WEBVTT 12:03.000 --> 12:09.420 combining to form microclots, that's all I'm totally down with that. But to then 12:09.420 --> 12:17.380 jump to the stage where that explanation now covers all of the problems with 12:17.380 --> 12:23.460 vaccines, it's another problem with vaccines. And I think that's the that's 12:23.460 --> 12:28.020 the that's the nuance that everybody needs to constantly be vigilant of 12:28.020 --> 12:32.900 because all all of the people that are trying to trip us up for sure are 12:32.900 --> 12:39.300 gonna give us easy explanations which replace other explanations. And that is 12:39.300 --> 12:43.500 a temptation that you cannot jump to because it's just like the DNA 12:43.500 --> 12:48.540 contamination of the RNA shot is now tempting to jump to that as the 12:48.540 --> 12:54.660 explanation where in reality we have this whole toolbox of of attributes of 12:54.660 --> 12:58.860 transfection which are bad and then the quality control and then this and then 12:59.340 --> 13:06.220 they all add together and the trick is to not let any one of us any one of 13:06.220 --> 13:10.100 these these explanations start to dominate to the point where all of the 13:10.100 --> 13:15.900 other impacts which we know are real are diluted or lost and that's basically 13:15.900 --> 13:19.500 what they're trying to do with the with the protocols right they're trying to 13:19.500 --> 13:23.580 tell stories and stories and stories so that the protocols go farther and farther 13:23.580 --> 13:30.060 into the past so that the rising number of opioid deaths from 2020 to 2021 to 13:30.060 --> 13:36.100 2022 go farther and farther in the past that's what they want and so that's why 13:36.100 --> 13:41.700 we have to be very vigilant and and work constantly to to update this list and 13:41.700 --> 13:47.060 not let the list be changed but rather just added to and that that was 13:47.060 --> 13:51.820 especially true with regard to what Kevin McCurn and I believe has been 13:51.860 --> 13:58.940 basically up to is he's populating this list but then he's he's not reciting it 13:58.940 --> 14:03.300 anymore and so if he if he has all this publicity has all this chance to tell 14:03.300 --> 14:07.660 people how bad transfection is he's definitely not taking advantage of it 14:07.660 --> 14:12.100 and instead he's focused on the latest greatest thing which is this CD and A 14:12.100 --> 14:16.340 contamination which of course is a very different objection than what he was 14:16.340 --> 14:22.820 making in 2020 in 2021 in 2022 and it's really important that we pay 14:22.820 --> 14:28.980 attention to these people because it's over time it's it's very hard to lie 14:28.980 --> 14:34.540 consistently and you can be like Mark and you can have made mistakes you can be 14:34.540 --> 14:39.380 like Mark and have sort of followed false leads or chased things that didn't 14:39.460 --> 14:47.100 need to be chased anymore but if he's correcting himself and he's he's if he's 14:47.100 --> 14:52.180 reevaluating where he stands like I try to do and like others try to do then 14:52.180 --> 14:56.180 then there's nothing wrong with playing a few notes as long as you're playing 14:56.180 --> 15:03.820 with passion and with the idea that that you want to get better and so you know 15:03.820 --> 15:07.620 it feels really good but I'm gonna try and take a little easy on my throat just 15:07.620 --> 15:12.060 to make sure I don't go nuts because I'm really excited and and I want to just 15:12.060 --> 15:19.620 talk all day but we're gonna go a little slower here so the show must go on and 15:19.620 --> 15:29.220 we did a number of shows recently about about companies related to Canadian 15:29.220 --> 15:37.620 biosciences and it's become really increasingly curious to me how so much 15:37.620 --> 15:41.100 of this story is just crossed that border and how much of that story 15:41.100 --> 15:44.940 actually was already across the border at the very early parts of the pandemic 15:44.940 --> 15:51.580 with this with this researcher who a Chinese researcher who was working on Z 15:51.580 --> 15:56.100 map and had this antibody cocktail that was really good and was on the front of 15:56.140 --> 16:03.660 time and and then accused of being a spy and stealing and disappearing and it 16:03.660 --> 16:08.580 wasn't really clear what it was about this lady that was weird but Mark was 16:08.580 --> 16:13.260 one of the first people to really point out that this Z map was this antibody 16:13.260 --> 16:19.420 cocktail that might have been useful for this or or at least wasn't 16:19.420 --> 16:23.780 antibody cocktail that had been previously compared to remdesivir and so 16:23.780 --> 16:29.980 there was reason to believe that that this Z map would would somehow muddy the 16:29.980 --> 16:35.540 waters around remdesivir and maybe it needed to disappear anyway we have more 16:35.540 --> 16:41.780 recently crossed the border into Canada with regards to Peter colas and Peter 16:41.780 --> 16:47.180 colas brought us to Canada because number one he's the guy who apparently is 16:47.180 --> 16:50.600 responsible for the intellectual property of the lipid nanoparticles 16:50.600 --> 16:55.760 that are now used for all of these gene therapies and vaccines I shouldn't 16:55.760 --> 17:02.200 call them vaccines and transfections but more importantly Robert Malone was the 17:02.200 --> 17:08.600 one who actually pointed us to him in one of his sub-stacks which was being 17:08.600 --> 17:14.000 quite critical of the awarding of the Nobel Prize and said that you know if 17:14.000 --> 17:20.800 anybody had and he used the patent law language enabling technology it would 17:20.800 --> 17:28.080 be Peter colas and so in my offline discussions with my friend regular guy 17:28.080 --> 17:34.120 it became clear to him in his eyes that the language that Robert Malone had 17:34.120 --> 17:39.120 been using in that article was really intellectual property law type language 17:39.120 --> 17:43.320 when you say enabling technology in regards to a patent you really talking 17:43.320 --> 17:51.520 about patent law and in that sense it seemed almost as if the the idea of the 17:51.520 --> 17:56.040 intellectual property that was being you know acknowledged with the MR of the 17:56.040 --> 18:01.800 mRNA with the Nobel Prize that intellectual property may not be as solid 18:01.800 --> 18:09.200 as the award might make it seem and the reason why is because the enabling 18:09.200 --> 18:14.240 technology in Robert Malone's mind wasn't the chemical alterations of the 18:14.240 --> 18:18.760 mRNA but it was the lipid nanoparticles that allowed it so it's an interesting 18:18.760 --> 18:24.600 assertion because remember and I'm just breaking this down with this on screen 18:24.600 --> 18:28.560 I don't really know why maybe you can you can copy this video that we're gonna 18:28.560 --> 18:37.640 watch here but it's interesting because up until that statement it should be 18:37.640 --> 18:45.720 very clear to you that it was his it was his idea of using RNA as a vaccine 18:45.720 --> 18:53.840 and all of the he had like eight patents around this so as he first came out in 18:53.840 --> 19:01.840 2021 remember part of the reason why he came out was to claim in mentorship but 19:02.200 --> 19:08.320 now after the Nobel Prize is awarded it's almost a change in position if you 19:08.320 --> 19:14.800 think that well in 2021 I was the one who came up with all the relevant ideas for 19:14.800 --> 19:20.320 using RNA as a vaccine and then when the Nobel Prize is awarded you say well 19:20.320 --> 19:24.480 actually what they did isn't that impressive because the enabling 19:24.480 --> 19:31.680 technology is is Peter Kullis's technology so it's a very very 19:31.680 --> 19:36.240 interesting pivot that actually is not consistent with his his earlier three 19:36.240 --> 19:44.120 years of behavior and so you can only see this as a continuing evolving op or 19:44.120 --> 19:49.560 continuing evolving construct it's not just somebody struggling to tell the 19:49.560 --> 19:54.080 truth this is a person who is adjusting the narrative and adjusting what he 19:54.120 --> 19:59.600 says in order to keep aiming at whatever goal they're aimed at and I think that 19:59.600 --> 20:05.960 that's how you should start to see most of the people's changing behavior is if 20:05.960 --> 20:10.120 you think about what their goal was for the entire three years that I've been 20:10.120 --> 20:15.360 online my goal has been to free my children from this illusion into free our 20:15.360 --> 20:20.720 society from this illusion and that's not really the goal of a lot of other 20:20.760 --> 20:29.080 people that let's say compete for this this kind of airtime I mean Kevin McCarran 20:29.080 --> 20:36.000 has never been about stopping the thing Kevin McCarran has never been about 20:36.000 --> 20:41.560 breaking any illusions that are around it's been about maximum fear and 20:41.560 --> 20:47.160 confusion and really Kevin McCarran has never been about stopping anything he's 20:47.200 --> 20:53.720 never said that transfection and kids would be crazy and the list really goes 20:53.720 --> 21:00.280 on quite long how many people haven't said really anything useful about what 21:00.280 --> 21:05.880 about what should be done with regard to this technology and they certainly 21:05.880 --> 21:10.920 didn't say it with any in any useful length of time and so here we are this 21:10.920 --> 21:18.200 is the epivax presentation from January 26th 2021 so put that into your time 21:18.200 --> 21:24.400 frame we are about to roll out the we had already rolled out the vaccines in the 21:24.400 --> 21:31.400 UK so the the the J&J and the AstraZeneca are already out and we're about to roll 21:31.400 --> 21:37.640 out the mRNA and epivax is also trying to put together a vaccine candidate and 21:38.120 --> 21:43.840 and they were actually working with crazy enough the University of Seattle I 21:43.840 --> 21:49.320 believe or University why I don't know which one it is Mark would know where a 21:49.320 --> 21:55.560 guy by the name of Biesler was working and Biesler also surprisingly had a 21:55.560 --> 22:03.160 three-dimensional model of the spike like in in February or January of 2020 he had 22:03.160 --> 22:10.120 a candidate vaccine ready to go based on the spike in a nanoparticle and that's 22:10.120 --> 22:17.240 also where Sohomish County man just happened to be so epivax is a company 22:17.240 --> 22:23.440 who for whom Robert Malone was a consultant for a very long time from 22:23.440 --> 22:30.640 2005 to 2018 or something like this and epivax is also a a company that has 22:30.640 --> 22:40.320 worked with Abcellular which is this this 22:40.480 --> 22:45.760 which is this company in Canada that is developing antibodies or finding them 22:45.760 --> 22:50.680 with this new technology that we watched yesterday so I want to watch this 22:50.680 --> 22:56.720 video because in this video they're going to discuss the methodological 22:56.800 --> 23:04.480 technology that is epivax and I think it's really Mark sent me a video this 23:04.480 --> 23:09.120 morning from was it eight years ago or something like that where this lady is 23:09.120 --> 23:15.000 also talking about this technology on a news program and I thought to watch that 23:15.000 --> 23:18.200 one now but actually I'm gonna watch that one with Mark sometime and invite him 23:18.200 --> 23:22.040 to watch it because it's an older one and she makes some crazy references this 23:22.040 --> 23:24.920 one has a lot more biology and it's I thought it'd be more appropriate for a 23:24.920 --> 23:27.880 study hall and I can take some notes and then give my voice a little bit of 23:27.880 --> 23:32.520 rest in between but not that it really needs rest the only reason why sometimes 23:32.520 --> 23:37.400 sound doesn't come out I think is because there's that flap of skin or 23:37.400 --> 23:42.880 tissue or whatever it was that was full of blood and then it emptied so it's 23:42.880 --> 23:50.480 kind of still there but I mean I don't I don't hear it sometimes but it's not 23:50.480 --> 23:55.960 like it was I could never do that yesterday I mean it's it's crazy how 23:55.960 --> 24:04.960 happy I am to have my voice back so let's see if it's here yes it is great 24:10.640 --> 24:16.160 thank you guys for joining me tonight I know that I am the CEO and CSO heavy 24:16.240 --> 24:20.360 acts incorporated and I'll be talking to you today about prediction and 24:20.360 --> 24:25.040 validation of immunogenicity and tolerance to generic peptides and 24:25.040 --> 24:35.600 impurities clearly this refers to the FDA draft guidance in which the FDA 24:35.600 --> 24:42.920 requested sponsors to identify impurities in their generic drug products 24:43.000 --> 24:49.200 that were above a certain percentage of the final drug product and and their 24:49.200 --> 24:57.440 request and validation sorry I missed the title too 24:57.440 --> 25:02.800 hey everyone this is Annie DeGroote I am the CEO and CSO heavy-vax 25:02.800 --> 25:07.640 incorporated and I'll be talking to you today about prediction and validation of 25:07.640 --> 25:17.560 immunogenicity and tolerance to generic peptides and impurities clearly this 25:17.560 --> 25:25.640 refers to the FDA draft guidance in which the FDA requested sponsors to identify 25:25.640 --> 25:31.920 impurities in their generic drug products that were above a certain 25:32.000 --> 25:37.360 percentage of the final drug product and and their request related to the 25:37.360 --> 25:42.600 identification of T cell epitopes shown in this slide that could be present in 25:42.600 --> 25:48.640 the impurities in the drug product it turns out that we've been working in the 25:48.640 --> 25:54.960 field of immunogenicity risk assessment focusing on peptides and 25:54.960 --> 26:01.080 proteins and their T cell epitopes for the last 20 years so we developed a 26:01.080 --> 26:06.000 comprehensive program for assessing the immunogenicity risk of peptide 26:06.000 --> 26:11.120 impurities building on decades of experience with biologics this program 26:11.120 --> 26:17.760 is called the panda program for peptide accelerated new drug application no and 26:17.760 --> 26:23.280 one of the things that I want to point out here is that this T cell epitope thing 26:23.280 --> 26:28.280 that she's speaking about is actually very serious the reason why I think it's 26:28.320 --> 26:33.960 serious is because in 2020 late 2020 when people were still screaming about the 26:33.960 --> 26:42.920 spike protein being a designed protein and I was still playing along it really 26:42.920 --> 26:51.680 felt like to me that one possibility was that they lied about the virus but they 26:51.680 --> 26:57.000 they lied to the extent to which they actually lied about the spike and so 26:57.000 --> 27:02.440 the spike was a designed spike on purpose with the idea that okay we're 27:02.440 --> 27:06.840 gonna lie about this virus but then we're gonna say that it has this spike and 27:06.840 --> 27:12.080 this spike protein we're gonna use it for the vaccine and why because we've 27:12.080 --> 27:18.560 already altered it we've already added a couple let's say universal T cell 27:18.560 --> 27:23.840 epitopes so that we know that this spike protein will generate antibodies and 27:23.880 --> 27:29.240 more importantly we can predict with a reasonably high accuracy what antibodies 27:29.240 --> 27:34.120 they will produce or at least what epitopes they will target and so then 27:34.120 --> 27:39.520 all you really need to know is how that epitope will be evaluated and 27:39.520 --> 27:44.800 represented given the different HLA subtypes of a particular person and 27:44.800 --> 27:50.840 that's what she's talking about for 20 years they've been working on finding the 27:50.840 --> 27:58.520 T cell epitopes that are chosen by your immune system when exposed to 27:58.520 --> 28:06.600 proteins and trying to see if there's some pattern based on the HLA receptors 28:06.600 --> 28:14.960 of the animal or the human what is the pattern of T cell epitope selection and 28:14.960 --> 28:19.560 is that pattern predictable and she's been working on it for a very long time 28:19.560 --> 28:26.580 now the reason why I find it enticing is because before I got to clones and 28:26.580 --> 28:31.080 swarm and all this other stuff and I was still really playing along with the idea 28:31.080 --> 28:34.280 that it was a lab leak and something is spreading around which again is still a 28:34.280 --> 28:41.520 possibility that the idea would be to lie about the sequence so that you could 28:41.520 --> 28:51.720 get your guaranteed shoe in immunogen into the vaccine and so you could imagine 28:51.720 --> 28:56.400 this story where you everybody insists that this spike bro spike bro spike 28:56.400 --> 29:01.960 protein all the time and all these people are laughing because while the spike 29:01.960 --> 29:09.400 protein is being portrayed as this evil evil protein it's really not it's a 29:09.400 --> 29:14.760 design protein guaranteed to produce seroprevalence and even more importantly 29:14.760 --> 29:22.960 it could be guaranteed to produce seroprevalence on many serial exposures 29:22.960 --> 29:28.200 because it's a T cell epitope that in theory the immune system would have a 29:28.200 --> 29:35.440 harder time ignoring it and that's the product that's the that's the product 29:35.440 --> 29:39.840 design from a vaccine designer's perspective that's the product design 29:39.840 --> 29:46.560 you want because the NIH says that antibodies are the indication of immunity 29:46.560 --> 29:49.880 and the antibodies are how they're going to measure the effectiveness of your 29:49.880 --> 29:56.280 product and so if you have a product where you can you can administer it 29:56.280 --> 30:01.120 multiple times and every time you get a good robust antibody response it's the 30:01.200 --> 30:08.600 ideal vaccine and so what she has been working on with this company basically 30:08.600 --> 30:15.640 for 20 years is that this ability to hijack what the T cells are doing and 30:15.640 --> 30:22.640 force them to react and it's doing it by selecting these epitopes out of the 30:22.640 --> 30:26.520 natural proteins and looking at the responses and screening them and using 30:26.520 --> 30:33.120 computational approaches to narrow down see it's still there to the proteins 30:33.120 --> 30:38.760 that will that will produce this I'm sure it's just scar tissue that's got to 30:38.760 --> 30:44.480 work his way out now I mean maybe my voice will get even lower peptide 30:44.480 --> 30:51.000 accelerated new drug application and the methods that I'm going to describe 30:51.000 --> 30:56.440 today are published in this recent compendium of methods in immunogenicity 30:56.440 --> 31:03.600 risk assessment that was written by a group of members of the APS therapeutic 31:03.600 --> 31:10.000 protein immunogenicity group so please refer to this article for additional 31:10.000 --> 31:14.720 details on the assays and in silico tools that I'll be talking about today 31:14.720 --> 31:21.200 members of the team that work with me include Brian Roberts who's the associate 31:21.200 --> 31:26.240 scientific director and manager of the protein therapeutics program at Epivax 31:26.240 --> 31:30.880 Francis Terry who is the director of analysis and immuno informatics at 31:30.880 --> 31:35.200 epivax and Bill Martin who is co-founder of epivax and the architect of our 31:35.200 --> 31:41.240 in silico tools of course I would be remiss not to mention the epivax panda 31:41.240 --> 31:45.240 team members who work in the laboratory to do the validation of the in silico 31:45.240 --> 31:51.000 predictions that we make and they are listed here on this slide my talk will be 31:51.000 --> 31:56.760 divided in four parts first why do immunogenicity screening peptides what 31:56.760 --> 32:01.080 what is it about these impurities that could generate a T cell response how to 32:01.080 --> 32:06.520 do it in silico and in vitro and I'll be describing the special tools that we 32:06.520 --> 32:11.040 have that evaluate tolerance and help find T reg epitopes I will also be 32:11.040 --> 32:17.080 describing some case studies that we recent T regulatory epitopes boy oh boy 32:17.120 --> 32:21.920 they're really they're doing it all like this is exactly what before I knew 32:21.920 --> 32:28.840 that's company existed I would have said that you need to be working on and so now 32:28.840 --> 32:34.920 you just imagine just imagine remember Robert Malone has been consulting with 32:34.920 --> 32:40.520 all of these companies that are in a position like absolera to make 32:40.520 --> 32:46.400 antibodies with mRNA she is in a position to make small peptides with 32:46.400 --> 32:56.680 mRNA so it's all kind of the same next generation game and now I might be going 32:56.680 --> 33:00.720 out on a limb here when I say this but it almost seems like that's the whole game 33:00.720 --> 33:07.560 that was the whole game all they have to do is preserve the national security 33:07.560 --> 33:12.600 priority of vaccinating against covid and then all they got to do is make the 33:12.600 --> 33:19.120 first generation of covid vaccines kind of lame and now guess what we need new 33:19.120 --> 33:24.040 vaccines for covid and those new vaccines will be much better because we 33:24.040 --> 33:29.080 won't use this silly spike protein we'll just use a few T cell epitopes or we'll 33:29.080 --> 33:36.480 we'll just make the antibodies and so after four years of brainwashing 33:36.480 --> 33:41.200 everybody or 40 years of brainwashing everybody the antibodies are important 33:41.680 --> 33:45.440 they've finally gotten around to saying well we'll just find the antibody and 33:45.440 --> 33:51.400 make the RNA and code for it that's not gonna work there's lots of reasons why 33:51.400 --> 33:54.840 that's not gonna work and we'll talk about that over the next weeks but I'm 33:54.840 --> 34:00.120 just trying to let you know that this sounds like reasonable immunology it 34:00.120 --> 34:06.560 sounds like really smack on point immunology that you we should have we 34:06.560 --> 34:11.560 should have been hearing so now the question I'm just burping from drinking 34:11.560 --> 34:21.440 too much if the question is how deceptive is she gonna be about antibodies and 34:21.440 --> 34:27.800 T cells how deceptive is she gonna be about the order in which those memories 34:27.800 --> 34:33.400 are made I think she's gonna have to do some hand waving here because she can't 34:33.400 --> 34:37.640 be too aggressive and say that actually the whole immune system is T cells and 34:37.640 --> 34:43.440 antibodies are dumb she can't say that but I'm sure that when they're in their 34:43.440 --> 34:46.680 own offices that's what they're always thinking they're always thinking that 34:46.680 --> 34:50.640 well yeah but anybody's come from T cells I mean they they require T cell 34:50.640 --> 34:54.040 help and all this other stuff and T regulatory cells to down like it I mean 34:54.040 --> 34:58.120 she obviously knows that so it's gonna be a good talk it's only 30 minutes long 34:58.120 --> 35:02.720 I'll try not to interrupt it anymore we completed under an FDA contract on 35:02.720 --> 35:07.440 calcitonin and terraperitide impurities and we'll show you some of the 35:07.440 --> 35:14.200 interesting findings that we uncovered while doing those projects now I'm gonna 35:14.200 --> 35:19.920 be primarily focusing on in silico so I do want to go over how a peptide drug 35:19.920 --> 35:24.880 impurity can potentially be presented to a T cell so that's illustrated in this 35:24.880 --> 35:29.320 slide here you can see a peptide drug which could be up to 50 amino acids in 35:29.320 --> 35:34.720 length and a peptide impurity which contains a modification to the amino acid 35:34.720 --> 35:40.840 sequence notice that this impurity is shorter mainly because in the antigen 35:40.840 --> 35:45.640 presenting cell it will be processed down to the length that will be presented on 35:45.640 --> 35:50.720 the surface of the HLA binding molecule on the surface of the antigen presenting 35:50.720 --> 35:56.220 cell the these so I'm I gotta say I'm actually confused because they're talking 35:56.220 --> 36:00.300 about impurities so that's I guess you produce proteins and there's a certain 36:00.300 --> 36:05.140 amount of impurities and so what she's trying to figure out how these impurities 36:05.140 --> 36:11.260 cause T cell activation this is like bait and switch and what she's trying to 36:11.260 --> 36:17.660 explain and what she's trying to talk around I'm sure of it is that T cell 36:17.660 --> 36:22.220 epitopes are only about nine amino acids long and so these very short 36:22.300 --> 36:27.260 proteins are actually extremely dangerous because they have a propensity to 36:27.260 --> 36:34.140 easily be presented as antigens unlike the larger proteins which need to be 36:34.140 --> 36:38.820 brought in and then process down to smaller fragments and then that process 36:38.820 --> 36:46.140 and this is the trick that process is the responsibility of this mature antigen 36:46.140 --> 36:52.140 presenting cell it's a process that we don't understand and so the larger 36:52.140 --> 37:00.300 protein goes in that's this one and it gets it gets processed by the antigen 37:00.300 --> 37:05.220 presenting cell and it's something we don't understand what she has noticed 37:05.220 --> 37:10.740 and pharmacological producers of biologics have noticed is that the 37:11.100 --> 37:16.860 impurities the small peptides are the ones that tend to generate the immune 37:16.860 --> 37:21.180 response and that's because of this and the way that the processing in a in a 37:21.180 --> 37:28.180 mature antigen presenting cell works if you don't give it anything to break down 37:28.180 --> 37:34.540 then there's nothing really to do but present and I'm not speaking from knowledge 37:34.860 --> 37:41.500 here I'm I'm imagining based on what I understand of antigen presentation and 37:41.500 --> 37:48.580 the the sorting of an antigen to the epitope that you present to the T cells 37:48.580 --> 37:54.140 and that selection process is extremely important in what she and her epivax 37:54.140 --> 37:58.940 company have been doing over the last decades is trying to figure that process 37:58.940 --> 38:06.460 out what parts of peptides are regularly chosen as T cell epitopes and how does 38:06.460 --> 38:13.580 that interplay connect with HLA diversity HLA being the MHC receptor that is used 38:13.580 --> 38:22.100 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