Hydroxychloroquine as a treatment for covid-19: summary of the best data we have as of Sept 2020 and an explanation of the statistics

 I'd like to start this post by drawing attention to the elephant in the room. It's clear that hydroxychloroquine as a treatment for covid-19 has become highly politicized.  I try to avoid consuming media on that sort of thing, to preserve my sanity, but I do get the general idea that at least some on the political right have hailed it as a miracle cure, and some on the left have painted it as having deadly side effects.  Early on, there was evidence that it might be a good drug candidate, and there was also evidence that it might have side effects that would preclude its use in covid patients, so each side had at least some grounds for their argument. Now that the randomized studies had been done, however, it appears that neither is correct.  

Many of the studies mention that patients receiving HCQ had more side effects than the control group, but they were not severe enough to prevent the drug from being used if it had helped with recovery from covid. 

But if there is a benefit to covid patients in taking hydroxychloroquine, it's so tiny that we would need a huge trial with tens of thousands of participants to actually see the effect.  Given the difficulties involved with setting up such a trial, and the small potential returns, it just doesn't make sense. But of course, I'm not asking you to take my word for it.  I'm going to show you lots of data.  

A search (on Google Scholar) of randomized, controlled trials for HCQ as a treatment for covid-19 came up with ten studies.  Randomized trials are superior to non-randomized trials because in a non-randomized trial, people who are more likely to recover without the drug are often the ones who are more likely to choose the drug.  People who are more educated are much more likely to trust the scientists enough to sign up for an experimental trial, and more educated people have better health outcomes all the way around.  People who are healthy enough to advocate for attention from medical professionals, or who have someone who is looking out for their interests and can advocate for them, or those with doctors who are highly motivated to get them the best care are more likely to end up with the drug.  Because of this, positive results in a non-randomized trial of a drug is taken as reason to do further experiments, but not to blindly accept the drug as effective. 

Placebo-controlled trials are superior to open-label ones, because taking a placebo, even if you know it's a placebo, makes people feel better.  I am including both placebo-controlled and open-label trials in this summary, but noting their category in my summary table.

These ten randomized controlled trials include a wide range of doses, and there is one each that includes zinc and azithromycin. There was a wide range of subjects with respect to how far they were in the course of the disease, from people who had been exposed but had not developed symptoms, to those who were intubated in an ICU. There was a range of outcomes observed, from the time it takes for patients to feel better, to how many patients died.

Of those ten studies, only two had a positive clinical outcome, and those were two of the three studies with fewer than 100 subjects.  Smaller studies are more subject to statistical error, and given how many larger studies we have that show no benefit, those two were most likely statistical flukes.  One of my tasks in this post is to explain how this can happen. 

What I do want to do, aside from presenting the data from those ten studies, is to explain why it looked like the drug was working, but then turned out that it didn't.  I can imagine that non-scientists might feel like someone is pulling something over on them somewhere, but as someone who as done early anti-viral drug discovery and design studies, I can tell you that this sort of thing is very much the norm.  Most of the drugs that look promising early on turn out to be false leads.  

P-values

In order to understand why a few smaller experiments found a positive result for HCQ, we need to talk about p-values. Technically, the p-value of an experiment is "the probability of obtaining test results at least as extreme as the results actually observed, under the assumption that the null hypothesis is correct." (Wikipedia).  But let's do an example with coin-flipping that hopefully will help clarify what this means.

Suppose someone gives you a coin, and tells you that it may or may not be weighted to give either heads or tails more than 50% of the time. Your job is to figure out if this is the case, by conducting coin-flipping experiments.  In this case the "null hypothesis" is that the coin is just an ordinary fair coin. 

You flip once and get heads.  You can say that 1/1=100% of the flips were heads.  But of course, you don't want to conclude that the coin is weighted, because a fair coin will give a result this extreme 50% of the time. In this experiment, you would say you got 100% heads, but the p-value is 0.50, so it doesn't count as good evidence of a weighted coin.  If we accepted this as evidence, half of genuinely fair coins flipped once would be described as weighted, and we don't want that.

So what if you did an experiment with two flips, and the coin came up heads both times?  That's a bit better.  You can say that 2/2=100% were heads.  A fair coin would give a result this extreme 25% of the time, for a p-value of 0.25.  But we still would not want to use this standard, because it would mean that a quarter of fair coins flipped twice would be called weighted. That's not good.  

With a three-flip experiment, if 3/3 came up heads, that's 100% with a p-value of 0.125.  One out of 8 fair coins would be described as weighted.

With a four-flip experiment, with 4/4 heads, the p-value is 0.0625. One out of 16 fair coins would be described as weighted.

With a five-flip experiment, with 5/5 heads, the p-value is 0.03.  Now we have crossed a threshold where this experiment would be considered publishable.  That threshold is p=0.05. If your experiment crosses this threshold, you can say that your 100% heads result is statistically significant.   

Note that as our experiments got bigger and therefore better, the p-values got smaller.  Lower p-values are better because they mean the odds that the result was from a fair coin giving an unusual (but possible) result is lower. If we go back to our definition of the p-value as "the probability of obtaining test results at least as extreme as the results actually observed, under the assumption that the null hypothesis is correct."  In this case, the null hypothesis is that the coin is just an ordinary fair coin.  Because 100% heads is the most extreme result possible, the math is easy.  

Also note that statistical significance is not the same as ordinary significance.  We say that something is significant when it is notable or important.  It's possible that you would get a result that is 51% heads and the divergence from 50% is statistically significant, but that result is not very significant in the ordinary sense of the word. 

There is nothing magical about p=0.05. It's just a standard that scientists (mostly) agree that you must meet to draw conclusions from your data.  If you have a result that has a p-value higher than 0.05, but still low enough to be suspicious, you can report it and say that there is a "trend."  But if you want to argue that the coin is weighted in favor of heads, and you send in a paper with p=0.07, the reviewers are (rightly) going to send it back to you and say "do more flips!" 

Now, there are some scientists who argue that the standard ought to be lower than p=0.05. If you give fair coins to 100 scientists around the world, and have them each do a five-flip experiment, there is a 3% chance that each individually will get 5/5 heads, and therefore a 97% chance that each will get something other than five heads.  But the chance that all twenty will get something other than five heads is 0.97 raised to the 100th power.  That's 5%. Turn it around and the chance that at least one of the 20 people doing the experiment will get five out of five heads is 95%. 

What if the 100 scientists around the world are all doing their experiments without knowing the others are doing the same experiment?  The majority that get an uninteresting result will do nothing, but the few that get the surprising result that the coin appears to be behaving strangely will publish their results, and the media will put out clickbait headlines about the extreme result. Remember, the math here is for a fair coin, giving a result that is unusual if you do the experiment once, but not if you do the experiment many times and cherry-pick the one result that is unusual.  If you have enough people doing independent coin-flipping experiments, there is a very high probability that a few people will think that the fair coin is actually weighted, if the standard for calling it weighted is a p-value less than 0.05. 

This is probably what happened with HCQ. Back in April, an experiment was published that looked really good.  It was a double-blind, placebo controlled experiment.  That's good experimental design.  It wasn't very big, but it did reach statistical significance.  The people who got HCQ were more likely than the placebo group to show improvement on their lung scans. The p-value for the difference was 0.047, which is just below the p=.05 threshold.  What we don't know is how many people tried similar experiments early on and found a negative result that they deemed not worth publishing.  There had been some early hints that it inhibited growth of the virus in a petri dish, and it was already an approved drug, and in non-randomized trials it looked promising, so it was definitely something worth checking out, and every scientist on the planet was aware of covid. So I wouldn't be surprised if there were quite a few.  And even if there were only one, there is a 4.7% chance that the improvement in the HCQ group was due to chance rather than to the drug actually being helpful. 

That was the first randomized trial to be released, then two more studies, both larger than the first, found p-values of 0.34 and 0.35.  One of those was a placebo-controlled double-blind study done in the US and Canada, and had over 800 participants. This was the study that was stopped early because it became clear that the drug wasn't helping.  At that point (in early June) doctors stopped using the drug.  During the big outbreak in NYC, hydroxychloroquine was commonly used as part of a "covid cocktail" of drugs that were promising but not fully tested. Despite its use in New York, the fatalities there were high, which is not surprising given the results of the studies. 

In late June, another small trial was released that had 48 people divided in to three groups. It found a p-value of 0.28 for days to get off oxygen (not statistically significant) but p=0.013 for chest CT score.  This is a better p-value, but remember that you will get this result by chance 1.3% of the time, assuming that the drug does nothing. It was also open-label, so the people reading the CT scans may have known which patient got the drug and which didn't.  

There was a third study with a statistically significant result, but it looks like a testing error.  They did a PCR test for shedding of viral RNA at day 7 and day 14.  At day 7 the HCQ group had more negative PCR results (with a statistically significant difference), but at day 14, the placebo group had more negative results.  So it looks like some people in the HCQ group tested negative on day 7, but positive on day 14.  In that study there weren't any clinical differences in disease severity between the groups, so there was probably just some sort of problem with their PCR testing. 

Of the seven studies that found no statistically significant benefit, most found a slight trend toward a benefit, which means that if someone did a huge study, they might find a small benefit that reaches statistical significance.  But it would be the equivalent of a coin that is weighted enough to give heads 51% of the time.  Not a miracle drug.  And there is one study that came out with a trend in the opposite direction. The UK did a large study in which the outcome they were looking for was death, not just how quickly those with mild or moderate illness improved.  In this case, the people given HCQ were more likely to die. The p-value for this difference was 0.18.  This study gave a fairly high dosage (the second highest on the list of ten studies) and it was the only study on the list giving the drug to people with more severe covid.  So it may be that a lower dose given to people with mild or moderate illness helps a little bit, while at the same time a higher dose given to those with severe illness leads to increased deaths.  But again those are trends, not statistically significant given the size of the studies.  

I've made a chart of the ten randomized trials of HCQ as a treatment for covid.  These studies are listed in the order in which they were released to the public.

Columns include:

• The title of the study
• The name of the first author of the study
• The date the data was released to the public
• The country where each study was done
• Whether or not it was placebo controlled or open-label
• The number of subjects in the study
• The characteristics of the subject in the study (i.e. were they people who were exposed and were given the drug in the hopes that it would prevent early infection?  Or people with more severe illness?)
• The groups the subjects were divided into, including information about dosage of the HCQ.  For reference, I found typical doses for already-established uses of the drug:
• 200-400 mg/day for lupus
• 400-600 mg/day for RA
• Treatment of Uncomplicated Malaria: 800 mg (620 mg base) followed by 400 mg (310 mg base) at 6 hours, 24 hours and 48 hours after the initial dose (total 2000 mg hydroxychloroquine sulfate or 1550 mg base)
• Total length of time the drug (or placebo) was given
• Summarized results. Any p-values less than 0.05 are shown in red type

  

Summary of major experimental covid-19 vaccines, as of August 2020

 Vaccines must undergo a series of tests in order to establish that they are safe and effective before they are available to the public.  In the US, the FDA must grant approval for a drug or vaccine to be sold. A company outside of the US can apply for FDA approval, but of course they still must meet the FDA standards. Other countries have their own regulatory systems for approval. 

The FDA requires three phases, although two phases are often combined. In order to even start phase 1, though, a vaccine will first be tested in animals.  Animal trials test for safety, and in the case of a vaccine, it's much easier to test for efficacy, because you can expose the vaccinated animal to the pathogen to see directly if the immune system fights off the pathogen. It is possible for a vaccine to produce a good immune response, but once the pathogen is introduced the immune response from the vaccine ends up being harmful.  There was some real worry that this would be the case for SARS CoV-2, as some animal trials with a different coronavirus found that the vaccine actually made the pathogen worse.  But happily, none of the animal trials for SARS2 vaccines have found any hint of that sort of outcome. 

Normally this sort of direct challenge trial with a potentially fatal pathogen would not be done in humans.  But in this case, the AstraZeneca team has said that they intend to do human challenge trials by the end of the year, in parallel with regular phase 3 trials.

Phase 1 is technically just to test for safety, although if you're going to give an experimental vaccine to some people, you might as well do the tests to see if it is likely to be effective, in which case it becomes a combined phase 1/2 trial. Phase 2, if you are doing it separately, tests for likely efficacy.  Both phase 1 and 2 are small studies, with dozens to maybe a few hundred volunteers, who must be young and healthy. 

Phase 3 is similar to phase 2, but with more people (thousands) and a wider range of people in terms of age and health status.  We know that in general, older people have weaker immune systems, and tend to have weaker responses to vaccines. At this point, only Pfizer has published data on older people, but all of the large phase 3 studies ongoing now are testing older people.  Unfortunately, the only country that is testing in children is China.  So this is going to leave parents in other countries with the decision of whether or not to give their kids a vaccine that has not been tested in kids. 

At this time, two vaccines have been approved for conditional use, although neither has been approved for use in the US or EU. One is the CanSino Biologics vaccine approved for limited use by the Chinese military. It has finished phase 2 testing, but not phase 3. The other is the vaccine developed by the Gamaleya Research Institute in Russia.  It has finished phase 1 testing.  

Eight vaccines are currently in phase 3 trials.  These are the ones that most of the world is waiting for. The three that are most relevant to the western world are Moderna's mRNA-1273, the Pfizer / BioNTech collaboration's BNT162b2, and the U. of Oxford / AstraZeneca collaboration's ChAdOx1 nCoV-19. I'm going to call these by the names of the largest company involved:  Moderna, Pfizer, and AstraZeneca. I will outline what we know about these three below.

The other five include four developed in China: CanSinoBio (the one approved for military use in China), Wuhan Institute of Biological Products, Beijing Institute of Biological Products, and Sinovac each developed a vaccine based on inactivated virus.  The last one is an Australian trial of the BCG vaccine, normally used to prevent TB, but in this case it's being tested against covid-19.

AstraZeneca / Oxford -- ChAdOx1 nCoV-19

This one was initially the front-runner in the competition to be the first through the approval process, but Moderna and Pfizer are catching up.  Although a common concern is that these vaccines are being developed too quickly, the people at Oxford have been working on the technology for this vaccine for a very long time, which gave them a big head start. They were trying to make a MERS vaccine, and so had inserted the MERS spike protein gene into their fancy vector, ChAdOx1.  This is a chimp adenovirus that is missing a gene needed to replicate.  In order to grow it for the vaccine, they inserted that one missing gene into a cell line, HEK293, which is used to grow the viruses.  These adenoviruses containing the gene for the MERS spike protein are injected as a vaccine.  Although they can't replicate to make new viruses, they can enter cells and do what viruses do -- hijack the cell's protein-making machinery to read the code of the foreign genes that it has brought into the cell.  So the cell makes the MERS spike protein, which the immune system recognizes as foreign, which triggers the development of immunological memory. The reason that they are using a chimp adenovirus is that many humans already have immunity against human adenoviruses.  They screened many different adenoviruses to find one that would infect human cells and not be attacked by the immune system before it can deliver its payload. If you search Google Scholar for ChAdOx1, and restrict the date to 2019 or earlier, there are 303 academic papers available. So the vector is not new.  And importantly, the vector has been tested in older people, who had about the same immune response as younger people.  When covid-19 happened, the researchers could quickly put the gene for the SARS2 spike protein into the vector and start animal trials immediately. 

Animal trials

The results of the animal (mice and monkeys) trials are published here.  I'm looking primarily at the monkey data, since they are more similar to humans than are mice. They used three groups of six rhesus macques -- one got a single shot of the vaccine, one two shots 28 days apart, and one got a placebo vaccine of the vector containing the gene for GFP.  

All of the monkeys that got the real vaccine had an immune response.  In the graph below, red symbols are the moneys with the single shot, blue are those with the shot plus booster, and green are the controls.  The left graph shows antibodies, the middle neutralizing antibodies (which can block the virus from infecting cells) and the right graph shows T-cell responses.  Both antibody and T-cell responses are better for the monkeys with two shots rather than just one, which is not surprising.  The booster shot is designed to mimic a natural infection, where the immune system sees foreign material for an extended time, rather than just once. All of the monkeys that got the vaccine had an antibody response.  Unfortunately, the T-cell response was above baseline for only 4 of the 6 monkeys.  

 

The monkeys were then given a high dose of virus in both the upper and lower respiratory tract.  The result was good but not as good as we'd like.  The best result is shown in the graph below.  The green symbols show the control monkeys are testing positive for virus growing in fluid from their lungs, whereas the immunized monkeys have at most a low level of virus that goes away quickly. This is why the title of the paper is "ChAdOx1 nCoV-19 vaccine prevents SARS-CoV-2 pneumonia in rhesus macaques."  The best news is that the vaccine protects the monkeys lungs. 

The less-good news is that the nose swabs are showing virus growing in the upper respiratory tract.  This is virus detected by PCR, so not the same as infectious virus. They did test for infectious virus as well. That data in "extended data table 1" below shows that the monkeys with two shots (prime-boost) had less infectious virus for a shorter period of time. The monkeys with one shot (prime) were about the same as the controls with respect to live virus in their nasal swabs. Less infectious is good, but we'd rather see not infectious at all.

Something I missed the first time I read this paper in preprint form is that the vaccinated monkeys were not free of symptoms when they were exposed to the virus. This data is presented differently in the preprint than in the final paper that is now available. Below, the left graph is from the preprint, and the right one from the final paper. The left graph looks like the vaccine led to milder symptoms for a shorter period of time, although the error bars are larger than I'd like to see.  The right graph, which separates out the monkeys that got one shot vs two, looks like a rather messy result, with the two-shot monkeys actually doing worse than the one-shot monkeys at later time points.  If you lump all the vaccinated monkeys together, the difference is statistically significant, but still, this is a bit concerning.

They then killed the monkeys (sorry animal lovers!) and looked at their lung tissue. The left image is from a one-shot monkey, the middle a two-shot monkey, and the right a control monkey.  The left and middle look totally normal.  The right looks like a really nasty case of pneumonia. This is good news, and consistent with the finding of low or no virus in the vaccinated monkeys' lungs

They also looked at cytokine levels for the monkeys. The little paired asterisks show statistically significant differences. Interferon gamma, is higher for the vaccinated monkeys, probably due to T-cells formed during exposure to the vaccine. IL-10 and IL-13 are both lower for vaccinated monkeys. These are both cytokines associated with a Th2 response, which makes sense.  Fighting a virus requires a Th1 response, so the exposure to the viral vaccine pushed the immune system toward Th1 and away from Th1.  The bad thing here is that there isn't a reduction in IL-6 with the vaccine.  This is an inflammatory cytokine that is highly elevated in people who have the most severe cases of covid-19. On the other hand, none of the control monkeys are showing the crazy high IL-6 levels that are happening in some humans, so maybe monkeys are not a great model for that particular part of covid-19.

Human trials 

The combined phase 1 and 2 results for testing this vaccine in humans are available here.

1077 healthy people aged 18-55 were randomized to receive the ChAdOx1 vaccine or a different vaccine as a control.  The nice thing about using a different vaccine is that subjects don't know if their side effects are due to the experimental vaccine or the control vaccine, so they can't guess which group they are in.  This is a fairly large group for phase 2, so we have the potential to get more data about rare side effects.  The dose was twice that given to the monkeys, which makes sense given that humans are larger than the monkeys.  Ten people who received the ChAdOx1 vaccine were given a second shot as a booster.  Those ten people were told that they were getting the real shot and not the control.

The side effects from the real vaccine were clearly worse than for the control (meningitis) vaccine. The symptoms experienced were the sort of thing you'd expect from activation of the immune system -- fatigue, chills, headache.  Think flu-like symptoms.  A few people described these as severe in the days following the injection, but none had severe or even moderate symptoms a week out from the injection. I suspect that those individuals are the ones who would have been more likely to have a nasty reaction to the real virus.  Remember, severe covid is severe at least partly because of immune system overreaction. 

Looking at antibody responses, those that got the real ChAdOx1 vaccine had a good response.  There does not appear to be an advantage here for the booster shot. Compared to people who actually got covid, they had roughly the same level of antibody response. 

A subset of people were tested for neutralizing antibodies.  These antibodies block the virus from entering cells. Here there is a clear advantage to the booster shot. Some people had a weak neutralizing antibody response with one shot, but everyone who got he second shot had a solid response.

A subset were also tested for T-cell responses.  Happily, the humans had *much* better T-cell responses to this vaccine than did the monkeys. This suggests that humans will have better immunity when faced with wild virus than did the monkeys. The best monkey had about 175 activated T-cells per million, and the average human is peaking around 800. There are a few humans in the one-shot group that didn't have a T-cell response. But the worst-responding humans in the two-shot group are roughly the same as the best-responding monkey.  

The official phase 3 description is here. They've changed the name of the vaccine to AZD1222. They are in the process of enrolling up to 30,000 participants. This time it is double-blind (the phase 1/2 was single-blind), and uses a saline placebo instead of a control vaccine. Instead of splitting people 50-50, the experimental group is twice the size of the placebo group. The dose is the same as the phase 1/2 trial, but this time everyone is getting two shots, four weeks apart. They are not accepting kids, but there is no upper limit for old people. They are not testing immunodeficient or seriously ill people.  

They started injecting people in late July, and claim on the official forms that they expect to have their primary results by 12/2/2020. I think it's possible that they might have results sooner, since immunity is pretty clearly kicking in within a month. To see if the immunity is actually protecting people, we need to see if the placebo group is getting sick and the vaccine group isn't.  Right now about 0.7 per thousand Americans is officially testing positive every day. If you have a placebo group of 10,000, that's 7 people per day on average. That's going to add up fast.

They are also planning on doing human challenge experiments, which are much faster, as the subjects will be exposed to live virus as soon as they have had time to develop immunity. They haven't given a hard date on that, although the general "by the end of the year" may mean that they are holding the option as a last resort if they don't get enough data from people catching the virus in the wild.

In April, they said that they would release results in June, and they didn't. In May, they said that they would be approved by September, and that seems like a long shot now. They have also said that they expect the vaccine to last a year, although without giving any hint of why they think that. 

Moderna -- mRNA-1273 

Because Moderna is a company rather than a university, we have less background on the past development of this one. The technology is RNA, a type of genetic code.  RNA is notorious for degrading in the environment, but for the vaccine, it is wrapped in a protective lipid nanoparticle. Moderna had also been working on a MERS vaccine when SARS2 hit, although they were not quite as far along as the Oxford group was on theirs.  The gene being used here is the spike protein, with a slight modification to stabilize the protein.

Animal trials

Like the Oxford group, Moderna tested their vaccine first on mice, then on monkeys.

The most notable thing about their mice study is that even though the mRNA-containing lipid vesicles were injected into a muscle, most of the expression of a reporter gene showed up in the liver. Upon reflection, this makes sense. One of the functions of the liver is to scavenge lipids, and so it's probably taking up the lipid vesicles. There are lots of immune cells in the liver, so this might actually help the vaccine provoke a strong immune response. 

The monkey study is here. They used three groups of eight monkeys each. One group got a saline control, one got 10 ug of the vaccine, and one got 100 ug. All monkeys were given a second shot a month later of the same thing they got the first time. The units here are micrograms, commonly written as ug to avoid having to look up the keyboard code for the greek letter mu. To give you an idea of how much this is, find a ruler with millimeters. A cubic millimeter of water weighs one milligram.  The larger 100 ug dose is 0.1 milligrams, so a tenth of the weight of that cubic millimeter of water.  The vaccine is made synthetically, not grown in cells like the AstraZeneca vaccine.  Because RNA is unstable and won't just go into cells by itself, it is encapsulated in tiny lipid vesicles.  Unfortunately the papers describing these lipid vesicles in detail are behind paywalls.  

The monkeys all developed general antibodies.  All but one (in the lower dose group) had neutralizing antibodies.  The purple dots show same test run on humans who have had covid.  So far so good, especially with the higher dose.

They also looked at T-cell responses. Unfortunately, they are not doing exactly the same test as was done with the AstraZeneca vaccine, so we can't do a direct comparison between the two.  But we can say that with the lower dose, four of the eight monkeys had T-cell activation, and with the higher dose, all eight did. The responses were all Th1 rather than Th2, which is what you want to fight a virus. 

A month after the second shot, the monkeys were challenged with live virus. The dose here was lower than with the AstraZeneca vaccine.  It was about 760,000 viruses for the Moderna study and 1.3 million for the AstraZeneca study. So again you can't do a direct comparison. We can say that once again, the vaccine is better at preventing the virus from replicating in the lungs than in the upper respiratory tract. With the higher dose of the vaccine, there is not much replication in the nose, but that could be due to the lower challenge dose. This group did not test the nasal swabs for infectivity, unfortunately.

They also killed the monkeys and looked at their lungs.  The placebo monkeys (top) had clear pneumonia, whereas the vaccinated monkeys did not.

Human trials

Moderna has said that they've done phase 1 and 2 trails, but they have only published phase 1, here. There is a supplement with additional data here. Being a phase 1 trial, it is small, only 45 people. They were divided into three groups of 15 that received 25 ug, 50 ug, or 250 ug, then the same dose 28 days later.  There was no placebo group, since this is phase 1 only. 

Once again, we see flu-like symptoms as a side-effect, some described as severe. One person was withdrawn from the study after experiencing a mild allergic reaction (hives) to the first shot. Unlike the AstraZeneca paper, this study does not give data on how long the side effects lasted. 

Unfortunately, one person in the highest dose group had a fairly serious reaction after receiving the second shot. Here is the description from the paper: 

"A participant in the 250 mcg dose group had severe fever, onset the evening of the second vaccination, along with severe chills and mild fatigue, myalgia, and headache. In the early morning of the day after vaccination the participant developed recurrent severe fever, chills, fatigue, and headache, moderate myalgia and nausea, and mild arthralgia. The participant was evaluated in an urgent care center and received symptomatic treatment prior to discharge. A nasal swab specimen was negative for SARS-CoV-2 by polymerase chain reaction and positive for adenovirus by a fluorescent antibody assay. After sleeping for several hours at home, upon standing the participant was lightheaded and nauseous, vomited, and then fainted. Lightheadedness persisted for several hours. Other systemic symptoms improved over the course of the day. Mild headache was present the next day and mild fatigue was reported through post vaccination day 6."

Because this is the age of covid, this subject has been interviewed about his experience, so if you want to read a more personal account, that's here.  There were several other people, also in the 250 ug group, who had abnormal labs, probably related to liver inflammation. Spoiler alert: Moderna is not using the 250 ug dose in the phase 3 trials. 

All three dosages gave good general antibody responses. This is seen in the top two rows below. The neutralizing antibodies were a bit weaker with the 25 ug dose.  Also, some individuals are losing neutralizing antibodies pretty quickly, but this happens with natural exposure as well.  Of course, not all immunity is antibody-based, which leads us to T-cells.

Here are the T-cell results for those that got the 100 ug dose. Once again the humans are showing a stronger T-cell response than the monkeys, about double.  The difference is not as dramatic as with the AstraZeneca vaccine, but in that case, the humans were given twice the vaccine dose as the monkeys, and here the doses were the same. As expected, the response is mostly Th1, not Th2. 

The official page for the phase 3 clinical trial is here. 

They are testing the 100 ug dose vs saline as the placebo. This makes sense, given that 250 ug had a lot of side effects, and 25 ug had a weaker neutralizing antibody response.  I do think that if the vaccine is made available to children, they shouldn't get the full adult dose.  If 250 ug causes problems in adults, 100 ug might cause problems in a child that has half the body weight of an adult. As with the AstraZeneca trial, the Moderna trial is enrolling up to 30,000 people.  They are not accepting kids or pregnant women, but they are accepting old people and those who have pre-existing medical conditions.  

Moderna has said that they need 150 people to test positive for the virus, after enough time has passed for the vaccine to kick in.

Pfizer --  BNT162b2

American biotech giant Pfizer has been quietly collaborating with the German company BioNTech. As far as I can tell, they have not released data from animal studies, although presumably they would have had to show those results to the FDA in order to go forward with human trials.  Their technology is very similar to that of Moderna.  They are using mRNA code for the spike protein, with slight modifications to stabilize the protein. The mRNA is wrapped in a lipid vesicle.  Neither company is making the composition of the lipids easy to find, so I don’t know if they are the same lipids.  The Pfizer vaccine has an additional modification.  Specifically, for the science people out there, it has pseudouridine in place of uridine.  This reduces the immunogenicity of the mRNA itself. You don’t want the immune system attacking the mRNA before it can be decoded. Theoretically, this should reduce side effects, and also reduce the dosage, since more of what you inject will get past the immune system to make the SARS2 protein that you actually want the immune system to attack. 

Pfizer was initially working on a slightly different vaccine, BNT162b1, which was a secreted form of the spike, with an extra immunogenic domain tacked on.  If you want to read those studies, they are here and here. But they switched to one that has the full code with membrane anchor, due to finding fewer side effects.  Their phase 3 trials are therefore testing BNT162b2.

The phase 1 trial for BNT162b2 is here.  

Although Pfizer is doing a similar vaccine to Moderna, their early experiments included a cohort that Moderna’s did not: elderly people.  As with the other vaccines, side effects were basically flu-like symptoms, and were lower in the older group.  This makes sense, as older people have weaker immune responses in general.

General antibodies were lower in the older group, but still higher on average than people who have had covid (far right bar)

Same goes for neutralizing antibodies:

They haven’t published data for T-cell responses with BNT162b2, but for the b1 version, their responses were good.  

The official clinical trial webpage lists both the b1 and b2 versions, but various media reports claim that they are moving forward with the b2, 30 ug dose, two-shot version.

Back in July, Pfizer said that they were on track to have 100 million doses of the vaccine available by the end of the year, and 1.2 billion doses in 2021.

A brief word about the BCG vaccine

Early on, there were lots of studies that showed a correlation between use of the BCG vaccine and lower rates of covid-19.  Correlation is not causation, so clearly more research was needed.  The Murdoch Children’s Research Institute in Australia is conducting that research. They are currently doing a phase 3 trial in which healthcare workers are given either a placebo or the BCG vaccine.  They are recruiting up to 10k participants, and already are following 2800 people from the first phase of the study. The fact that they have moved to phase 3 suggests that they found something interesting in phase 1/2.  But they have published nothing at all to that effect.  It’s possible that they found a benefit early on and are trying to prevent a run on the vaccine.  Currently, the main use of BCG is to prevent TB in children in the developing world.  If the manufacturer of the vaccine is notified secretly, they can ramp up production while phase 3 trials are being completed.  That’s pure speculation on my part, so take it with a grain of salt. 

There has been one non-randomized study done in the UAE that found that people who took the BCG vaccine didn’t get covid, while 8.6% of their colleagues who declined it did.  Because this isn’t randomized, it may just be showing that people who are taking the virus more seriously are more likely to take the vaccine if offered, and also to wear masks, wash hands, etc.  So we still need to see the Australian study, which is randomized and placebo controlled, finds a benefit. 

Concluding remarks

All three of the major contenders have good enough preliminary data that the vaccines are likely to show enough benefit for approval.  All three are also likely to have side effects that are worse than vaccines you’ve had in the past.  Still, it’s far better to have flu-like symptoms for a few days than to get actual covid, and if you’re someone who is prone to strong immune reactions, you are also someone who is at high risk from covid, because much of the severity in severe cases is linked to immune over-reaction.  It looks like 10-20% of young adults who get the vaccine will be curled up in bed for a day or two with chills/fatigue/body aches.  Older people have weaker immune systems, and so have fewer side effects.  I think this means that we should be cautious about giving the vaccine to kids, because they are both physically smaller, meaning the dose per body weight is higher, and they are also prone to strong immune reactions, like suddenly spiking a high fever.   If a vaccine is offered for kids, it could probably be given at a lower dose, as side effects for all three are proportional to dose, and we are giving a high dose to adults in order to protect the elderly people who are at most risk from covid, but also have weaker immunity to the vaccine.

Although the vaccines are likely to be good enough, they might not protect completely.  That is, it will probably still be possible to get a positive covid test after getting the vaccine, but with milder symptoms than you would have had without the vaccine.  We don’t know yet whether or not a vaccinated person will be able to give the virus to someone else, but at the very least, they will probably be less contagious than they would have been without the vaccine.  

I suspect that many people will rush to get the vaccine as soon as it’s available, but that many others will worry about safety, or in the case of the AstraZeneca vaccine, have objections to the HEK 293 cells (human embryonic, derived from an abortion in the 1970’s) used to grow the vaccine. Because the virus does not spread as quickly as does some other pathogens, we don’t need a very high percentage of the population to get the vaccine in order for spread of the virus to decline.  The percentage needed depends on a lot of other factors, like what percentage of people are wearing masks, but a rough estimate is 60%. That doesn’t mean the virus would go away overnight if 60% of the population gets vaccinated.  It means new cases would stop growing.   I think that there are many people who will let others go first, and then join in when they see that their friends and family got the vaccine and were okay. The more uptake there is with the vaccine, the faster we can all get back to normal life.