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MWM:
Me? I'm a long-time CYDY investor (over a decade) and recently started buying some HGEN as well.
The lenzilumab MOA is intriguing.
Dr. Bruce Patterson's pre-print on leronlimab in critically patients shows a rapid reduction in IL-6, which is a key pro-inflammatory cytokine. It looks like sarilumab is worthless against covid-19, and tocilizumab may have some marginal utility. Leronlimab is broader and targets the immune cell signaling that stimulates and recruits pro-inflammatories to the infection site. It does reduce IL-6 but also seems to reduce CRP, restore exhausted CD8+ cells, and reduce viral load. I would expect it to have significantly greater effect than a simple IL-6 blocker (not to mention having a much better safety profile).
Lenzilumab is broader still, targeting GM-CSF and reducing IL-6, CRP, and other pro-inflammatories, apparently leading to improved recovery statistics. Preliminaries look great; I can't wait to see the results of the P3 trials.
One question I had...after Dr. Patterson did the blood work on hundreds of patients in the leronlimab trials and EINDs, he said that only about 25-30% of covid-19 patients had elevated GM-CSF. Does anyone know anything about those numbers or what the consequences might be for the clinical trials or what it might mean for the general applicability as a treatment?
Dr. Patterson is not getting paid for promotions nor does he have any CYDY stock, but he has been working with CYDY as an s outside contractor for many years. He's the one that identified the possibility of leronlimab being effective against covid-19. He certainly wants to be proved right, but his primary motivations are reputation and saving lives/lifestyles/livelihoods, not money.
Dr. Yo came to the leronlimab train very recently. He did buy some stock a few weeks ago after he became convinced that it would be effective against covid-19, metastatic triple-negative breast cancer, and HIV.
He spends a lot of time thinking anout the score every second It's unseemly. I'd prefer he put all that energy on the actual game instead.
Why does he respond to this crap?
especially if you start in the non-mask-wearing countries like Norway, Denmark, Netherlands, and Australia, you would run out of space by the time you got to the more mask-compliant places like Italy, Spain, and France.
Ha! Perfect.
I'd been thinking along the lines of jail time for not wearing a mask.
Wow. As a thought experiment, let's take the most nervous, most totalitarian-minded among us and give them total control of our personal health decisions. What could go wrong?
I'm guessing long-haulers
massively diluted?! Let's see. At worst, $28M at $10 per share. That amounts to 2.8M shares out of some 800M, so dilution of 0.3% is a possibly.
Sounds like someone is massively deluded.
probably not...too risky.
...especially if they were planning to cover tomorrow morning or near the end of the day today.
I'm guessing the components of the clinical scores are highly correlated, so a patient that goes up 1 or 2 in fever or dispnea is much more likely to go up in 1 on myalgia or cough, which would mean there'd be more high and low scores than you'd expect if everything were normal.
I haven't been paying much attention to this discussion because we don't have any idea what the data will look like. I'm guessing that the efficacy endpoint of comparing changes in clinical scores between day 0 and 14 is going to get us a smaller p-value than the 0.06 that the crude test comparing SAEs gives.
In playing around with made-up data, it helps to try to do what the trials do, which is compare changes in clinical status. There are many ways to compare changes in scores, but they all require some idea of what the distributions of scores (or changes in scores) will be. We don't have much idea. I'm not going to put more time into it than is justified by the quality of data we have available, which is close to zero.
it makes a big difference what your wild guesses about the standard deviations are...
yup. We need to cut the mortality rate roughly in half. good chance it will done.
Before seeing the SAE and AE data, I was above 60:40 for the m/m trial meeting its primary endpoint. Now I'm way above. It took me a lot of reading and cogitation, but I think the m/m trial is going to be easier to show an effect in than the s/c trial will be. In a nutshell, it is probably easier to prevent the cytokine storm in the first place than to reverse it.
It doesn't take disease progression in very many placebo patients to make a clear separation from the leronlimab patients. They almost did it with crude binomial counts of SAEs and AEs. Two key differences in the clinical score are 1) the quantitative measure allows greater separation than a binomial 0-1, and 2) many AEs happen early--before full CCR5 occupancy with leronlimab--and the leronlimab patients will recover from their SAEs and AEs better than the placebo patients will recover from theirs. These two extra boosts to the separation between the treatment arms will drive that p-value down quite a bit from the 0.06 and 0.08 for the SAEs and AEs (resp).
The s/c trials are binomial, so there is very little information in each data point. Sample sizes need to be large to show differences. Leronlimab will need to have about 50% reduction in mortality (depending on what the ultimate sample sizes and mortality rates are) to show significant effect. It could easily happen. I'm about 60:40.
But who knows! This is only a guess.
After seeing the SAE data, I'm 90:10 or 95:5 that m/m primary outcome will be statistically significant. For crying out loud, the very crude measures of SAEs (p = 0.06) and AEs (p = 0.08) are very close to statistically significant. The primary outcome is a more sophisticated measure with more information and designed specifically to detect clinically and statistically significant differences. It is certainly possible there won't be a significant difference, but it would surprise me more than an 80:20 proposition would if it failed.
they wanted to take questions but the tech outfit they went with wasn't able to make it happen.
Yesterday's SAE data give a glimpse of efficacy because the SAEs are serious medical events, like death, hospitalization, ventilators, other serious interventions. The stats for the crude measures in the PR give p-values right around 0.05. Those measures weren't designed to demonstrate efficacy but come close to doing it any way. That gives a strong hint that the measures that truly were designed to show efficacy will start that flow of beautiful results that will shock the world. Everything is coming together in a spectacular way.
Not true! And therein lies the problem with the PR. Patients with adverse events was 6/28 for placebo vs. 5/56 for leronlimab, or 1 - (5/56)/(6/28) = 58%. It's the rate of adverse events that was decreased by leronlimab from 11/28 = 0.39 SAE/patient to 8/56 = 0.14 SAE/patient, or 1-(8/56)/(11/28) = 64%.
tikotiko, scoring the "events" would indeed (most likely) give greater power, and that is essentially what the primary outcome for the m/m trials does---but with more detail and nuance, which will probably give even more statistical power. And then the effect will most likely be magnified because the events that are recorded in the safety data are most likely early in the trials---before leronlimab has much of a chance to work its magic. The p-value is likely to go from around 0.06 for the 6/28 vs. 5/56 to something WAY less than 0.05 in the m/m clinical score at day 14. Headline in OAN News and CNN describing Dr Fauci's announcement with Trump at his side will be: "Clinical trial for leronlimab in m/m covid-19 patients crushes the primary endpoint as the drug stops disease progression in its tracks. We can all go back to work just as soon as they can ramp up production."
Leronlimab for the win.
or, more simply, (11/28)/(8/56) = 2.75 => rate of SAEs in placebo arm was 2.75x that of the leronlimab arm. As you point out, that's huge.
Bigger than expected by random chance? Not easy to determine because the SAEs are not independent, but somewhere between 0.06 (using simple counts of patients with SAEs in the two groups via Mantel Hanszel test or logistic regression) and 0.024 assuming complete independence (binomial test on the distribution of SAEs between the groups).
Better would be to score the events and put them on a quantitative or ordinal scale, like is done for the primary outcome (but with measures of clinical status of patients). The ordinal scale will add power to the test and give a lower p-value. Also, I suspect that the leronlimab adverse events were early, before the drug fully kicked in, and that the patients had time to recover by day 14, when the primary outcome is complete. The placebo SAEs are much less likely to have recovered by then (I'm guessing).
The ordinal scale + the recovery time => p-value well below 0.06 for the primary outcome = huge success.
5/56 and 6/28 patients had SAEs, totaling 8 and 11 events.
misiu143,
Thanks, lifeLongLearner. I was really hoping we'd have beautiful results that would shock the world today, but it looks like we're going to have to wait a little longer.
p-value is the probability that the trial results you observe could happen strictly by chance if there was no difference between the treatments. Very small p-value means very small chance that results are due to chance, so effect must be due to the treatment.
not sure where you get the info, but it is plausible and matches the impression given by the glimpse of the safety data that we've seen.
Math is easy; the hard part is getting the language to match the math (as the botched PRs and your little table so clearly illustrate).
"clinically meaningful" for the particular patients in the trial.
"Statistically significant" would mean that the results are clear enough to extrapolate to patients in general because they cannot be reasonably explained by random chance.
Example...suppose one patient was given a placebo and ended up on a ventilator, while another got the treatment drug and went home the next day. That's clinically meaningful for those two patients but the sample size is too small to say anything about whether the same kinds of results could be expected for patients in general.
maybe...I did two tests (one-sided): a Mantel-Haenszel test on the odds ratio (p = 0.06) and a two-sample binomial test on the difference in proportions (p = 0.07). I presume they are using Mantel-Haenszel (or, equivalently, logistic regression), but what did you use to get p < 0.05?
[NOTE: One-sided tests on the efficacy side, but they might instead be doing two-sided tests on the safety data, in which the p-values would double.]
Suppose there were 10 patients in the placebo group, and 5 of them each had 2 SAEs and one other had 1 SAE. Would you say "110% SAEs in the placebo group"? It makes no sense.
The "corrected" subhead makes no sense. In the same way that my 5 toes on my left foot is not 500%, 11 SAEs in 28 patients is not 39%. "%" is unitless. The fraction of my feet that have 5 toes is 100%, and the fraction of placebo patients with SAEs was 6/28 = 21%.
Summary:
21% of patients in the placebo arm had SAEs while only 9% of leronlimab patients had SAEs, so the relative risk of patients experiencing SAEs was cut in half (but not statistically significant p = 0.06).
In addition, leronlimab was able to cut the rate of SAEs by nearly 2/3, from 0.39 SAEs/patient in the placebo arm to only 0.14 SAEs/patient in the leronlimab arm. This suggests that the primary endpoint of clinical score on symptoms at day 14 will be a grand slam. The simple binomial effect of number of patients with SAEs (which was marginally non-signficant) is amplified by the disproportional increase in number of SAEs in the placebo arm. SAEs are things like hospitalization, need for ventilator, etc. Most of the SAEs, if they are going to happen at all, tend to happen early. But it takes several days for full CCR5 occupancy with leronlimab and a few more days to reverse the SAEs. The SAE conditions in placebo group are not likely to resolve by day 14, but most of the leronlimab SAE conditions are, which will further separate leronlimab from the placebo.
Very bullish. Shorts NEED to cover before closing bell because the risk of an insistent gap is too great.
because whoever writes these things doesn't know what they are doing...sloppy
yeah, right.
High expectations and then a muddled PR adds a lot of doubt, which makes it is easy for AF et al to exploit.
hang in there...it will come
Thanks, mgd12070.
Unfortunately(?), I pulled the plug on the TV 34 years ago and haven't looked back, so no OAN for me. <sigh>
what's OAN news?
"knowing"?