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.
#DDDD $LBPS $LOAC
A thread on the potential near term catalysts behind why I have increased my position in 4d Pharma @4dpharmaplc (LON: #DDDD):
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.
https://t.co/O8Fd9uuR7f
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
https://t.co/EHvA8xVbep.
This is for #DDDD's treatment group who had exhausted many previous options and had metastatic cancer.
https://t.co/GFsF6ZYraA
This is without taking into account #DDDD's trial of MRx0518 with Merck $MRK's Keytruda, which is likely to yield as high if not greater efficacy than MRx0518 alone.
More from Science
JUST ONE PERSON—UK 🇬🇧 scientists think one immunocompromised person who cleared virus slowly & only partially wiped out an infection, leaving behind genetically-hardier viruses that rebound & learn how to survive better. That’s likely how #B117 started. 🧵 https://t.co/bMMjM8Hiuz
2) The leading hypothesis is that the new variant evolved within just one person, chronically infected with the virus for so long it was able to evolve into a new, more infectious form.
same thing happened in Boston in another immunocompromised person that was sick for 155 days.
3) What happened in Boston with one 45 year old man who was highly infectious for 155 days straight before he died... is exactly what scientists think happened in Kent, England that gave rise to #B117.
4) Doctors were shocked to find virus has evolved many different forms inside of this one immunocompromised man. 20 new mutations in one virus, akin to the #B117. This is possibly how #B1351 in South Africa 🇿🇦 and #P1 in Brazil 🇧🇷 also evolved.
5) “On its own, the appearance of a new variant in genomic databases doesn’t tell us much. “That’s just one genome amongst thousands every week. It wouldn’t necessarily stick out,” says Oliver Pybus, a professor of evolution and infectious disease at Oxford.
2) The leading hypothesis is that the new variant evolved within just one person, chronically infected with the virus for so long it was able to evolve into a new, more infectious form.
same thing happened in Boston in another immunocompromised person that was sick for 155 days.
3) What happened in Boston with one 45 year old man who was highly infectious for 155 days straight before he died... is exactly what scientists think happened in Kent, England that gave rise to #B117.
Immunocompromised 45 year old suffered from #COVID19 for 155 days before he died. The virus was changing very quickly inside the man's body\u2014it acquired a big cluster of >20 mutations\u2014resembled the same ones seen in #B117 & #B1351. (NPR audio Part 1 of 2)\U0001f9f5https://t.co/7kWiBZ1xGk pic.twitter.com/ZJ7AExB78Y
— Eric Feigl-Ding (@DrEricDing) February 8, 2021
4) Doctors were shocked to find virus has evolved many different forms inside of this one immunocompromised man. 20 new mutations in one virus, akin to the #B117. This is possibly how #B1351 in South Africa 🇿🇦 and #P1 in Brazil 🇧🇷 also evolved.
2) NPR report audio part 2 of 2:
— Eric Feigl-Ding (@DrEricDing) February 8, 2021
Dr. Li couldn't believe what they found. "I was shocked," he says. "When I saw the virus sequences, I knew that we were dealing with something completely different and potentially very important." pic.twitter.com/HT3Yt6djFd
5) “On its own, the appearance of a new variant in genomic databases doesn’t tell us much. “That’s just one genome amongst thousands every week. It wouldn’t necessarily stick out,” says Oliver Pybus, a professor of evolution and infectious disease at Oxford.
1/ Automobiles and Intake Fraction. Since cars are back in the news I thought I would retweet this model result I offered in early April 2020. I focused only on 1 micron particles & accounted for windows completely closed & cracked slightly open.
2/ Related air exchange rates were based on experimental results in literature for mid-sized sedans. Particle deposition to indoor surfaces were accounted for, as the surface to volume ratio in a 3 m3 cab is large. An important outcome was the intake fraction (IF)
3/ Here, IF is the number of particles (or virions in collective particles) inhaled by a receptor DIVIDED BY the number or particles (or virions in collective particles) emitted by an infector.
4/ Integrated over the two hour drive (in this example) the IF for all windows closed & a receptor at rest is 0.08 (8% of what comes out of the infectors respiratory system ends up in the respiratory system of the receptor). 8%! That is a very high intake factor.
5/ With additional ventilation from cracking a window open drops the IF to 0.012 (1.2%) still relatively high. Can get lower by opening more windows.
Simulation: Riding in car for 120 min w/ infected passenger who seems fine other than a cough every few mins. (1) a lot of SARS-CoV-2 virus (in fine aerosol particles) accumulation in car cabin w/ windows closed; (2) cracking window open slightly = dramatic reduction. #COVID19 pic.twitter.com/bCmrmnLUPG
— Dr. Richard Corsi (@CorsIAQ) April 4, 2020
2/ Related air exchange rates were based on experimental results in literature for mid-sized sedans. Particle deposition to indoor surfaces were accounted for, as the surface to volume ratio in a 3 m3 cab is large. An important outcome was the intake fraction (IF)
3/ Here, IF is the number of particles (or virions in collective particles) inhaled by a receptor DIVIDED BY the number or particles (or virions in collective particles) emitted by an infector.
4/ Integrated over the two hour drive (in this example) the IF for all windows closed & a receptor at rest is 0.08 (8% of what comes out of the infectors respiratory system ends up in the respiratory system of the receptor). 8%! That is a very high intake factor.
5/ With additional ventilation from cracking a window open drops the IF to 0.012 (1.2%) still relatively high. Can get lower by opening more windows.
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.
