I read the latest paper on Anavex 2-73 and precision medicine, the link can be found here, top author Dr.Harald Hampel.

My interest is in the groups showing High Concentration. Their performance is key to assessing the success of Blarcamesine (Anavex 2-73). So I looked into Groups 1 & 2. I reached the following conclusions;

- Both groups consist of patients with MMSE scores above 20. This can be controlled by better screening, testing, and future biomarkers.
- Group 1 is composed of subjects carrying the APOE 3 alleles. This fact correlates (causation?) with decreasing odd of the late-onset Alzheimer disease (LOAD), and its severity. Yet unknown is the genotype, are they e3e3 only, or e2e3 or e3e4 possibly?
- Group 2 by extension includes Group 1 and Group A, which is made from those patients who are, MMSE>20, SIGMAR1 wild, High Concentration, and don’t carry APOE3 alleles. Are they e2e4 or e4e4, only?
- The performance of Group A can be inferred from the performance of Group 1 & 2.

See Illustration.

Before we discuss anything, on Table 1 in Study on APOE alleles in LOAD population ( link in text below) is given distribution of genotypes in LOAD. If e2 is neutral, e3 “good” and e4 “bad” , then 12% are poor prospects patients, 38% good prospects and 50% in varying degrees between.

What insight did I gain from this plot?

Regarding High Concentration Cohort.

- Group 1 of 2 patients out of a total of 8 in the High Concentration cohort did not deteriorate. That is n=2 (@38% this should be 3)
- What is the probability of patients belonging to this group? Since I am not smarter than my computer; I don’t know much about statistics or probability, I just multiply probabilities of factors going into Group 1. .80 (SIGMAR1 wild) * .37 (APOE3 alleles carriers among LOAD, see link below) = ~.30 or 30 percent of patients in a large population of LOAD. See Study on APOE alleles in LOAD population.
- Group 1 is just 2 patients which are a bit less than 30% of the High Concentration Cohort population, yet in line with the calculations.
- If Group A is just the same as Group 1 but with at least one copy of APOE4, then the same calculations are, .8*.56=.44 (~50) which is 44% of the LOAD population. Out of 8, this is ~4 patients.
- We are not accounting for 2 other patients within the Cohort. Those two are quite different, one is moderate (patient 2008, -4 ADCS-ADL @ 57 weeks) and other extreme (patient 2002, -22 @ 57 weeks). If we assume that patient 2002 is SIGMAR1 mutated and e4e4 then .20*.13=3%, which is rare but not impossible. These patients can be assumed to be SIGMAR1 mutated, Low on MMSE, and any APOE3 genotype carriers. There is negative synergy in the two former and possible negative in the third, but not necessarily since e3 can not be taken from the equation here.
**From the calculation (very crude) of the distribution of factors affecting Blarcamesine performance, it seems that the Cohort should be a good approximation of performance in Phase 2b/3.**

Before I discuss the performance of Group A, I would like to described possible composition of Phase 2b/3 cohorts. If indeed these selection criteria are going to be used as the paper suggests then by selecting the patients to be SIGMAR1 Wild + MMSE > 20, and having the distribution of APOE alleles among them in accordance with the data in the referenced Brazilian study, the overall performance should be the similar to Group 2. The breakdown goes like this, 30% patients “stable”, 60% “delayed” and 10% ‘dropouts”. This is just a working hypothesis based on very crude thinking. The expectations here provided seem not violate, in my very poor understanding, the statistics of LOAD population. Sometimes such rudimentary analysis can be helpful as it sets boundaries for what is possible, but within these boundaries all sorts of phenomena can change the efficacy of the drug. In final reckoning the future will either void my model or validate it.

Regarding the performance of Group A

- The difference between Group 1 and Group A is the absence of APOE3 alleles.
- To access the impact of APOE3 and APOE4 on LOAD see the reference Brazilian study.
- The meaning of Odds Ratio is that if events are statically independent the ratio is 1, above 1 there is indication of events not being independent , and below the opposite effect is found.
- The ratio for LOAD and e4e4 is ~14, e3e4 ~2.33, and for e3e3 is 0.36. So the genotype has very significant impact on LOAD and its severity, creating great variance in the outcomes among patients in Group A.

- At the worst, the plot of data for Group A providing information till 148 week of study (~3 years) implies that the top benefit is accrued till year 3, and then there is sharp decline. If one linearly extrapolate the last period slope in about just year 4 the outcome is the same as STANDARD of CARE.
- Taking another shot at extrapolation, the curve fitting software produces quadratic equation curve making the patients on average reaching score about ADCS-ADL=20 at 5.5 years.
- If Group A follows the characteristics of STANDARD of CARE curve, there should be transition to accelerated decline sometime after 57 weeks. Is the accumulation of amyloid plaque adding its destructive synergy to the disruption in homeostasis? If so, removal of plaque might be beneficial to these patients, yet till now, the removal by itself has not worked. Is e4 doing its destructive job?
- The transitions seems to be taking place at 57/70 week mark. The fitted curve slop is y=-1.78 points/13 weeks. Since the point of departure for this regime is about 0 loss, at 5.5 years from treatment start the decline for the group could be -30, leaving the score ADCS-ADL=30 @ 5.5years.

Remaining questions.

- How effective is Blarcamesine in patients with APOE4+SIGMAR1 Wild vs. STD of Care?

The partial data to answer this question is now in the genotypes and performance of the remaining 4 patients in High Concentration Cohort. There are six genotypes, two of them, e4e4 and e3e4 having varying degree of detrimental effects on LOAD patients.

Leave at this, I think that I will not live to see the stock reach the promised land of $100, and certainly not the $1000, making us all millionaires. As it lasted it was fun.

Thanks.

]]>- I looked into seasonal patterns of $TM in the US market as a measuring stick.
- The data was for every month in 3 years, 2016-2018.
- The goal was to see how the numbers of cars sold in two first months of a quarter are to the third and final month multiplied by 2. I called this ratio the “Sales Effort”
- I created a measure of demand which I called “Organic Demand”; the sum of the first two months of a quarter. If the “Sale Effort” is one or near then we have perfect “Organic Demand”, otherwise companies need to incentivize or push for sales.
- I can see seasonal patterns; “Organic Demand” in Q1 is low and in Q3 high.
- “Sales Effort” reverse pattern; Q3 low and Q1 high.

Illustration:

Data points to the following pattern.

- “Organic Demand” steady, seasonal small change, a top average of 3 years (Q3) = 435k, 129% of low Q1.
- “Sales Effort” steady, seasonal small changes, average Q1 (high) = 337k, 130% of low Q3.

Q4 2018 was the last quarter where $TSLA was fulfilling preorders (production constrained). Since Q1 2019 $TSLA has been demand constrained or assumed to be since the number of sold cars in the US per quarter reached a peak in Q4 2018. The delivery lead time might serve as a good indication of that assumption. When comparing the patterns of $TSLA and $TM we have to keep in mind that $TM has a network of dealers that smooth out the variations in demand and production. I hope that the statistics capture the final demand with some reasonable fidelity so that we can venture to make somewhat valid comparisons and conclusions of the current state of $TSLA demand.

In this plot, I encluded Q4 2018 results and software applied trend line polynomial 3 degrees. The trendlines capture the periodic character of limited data, but at the end of the plot incorrectly anticipate a severe decline of values plotted. Also, the Q1 2020 is just a copy of Q1 2019 data.

Our attention should be directed at Q1 2019 to Q4 2019. In general, Q1 is seasonally a poor quarter, Q3 is the best. This pattern is maintained here (validation?). “Organic Demand” for Q3 is 203% of Q1. This was 130% for $TM. “Sale Effort” in Q3 is the lowest 93% of Q1 but in Q4 it becomes to be 270% of Q1. It seems $TSLA starts with organic demand and keeps selling Models M3 with the same “Sales Effort” it had in Q4 2018 when it had been delivering to preorder holders (production constrained) then in Q4 2019 the “Organic Demand” deteriorates (-20%) and the quarter numbers are made up in the 3rd month which presupposes incentives or aggressive marketing. For $TM this drop would be within the bonds of full seasonal change.

What remains of demand for $TSLA Model 3 after the rush of virtue signaling subsides? The answer is the organic demand, scratch a bit deeper, not much!

Neither the US or the EU are homogenous markets but the differences within the EU are much more pronounced, or easier to document than in the US.

The plots represent data collected by @fly4dat twitter post including a spreadsheet with data on EU countries. I only replotted this data, so that you can with a glance visualize the decline or assent of the demand in a given country.

The data covers with some uncertainty the partial data in Q1 2020, otherwise, the rest is actual numbers.

The EU overall;

$TSLA moves from one market to another, once the V-S demand is gone and what left is the organic demand it pours its cars in the next target preserving the perception of at least constant demand. Examples follow.

France, not yet New Hope!

Is this the New Hope or the Last Kick in the Pants?

Is this the New Hope dud?

$TSLA pursuits throng of the demand minnows in the EU, all mostly under 1000 cars/ month. I left out some markets above that, but now $TSLA is picking pennies of a demand.

Just the same for the US market.

Nothing on Q1 2020 from $TSLA yet. It seems that the Great $TSLA Demand Hoax will only be put to death but a thousand cuts, it will just bleed to death the Longs along with Elon, or Federal gov. will save for few quarters the “jobs” of $TSLA workers. These are just transfers from the investors, and anybody involved, to the buyers and workers. **If you think otherwise you are a Marxist**.

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So we play with numbers here first then we make some conclusion.

Empty Starship we are told weighs about 120,000 kg. At 200 miles orbit, this translates into 30,175,000 Joule/kg (kinetic energy per kilogram). You have to dissipate this energy on re-entry into the atmosphere and things get hot and violent. For 30 minute descent, this translates into heating and compression air 557,112 horsepower machine (for full 30 minutes).

So there is the ballistic factor:

- Ballistic Factor=W/(Cd*A)
- You cannot change the W=weight
- But you can change the A=area resisting the flow, and together with
- The Coefficient of Drag=Cd, by maneuvering the Starship during reentry.

There is another measure involved which is Cl=Coeefficient of Lift, as a body goes through the atmosphere it produces lift together with drag (or if it is a ball Cl=0). Starship is designed to produce lift so Cl is not zero. L/D ratio relates Lift to Drag. If Lift is larger than zero the reentry can be spread in time.

The Ballistic Factor ratio of the kinetic energy to the innate ability to convert it into heating of air by compression and friction.

The Ballistic Factor for Starship can go from 205kg/m2 just hitting air sideways to 8000 kg/m2 going the cone first. The coefficient of Drag respectively from Cd=1.5 to .3. The coefficient of Lift Cl=0 to .8. (all numbers estimates, and approximations to show the possible ranges, not precise values)

So what is the gamble quoted in the title? Let’s see a graph;

As Starship enters the atmosphere at 120km above Earth the air is so thin that it flows over the ship like water from your faucet producing a clear orderly column of water, before you open it up so that it becomes turbulent (mixing) – it is called laminar flow. The surface of the ship heats up from the friction of the air, in the greatest proportion to overall dissipated energy. As the density of air rises and velocity drops a lesser fraction of the energy goes into spacecraft, but the overall amount of energy is dissipated grows so that the rate at which the heat flows into the spacecraft skin does not drop precipitously. At 30km the frictional heating and compression of air become so intense that besides convection the surface heats up due to radiative heat, as the air surrounding the ship heats up to thousands of degrees. The energy radiated toward the ship is proportional to the fourth power of the absolute temperature of the air. As air dencity increases and velocity drops further, there is appearing turbulent convection (transfer of heat when there is a contact of the fluid with solid) besides radiative heating.

Elon’s gamble is to maneuver (change A, Cd, and Cl) the Starship so that heating rate (a critical parameter) can be controlled and spread over time as well as the dominant type of heat transfer, with radiative heat transfer being most advantages because Starship cladding is made from stainless steel polished to reflect at least 90% of the radiative heat.

The above graph shows the results of optimization to strike a balance between structural capabilities and limits on heat transfer. Elon’s gamble is to move beyond that and aggressively manage the velocity of reentry and by extension the heat transfer rate to the ship, by leveraging the reflective surface of stainless steel Starship. This might be much more complicated and daring than it seems. The descent engines and the movable surfaces are available to achieve this. This has never been tested.

Structurally, Starship aimed at integrating the fuel tanks into the shell. There was also a change in material from ANSI 316L to 304L which is unclear why since both are low carbon (since L(ow)) versions of 316 and 304 respectively. The cryogenic test failed as stainless steel might experience corrosion in welds and that depends mostly on carbon content. Liquid oxygen is not a forgiving fuel to store as over certain pressure the stainless tank might ignite. (over 1000lbf/in2). The structural issue lingers since many aerospace material are lighter and more suitable with greater strength to weight ratios than stainless. Eleven % of the Space Shuttle weight was dedicated to ceramic tiles. That was 8,600 kg out of 78,000 kg. The stainless steel cladding, if 3mm single layer thick, weighs about 34,000kg out of 120,000kg estimated Starship weight (28%). There seems to be a stainless steel weight penalty. Will its benefits outweigh the cumbersome process to which was subject the Space Shuttle? Add on top of this the fuel penalty since you take this mass up and then land the Starship just like the other SpaceX rockets.

**I am not able to run a detailed analysis, but all I see are weight penalties traded for an idea of getting rid of ceramic tile insulation, at least at this stage of design. The idea can fail altogether or can pass (not likely) the test of reentry but the weight penalties can be such that it might be equally, or even more, unprofitable as the idea of landing the booster stages of rockets on ships. **

Let’s put it into narrative.

The Space Shuttle had aerospace alloy + composites (if am correct) construction and used ceramic tiles to protect the craft from reentry heating.

The tiles were causing maintenance and reliability problems, but allowed to lower structural weight of the craft. The problems with the tiles basically defeated the concept of reusable craft. One mission was lost due to their failure.

Elon is constantly looking to beat the paradigm of space travel and picks following strategy.

- Get rid of tiles, and use mirror (as close as possible) stainless steel to reflect off radiative heat of reentry.
- Provide the Starship with lift capability to extend the reentry time and control the amount of heat absorbed.
- Allow the Starship to increase its ability to change ballistic factor by change in Area and Cd, all by maneuvering but this is limited by the maximum deceleration forces.
- Maneuvering allows control of velocity at given density of air so that desired heat transfer regime can be extended. Mirror finish might reflect 90% radiative heat.
- Passive controls by low heat conductivity of stainless steel, and possibly second stainless skin.
- Under this scenario reentry becomes complex and dynamic maneuvering procedure with larger g forces on the frame and crew (possibly at times). Also heating becomes complex and dynamic due to maneuvering, not to mention heating stresses due to temperature differences.

Now, let’s turn our attention to structural weight problems and their tried solutions.

- Use of the cavity enclosed by the skin as structurally integrated fuel tank. Cryogenic test failed probably due to field welds quality. Other issue is the weld chemical corrosion besides just quality issues.
- If indeed fuel tanks would be integral and exposed to directly absorb heat energy upon reentry the pressure in them can rise toward catastrophic failure. (Playing devils advocate)
- If the idea of integral tanks to survive we can not dismiss the possibility of second skin (3mm thick?) to isolate them from reentry heat.
- Non stainless steel internal tanks might the best solution here. Increased weight though.
- The weight of single 3mm thick skin layer is about 34 tons. This is 28% of the 120 ton empty weight. For the Space Shuttle the weight for all the tiles was just 11% of empty weight of ~75 tons. Add second skin?

Assessment of stainless steel as structural material is rather to disadvantage of stainless steel. Light aerospace type aluminum alloys beat stainless with 4 time that strength per unit of weight. Stainless is either chosen for corrosion resistance, for forming, esthetics, and low thermal conductivity (but not refractory or high temperature applications).

- To eliminate tiles and its shortcomings stainless steel and the above reentry method is chosen.
- The solution using stainless steel has weight penalty and increases complexity and risk
- The struggle to lower weight of the Starship and manufacturing shortcomings of #SpaceX lead to structural test failures.
- The goal of making rapid turnover between launches spaceship seems to be defeated by excessive structural weight as it imposes economy penalty vs. other methods, even if complex maneuvering during reentry will be successful.
- Every pound of structure requires some amount of extra fuel to be lifted into the orbit. This phenomenon has doomed the efficiency of reusable lift rocket.

The Starship can fail in few ways.

- Failure of the concept of maneuverable reentry, by way of structurally failure due large aerodynamic forces, by excessive heating of spaceship. These can quite complex.
- The structural weight penalty making the Starship not economical
- The attempts to lower structural weight of the Starship can expose it to structural failure.
- Manufacturing methods are already exposing the craft design to failure.

One very ugly remark on the Starship; I does not look *elegant* as a solution to engineering problem, it look downright UGLY. You know engineers have intuition too!

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I stole data on $TSLA Model 3 registration from @PlainSite (Twitter).

$TSLA New York registration data is out. And it is ugly. pic.twitter.com/Cjpvmi6tLF

— PlainSite (@PlainSite) March 2, 2020

I created two metrics to describe the demand situation in NY. The first is “Organic Demand”. I know that this is a lame idea but for a lack of a better one, I assumed that at my level of intelligence it suffices. The idea goes that the “Organic Demand” can be measured by the 2 front months of the quarter, and it is relative to other quarters and the final 3rd month of the quarter. The ratio of 3rd Month to 2 Front Months I called “Sales Effort”.

The Q4 of 2018 is the last Q when $TSLA was filling preorders. The green bar for 2 Front Months is larger than for 3rd Month (Yellow). This is the yardstick of “Full Demand”. The other special case is the seasonal pattern for Q1 of 2019 and Q1 2020. Since there is no data for March 2020 (3rd month) I assumed that 473 Model 3 just like in Q1 2019 are registered, and hinting at 18% larger “Sales Effort” than of Q1 2019.

- The trend for “Organic Demand” is down.
- The trend for “Sales Effort” is up.
- The trend for the 3rd Month is more or less flat.

Notice that “Sales Effort” in Q3 2019 is as slightly larger than in Q1 2019, the collapse of demand after year-end and fulfilling preorders.

This graph can be validated or disproved by the March 2020 data.

P.S.

Thank you, @PlainSite! I am just a draftsman.

Do you remember these slides from CTAD 2017?

I decided to plot all patients in High Concentration Cohort from CTAD 2017 for the first 57 weeks. From slide 29, we have the starting MMSE scores for 6 patients, all those who improved or barely declined. The remaining patients declined very quickly but their initial scores are not known.

- I assumed that the mean MMSE score for all patients in the Cohort was 21 MMSE.
- The mean for the other 3 patients turned out to be 18.
- We know that 65 years old patients diagnosed on average 3 years after first symptoms should have on average MMSE 0 scores after 8.7 years, after declining from MMSE 20. Avg. Δ=-2.4 MMSE
- The 90 years old patients decline rapidly from 20 MMSE, and in 3.4 years we assume they have MMSE scores 0. Avg. Δ=-5.8 MMSE
- 75% of patients in High Concentration Cohort are APOE ε4 (rapidly declining)

So let’s see the graph:

Discussion:

**Three classes of patients are clearly visible.****We can assume that the rapidly declining patients can be carries affected by all the detrimental conditions to improvements while dosing with Blarcaminase.****The 3 patients are just 30% of the Cohort. From CTAD 2019 presentation we know that 75% ~6 patients are carriers of APOE e4. In the general population ~50%.****These numbers suggest that Blarcamenise helps even patients carrying the APOE e4 gene, the most destructive form of the gene connected with a specific form of amyloid plaque.****The number of 30% of rapid decliners is in line with SIGMAR1 mutation prevalence in the general population. Yet $AVXL did not have SIGMAR1 info on 11 patients, as of 2019 this information is only provided for the 21 patients who are currently enrolled in the Phase2a extension. I bet that as of 2019 the 3 patients are dropouts, so it is highly likely they had the SIGMAR1 mutation.****There is a disparity between the 75% of patients, either 6 or 7 carriers of APOE e4 of 9, and 60% of patients who are helped by Blarcamesine. If indeed the SIGMAR1 mutation is the harbinger of the worst outcome so that the 3 patients could be carries of all the “bad” genes that makes at least 4 remaining patients be APOE e4 carriers, then Blarcamesine lifts this disadvantage.****The only conclusion that I can, in light of the point above, make is that the plaque theory of Alzheimer’s is not identifying the prime cause, so homeostasis might be a better candidate for it.**

Beer Fund

$1.00 is about one bottle so .....please contribute..

$1.00

I suspected that the curve for deterioration of the general progress of Alzheimer’s disease might likely contain a region of acceleration. I was for a short while looking for it on the internet but quit it all too soon. Fortunately, I looked today at iHub and gentleman going under the moniker of **amstock82** shared a paper illustrating the general trendline for Alzheimer’s patients. Here is a link to the paper:

Rate of decline in Alzheimer’s disease measured by a dementia severity rating scale

again the illustration will tell more:

- First 2 years: y=-2.15*x
- After year 2 to year 5 y=-3.88x
- After year 5 y=-1.6*x

I have included this new info in a graph which old version can be found at this link:

So after updating the graph, it looks like this:

**Blue Line**: Actual data till week 148 (~3 years) Blarcamesine High Concentration Cohort Phase 2a; extrapolated to year 9 with the rate of decline the same as between year 2 and 3; y=-1.3*x (236%) (previously y=-.55*x). This is an assumption which is in line with the general trend line for Alzheimer’s found in the above paper as after year 2 the rate increases from y=-2.15 to y-3.8 (173%) The prevalence of APOE e4 gene among High Concentration cohort (75% of carriers) has not been addressed or adjusted. If we follow the adjustment numbers for the decline rate due to additional 27% of patients with APOE e4, from the previous post (link provided here), then the rate becomes

y=-(1.3-.3525)*x=-.97*x and**y=-.97*x is 172% of y=-.55*x**(wow, what a coincident (?))**Yellow Line**: Quadratic equation fitted to 3-year data for the above cohort. The worst-case possible with ever-increasing acceleration in the decline of patients.**Green Line**: ADNI Synthetic Placebo Trend Line y=-2.2*+20, actual data till year 2, afterward extrapolated till year 9. This should not be the case as far as we know from the above paper. 48% APOE e4 of carriers.**Red Line**: General Trend Line, from the above paper. Described above. 30% have no APOE e4 and 45% at least one copy of the gene. Comparable to ADNI synthetic Placebo. The first 2 years for both are identical. It is expected for the GREEN Line to follow Red Line.

The above-mentioned post was looking at the worst case. Now let us look at the best case, but still not adjusted for APOE e4 gene prevalence in the High Cohort, at least not yet:

- The Blue Bar: MMSE points over General Tred Line for Alzheimer’s. Observe that the difference between Blarcamesine patients and placebo grows up to the sixth year, and even there it holds.
- The most important Green Bars illustrate the expected delay Blarcamesine gives to an average patient at the current rate of decline. An average of 65 year old patient expects to live with Alzheimer’s 8.3 years, for 90-year-old this is 3.4 years. In reality, at about 10 MMSE independent life is impossible but Blacamesine can buy about at least 6 years of life.

- The CTAD 2019 presentation dwells only on the period of 2 years because data from ADNI had been collected only for that period, and I suspect that the regulators recognize as Synthetic Placebo Arm only the data from ADNI due to its versatility and statistical significance. ADNI was set up by the industry with this purpose in mind. Most trials might never extend for more than 2 years so there is valid logic for limitation on the duration of collection.
- Some people raised questions as to why $AVXL used only 2 years of data, suspecting that Blarcamesine did not hold much efficacy as treatment advanced. The prevalence of APOE e4 gene in the Cohort might lower the comparative performance of Blarcamesine over ADNI Synthetic Placebo Arm. In the very imperfect study conducted by me, the APOE e4 could have contributed about 60% of the absolute decline rate these two years. Is this correct; I don’t know.
- The ADNI Synthetic Placebo Arm for 2 years behaved as the above reference would call for. The graph of general Alzheimer’s progress was done with over 500 subjects. I don’t think there are problems with being not statistically significant. Yet this data might not be valid to create a synthetic placebo arm since placebo effect exists. Is this applicable to dementia? I am not an expert.

Just don’t, It isn’t worth it!

Only $3 Bills Accepted

$3.00

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as 4 billion years of evolution and

possibly God is against my judgment

The beginning of the presentation dwells on advancement in clinical studies beyond the slow process of adding sources of information to process which might have not changed for decades towards a revolution. It seems that now due to the availability of data placebo arm might be eliminated and the evaluation of efficacy and safety might be dynamic (adaptive) that is done in real-time i.e. responding to changes in the data.

I will leave this to more knowledgeable people and let’s graph some adjusted lines on paper for fun.

Before I do this, let me show you what I got in response to my blog (the previous post on Blarcamesine):

I am not petulant, and I have to give partial credit to this gentleman. Since my training is in engineering I can say that I am not a scientist. I have been told that an engineer needs to get to +/- 20% of the real-world numbers in his model of reality. If you want to do something in this field of the human endeavor, you model something, or use some heuristics (rules of thumb), and then validate model just test what you build and if everything works out you call it a success if not you go back to your model and start thinking harder.

I know that I am not exactly right but I am not entirely wrong either. A number of people who were called geniuses were lionized in the public minds. When you read their biographies the success is the result of much efforts and some of these attempts might not be rewarded as the public tends to think. If somebody tells you that you are smart, run away because humans have their limitations and could be easily duped. However, limited my thinking is it is really fun to get on a limb and theorize, extrapolate, play with numbers and make yourself look plain stupid. At least you don’t watch TV.

At now back to the main program:

- The carries of APOE e4 deteriorate quicker but they also get Alzheimer’s when they are younger.
- In a simple search, I did not find any clear number to assess the amount of acceleration in decline vs. noncarriers.
- The difference in the prevalence of APOE e4 gene in ADNI Placebo vs. High Concentration A2-73 is -27%.
- The ADNI Placebo Arm fidelity to Phase 2a conditions is limited by the data gathered by the organization. Only 104 weeks of data available and mismatch on APOE e4 carriers prevalence.

Let us go to the next slide:

- There are trend lines for Low & Medium and ADNI Placebo. Since there is 0.47 point difference between these at 104 weeks we can make assumptions about the bias due to the impact of the prevalence of APOE e4. (30% and 48% respectively)
- Simple grammar school math gives us (y=+.3525*x+20) bias to be applied to High Concentration trend line (y= -.5500*x+20)

Let’s see the adjusted Blaracamesine trend line. (this is just a trend line, nothing more):

More info can be found in the previous post about Blarcamesine and $AVXL regarding the colors of different trend lines and how they were generated.

- The top continuous black line depicts adjusted for APOE e4 bias the High Concentration trend line.
- The trend line runs so close to the line for MMSE=20 that the portion of that line was removed.
- After 6 years the drop would be -1.2 MMSE points.
- The previous unadjusted line would be -3.2 MMSE points.
- The unadjusted but extrapolation attempting to account for accelerated deterioration would be at -6.7 MMSE points.
- APOE e4 is present in 15% of the population and increases the probability to have Alzheimer’s disease 12 times.
- By attempting to apply the bias to the High Concentration trendline the comparison with ADNI Placebo is more realistic. After 6 years there is a projected 1000% greater deterioration in Placebo Arm than Blarcamesine patients of identical groups of patients.

In the process of making this exercise, no patient was harmed and if I were wrong only I would look stupid. This is in the realm of possibilities but not a certainty.

One word here, I realize that extrapolations in the physical world are rather dumb. Most biological systems are comprised of elements and under the influence of so many factors that highly non-linear behavior is possible, and each of us has experienced this with our health 9this wisdom comes with age). The word non-linear means in vernacular that you wake up one day and you feel sick or health all of sudden. Yet, $AVXL did extrapolate the ADNI trend line to 148 weeks. LOL. I bet that they are just mocking me since I have been doing it for a time.

I hope that this gives you insight into the possible factors involved in the evaluation of trial results. I know that the real curves for the deterioration of Alzheimer’s patients are much more complex as they involve dropout as well as MMSE scores decline. Now I am waiting for my 15 minutes of fame! LOL. With a beer in my hand.

Beer Fund

$1.00 is about one bottle so .....please contribute..

$1.00

]]>

Seekingalpha.com had on 12 04 2019 this press release.

The only problem with this press release is that it is warmed up info already given by $AVXL when it for the first time revealed the 148 weeks data adjusted for Precision Medicine a.k.a. genetic markers. The following graph can be seen on page 20 of ANAVEX Corporate Presentation from October 2019, the first publication even predates this.

Following trend lines are shown:

- Blarcamesine High Concentration adjusted for genetic markers MMSE scores ~ y=-1.1*(x/52) (for this graph) (x in weeks)
- Blarcamesine Low and Medium Concentration adjusted for genetic markers MMSE scores ~ y=-4.4*(x/52)
- Blarcamesine High Concentration decline accelerates with the duration of the dosing, by data points, not the trend. The rate of which between 96 weeks and 148 weeks (52 weeks difference) is y=-1.3*(x/52) (x is weeks).
- Better fit to Blarcamesine High Concentration decline data points can be given by quadratic equation y=-.21632*x^2+.025*x+20 (x in years).
- Ultimately, it is entirely possible that ADNI Placebo might have an accelerated decline curve as well since Blarcamesine Low Medium Concentration might be affected by the drug.
- At this time we have no further information on ADNI Placebo arm than the assumed linear decline of y=-4.4*x+20 (x in years) (2 days before the presentation)

These numbers are about the same as in the Press Release. Our goal at first is to present the data extrapolated to 6 years. For both ADNI placebo and Blarcamesine. The case of acceleration in the deterioration rate after 96 weeks can be presented by extrapolating the rate between years 2 and 3 into next year ( year 4).

- What can be seen here:
- ADNI Placebo (yellow line) for the first 2 years declines -4.4 points. The extrapolation (black line) into an additional 4 years gives the final score of 6.8. (y=-4.4*x+20)
- The extrapolation of Blarcamesine y=-1.1*x+20 gives a final score at 16.7 (magenta line)
- Extrapolating Blarcamesine decline till year 6 and using the rate of decline from the last data points as specified on the graph from the corporate presentation, gives a score of 13.2.

Word of caution; the decline of Alzheimer’s patients over time can be accelerating.

(the legend says “probability” but it should say “population fitting given decline”). One can see that with time number of patients is dwindling. This image gives the idea about the rapid decline in the number of patients whose decline can be keeping the average for the group higher (MMSE score). At this point, 2 days before the presentation we have no data to present any conclusion on this. The assumption then is that the decline is linear but the decline of Blarcamesine patients follows **the worst-case scenario**:

- We assuming that decline starts from MMSE score 20
- Blarcamesine High Concentration Adjusted data points (4 data points in blue) are well fitted by the yellow curve (the quadratic equation)
- The ADNI Placebo is showing a steady rate linear decline in scores.
**In the real world, that might not be the case.**

To evaluate the efficacy of Blarcamesine vs. ADNI Placebo we looked at

- The advantage in retained MMSE points by Blarcamesine patients over ADNI Placebo
- The delay in years in the score for Blarcamesine patients over ADNI Placebo
- The relative performance of Blarcamesine in terms of declining ADNI Placebo score.

- The Green Bar: the MMSE score difference grows till it reaches almost 6 points at 5 years into treatment
- The Yellow Bar: Years need for average Blarcamesine patient to reach deterioration of ADNI Placebo patient; starts at 3.5 years and steadily declines as shown.
- The Blue Bar: The MMSE score difference between arms as a percentage of the current score of the ADNI Placebo patient; Steadily increasing.

In general, the age of diagnosis determines the average life expectancy after diagnosis; For a 65-year-old patient that can be 8.3 years, for a 90-year-old patient this is 3.4 years. Assuming that at the score of 10 MMSE points patient is hospitalized; Blarcamesine buys additional 3 years out of an institution for a 65-year-old patient. That is notwithstanding the benefit of retaining a person’s cognitive abilities.

- In the first 2 years, Blarcamesine patients are expected to decline 4 times slower than placebo patients.
- If the extrapolations have any predictive value; Blarcamesine keeps the patients, in 6 years, from reaching the point of being classified as “Severe” and institutionalized.
- Presented data on Blarcamesine Advantage is the
**WORST CASE SCENARIO;**ADNI Placebo arm is declining in a linear fashion, that might no be the case in the real world. - Performance of Blarcamesine over Standard of Care is about six times greater than that of Donepezil.

I used some sources to make educated guesses to the number of all models of $TSLA car sold in the 3 major markets; the US, EU, and China.

From data on one place in Colorado ( have a history for good correlation to months numbers) I estimated all models in the US and multiply by 2.24 the number of avg 2 months in the quarter in order to account for those fabulous surges in sales at the end of the quarter, especially this one. So that is how I got Oct 19, Nov 19 and Dec 19.

I kept China months numbers steady for Nov 19 (comparing to know Oct 19) and applied scaling for the last month of the quarter (1.61 multiplier).

Europe was a bit more complicated. I have data for NL, NO, and SP, (Oct, Nov 19 (till 25th)), but I looked at the patterns in other quarters for the ratio between these countries and the missing rest of Europe. so the average of 2 front months was multiplied by 3.44 to get Dec 19. The number from Oct 19 was scaled by 1.55 from know deliveries to NL to give an estimate for Nov 19.

Since I am not that bright I just press a few keys and this is a plot of global demand all models of $TSLA cars by 3 main markets.

I am also lazy so I pressed another button and I got this trendline (second-degree polynomial) for all models demand 3 main markets.

I only pressed buttons, blame Gig Oil. It seems that the trend is right here and $TSLA’s demand is starting in even very continuous fashion declining (as smoothed over quarter’s 3 months). The information is in the monthly data. I want to find out what the final numbers for the Q4 2019 are to update.

The clues point to the descending trend in global demand. All markets seem to be poised to roll over. Crucial to this being true are the numbers for the last month of Q4 2019. Each market peaked and then started rolling over after going organic. The trendline equation parameters are given their current values by the weight of a sudden drop in the last 3 months. If my estimates are off than we probably have to stay content with steady demand or even rising, but that would imply going from the bottomless pit to blue heaven.

There is a sudden drop in the world number for Oct 2019. A similar situation was seen in Q1 2019 with demand in the US. The explanation was that tax breaks and the backlog of backorders were exhausted or ended. $TSLAQ has begun to appreciate the resourcefulness of Elon Musk in “keeping up appearances”, both in accounting and demand. So I heard voices claiming that the Oct, Nov 2019 drop is a “honey trap” for the shorts. Having lived once in a dictatorship of the proletariat reading between the lines can save you a lot of grief. There is the expectation of disappointment when Elon conjures up ~70,000 cars sold in Dec 2019. Mr. Musk sealed all leaks and is baiting potential longs with numbers, both in the accounting of his business and the future orders of the already notorious #Cybertruck.

Model 3 has died (dying a slow death), long live Model Y. Oh! Wait it is a reincarnation of Model 3. If indeed the numbers for reservation are in tens of thousands then #Cybertruck has been sorely needed. $TSLA Q3 2019 results and the reservation numbers for Cybertruck seems sounding like a Wall Street’s siren song to Longs and a $TSLA’s swan song to $TSLAQ. Let’s see how the snowfall in the US affected global sales.

$TSLAQ community had many more clues on production and sales. Now, it seems that the expectations for the quick demise of $TSLA have subsided the information has been a bit sparse. That is why the clues and the methods rely on correlations from previous quarters. Are they false? I don’t know till it will be too late to explain to anybody how Elon Musked my numbers. In this way, Elon will disgrace another Short.

By my reckoning, only God knows things and we all are kept in the dark. Since I am humble enough to celebrate my ignorance I think I shall be rewarded. Please, spare me standing on the street corner and begging.

Sincerely yours, Beer Fund

Beer Fund

$1.00 is about one bottle so .....please contribute..

$1.00

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