:37.580 --> 01:21:41.060
there are more T-cell epitopes less human so what do our generic peptides look

01:21:41.060 --> 01:21:43.780
like and what do their impurities look like well let's just take the two peptides

01:21:43.780 --> 01:21:47.060
that we worked on with the FDA in the context of our contract here you can see

01:21:47.060 --> 01:21:53.020
on this quadrant analysis all of that so she's working on FDA approved

01:21:53.020 --> 01:21:59.020
biologics which have known impurities in them and she's trying to explain whether

01:21:59.020 --> 01:22:05.060
the impurities are generating the unwanted immune responses or the peptide

01:22:05.060 --> 01:22:13.660
itself and so she's analyzed peptides for the FDA this is like right years

01:22:13.660 --> 01:22:17.820
years later after she had that interview which I described where she

01:22:17.820 --> 01:22:21.380
said well we charged him a thousand and they paid it and the next people had

01:22:21.380 --> 01:22:25.180
called we said five and they paid it and the next people called we said 20 and

01:22:25.180 --> 01:22:31.860
they paid it so we knew we had a company and so what you see here is epi matrix

01:22:31.860 --> 01:22:38.540
and Janice matrix supposedly probably just two software programs that are

01:22:38.620 --> 01:22:46.860
going to be patented and that makes the results of this patentable or trade

01:22:46.860 --> 01:22:50.340
markable or something I'm sure

01:22:51.300 --> 01:22:55.580
generic peptides that are of interest in the market right now and you can see

01:22:55.580 --> 01:22:59.020
that a lot of them fall in this low epitope density but not necessarily very

01:22:59.020 --> 01:23:02.740
human quadrant which suggests that there is potential for some immunogenicity but

01:23:02.740 --> 01:23:05.260
there are two that fall in other quadrants tear peritide falls in the

01:23:05.260 --> 01:23:07.320
epitope dense but highly human so potentially taller

01:23:07.320 --> 01:23:11.160
genetic quadrant and the serotide is epitope sparse and highly human so

01:23:11.160 --> 01:23:14.000
probably not a problem as an API and what about their impurities you can

01:23:14.000 --> 01:23:16.800
actually see on this quadrant graph that the tear peritide impurities that we

01:23:16.800 --> 01:23:20.280
worked on with FDA really travel around the parent drug small modifications

01:23:20.280 --> 01:23:23.400
really don't expect you don't can't expect deviation to be too great from

01:23:23.400 --> 01:23:26.000
the parent drug but there are examples and we actually designed the peptide

01:23:26.000 --> 01:23:29.840
shown here to be to erase the human like a face of tear peritide and I'll get to

01:23:29.840 --> 01:23:33.200
that later so we actually found using an algorithm the peptides that were

01:23:33.200 --> 01:23:36.520
furthest from the parent molecule and could potentially be immunogenic obviously

01:23:36.520 --> 01:23:39.440
those are impurities that you want to watch out for here's salmon calcitonin

01:23:39.440 --> 01:23:42.880
again most of the impurities kind of traveled around the salmon calcitonin

01:23:42.880 --> 01:23:46.600
API itself and didn't get up into the very high risk but there were they were

01:23:46.600 --> 01:23:49.600
in the kind of moderate risk category here you can see in gray some well-known

01:23:49.600 --> 01:23:53.080
peptides that are immunogenic generally derived from tetanus toxin and others

01:23:53.080 --> 01:23:55.840
those are used as controls in our assays and here the T regitope showing

01:23:55.840 --> 01:23:59.120
that they are epitope dense but highly human and or as I've mentioned have been

01:23:59.120 --> 01:24:03.400
found to be taller genetic now how do we validate the in silico I'm just going

01:24:03.400 --> 01:24:06.400
to point to a couple of assays that we use we use HLA binding assays to see if

01:24:06.400 --> 01:24:09.840
the agritope binds to the HLA DR molecules as predicted then we use T cell

01:24:09.840 --> 01:24:12.720
assays with naive donors looking to see if we can generate a T cell response in

01:24:12.720 --> 01:24:16.360
vitro not the most accurate assay but it's a good way of validating something

01:24:16.360 --> 01:24:20.800
that you've looked at in silico to see if the two two expectations align so it's

01:24:20.800 --> 01:24:24.240
a way of doing orthogonal analysis of your impurity looking at it from two

01:24:24.240 --> 01:24:27.320
different directions and seeing if you get sort of the same signal the binding

01:24:27.320 --> 01:24:31.600
assay that we use is actually an assay that uses a fluorescent tracer peptide

01:24:31.640 --> 01:24:36.200
that's a known binder I'm still I'm still confused about how you can use a blood

01:24:36.200 --> 01:24:43.400
sample from a person to take T cells out and then try to find T cells my

01:24:43.400 --> 01:24:49.200
problem with that is that it is inevitably an incredibly small portion of

01:24:49.200 --> 01:24:53.200
the T cells of that person because most of the naive T cells are hanging out in

01:24:53.680 --> 01:25:02.480
not in circulation but they're in the lymph nodes pre activation so I'm having

01:25:02.480 --> 01:25:09.720
a hard time understanding what kind of sample one gets from circulation my

01:25:09.720 --> 01:25:12.960
assumption would be that that sample would be composed of T cells that have

01:25:12.960 --> 01:25:16.240
already been educated T cells that are already on patrol T cells that are

01:25:16.240 --> 01:25:21.360
already working including cytotoxic T cells as well T regulatory cells and

01:25:21.360 --> 01:25:27.160
T helper cells I don't so she said it was not a very reliable essay or not a

01:25:27.160 --> 01:25:32.840
perfect assay but I think it's it's worse than that it's it's not even she

01:25:32.840 --> 01:25:36.320
should really say that it's like a biological shot in the dark and if we

01:25:36.320 --> 01:25:39.120
get something from it we're lucky but most of the time we don't know for sure

01:25:39.120 --> 01:25:46.480
because the vast majority of the catalog of T cell memory is encased in a place

01:25:46.480 --> 01:25:51.760
that you can't just get to with a small blood sample that really bothers me a

01:25:51.760 --> 01:25:56.480
lot I don't I don't know I don't tie it off with the test peptide which is our

01:25:56.480 --> 01:26:01.920
API or in our impurity we do a seven concentration range of binding assay

01:26:01.920 --> 01:26:05.640
so that we can get a nice dose ranging curve and that gives us the ability to

01:26:05.640 --> 01:26:09.840
calculate the I C 50 of the binding affinity here are some examples strong

01:26:09.840 --> 01:26:13.400
binder competes off the tracer peptide moderate binder only at high

01:26:13.400 --> 01:26:16.960
concentrations non binder doesn't compete at all T cell assay we use is is

01:26:16.960 --> 01:26:20.120
adopted from walnut at all it basically expands up T cells in vitro with the

01:26:20.120 --> 01:26:22.880
test peptide and then you do a measurement of the immune response either in a

01:26:22.880 --> 01:26:26.480
fluorescent at least by assay measuring T cell responses or you can do a flow

01:26:26.480 --> 01:26:30.640
assay the last assay I want to mention is the tetanus toxin bystander assay I'm

01:26:30.640 --> 01:26:34.480
not an expert on those assays I'm gonna try to read a few of her papers and see

01:26:34.480 --> 01:26:37.600
if we can't do some journal clubs on them till we can understand this because I

01:26:37.600 --> 01:26:42.800
am interested in T cells and understanding what is supposedly known so it

01:26:42.800 --> 01:26:47.200
looks like this is gonna be a a lot of work for me but it's good okay or we we

01:26:47.200 --> 01:26:50.120
actually measure for T-reg response but since you can't it's or it's difficult

01:26:50.120 --> 01:26:52.680
to measure directly the T-reg response we usually try to measure it by looking

01:26:52.680 --> 01:26:55.920
at how much a T-reg epitope will suppress another response here we use

01:26:55.920 --> 01:26:59.960
tetanus toxin and we try to suppress that response in vitro we publish that assay

01:26:59.960 --> 01:27:04.040
it's in a new paper looking at a new T-regital tube called factor 5 6 21 please

01:27:04.040 --> 01:27:07.760
take a look at the light Jack paper looking at a new T-regital tube called

01:27:07.760 --> 01:27:11.400
factor 5 6 21 please take a look at the light Jack you're just gonna have to

01:27:11.400 --> 01:27:15.880
wait a few minutes so to the case studies really interesting peptides

01:27:15.880 --> 01:27:19.160
salmon calcitonin and teraparitide salmon calcitonin because it is immunogenic

01:27:19.160 --> 01:27:23.080
it is a foreign peptide only 50% conserved with human calcitonin teraparitide a

01:27:23.080 --> 01:27:25.800
fully human peptide and as it turns out it looks tolerogenic in our in

01:27:25.800 --> 01:27:29.640
silico assays here is salmon calcitonin interestingly enough it has one of those

01:27:29.640 --> 01:27:33.600
every day talked about and that epibar is in the book I can't tell whether if

01:27:33.600 --> 01:27:38.120
tolerogenic is good in her mind or what it is I can't tell we foreign immunogenic

01:27:38.120 --> 01:27:40.680
region others have looked at this region and found it to be immunogenic so that's

01:27:40.680 --> 01:27:43.980
confirmed in our in silico assays and we also looked at impurities you can see

01:27:43.980 --> 01:27:46.520
as I've mentioned before that all of the salmon calcitonin impurities were in

01:27:46.520 --> 01:27:50.320
the left lower quadrant not traveling too far from the a salmon calcitonin

01:27:50.320 --> 01:27:53.480
API and we test all of these in finding assays all these in T-cell assays it's

01:27:53.480 --> 01:27:56.520
one of their controls the tetanus toxin is there so just to give you kind of a

01:27:56.520 --> 01:28:01.120
high-level view of salmon calcitonin is immunogenic in a 16 donor set of assays

01:28:01.120 --> 01:28:04.560
we found that 44% of donors responded again see there you go now they're

01:28:04.560 --> 01:28:09.120
using 16 different donors to find out if there's a T-cell response that

01:28:09.120 --> 01:28:14.920
recognizes one of these epitopes or recognizes their responses to calcitonin

01:28:14.920 --> 01:28:20.640
so I'm still not completely clear what they're doing but I do think it is that

01:28:20.640 --> 01:28:25.800
from this test they can take those T-cells and then find out a little bit

01:28:25.800 --> 01:28:30.760
about what they memorized in this I don't know what an API is I think that

01:28:30.760 --> 01:28:37.160
this is some abbreviation for a protein artificially protein something you know

01:28:37.160 --> 01:28:39.760
like I don't know what that means I could you could google it and find it

01:28:39.760 --> 01:28:43.080
out but it's she's used it a couple times in the talk as if everybody in the

01:28:43.080 --> 01:28:46.240
audience understood it so I didn't that matrix a score is above that random

01:28:46.240 --> 01:28:50.680
zero that's the median of what we would expect from random chance so higher

01:28:50.680 --> 01:28:55.560
than I would say expected immunogenicity for a peptide or expected in silico and

01:28:55.560 --> 01:28:59.840
validated in vitro we also found that the impurities listed here some of which

01:28:59.840 --> 01:29:04.200
had higher scores a higher it means a small number of donors who put you in

01:29:05.200 --> 01:29:09.800
salt and but you can see that yes although salmone calcitonin is immunogenic

01:29:09.800 --> 01:29:13.440
the impurities do look like they're slightly more immunogenic as predicted

01:29:13.440 --> 01:29:17.040
in silico so the findings in our salmone calcitonin studies were that the

01:29:17.040 --> 01:29:19.440
salmone calcitonin impurities had similar epimatrix scores that were

01:29:19.440 --> 01:29:22.520
moderate and Janus matrix scores which were non-human and they had

01:29:22.520 --> 01:29:27.360
application program to the API so they were immunogenic as predicted but not

01:29:27.360 --> 01:29:30.440
more so than the API itself so that was a nice finding now what about terra

01:29:30.440 --> 01:29:34.600
peritide terra peritide is fully human it is a sub sequence of the parathyroid

01:29:34.600 --> 01:29:37.920
hormone it's a drug called fordio it's generally not immunogenic in the

01:29:37.920 --> 01:29:41.480
clinic but it does have an epibar so if you look at this this peptide sequence

01:29:41.480 --> 01:29:43.960
you see that epibar and you go to yourself that's got to be immunogenic

01:29:43.960 --> 01:29:47.440
however when we look at it with Janus matrix I do not know what an epibar is

01:29:47.440 --> 01:29:50.960
apparently everybody else in the audience does but I don't know what an

01:29:50.960 --> 01:29:54.720
epibar is I could also google that but I'm not gonna do it right now it has a

01:29:54.720 --> 01:29:58.080
very high Janus matrix human homology score meaning that it has a

01:29:58.080 --> 01:30:00.600
conservation with the TCR facing residues of peptides found in the

01:30:00.600 --> 01:30:04.200
human genome and actually it's related to the human protein tubulin which is

01:30:04.200 --> 01:30:08.680
found in most cells all cells it's the protein that basically gives each cell

01:30:08.680 --> 01:30:11.440
its structure so this is very similar promiscuous T cell epitope is found in

01:30:11.440 --> 01:30:14.920
tubulin here's the cytoscape diagram showing that relationship again as I've

01:30:14.920 --> 01:30:18.800
mentioned terra peritide actually has a high epimatrix score higher epitope

01:30:18.800 --> 01:30:21.320
density than what we would expect from random chance lots of T cell epitopes

01:30:21.320 --> 01:30:24.640
epibar and it's highly human as we've mentioned in terms of its Janus matrix

01:30:24.640 --> 01:30:28.320
score and so are most of the impurities and we actually had to generate an

01:30:28.320 --> 01:30:31.760
impurity that erased that TCR face and we did do that with what we call the

01:30:31.760 --> 01:30:35.320
WIM machine which generates all the impurities at every single amino acid

01:30:35.320 --> 01:30:39.040
position for every generic peptide that we want to look at and that actually gave

01:30:39.040 --> 01:30:43.600
us a couple of solutions that we could test in vitro and in vivo here again are

01:30:43.600 --> 01:30:47.120
the impurities again this publication is in preparation we promise that you'll

01:30:47.120 --> 01:30:50.080
get a look at the detailed results for these impurities when we finish now I'm

01:30:50.080 --> 01:30:52.560
just gonna cut to the chase because I thought this was really interesting we

01:30:52.560 --> 01:30:55.280
actually did that tetanus toxin bystander assay with the API so here I'm

01:30:55.280 --> 01:30:58.880
just using the API to see if it was tolerogenic in vitro so we added

01:30:58.880 --> 01:31:01.880
tetanus to this the wells and in this example donor what we're looking for

01:31:01.880 --> 01:31:05.160
is proliferation of the cells the cells travel left in the flow when they

01:31:05.160 --> 01:31:09.520
start proliferating and when we actually see a T-reg have an effect on that

01:31:09.520 --> 01:31:13.040
proliferation you can see less proliferation here and less activation

01:31:13.040 --> 01:31:16.960
and presentation of activation cell surface markers on the T cells so two

01:31:16.960 --> 01:31:19.920
different tests of the same thing and what you see here is two standard T

01:31:19.920 --> 01:31:24.000
regitope controls and terra peritide all three are suppressing the

01:31:24.000 --> 01:31:26.640
proliferation response to tetanus toxin and our tetanus toxin bystander

01:31:26.640 --> 01:31:29.360
assay and they also reduce activation this was repeated with I think five

01:31:29.360 --> 01:31:33.320
total donors which demonstrated that that terra peritide does actually have a

01:31:33.320 --> 01:31:36.800
tolerogenic effect in vitro could be what we would call a T regitope and we're

01:31:36.800 --> 01:31:40.120
going to be exploring that further do the impurities induce an immune response and

01:31:40.120 --> 01:31:43.840
the answer is yes if you remove the TCR cross-conserved residues the terra

01:31:43.840 --> 01:31:46.880
peritide impurities still bind but they are more immunogenic and we look forward

01:31:46.880 --> 01:31:50.960
to publishing that data so you can review it in detail so in brief we had

01:31:50.960 --> 01:31:54.320
an opportunity to look at these two case studies and then the most recent

01:31:54.320 --> 01:31:57.680
project is to prospectively predict and rank all peptide impurities well at

01:31:57.680 --> 01:32:02.200
least for the generic peptides using a machine called whim what is whim is whim

01:32:02.200 --> 01:32:06.240
stands for the what if machine and basically since we don't know all drug

01:32:06.240 --> 01:32:09.360
impurities but there are certainly many that could occur we basically just made

01:32:09.360 --> 01:32:12.120
an in silico tool that performs all possible changes to the natural amino

01:32:12.120 --> 01:32:15.080
acid sequence of the drug substance and measures their impact on the epitome

01:32:15.080 --> 01:32:19.800
content not only epitope density but also their humanness the what if machine

01:32:19.800 --> 01:32:24.480
sounds a lot like the domain server or like what Robert Malone did at the

01:32:24.480 --> 01:32:29.920
beginning of the pandemic where he tested all possible drugs all known drugs and

01:32:29.920 --> 01:32:40.680
pharmaceuticals against the the virtual 3D crystallization model of the of the

01:32:40.720 --> 01:32:46.240
enzyme that he made in like three weeks he made the model of the enzyme of the

01:32:46.240 --> 01:32:52.320
protein that he wanted to bind to which was the 3 CL enzyme or protease 3 CL

01:32:52.320 --> 01:32:58.520
protease of the of the virus he made a crystal a virtual crystal model of that

01:32:58.520 --> 01:33:05.080
which again is a simulation and then he took that simulation he interfaced it

01:33:05.080 --> 01:33:12.600
with all known drugs and pharmaceuticals in the domain server or in the domain

01:33:12.600 --> 01:33:21.160
program or something and the crazy thing is is that program kicked out remdesivir

01:33:21.160 --> 01:33:28.360
femotidine and a few other things apparently it didn't kick out a hydroxy

01:33:28.360 --> 01:33:33.280
chloroquine or ivermectin and that could be because they don't interact with the

01:33:33.280 --> 01:33:39.400
virtual 3D crystallography model that Robert Malone made of the 3 CL protease

01:33:39.400 --> 01:33:45.640
in January of 2020 after being told by Michael Callahan to spin his team up

01:33:45.640 --> 01:33:53.080
largely volunteers this sounds like a very similar you know magic box where you

01:33:53.080 --> 01:33:59.280
just kind of crank it out and it gives you answers all possible changes but this

01:33:59.280 --> 01:34:04.360
is easier because it's only nine only nine amino acids in their in their

01:34:04.360 --> 01:34:09.000
epitopes and so they only need to change those nine amino acids in relation to

01:34:09.000 --> 01:34:13.880
the other chemical amino acids that are related to them so 20 amino acids you

01:34:13.880 --> 01:34:19.160
know it's maximum it's not that many so it it's a little less than all the drugs

01:34:19.160 --> 01:34:23.880
and pharmaceuticals known to man were tested against my my thing like then

01:34:23.880 --> 01:34:27.720
calculate a predicted immunodulus score that combines those two things and we

01:34:27.720 --> 01:34:30.320
basically identify whether certain impurities with modifications at a

01:34:30.320 --> 01:34:34.160
certain location are more risky than others this generates a list list that

01:34:34.160 --> 01:34:37.360
could be used to identify impurities that could be removed from generic drugs

01:34:37.360 --> 01:34:42.600
prior to approval so this just shows you an example of past pogutide which is a

01:34:42.600 --> 01:34:45.800
glip one that was taken off the market because it had a lot of impurities a lot

01:34:45.800 --> 01:34:49.200
of those impurities were immunogenic there wasn't HLA association none of

01:34:49.200 --> 01:34:52.960
that ever got published but we have seen the data and what is shown here is just

01:34:52.960 --> 01:34:57.280
a duplication modification that we generated with whim you can see that some

01:34:57.280 --> 01:35:01.420
of those some here's the RLD or the API and here are the scores of the

01:35:01.420 --> 01:35:05.480
modified impurities generated in the wind machine you can also see the Janus

01:35:05.480 --> 01:35:08.400
major comology score obviously a high homology score would be less immunogenic

01:35:08.400 --> 01:35:11.800
and what's really interesting that is that the HLA that restricted were

01:35:11.800 --> 01:35:15.440
present in the patients that had the adverse events with TASPO correlated

01:35:15.440 --> 01:35:19.280
with the highest risk immunogenicity in this table so maybe we identified the

01:35:19.280 --> 01:35:22.880
impurities that were a problem in that TASPO clinical trial so this is just to

01:35:22.880 --> 01:35:26.760
tell you what's coming up we so in a way it's very interesting because what she

01:35:26.760 --> 01:35:34.860
is doing is not dissimilar to what San Sashalatopova was doing back in the

01:35:34.860 --> 01:35:40.600
90s where she was giving them and giving pharmaceutical companies another way to

01:35:40.600 --> 01:35:48.640
read the P wave of cardiac of cardiac signals so one of the things that they

01:35:48.640 --> 01:35:54.240
look for in all drug trials is the the signature of the heart of the patient

01:35:54.240 --> 01:35:58.080
and you know that the signature of the heart of a patient has this you know

01:35:58.080 --> 01:36:02.880
thing that they call I don't know what it is electrocardiogram and it has this

01:36:02.880 --> 01:36:06.960
little shape and they show it on the ER show and all other stuff and it's got a

01:36:06.960 --> 01:36:12.600
P wave and a Q something and whatever and cardiologists that are really good at

01:36:12.600 --> 01:36:15.800
looking at these things can tell what's wrong with you by just looking at the

01:36:15.800 --> 01:36:20.800
distances and the sizes between these these characteristic electrical signals

01:36:20.800 --> 01:36:30.080
that come from the heart and so Sandra a lot of Sashalatopova was the CEO of a

01:36:30.080 --> 01:36:36.480
company which sold software which essentially analyzed the the cardiac

01:36:36.480 --> 01:36:44.800
data of in clinical trials and so in a way it was a what-if machine about the

01:36:44.800 --> 01:36:52.000
cardiac data in a in a way that could as Sashal that be more sensitive so that

01:36:52.000 --> 01:36:58.120
let's say bad signals cut bad cardiac signals would not prematurely end a

01:36:58.120 --> 01:37:04.720
trial or not allow a drug to come to market because their software allowed a

01:37:04.720 --> 01:37:10.240
more subtle analysis which you could just construe as the software allowed them

01:37:10.240 --> 01:37:15.640
to get away with more signal change then they should have been able to and I

01:37:15.640 --> 01:37:19.600
would I would argue that this might be something very similar to that where

01:37:19.600 --> 01:37:29.280
we're looking at a a kind of a screen of a biologic where you can actually look

01:37:29.280 --> 01:37:33.560
at the the results of a clinical trial and then come up with a model where the

01:37:33.560 --> 01:37:39.440
where the impurities that you found are responsible for the for the adverse

01:37:39.480 --> 01:37:44.000
events rather than for the the product as a whole and so you can blame it on this

01:37:44.000 --> 01:37:53.200
what how well with using software three layers of software can identify where the

01:37:53.200 --> 01:38:00.440
potential immunogenic epitopes are in your designer drug and its impurities and so

01:38:00.440 --> 01:38:04.120
unless you're able to go into this code and find it yourself which you can't

01:38:04.120 --> 01:38:08.000
unless you're part of this company you're never going to be able to verify

01:38:08.000 --> 01:38:13.600
whether or not this score is actually proving that the intended protein is

01:38:13.600 --> 01:38:19.520
fine but the purity impurities are wrong it's a really wonderful wonderful tool

01:38:19.520 --> 01:38:26.080
if it's honest but it's also a hugely potential source of fraud because just

01:38:26.080 --> 01:38:31.240
because you use a computer program doesn't make it correct and if you can

01:38:31.240 --> 01:38:34.280
launder anything you want through through the domain program and get

01:38:34.280 --> 01:38:40.520
everybody to take remdesivir and that's kind of the same trick which I think

01:38:40.520 --> 01:38:47.840
was done with remdesivir and potentially could be done with a software suite like

01:38:47.840 --> 01:38:52.800
this one. We identified the impurities that were a problem in that TASPO clinical

01:38:52.800 --> 01:38:56.480
trial so this is just to tell you what's coming up we are really delighted to be

01:38:56.480 --> 01:38:59.920
working with our OGD colleagues on these projects of the last one being

01:38:59.920 --> 01:39:03.040
Selen Calcitonin and Terra Peritite and the current one being Wim I have

01:39:03.040 --> 01:39:06.560
included publications in my presentation and I want to thank my team for doing

01:39:06.560 --> 01:39:09.280
the actual work that I get to present and I'll take any questions that you may

01:39:09.280 --> 01:39:15.600
have thank you for your attention. So we're going to leave it at that right yes

01:39:15.600 --> 01:39:24.360
we are it whoops that was a mistake. Hello and welcome to our webinar the

01:39:24.360 --> 01:39:27.160
webinar is entitled does nature always know best the role of regulatory

01:39:27.160 --> 01:39:30.360
T cell epitopes in biologics and vaccines my name is Annie DeGroote I'm

01:39:31.320 --> 01:39:35.400
guess we have to I'll have to watch that one sometime because that's really

01:39:35.400 --> 01:39:38.880
cool too I want to know what they're thinking I want to know what's going on

01:39:38.880 --> 01:39:44.680
here. So if you don't mind I'm just gonna take this chance to present the

01:39:44.680 --> 01:39:51.920
last part of my running slide set right now in a different voice. Ladies and

01:39:51.920 --> 01:39:56.840
gentlemen we have been misled about the potential for pandemics in bad caves

01:39:57.040 --> 01:40:02.080
and we've also been misled about the potential of this pandemic potential

01:40:02.080 --> 01:40:07.960
that can be accessed via cell culture and via animal passage over several

01:40:07.960 --> 01:40:12.920
decades we have been led to believe that this pandemic potential could be

01:40:12.920 --> 01:40:18.240
accessed in a laboratory and even worse you could stitch pieces of potential

01:40:18.240 --> 01:40:21.920
pandemic viruses together and create something that mother nature herself

01:40:21.920 --> 01:40:28.440
never would have and it is this illusion of consensus about the potential for

01:40:28.440 --> 01:40:34.400
pandemics in bad caves and in laboratories that is the new mythology

01:40:34.400 --> 01:40:41.000
with which they intend to govern you and more importantly your children because

01:40:41.000 --> 01:40:45.680
it's different than terrorism it's different than patriotism it's different

01:40:45.680 --> 01:40:51.280
than communism it's different than aliens it affects everyone and it's

01:40:51.320 --> 01:40:58.360
invisible and it's under and you can't investigate it there's nothing that the

01:40:58.360 --> 01:41:04.000
average person can do to disprove this without learning years of molecular

01:41:04.000 --> 01:41:11.800
biology to the extent to which you can see through this but there is a way to

01:41:11.800 --> 01:41:15.280
see through it if you don't bother with the biology you can just bother with the

01:41:15.280 --> 01:41:22.240
numbers and the way to think about it is in 2020 they told you that there was a

01:41:22.240 --> 01:41:31.640
new cause of death a new way of dying for which everyone was vulnerable and that

01:41:31.640 --> 01:41:39.600
new way of dying should have shown up as a spreading respiratory virus and so

01:41:39.600 --> 01:41:45.640
when you say this out loud there's a novel cause of death that's spreading that is

01:41:45.640 --> 01:41:53.680
a certain level of expectation in the data and Denny Rancor has shown us that

01:41:53.680 --> 01:42:00.320
those expectations are never met so the excess deaths are not correlated with

01:42:00.320 --> 01:42:08.600
the spread they are correlated with poverty excess deaths are not correlated

01:42:08.600 --> 01:42:17.440
with spread they are correlated with poverty in America and that's because

01:42:17.440 --> 01:42:26.480
hospital protocols were destroyed the autonomy of a doctor to treat the

01:42:26.480 --> 01:42:33.360
symptoms in front of him or her was destroyed and with a high financial

01:42:33.360 --> 01:42:41.360
incentive and top-down control in hospitals they were able to use a

01:42:41.360 --> 01:42:49.760
nonspecific PCR test to rope an ever larger portion of all cause mortality

01:42:49.760 --> 01:42:57.040
into a new national security priority that they called COVID when in reality

01:42:57.040 --> 01:43:02.040
all they really needed to do was kill a bunch of people with pneumonia and call

01:43:02.040 --> 01:43:08.360
it that that's all they need to do if you need antibiotics for secondary

01:43:08.360 --> 01:43:13.440
pneumonia and you don't get it and instead they test you with PCR and then

01:43:13.440 --> 01:43:20.240
put you on a ventilator and gave you remdesivir you were added to the list and

01:43:20.240 --> 01:43:25.320
in fact with a lot of these people early on there was no remdesivir there was

01:43:25.320 --> 01:43:32.160
just a lack of treatment from not do not resuscitate orders in New York City

01:43:32.160 --> 01:43:38.160
all the way to ventilating people they could talk and these protocols were

01:43:38.160 --> 01:43:44.600
enacted wherever the government or the CDC had sufficient push and we're gonna

01:43:44.600 --> 01:43:48.680
find more and more that the places where these protocols didn't occur there's

01:43:48.680 --> 01:43:54.000
gonna be some administrative reason why one doctor got in the way or several

01:43:54.000 --> 01:44:00.080
nurses got in the way and so the death toll never happened there that's the

01:44:00.080 --> 01:44:05.080
reason why the death toll doesn't cross borders ladies and gentlemen that's the

01:44:05.080 --> 01:44:11.280
reason why why Germany had almost no deaths until the vaccine was rolled out

01:44:11.280 --> 01:44:17.960
no excess deaths ladies and gentlemen they intend a total surrender of

01:44:17.960 --> 01:44:21.480
individual sovereignty and enforcement of a fundamental inversion from basic

01:44:21.480 --> 01:44:26.600
human rights to basic granted permissions and this illusion of consensus is how

01:44:26.600 --> 01:44:30.160
they've done it they have convinced us that there was a novel virus that

01:44:30.160 --> 01:44:33.960
everybody had to act on that the Army RNA saved a lot of people but it could

01:44:33.960 --> 01:44:37.080
have been better and because this was likely again a function virus this will

01:44:37.080 --> 01:44:42.480
definitely happen again and that illusion of consensus I'm calling a scooby-doo

01:44:42.480 --> 01:44:46.840
which is a bunch of teenagers being fooled into solving a mystery that ends

01:44:46.840 --> 01:44:53.560
up to be bad guys with monster masks and so while the entire population is

01:44:53.560 --> 01:44:58.640
afraid of a monster called COVID-19 we are now being tricked into taking the

01:44:58.640 --> 01:45:04.040
masks off of the people who are responsible for that monster instead of

01:45:04.040 --> 01:45:09.040
being told the truth which is the monster could only exist in the context

01:45:09.040 --> 01:45:16.000
of lies lies about your immune system lies about mother nature lies about the

01:45:16.000 --> 01:45:22.600
fidelity of RNA lies about the immune response lies about vaccination lies

01:45:22.600 --> 01:45:30.400
about intramuscular injection lies about everything and in 2020 in 2021 the

01:45:30.400 --> 01:45:38.520
lies were so succinct and so synchronized and so perfect the doctors

01:45:38.520 --> 01:45:42.880
around the world were ventilating people to prevent spread doctors around

01:45:42.880 --> 01:45:49.520
America were using remdesivir with no no bother to read where it had been used

01:45:49.520 --> 01:45:56.040
before no bother to look into how it worked no bother at all just following

01:45:56.040 --> 01:46:02.720
the CDC protocol don't use antibiotics anymore don't use antibiotics anymore on

01:46:02.720 --> 01:46:11.680
a viral disease was something that people on twiv are still saying then they shut

01:46:11.680 --> 01:46:16.840
down schools they masked our little babies and they terrified everybody who

01:46:16.840 --> 01:46:23.400
wasn't sophisticated enough to know they terrified every skilled TV watcher

01:46:23.400 --> 01:46:28.320
with this illusion of consensus and those skilled TV watchers are just now

01:46:28.320 --> 01:46:32.840
thinking that they finally figured out what happened which is that we covered

01:46:32.840 --> 01:46:37.960
up our role in the production of this virus in China and then it got out in

01:46:37.960 --> 01:46:44.800
China tried to cover it up and so did we and the reason why this is so enticing

01:46:44.800 --> 01:46:48.840
this illusion of consensus of a laboratory batcave zoonosis is that that

01:46:48.840 --> 01:46:53.880
means that this potential is there forever that means that we need the who

01:46:53.880 --> 01:47:02.640
and vaccines and viral surveillance and all of this stuff forever and that's what

01:47:02.640 --> 01:47:07.120
I mean by trying to govern your children on this mythology this mythology

01:47:07.120 --> 01:47:12.280
that we defeat viruses with vaccination all the time like Annie DeGroote said in

01:47:12.280 --> 01:47:19.040
that brief piece on the TV channel she said that we have to develop immunity

01:47:19.040 --> 01:47:26.840
with vaccines for this one and novel viruses can jump from species and

01:47:26.840 --> 01:47:32.640
can pandemic be false PCR false positives are rare asymptomatic spread

01:47:32.640 --> 01:47:38.360
was real and they still believe it is real my neighbors still believe it's

01:47:38.360 --> 01:47:45.760
real she tested positive for covid and her husband made her isolate in her

01:47:45.760 --> 01:47:52.640
bedroom for five days and she didn't come out I mean it's people are that they

01:47:52.640 --> 01:47:58.040
believe it and they live right next door to me they were just here for the party

01:47:58.040 --> 01:48:06.320
and they know how we think but they can't snap it they can't turn it off they

01:48:06.320 --> 01:48:11.880
can't stop because they took the test and the test said positive you know it's

01:48:11.880 --> 01:48:16.160
like if your husband took a pregnancy test and it said positive would you

01:48:16.160 --> 01:48:20.800
believe it was right I mean the conflated background signal is the thing you have

01:48:20.800 --> 01:48:24.680
to understand because we don't know what that conflated background signal is is

01:48:24.680 --> 01:48:31.760
it really coronaviruses is it really human RNA is it human RNA sequences see

01:48:31.760 --> 01:48:36.760
the problem with it is is that up until 2020 we knew that coronaviruses had a

01:48:36.760 --> 01:48:42.240
lot of homology with our own proteins and in fact that's what everybody was

01:48:42.240 --> 01:48:45.600
complaining about in the very beginning when we decided to use the spike as the

01:48:45.600 --> 01:48:49.400
transfection because using the spike is the transfection it's got a lot of

01:48:49.400 --> 01:48:54.480
epitopes in it that aren't so not human and so we risk mimicry we risking this

01:48:54.480 --> 01:48:58.680
potential auto immunity there's lots of things in there that we might not want

01:48:59.160 --> 01:49:04.720
platelet factor for a mile in this kind of thing and that's what makes

01:49:04.720 --> 01:49:07.800
epi-backs so interesting is they knew that already they knew that that was

01:49:07.800 --> 01:49:15.120
screenable and so it's not like all this stuff wasn't known Robert Malone was

01:49:15.120 --> 01:49:22.280
working with that company for 15 years no eight years he definitely knew just

01:49:22.280 --> 01:49:29.040
like he knew Peter Colis knew that you can't target an mRNA encased in a lipid

01:49:29.040 --> 01:49:34.680
nanoparticle to a particular part of your body by intramuscular injection so when

01:49:34.680 --> 01:49:41.360
Robert Malone went on Brett Weinstein's podcast in 2021 and said that he thought

01:49:41.360 --> 01:49:47.240
that they probably fixed that problem that's absolute bullshit and there's no

01:49:47.240 --> 01:49:53.240
other word for it that was a bald face lie he knew that Peter Colis had never

01:49:53.240 --> 01:49:57.120
solved that problem and that's Peter Colis knew he never solved that problem

01:49:57.120 --> 01:50:07.880
he had burnt five postdocs on it five postdocs were burnt on trying to get the

01:50:07.880 --> 01:50:15.720
LNP's of Peter Colis to target somewhere and they couldn't even get the real easy

01:50:15.880 --> 01:50:22.840
target like the liver to work very well that's where we're at lies upon lies

01:50:22.840 --> 01:50:28.000
upon lies and some of them are assumptions some of them are dumb when you hear

01:50:28.000 --> 01:50:33.000
Peter Colis say that when that when that Pfizer report came out and they said

01:50:33.000 --> 01:50:38.720
that the vaccine was 95% effective what did Peter Colis do he went and got a

01:50:38.720 --> 01:50:45.240
glass of scotch and started drinking that's exactly what he said I'm not really

01:50:45.240 --> 01:50:50.040
sure what to what to think about this I'm not really sure how to push it I do

01:50:50.040 --> 01:50:52.800
know that my voice needs a little bit of a break though I can feel it a little

01:50:52.800 --> 01:50:55.680
bit in the back so I'm gonna give it a little rest because I did rupture

01:50:55.680 --> 01:50:59.800
something back there so something needs healing I'm sure but there was

01:50:59.800 --> 01:51:04.680
definitely endemic background of something that they misconstrued as the

01:51:04.680 --> 01:51:09.720
novel spread of something else and they use the protocols to fill it to fool us

01:51:10.120 --> 01:51:13.200
and protocols were actually murder not COVID and

01:51:13.200 --> 01:51:18.040
transfection is not medicine now it could have been an infectious clone

01:51:18.040 --> 01:51:22.800
release that's my favorite explanation the only question is how far does the

01:51:22.800 --> 01:51:26.320
clone spread how much did they spread was it really only three people and three

01:51:26.320 --> 01:51:31.800
sequences and and and three seeded narratives or was it really a whole

01:51:31.800 --> 01:51:37.200
hospital of in northern Italy and a whole bunch of people in Wuhan and in Iran I

01:51:37.280 --> 01:51:42.280
don't know but if we actually considered it a crime and actually started to think

01:51:42.280 --> 01:51:46.280
about the plausible biology that's available to these people that's what

01:51:46.280 --> 01:51:50.520
we would be considering an infectious clone release could have been a

01:51:50.520 --> 01:51:54.680
transfection agent as well what's the difference between a transfection agent

01:51:54.680 --> 01:52:01.120
and an infectious clone an infectious clone release is is the mRNA or sorry

01:52:01.120 --> 01:52:08.840
the RNA of a coronavirus which is presumed to be self-replicating so it

01:52:08.840 --> 01:52:14.000
has a RNA dependent RNA polymerase and 30 other accessory proteins which allow

01:52:14.000 --> 01:52:21.080
the assembly of coronavirus particles whereas a transfection agent release

01:52:21.080 --> 01:52:26.280
might have just been mRNA which expresses a spike or expresses an

01:52:26.320 --> 01:52:32.520
immunogen which would produce the the false positives that they needed or

01:52:32.520 --> 01:52:38.040
produce the I can only be really the false positives that they needed so the

01:52:38.040 --> 01:52:41.840
transfection agent might work if you transfected the spike protein and an

01:52:41.840 --> 01:52:45.920
end protein or something like that and then you knew that your PCR was going to

01:52:45.920 --> 01:52:50.040
test for those then a a transfection agent of just the end protein would work

01:52:50.040 --> 01:52:55.080
if your PCR is only going to test for the end protein but I think it depended on

01:52:55.080 --> 01:52:57.880
what country you're in some countries were testing for this bike some were

01:52:57.880 --> 01:53:01.440
doing RNA dependent RNA polymerase some were doing RNA dependent polymerase and

01:53:01.440 --> 01:53:06.680
and I think America now is doing three different ends or something like that so

01:53:06.680 --> 01:53:11.280
it's changed over time but the protocols have always been murder and

01:53:11.280 --> 01:53:16.360
transfection has always not been medicine and the same thing goes for this

01:53:16.360 --> 01:53:20.400
trap we've been working on this one for a long time but I'm pretty sure that the

01:53:20.400 --> 01:53:26.320
no-virus people are gonna vanish into sort of insignificance at some point as

01:53:26.320 --> 01:53:33.000
the the ridiculousness of their stance becomes more and more obvious and the

01:53:33.000 --> 01:53:38.760
way that they will continue to sort of push for is instead of protocols and

01:53:38.760 --> 01:53:47.920
push you know unrelated cursory issues to make you ask the wrong question is the

01:53:47.920 --> 01:53:53.200
best the absolute best way to put it because if your question is are there

01:53:53.200 --> 01:53:58.040
viruses or not you're asking the wrong question because we know that they can

01:53:58.040 --> 01:54:03.560
aerosolize lipid nanoparticles we know that they can aerosolize RNA and DNA we

01:54:03.560 --> 01:54:08.320
know that they can make infectious clones by the leader we know that they can make

01:54:08.320 --> 01:54:16.800
recombinant DNA and recombinant RNA in the laboratory in quantity and so are

01:54:16.800 --> 01:54:21.880
there real viruses in nature is secondary to the fact that so much of the

01:54:21.880 --> 01:54:25.920
technology necessary to make it appear as though there was a pandemic was

01:54:25.920 --> 01:54:31.880
already rare to go and I think the best way to look at the no-virus people is

01:54:31.880 --> 01:54:39.600
just go back to 2020 go back to 2020 and watch a few Andy Kaufman Andrew

01:54:39.600 --> 01:54:45.480
Kaufman lectures and you'll see that I'm totally right because in those

01:54:45.520 --> 01:54:50.360
lectures he doesn't talk about there being no virus he talks about them being

01:54:50.360 --> 01:54:55.880
unable to isolate it he talks about them lying about it and he talks about them

01:54:55.880 --> 01:55:01.560
probably being exosomes and the PCR is just detecting that that the that the

01:55:01.560 --> 01:55:06.200
sequencing reaction is really amplifying amplicons it could easily be parts of

01:55:06.200 --> 01:55:12.480
the human the human genome or the human proteome and if you do that and then

01:55:12.480 --> 01:55:17.120
use a computer program to assemble them into a coronavirus it's not the same

01:55:17.120 --> 01:55:23.320
thing as finding a coronavirus and all of those arguments from 2020 of the no

01:55:23.320 --> 01:55:32.000
virus people are totally 100% right and then they stopped and then they started

01:55:32.000 --> 01:55:39.440
talking about foyas and they started talking exclusively about isolation

01:55:39.440 --> 01:55:45.320
and purification and they made it seem like that was because this is the

01:55:45.320 --> 01:55:52.160
argument this is how you win but it's not that's not how you win that's how you

01:55:52.160 --> 01:55:57.440
stall people out and that's how you get people asked the wrong question I'm

01:55:57.440 --> 01:56:00.800
convinced of it otherwise they would have been saying a long time ago that the

01:56:00.800 --> 01:56:04.720
protocols are murder and transfection is a medicine and we don't think there are

01:56:04.720 --> 01:56:09.000
real viruses but instead they just say we don't think there are real viruses we

01:56:09.000 --> 01:56:14.360
don't want to talk to anybody who doesn't think the same it's ridiculous and so

01:56:14.360 --> 01:56:19.360
in this conflated background signal the PCR test used in 2020 in 2021 were not

01:56:19.360 --> 01:56:23.960
specific for a particular coronavirus the PCR tests have many sources of false

01:56:23.960 --> 01:56:30.680
positives besides high cycle count including human transcripts the respiratory

01:56:30.680 --> 01:56:34.120
disease that leads to a secondary ammonia is a regular cause of death that we

01:56:34.120 --> 01:56:40.720
stopped treating appropriately in 2020 say it again respiratory disease that

01:56:40.720 --> 01:56:44.080
leads to secondary pneumonia is a regular cause of death that we stopped

01:56:44.080 --> 01:56:52.080
treating appropriately in 2020 in 2021 and I have been saying this for years

01:56:52.080 --> 01:56:58.200
years changing the standard protocols for treatment of a general respiratory

01:56:58.200 --> 01:57:01.240
disease that was the primary cause of excess death during the pandemic

01:57:01.240 --> 01:57:04.800
additional harms were also caused by the response that included lockdowns

01:57:04.800 --> 01:57:09.400
etc. Additional arms can also be matched to the use of specific agents

01:57:09.400 --> 01:57:13.360
including Madazlam and Rendezivir. Her early treatment must be considered

01:57:13.360 --> 01:57:18.520
protocol by protocol because no single agent is likely to have been the silver

01:57:18.520 --> 01:57:22.760
bullet for whatever they sprayed distributed or lied about and it certainly

01:57:22.760 --> 01:57:26.040
wasn't going to cure anybody that was being improperly ventilated or treated

01:57:26.040 --> 01:57:30.960
with Remdesivir. We spend money to make sure the danger of zoonotic pandemics

01:57:30.960 --> 01:57:36.200
is taken very seriously that's really the bottom line if we spend money to get

01:57:36.200 --> 01:57:42.240
papers published that that claim pandemic potential and we we fund

01:57:42.240 --> 01:57:46.680
organizations like the Equal Health Alliance to make sure that people think

01:57:46.680 --> 01:57:52.280
that there's pandemic potential and and Nathan Wolf's organization metal

01:57:52.280 --> 01:58:01.200
biota same thing it is the governing forces trying to set up this mythology

01:58:01.200 --> 01:58:08.320
this mythology of a natural pandemic and a man-made one and unavoidable because

01:58:08.320 --> 01:58:14.560
with climate change and our population and deforestation and guess who's one of

01:58:14.560 --> 01:58:24.000
the biggest donors of Equal Health Alliance palm oil companies palm oil

01:58:24.000 --> 01:58:33.680
companies deforestation donating to to organizations that are helping with

01:58:33.680 --> 01:58:42.040
climate change is a great way to launder money for palm oil companies

01:58:42.200 --> 01:58:46.240
ladies and gentlemen you can tell the liars because they're not talking about

01:58:46.240 --> 01:58:49.600
the PCR anymore they're not talking about variants and sequencing fraud

01:58:49.600 --> 01:58:52.160
they're not talking about death certificate fraud that happened from

01:58:52.160 --> 01:58:58.760
California to New York and not talking about purity fraud protein folding

01:58:58.760 --> 01:59:03.240
transfection in general and they forgot completely about natural immunity that

01:59:03.240 --> 01:59:08.000
I was talking about in 2020 so that's how you tell they're doing this for a

01:59:08.000 --> 01:59:11.760
reason because they've got only this chance to get this many people on board

01:59:11.760 --> 01:59:15.680
to brainwash this many people to collect this much data that's really all

01:59:15.680 --> 01:59:20.600
this is about they don't need all of us they need our kids time they've got 30

01:59:20.600 --> 01:59:24.720
years they've got 40 years and in 40 years I might be dead so it's only my

01:59:24.720 --> 01:59:29.720
sons that'll be here and those are the ones they're trying to get not us

01:59:29.720 --> 01:59:33.120
ladies and gentlemen intramuscular injection of any combination of

01:59:33.120 --> 01:59:36.600
substances with the intent of augmenting the immune system is dumb and

01:59:36.600 --> 01:59:42.440
transfection is not immunization please stop all transfections in humans

01:59:42.440 --> 01:59:49.880
full stop full stop please just really stop it stop it all don't do it any more

01:59:49.880 --> 01:59:55.840
please get in there right there yes Mike Vandenberg mixed-day no that's not

01:59:55.840 --> 02:00:01.640
what I did I did this one oh I see

02:00:01.640 --> 02:00:09.080
I don't know what to say ladies and gentlemen I hope that the surprise was

02:00:09.080 --> 02:00:14.040
worth it I hope that you weren't let down we have 137 viewers which is a new

02:00:14.040 --> 02:00:19.040
record for giga-home biological so maybe my voice did something for us ladies and

02:00:19.040 --> 02:00:21.800
gentlemen stop all transfections and humans because they're trying to eliminate

02:00:21.800 --> 02:00:26.160
the control group by any means necessary this has been giga-home biological a

02:00:26.160 --> 02:00:29.840
high-resistance low noise information brief brought to you by a biologist it's

02:00:29.880 --> 02:00:38.320
21st of October 2023 this is day 46 in a run of several hundred so grab your

02:00:38.320 --> 02:00:44.720
popcorn and your hot tea and I will see you again tomorrow ladies and gentlemen

02:00:44.720 --> 02:00:51.480
hopefully the voice will be the same or even deeper yeah I still have a little

02:00:51.480 --> 02:00:54.000
you know I gotta

02:01:00.120 --> 02:01:15.480
thanks a lot Pamela thanks everybody for all your support it's really been

02:01:15.480 --> 02:01:21.280
unbelievable and I don't know what to say other than I'm when I coughed that

02:01:21.280 --> 02:01:25.160
thing up this morning and I spoke to my sons I started crying pretty fast

02:01:25.240 --> 02:01:30.440
afterward because it I've been saying it for a long time if you give me my

02:01:30.440 --> 02:01:36.920
voice back I'm gonna definitely use it and here we are so it's not a matter of

02:01:36.920 --> 02:01:40.280
what is true the counts but a matter of what is perceived to be true ladies and

02:01:40.280 --> 02:01:45.880
gentlemen so let's start teaching biology share this stream please ladies and

02:01:45.880 --> 02:01:49.680
gentlemen share my work that's the best way to support it I love you guys see

02:01:49.680 --> 02:01:51.960
tomorrow

02:01:55.160 --> 02:01:57.220
you

