Pfizer's "vaccine design" and superantigens

Hidden things inside antigens

Another rather spur-of-the-moment substack, although the entire basis of it has been laying in pen and paper for a little while. First a little bit of history, and a little bit of science, and this is a topic I have both hinted at and must cover before I write about the last piece of my side of the puzzle.

Also, full disclosure, I want to thank the user

vdr’s Newsletter

(more active on Twitter) for a few questions, and in the process of answering the questions, led me to many answers that I have been looking for.

First, what are superantigens ?

Antigens are like the connecting part of Lego blocks, and if one of these Lego blocks is not recognized by your immune cells, it starts an immune reaction to remove all the blocks that created that particular foreign Lego block. So, antigens are like the unique Lego blocks that the immune system uses to identify and respond to invaders in the body.

Superantigens, as the name implies are “super Lego blocks”, unlike traditional antigens, which interact with a small subset of T cells through the conventional antigen-presenting cell pathway, superantigens can activate a large proportion of T cells simultaneously. This indiscriminate activation leads to an overwhelming immune reaction characterized by the release of excessive amounts of inflammatory cytokines. Consequently, the immune system's balance is disrupted, resulting in symptoms ranging from fever and shock to organ failure.

A particular not widely known fact from the field of immunology and vaccine research is the hidden, decades-long holy grail of vaccine design. Superantigens.

Just a few examples

Using superantigens in vaccine development has two main goals. The first, proposed many years ago was using said superantigens to “immunize” the population, the goal is avoiding the strong, often damaging immune response, and some of these responses can lead to TSS (Toxic Shock Syndrome). The secondary goal, a fairly more complex is vaccine design, meaning using the superantigen as an “adjuvant” to create a potent immune response against whatever you are designing a vaccine against, or otherwise using it together with other proteins that have a poor immunogenic profile, that you need immunity against but in “vaccine form” fail to do so.

The following is a massive point of contention between the many different types of experts researching SARS-CoV-2, especially when it delves into the origins conversation, but this is important before going to the “history” part of this piece. The Furin Cleavage Site (FCS), the specific “part” of the virus that enabled it to infect humans so well, so fast, and so aggressively has the hallmarks of mimicking a well-known and researched superantigen, the Staphylococcus Enterotoxin B (SEB). The contentious paper Superantigenic character of an insert unique to SARS-CoV-2 spike supported by skewed TCR repertoire in patients with hyperinflammation.

The origins of the FCS are inconsequential to its biological effects, and while there is a small room for argument on how “valid” the comparison of SEB towards the specific sequence and structure highlighted by the researchers, two verifiable facts remain.

• A monoclonal antibody against SEB binds to the region and inhibits SARS-CoV-2

• Together with a few other “parts” of the Spike Protein, the FCS is effectively a magnet toward LPS (Endotoxin)

The Spike Protein can also interact, and “bursts” biofilms (little houses bacteria build inside us, thus guaranteeing many of these interactions and cascade effects. An argument by a few highly gifted molecular biologists was that the FCS is in fact a immunogenic target, something to make your body “attack” a protein or pathogen and create a immune response. The following image speaks for itself.

To summarize for the layperson. Superantigens make your immune system go into turbo mode, causing massive damage. Superantigens are also a target for vaccine development. FCS looks like and behaves like a Superantigen, it is an amazing target for the immune system under specific circumstances. FCS binds to LPS. LPS is used as a potentiating agent for SEB in research. Thus leading to the title in question.

Before going further another explanation. An algorithm is a set of instructions, from simple to incredibly complex, where a computer/program will do what you ask. You give it data, and if you give the same data 10 times, you will get the same result 10 times. An AI is adaptable, it can contextualize, it can change “midway”, and it can learn. This was heavily simplified, but the distinction here is necessary.

A specific news clip that my brain instantly committed to memory was an early interview from Pfizer’s CEO, in a moment where the interviewer asked how Pfizer came up with the vaccine so incredibly fast, Bourla replied: “We used an algorithm, and after some time the machine gave us the result”. I tried very hard to find the clip, but couldn’t and can’t spend 6-12 hours going through hundreds of useless videos, but I did find something “better”.

Source

Pfizer Discusses Use of Supercomputing and AI for Covid Drug Development

Over 16 months ago, Pfizer achieved a historic scientific moonshot — the unprecedentedly swift development and authorization of a novel vaccine for a novel virus using methods that hitherto had not been used in approved drugs at scale. Throughout the pandemic, nearly every public research supercomputer pivoted to some form of Covid research, but the pharmaceutical giants were characteristically cagey about their use of advanced technologies for vaccine and therapeutic development.

“Pfizer is applying digital data and AI across the entire value chain,” Fonseca said, “making our work faster and easier and enhancing every aspect of our business. We’re driving this end-to-end innovation with three strategic priorities in mind: first, to improve patient health outcomes; second, to bring medicines to patients faster; and third, to fuel tomorrow’s breakthrough therapies.”

“In research and discovery, we leveraged supercomputing, AI and machine learning to accelerate the identification of the most promising target compounds…”

The following quote comes from this Mckinsey article, both of the article above and the one below are worth reading. This last one is more of an observation and extrapolation case study, you can deduce how Pfizer and many other big pharmaceutical companies hedged their bets and acquired so many “peculiar” biotech companies to treat“rare” conditions.

Siddhartha Chadha: What difference did digital make when Pfizer faced the urgent need to develop a COVID-19 vaccine?

Lidia Fonseca: Simply put, digital enabled us to develop a safe, effective vaccine in record time, without cutting corners.

I will leave the arguments for or against Algocracy (a state we definitely live on, and have for years now) for another time or better writers, but the point I am making here, and a question people from all walks of life, from the average person to the some of the most talented scientists had was “Why they chose the Spike Protein as a vaccine target ?”

Simply put, their algorithms were either trained on insufficient data or badly designed from the group up, perhaps both at that point in time, chose the Spike precisely because depending on how you design your software or algo, the Furin Cleavage Site lights up like a Christiams tree. Until ChpatGPT 3.5 and 4, most AI was not that sophisticated, in the near future such systems will be defined as either “sophisticated algorithms” or “very rudimentary AI tools”, and as such the software picked up the Spike Protein because of that. (or it was DARPA’s ADEPT software ? A conspiracy contemplation for your Friday).

As a matter of fact, to demonstrate how powerful properly designed algorithm, especially aided by properly trained AI, can be, here is a Nature paper published by Chinese scientists, with the help of the Baidu AI department (Baidu being one of the biggest tech companies in China, focused on AI now).

Algorithm for Optimized mRNA Design Improves Stability and Immunogenicity

On both COVID-19 and varicella-zoster virus mRNA vaccines, LinearDesign substantially improves mRNA half-life and protein expression, and dramatically increases antibody titer by up to 128× in vivo, compared to the codon-optimization benchmark. This surprising result reveals the great potential of principled mRNA design, and enables the exploration of previously unreachable but highly stable and efficient designs. Our work is a timely tool not only for vaccines but also for mRNA medicine encoding all therapeutic proteins (e.g., monoclonal antibodies and anti-cancer drugs (7, 8)).

This shorter article serves a few purposes. The first, explaining what SAgs (Superantigens) are, and why they are powerful/useful, possessing this cursory understanding before delving deeper into a more complex picture is necessary. Second, how badly trained or designed AI can backfire monumentally, such is the case for the mRNA vaccines. Lastly, the intricate relationship between the Spike, FCS, and toxins, because they will play a role when I refer back to SAgs. Except for the “Pfizer” part of this conversation, all the information here is necessary, and I am aware I am repeating myself =P.

A hint for my future writings.

If you use the search tool on Substack website (not present in the app) you can find the other 3 times I mentioned superantigens, with 2 being roughly a year ago and very significant.

Also to be abundantly clear, I will never touch an mRNA product in my lifestyle unless I or people I trust designed it from the ground up.

I am thankful for the people who chose to support my work and everyone who reads it.