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Update On Two AAT Clinical Trials

Discussion in 'Research' started by joshualevy, Feb 16, 2018.

  1. joshualevy

    joshualevy Approved members

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    This is a update on two AAT (Alpha-1 Antitrypsin) clinical trials, with a little more general summary of AAT status at the end. AAT is an anti-inflammatory/immunomodulatory drug, which the body makes naturally, and which is already FDA approved for people who have a rare condition where they don't make enough of it on their own. Using AAT to treat type-1 diabetes is based on the idea that one of AAT's effects (lowering inflammation, immune modulation, or wound healing) can cure/prevent/treat the disease. My previous blogging on AAT is here:

    http://cureresearch4type1diabetes.blogspot.com/search/label/AAT

    Grifols' Phase-II AAT Clinical Trial is Unsuccessful

    Grifols terminated their Phase-II AAT Clinical Trial in October 2017. "Terminated" in the sense that they ended it earlier than expected. Their official comment was "Wk 52 primary endpoint results would be unaffected by follow-up data so trial was discontinued prior to wk 104. No safety data was collected after wk 52." I have unofficially been told by a participant, that they were told "it was found not to be beneficial enough".

    I interpret Grifols termination statement to mean: The primary results (after 52 weeks) were bad enough, so that no matter what the results from 104 weeks, it is not worth it to them to complete the trial.

    Discussion

    In my opinion, it is morally wrong (and should be illegal), for a company to stop safety monitoring during a trial. Even if the efficiency results are bad enough such that they don't care about that data any more, they still have a commitment to the patients in the trial to continue the promised safety monitoring. The patients have already gotten the experimental treatment, so bad side effects or safety issues could still happen.

    Clinical Trial Record: https://clinicaltrials.gov/ct2/show/NCT02093221

    Kamada Announces Results From Their Phase-II AAT Clinical Trial

    This trial had three groups: a "low dose" group, a "high dose" group, and an untreated (placebo) group. The primary end point was change in C-peptide generation, and secondary end points included A1c, insulin dose, and safety data. The results have not yet been published, so I'm working off of a Kamada Press release and email interactions with the team at Kamada. The basic results are:
    • No statistically significant results for the primary or secondary outcomes for the study as a whole (ie. "No significant treatment effect was observed in the overall study population").
    • For one subgroup (patients aged 12 to 18), there were "close to" significant results for the primary and some secondary results. The researchers call this a "positive trend". The full quote is "Efficacy trend was demonstrated in the pre-determined sub-group of patients between the ages of 12 to 18, treated with the higher dose of 120mg/kg. The positive trend was observed in this age group for all three key efficacy measures of Type-1 Diabetes".
    I consider this trial unsuccessful, because the primary end point was not met. I would not consider the subgroup data to be successful either, because none of it was statistically significant. You can read a lot more about my definition of study success here:
    https://cureresearch4type1diabetes.blogspot.com/p/recently-on-couple-of-occasions-ive.html

    However, the researchers do consider it successful; successful enough to continue the work on a follow on trial aimed more specifically at the 12 to 18 year old group that had the best results here.

    Discussion

    Differences of Opinion on Success

    So, why do I think this study is unsuccessful, while the researchers think that there is a success in there, and another clinical trial will find it? To understand this, let's look at the three results that they consider most important:
    • Better preservation of beta-cell function, as measured by less loss of C-peptide during the honeymoon (p =0.543).
    • Lower average HbA1c and more patients with A1c below 7% (p=0.052, p=0.048, p=0.073).
    • Lower insulin daily dose, for the higher dose treatment group versus placebo (p=0.086).
    The standard cut off for statistical significance is p=0.05 or below. So if you look at the numbers above, one is just in that range, three are close, and one is way out of range. My view is out of range is out of range, and also the most important number (C-peptide) is not even close to an acceptable p-value. C-peptide is most important for me, because it's the one that the FDA has previously said is the appropriate measure for curing type-1 diabetes. A1c and insulin usage can be impacted by eating fewer carbs and having better control during the trial, but the C-peptide that they measured is inherent to how much insulin the body is producing itself. The fact that they got the worst p-value there makes me profoundly nervous.

    The researchers point out that they see good trends in three different measurements: insulin measurements, A1c, and C-peptide, and it is unlikely that you'd get three good trends in the same group of people, just by luck. P-value is designed (more or less) to show the chance that you got a good result by luck, rather than by the effectiveness of the treatment. P-values above 0.05 are considered too likely to be due to luck. However, in this case, the researchers point out, there are three different results, all of which are slightly above the cut-off. Even if one was due to luck, it is unlikely that all three would be due to luck. So the researchers look at all three together and view that as sufficiently unlikely to happen by luck, that it must be due to effectiveness. Most statisticians would look at each measurement separately, and say that each of them looked like it was due to luck, rather than effectiveness.

    Since this was a phase-II trial, it was not large to begin with, and focusing on just the 12-18 year olds makes size even smaller, which is a handicap in a study like this. Another way to view this conflict is as follows: was the clinical trial unsuccessful (poor p values) because the treatment was not effective, or was it unsuccessful (poor p values) because it was small? Basically, if the trial were larger, would the p values have improved or would the effectiveness have diminished?

    However, the path forward is the same in any case. The researchers must do a follow on trial, which specifically recruits enough people between the ages of 12 and 18, to if the treatment is effective or not. It is the success of that follow on study which will determine if the research continues or not.

    Clinical Trial Record: https://www.clinicaltrials.gov/ct2/show/NCT02005848
    Press Release: https://globenewswire.com/news-rele...Newly-Diagnosed-Type-1-Diabetes-Patients.html

    The Scorecard

    However, there is another issue with AAT. This is not the first data we've seen on this treatment. Part of the reason I'm nervous about the results from this study, is that I know the results from previous studies, and none of them are particularly good.

    Study Number Phase Size Sponsor Duration Completion Date Results
    NCT01304537 I 24 Kamada 1 year November 2012 No strong results
    NCT01319331 I 15 Omni Bio 2 years September 2013 No strong results
    NCT01183468 II 16 NIAID 2 years November 2014 Terminated
    NCT01183455 II 66 NIAID 2 years November 2014 Withdrawn
    NCT01661192 II 12 Kamada 3 years December 2016 Future publication
    NCT02005848 II 71 Kamada 1 year December 2017 This study
    NCT02093221 II 76 Grifols 1 year November 2017 Unsuccessful


    Here is my previous blogging on the first two:
    http://cureresearch4type1diabetes.blogspot.com/2015/09/aat-completes-phase-i-trial-no-strong.html
    http://cureresearch4type1diabetes.blogspot.com/2012/06/possible-cures-for-type-1-in-news-june.html


    Joshua Levy
    http://cureresearch4type1diabetes.blogspot.com
    publicjoshualevy at gmail dot com

    All the views expressed here are those of Joshua Levy, and nothing here is official JDRF or JDCA news, views, policies or opinions. My daughter has type-1 diabetes and participates in clinical trials, which might be discussed here. My blog contains a more complete non-conflict of interest statement. Thanks to everyone who helps with the blog.
     
  2. rgcainmd

    rgcainmd Approved members

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    Thank you, Joshua, for your continuing diligence.
     
  3. MomofSweetOne

    MomofSweetOne Approved members

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    Joshua, thank you for your posts. I do have a question about your c-peptide comment. Wouldn't amount of insulin used (diet, etc.) also affect the amount of c-peptide produced? Or does even whether basal rates being set slightly too high reduce the amount of c-peptide since hopefully the body is adjusting down whatever it is producing itself to prevent lows? Just wondering if there is a way to truly measure how much our kids' bodies are actually producing with so many variables?
     
  4. joshualevy

    joshualevy Approved members

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    When you inject insulin, you inject insulin, nothing else. However, when your body creates insulin, it creates the insulin attached to a C-peptide. One c-peptide molecule is created for each insulin molecule. At the last moment, the insulin molecule and the C-peptide molecule are separated, and each goes into the bloodstream separately. So by looking at the amount of C-peptide in the blood, researchers can see how much insulin the body is actually producing. Injected insulin does not have C-peptide. So measuring C-peptide is a great way to measure the insulin a person produces, even if they are injecting insulin at the same time. You can inject as much insulin as you want, and it will not contribute to C-peptide numbers.

    Joshua
     
    rgcainmd likes this.
  5. MomofSweetOne

    MomofSweetOne Approved members

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    But why wouldn't it? Wouldn't the body adjust down the amount of produced insulin if the injected insulin level was too high? Which would then adjust the c-peptide? I was told once that the BG needs to be around 180 or higher so that the body is trying to bring it down in order to get an accurate c-peptide, that an in-range number doesn't give an accurate read.
     
  6. joshualevy

    joshualevy Approved members

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    For research studies, c-peptide is measured in two specific ways: "fasting" and "post-meal". I'm pretty sure the post-meal test is done without injecting insulin, so there would be no interference there. I'm not sure about the details of the fasting test.

    Joshua
     

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