AGI Significance Paradox: As progress accelerates towards AGI, the number of people who realize the significance of each new breakthrough decreases.
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#DDDD $LBPS $LOAC
A thread on the potential near term catalysts behind why I have increased my position in 4d Pharma @4dpharmaplc (LON: #DDDD):
1) NASDAQ listing. This is the most obvious.
The idea behind this is that the huge pool of capital and institutional interest in the NASDAQ will enable a higher per-share valuation for #DDDD than was achievable in the UK.
Comparators to @4dpharmaplc #DDDD (market capitalisation £150m) on the NASDAQ and their market capitalisation:
Seres Therapeutics: $2.33bn = £1.72bn (has had a successful phase 3 C. difficile trial); from my previous research (below) the chance of #DDDD achieving this at least once is at least
Kaleido Biosciences: $347m = £256m. 4 products under consideration, compared to #DDDD's potential 16. When you view @4dpharmaplc's 1000+ patents and AI-driven MicroRx platform (not to mention their end-to-end manufacturing capability), 4d's undervaluation is clear.
A thread on the potential near term catalysts behind why I have increased my position in 4d Pharma @4dpharmaplc (LON: #DDDD):
1) NASDAQ listing. This is the most obvious.
The idea behind this is that the huge pool of capital and institutional interest in the NASDAQ will enable a higher per-share valuation for #DDDD than was achievable in the UK.
Comparators to @4dpharmaplc #DDDD (market capitalisation £150m) on the NASDAQ and their market capitalisation:
Seres Therapeutics: $2.33bn = £1.72bn (has had a successful phase 3 C. difficile trial); from my previous research (below) the chance of #DDDD achieving this at least once is at least
While looking at speculative pharmaceutical stocks I am reminded of why I am averse to these risky picks.#DDDD was compelling enough, though, to break this rule. The 10+ treatments under trial, industry-leading IP portfolio, and comparable undervaluation are inescapable.
— Shrey Srivastava (@BlogShrey) December 16, 2020
Kaleido Biosciences: $347m = £256m. 4 products under consideration, compared to #DDDD's potential 16. When you view @4dpharmaplc's 1000+ patents and AI-driven MicroRx platform (not to mention their end-to-end manufacturing capability), 4d's undervaluation is clear.
Hard agree. And if this is useful, let me share something that often gets omitted (not by @kakape).
Variants always emerge, & are not good or bad, but expected. The challenge is figuring out which variants are bad, and that can't be done with sequence alone.
You can't just look at a sequence and say, "Aha! A mutation in spike. This must be more transmissible or can evade antibody neutralization." Sure, we can use computational models to try and predict the functional consequence of a given mutation, but models are often wrong.
The virus acquires mutations randomly every time it replicates. Many mutations don't change the virus at all. Others may change it in a way that have no consequences for human transmission or disease. But you can't tell just looking at sequence alone.
In order to determine the functional impact of a mutation, you need to actually do experiments. You can look at some effects in cell culture, but to address questions relating to transmission or disease, you have to use animal models.
The reason people were concerned initially about B.1.1.7 is because of epidemiological evidence showing that it rapidly became dominant in one area. More rapidly that could be explained unless it had some kind of advantage that allowed it to outcompete other circulating variants.
Variants always emerge, & are not good or bad, but expected. The challenge is figuring out which variants are bad, and that can't be done with sequence alone.
Feels like the next thing we're going to need is a ranking system for how concerning "variants of concern\u201d actually are.
— Kai Kupferschmidt (@kakape) January 15, 2021
A lot of constellations of mutations are concerning, but people are lumping together variants with vastly different levels of evidence that we need to worry.
You can't just look at a sequence and say, "Aha! A mutation in spike. This must be more transmissible or can evade antibody neutralization." Sure, we can use computational models to try and predict the functional consequence of a given mutation, but models are often wrong.
The virus acquires mutations randomly every time it replicates. Many mutations don't change the virus at all. Others may change it in a way that have no consequences for human transmission or disease. But you can't tell just looking at sequence alone.
In order to determine the functional impact of a mutation, you need to actually do experiments. You can look at some effects in cell culture, but to address questions relating to transmission or disease, you have to use animal models.
The reason people were concerned initially about B.1.1.7 is because of epidemiological evidence showing that it rapidly became dominant in one area. More rapidly that could be explained unless it had some kind of advantage that allowed it to outcompete other circulating variants.
