This blog posting covers two topics which I found interesting at ADA's Scientific Sessions 2014. It contains much more of my personal opinions than a regular post.
Barriers to Pediatric Treatment Approval
This was an ADA session on getting drugs/treatments/devices approved for pediatric use. It focused on one topic: why is it so hard to get pediatric approval for diabetes treatments (both in type-2 and type-1)? The entire discussion panel took the position that getting pediatric approval for new treatments was too long/slow/difficult, and so there was no discussion of safety trade offs, or the that the extra work of pediatric approval led to commensurate increased safety. Everyone just assumed that it didn't.
The answer to the question of why pediatric approvals were so hard was basically this: The FDA requires testing on adults before testing on children (part of the Helsinki protocol on medical testing). So treatments get approved on adults. But the FDA requires separate testing on children. So approval for children comes years after approval for adults. You don't want to start your pediatric testing before you know that the treatment is going to be approved for adults, so there will always be a gap of many years. However, doctors can prescribe a treatment for anyone, once it is approved. So if the treatment looks safe for kids, doctors will prescribe it, once it is approved for adults. Many parents will encourage this, by clamoring for the newest treatments available. Now the drug companies think to themselves, why bother testing on kids? We're already getting profits from kid's prescriptions, and that's only going to increase. So there is little motivation for them to do pediatric testing. Of course insurance companies try to say "we won't pay for it for kids, because it hasn't been approved for kids", and sometimes they succeed and sometimes not. At the end of the day, that just limits the treatment to more wealthy families, which creates another problem; newer pediatric treatments are limited to the wealthy, even for people who have insurance.
All of this leads to the worst possible outcome, from a patient's point of view: drug companies don't test in children, and doctors routinely prescribe for children. We have use without testing. Officially, the FDA can wag it's finger at both industry (for not testing) and doctors (for prescribing), but the bottom line is that it's the FDA's policies that promote this sad state of affairs. (And the FDA can blame the Helsinki protocols for the problem, when the problem might really be their simple minded implementation of those protocols.)
In Europe, they have shifted towards requiring a plan for pediatric testing as part of the adult approval process, and the FDA is considering a similar change. The idea, is that since some doctors will prescribe a treatment for children once it is approved for adults, the regulator agency should require some testing (or at least planning for testing) on children even as part of adult approval.
Another idea, promoted by researchers and industry, is to share control groups. Right now, if you want to test a treatment, you need a treated group and a control group. That often means you must recruit twice as many people than actually get the drug. For example 150 people get the drug, and another 150 go through the study, but never get the drug. To test a second drug you need to recruit another 300 people, and so on. The idea here is to test several drugs at once, all sharing the same control group, so maybe recruiting 600 people to test 3 new drugs, instead of the 900 people it would take now.
Discussion (My Opinions)
I don't have a easy solution to this problem, but I think there are a few obvious things to consider:
Two side-discussions on childhood type-2 diabetes:
1. In general, ADA 2014 sessions labeled "pediatric" were about half type-1 diabetes, and about half type-2 diabetes. In itself, this shows how fast childhood type-2 diabetes is growing, since even 15 years ago, pediatric diabetes was almost a synonym for type-1 diabetes.
2. I was really shocked by how bad the outcomes were for people diagnosed with type-2 diabetes in childhood. In type-1 we are used to serious side effects that happen decades down the line, and can be minimized with good control during all that time. But that's not the reality of type-2 diabetes in childhood. Very serious side effects can happen during childhood. They are hit with bad complications much earlier and much more commonly than people with type-1 diabetes.
The term "big data" refers to using huge amounts of data to answer questions that were not even considered when the data was collected. A "classic" data base task might be to record all the books a person buys, so that you can see what authors they like. A "big data" task might be to record every book a person views while shopping, and every book they discuss on-line, and how quickly they read each page of each book they own, in order to answer questions about what they like, why they like it, and what they do based on their likes, etc.
There was an entire session on "Big Data" at ADA 2014. Although I don't work in Big Data specifically, I am a software engineer, and I do understand the topic. It was interesting to hear how medical researchers view big data, and also interesting that none of the papers in this session would be considered "big data" by software engineers.
Monitoring Data from Doctor Office Visits
Two of the talks focused on what I would call "more data" (but no where near "big data"). These guys were talking about integrating more medical data from more sources. But the amounts of data they were talking about was so small that they would not qualify as "big data" for anyone in the industry. (Indeed, the data was so small, it would easily fit "in memory" for a mid-sized virtual machine at my work site.)
One talk focused on scraping information from medical records and aggregating it. Basically, they installed a server at a 100 or so doctors' offices ("medical practices") that used electronic medical records. Every night, the server software would look at the newly updated records, and pull useful information and send it to a central server for analysis. No identifying information was sent, so all data was anonymous and there were no privacy issues.
This data could be used to get an early warning of a flu outbreak or a rare side effect in an approved drug or an unusual drug interaction. I very much hope that this can be used as a "safety blanket" to encourage more streamlined drug approvals, followed by more rigorous real use surveillance. I think this combination can lead to the win-win of faster approvals and safer drugs.
In a real world (although small) application on this idea, the researchers looked at all problems reported by type-2 diabetics. They noticed that many people who took two specific drugs at the same time had complaints about high BG numbers. Now each of these drugs were supposed to lower BG numbers. Both had been extensively tested and found safe and effective in lowering BG numbers. But by looking through 1000s of medical records, they found over 100 people who happened to take both, and they often had complaints (also in the their medical records) of high BG numbers. The two drugs had never been systematically tested together. The researchers gave both drugs to mice, and saw the mice BG numbers go up. So the statistical discovery was confirmed in animals. (Since it was a bad side effect, confirmation in animals was enough, you don't need to test something like that in people.)
A factoid from another talk at ADA 2014: In the US, 76% of people over 60 are taking more than one prescription drug. And I'm sure the number is much higher for people diagnosed with a chronic disease like diabetes. Yet drugs are often not tested together; indeed, people taking other drugs (especially for the same disease) are often specifically excluded from clinical trials to avoid uncertainty as to which drug is having what effect.
Recruiting and Running Clinical Trials on a Social Network
Another talk focused on using members of type-1 diabetes on-line groups as recruitment pools for studies, and making study participation much more like social networking. I would view this as "crowd sourcing" clinical trials. Therefore, it would be more natural to people who grew up updating Facebook status and sharing pictures. Presumably such people are comfortable sharing their A1c, which drugs they take, and complications they experience. These researchers have published some studies based on data from members of the TuDiabetes on-line community.
The researchers were generally worried about such things as "informed consent" to participate in a research study (and ethics in general), and also the quality of the data (especially self selection of the participants). They did notice that early adopters tended to have better A1c numbers than expected, which suggested to me that they were "skimming" the people with good control, rather than a representative sample.
Discussion (My Opinions)
Personally, for the "trials via social networks" idea, I'm more worried about deliberate manipulation, and I asked the speaker about this issue. Not in type-1 diabetes, but in some other diseases, there are organized patient groups that have very strong points of view about their disease, and have actively tried to manipulate scientific research to agree with their views. (See discussion below.) The researcher I questioned hoped that by using reputable groups (in this case TuDiabetes) they could minimize manipulation, and that statistical analytics might detect or prevent it.
I think that is wishful thinking because people can organize on one forum and then move to another to implement their manipulation, and also because the kinds of statistics usually used are not designed to detect or prevent deliberate, organized manipulation. At the very least, we would need new kinds of statistics and new kinds of surveillance to protect these studies.
Details about Manipulation
For example, already in vaccine, abortion, and chronic fatigue syndrome (CFS) research, I have seen organized attempts to bias research by selectively submitting reports of side effects, boycotting research that might show something they don't believe, influencing participants in the research, etc.
Currently, the most common form of manipulation is creating spurious VAERS reports. VAERS is a reporting system for vaccine side effects. However, any medical professional can submit anything they want into the system. So anti-vaccine groups run organized campaigns to ask doctors and nurses they work with to submit "side effects" that they claim are caused by vaccines. Anti-abortion groups do the same for those vaccines which were developed using cell lines from aborted tissue. Conservative religious groups do it for vaccines targeting sexually transmitted diseases.
More advanced forms of manipulation are also already in use. Some chronic fatigue syndrome patients have started campaigns to actively discourage fellow patients from signing up for studies that might disprove their pet theories, boycott all studies by researchers who have previously published results they don't like, and even sabotage studies by getting patients to drop out of studies they have already started if those studies might come to a conclusion they don't agree with. Both CFS groups and anti-abortion groups have organized mass ethics complaints against researchers whose work they disapprove of.
Research done via social networks would be even more open to similar organized attacks.
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All the views expressed here are those of Joshua Levy, and nothing here is official JDRF, JDCA, or Tidepool 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.