This page describes various words, phrases and ideas that get used over and over again in my blog:
Scientific, Medical, or Research Theories
Majority (or Dominant) Theory. Consensus Belief.
These terms refer to the most commonly held theory. This is sometimes called "the scientific main stream" or something like that.
This is a scientific theory which has support from some researchers, but most researchers do not believe it.
This is a scientific theory which has very little research support, or maybe no support at all. It is typically believed by one person, or possibly a very small group of people.
This is a scientific theory which has been disproven or is obviously wrong, but is still believed in by a small group of people or maybe just one very vocal person.
Trials and Experiments
Why is this important? For two reasons. First, because it is now possible to predict when they will finish collecting data. (This study runs for DURATION, so they should have data collected by DATE.) Second, because much of the uncertainty that surrounds clinical trials, is involved with recruiting participants. It is often unclear how hard it will be to recruite people, and long it will take. But that this point, all that cunertainty is behind the researchers. From now on, it is just gather data, then analize data, and then publish data. Researchers have a lot more control over those later stages, then over recruiting people in the first place.
Placebo vs. Control vs. Comparison Group
I use these terms to mean three different things:
- A placebo group gets a "sham" treatment of some kind, so the patients in that group don't know if they got the real treatment or not. In general, placebo groups (or placebo control groups) are the best type of comparison.
- A control group is a group that does not get the experimental treatment. A control group is the same as the treated group, except they don't get the treatment
- A comparison group is any group that is compared to the treated group. It starts out different from a control group.
The best studies have placebo control groups (often shortened to placebo groups). The worse have not comparison group of any kind. Control groups are better than comparison groups, but not as good as placebo groups.
The Real Publication Requirement
A lot of people seem to think there is some rule that says you must publish the results of clinical trails. Twenty years ago, the answer was simple: no. There was no law saying anyone had to publish anything. Sure, academics built their reputations by publishing, so there was pressure for them to publish. But for commercial companies, there was no reason to publish anything that would not increase their profits. Then the USA passed a law saying that any clinical trial that was part of a new drug application must be listed on the FDA's clinical trial registry. That was enforced as part of drug approvals, so suddenly there was a trial registration requirement, but no requirement to publish results. (Companies had to submit results for FDA approval, but not publish them in the scientific literature or make them available to anyone else.)
Years later the law was updated to require results be added to the clinical trial registry. However, there was no enforcement, and therefore few researchers did. Even researchers who published results rarely added them to the clinical trial registry. Years later, a news service published a series of articles describing the legal requirement, the fact that it was widely ignored, that there was no enforcement, and that most of this research was funded by US taxpayers via government agencies. Finally, there was some movement. Over the next few years many old clinical trial registries were updated with results, and now more results are posted when the studies are completed. But the number is still low.
So the summery right now,
in the US, is that there is a paper rule that researchers must make
results of clinical trials public, but, in fact, this data is not
available for many clinical trials, including many focused on T1D. I
think the situation is worse in the rest of the world, but I've never
investigated in depth.
For example, in 2015 none of the
research results previously funded by JDRF were available in the FDA's
clinical trial registry. The publication of this information by
Statnews resulted in several JDRF funded clinical trials posting results
to the clinical trial registry, in some cases years after the research
finished. I'm sure JDRF funded researchers are doing better now, but I
don't have any more recent data.
As of 2019, the two local (to
me) universities who do the most T1D research were University of
California San Francisco (reporting less than 50% of results) and
Stanford (reporting less than 75%). These numbers are for all the
clinical trials done at those universities, not just their T1D trials.
More to read:
Off Label Use
In the USA once a drug or device is approved, a doctor can prescribe it in situations different than it was originally approved for. As an example, a doctor may prescribe it for a different disease, at a different dose, or for a different type of person than it has been approved for. In the world of type-1 treatments, drugs or devices that have been approved for use on adults are often prescribed for children. This is a classic "off label" use. Off label use is based on the professional opinion of a doctor, and consent of the patient. Therefore safety data (such as from this study) can make doctors more willing to prescribe "off label", and patients more interested in trying it.
A case study is the presentation of something very unusual that happened to one person. Occasionally a case study will motivate a researcher to run a clinical trial. Case studies are part of the scientific literature, but they are not clinical trials; rather they occur before clinical trials in the scheme of things.
This leads into discussion of the symposium's magic word: "platform". You might think that a platform is a wooden box that you stand on. I'm a software engineer, and we use "platform" to mean a bunch of software that helps develop new software and can be used over and over again for that purpose. The pharma guys use the word in much the same way that software guys do. A pharma "platform" is a way to speed the development of multiple drugs. Everyone who is working on a drug, talks about their platform. They hope that their development can be used again and again to develop multiple different cures for different diseases. The word "platform" represents the unbridled optimism common to researchers; and the funding opportunity that every venture capitalist, pharma company, and non-profit is looking for. They haven't even started a clinical trial, and are already talking about how they will cure multiple diseases in the future. "Platform" is the pot of gold at the end of the rainbow.
To be a little more specific, two types of platforms were discussed. The first was a common collection of ingredients that you can customize to cure different diseases. Consider this bread analogy: you have a recipe for pecan bread. You try replacing the pecans with almonds, now you have almond bread. You replace them with blueberries and now you have blueberry bread. Your bread is what the pharma guys would call a "platform". You drop in one new ingredient to create a new bread. If your bread recipe is good at this sort of flexibility, then a commercial bakery would be very interested in it, as well as all the "specialty" bread recipes.
Bayhills has exactly this kind of platform: it is a ring structure of several chemicals, one of which is specific to type-1 diabetes. That drug is called BHT-3021 and is targeted at type-1 diabetes. But if you take the same basic ring, and replace the type-1 chemical with a different one aimed at multiple sclerosis, then the drug is called BHT-3009 and is aimed at MS. And so on....
Another kind of platform is a method to find drugs, which you can use again and again on different diseases. Again, to use a cooking analogy, let's say you are looking for a dinner recipe and so you grab a can of chicken soup and pour it over chicken meat and bake it. A week later you grab mushroom soup, pour it over beef and bake that. You now have a "platform" for making recipes. The platform is this: pour a can of soup over a meat and bake it. None of the ingredients are reused (as they are above), but the basic technique is reused. There were a couple of different researchers with this kind of platform at the meeting. Including Apitope and Dr. Mannie (neither in clinical trials as yet).
The second most important word at the symposium was "bio-marker". A bio-marker is a way to do an experiment for cheap. For example, lets say you have a drug and you think it cures type-1 diabetes. Running an experiment to see if it does will take years: you need to make sure type-1 doesn't come back. However, if you had some blood test that told you the person no longer had type-1, then you would not need to follow them for years. You would just need to do the blood test. That blood test would be for a "bio-marker". Something that showed you the drug had worked, but was cheaper, quicker, and easier than seeing if it had really worked. Finding a bio-marker for a disease speeds up ALL research aimed at curing that disease, and it attracts research money to that disease, since research there is less risky.
C-peptide is such a bio-marker for type-1 diabetes, but the pharma guys are always wishing that there were more. It makes research, cheaper, quicker, less risky, and less unknown. It is especially important that the government regulators agree to the use of the bio-marker. If they do, then you can get government approval that the drug is useful, based on the bio-marker: quicker, cheaper, less risky, and you know approval will be granted.
So here is a quick introduction to treating inflammation as a cure for type-1 diabetes: Everyone knows that type-1 diabetics have a lot of inflammation in their pancreas and especially around their beta cells. Most researchers believe that inflammation is a result of the body's immune attack on it's own cells. That is, the underlying immune problem causes inflammation and also causes beta cells to die (which causes the symptoms of type-1 diabetes):
However, some researchers believe that the underlying immune problem causes inflammation, and that this inflammation kills the beta cells, which then causes the symptoms of type-1:
The difference is that, in the second model, if you stop the inflammation you can stop the symptoms of type-1 diabetes (the high BG numbers and the low numbers). And that is a big difference. But this second model is still a minority opinion.
A monoclonal antibody is an artificially created antibody which targets one very specific cell in the body. Different monoclonal antibodies target different cells. So if a disease is caused by a bad cell, then using a monoclonal antibody to target that cell is a promising treatment. Because several have been successful in other autoimmune diseases, they are an active area of research for curing type-1 diabetes.
Because monoclonal antibodies are usually named with names ending in "-mab", they are sometimes called "mabs". Several have been tested in clinical trails as possible cures for type-1 diabetes. At one time, approximately 1/3 of new drug approvals were for different kinds of monoclonal antibodies. They are most commonly used as anti-cancer and anti-inflammatory treatments. The most well known is probably Rituximab.
General info on mabs: https://en.wikipedia.org/wiki/Monoclonal_antibody
List of mab drugs: https://en.wikipedia.org/wiki/List_of_therapeutic_monoclonal_antibodies
Black Box Warning
A "black box warning" is a serious warning placed inside a black box in the FDA-mandated drug labeling. The text will be printed on the drugs "package insert", on the FDA's web pages and on many other web pages and reference books that doctors commonly use to get information on he drugs. (They are not printed on the label of the pill bottle, however.) About 9% of prescription drugs have "black box warnings", and the etanercept used in this trial is one of those that has this warning. More details are here: https://en.wikipedia.org/wiki/Boxed_warning
CD numbers (such as CD34 or CD52?)
CD stands for "cluster of differentiation", which is a unique chemical (often a protein or peptide) on the surface of a cell which is part of the immune system. Different types of cells can have different CDs on them, and one cell can have more than one. These CDs control how the cell interacts with other cells and are also markers which identify the cell. For example, the CD34 marker is found only on stem cells, while the CD52 marker is found only on mature cells, and CD4 marker is found only on a specific immune cell called a "helper T-cell".
Each person has to decide for themselves which results excite them, and
which don't. For my part, in honeymoon trials, results where the
C-peptide numbers go up (after a year) excite me. Results where they
stay constant are unexciting. And, results where they go down are
This is one of several studies which preserved beta cell function for a year during the honeymoon. Early on, these studies gave me a lot of hope that they could be improved on, and eventually even lead to a cure. However, so far I haven't seen any movement in that direction. Treatments that preserved beta cells for one year, have (so far) not been extended to last longer.
Organizations of Researchers
INNODIA and T1DUK
This study is part of the INNODIA and T1DUK networks. INNODIA is a European collection of research universities, commercial companies, and patient organizations aimed at fighting T1D. Their research goal is "to advance in a decisive way how to predict, stage, evaluate and prevent the onset and progression of type 1 diabetes (T1D)." T1DUK is a UK collection of research universities focused on Immunotherapy research. Its principal aim is "To help get immune therapy into the market as part of the management for type 1 diabetes". Both networks are funded by JDRF. T1DUK is also funded by Diabetes UK (and others) while INNODIA is funded also funded by the Helmsley Trust (and others).