Category Archives: Biology

3D printers, DIY Bio, French bistros and one possible future path for drug development

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All opinions are my own and do not necessarily reflect those of Novo Nordisk

The Long Tail is Everywhere There’s Information

Several years ago I happened upon Chris Anderson’s great book The Long Tail.  He wrote about the amazing changes that were taking place in commerce because of the digitization and electronic dissemination of information.  Mix incredibly cheap (essentially free) data storage with the Internet and reasonable bandwidth, throw in the power of search and individual customization algorithms, and suddenly business models no longer had to rely on bulk consumption and the generation of popular hits.

The first industries to feel the change were in entertainment:  music, movies, books, where having a physical copy was once necessary to enjoy Madonna, Star Wars, or Carl Hiaasen’s latest thriller.  Digitization turned that upside down.  It became clear that what we’re really paying for is information, and it’s a lot harder for the entertainment industry (or any industry) to keep control over the dissemination of information than when they sold that information packaged in shiny plastic discs.

Anderson also described how in this digital world, and aided by the powers of personalized search, niche markets could not only survive but thrive.  Once, something like Tuvan Throat Singing was a niche musical form that you might have heard of on a trip to Siberia, but you’d have had no luck finding a CD at your local Tower Records (remember them?).  Now, you can not only find several tracks from iTunes or Amazon, you’ll also get suggestions for what else you might like based on your fondness for overtone singing.  Since it costs Amazon basically nothing to store the music and associated information, they can afford to have it available for the 20 people who might want to buy it.  Tally that up across all the niches in the world and it’s a hefty sum.

This is pretty neat.  But it’s still uncertain how the business of entertainment will shake out financially and logistically among the producers, distributors and promoters.  I’m not real fond of chaos like that in my professional life, and for a long time felt secure that my job–drug development scientist–was not in danger of becoming part of a long tail phenomenon.  Only now I’m not so sure. Continue reading →

Fielding percentage for UK surgeons

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All opinions are my own and do not necessarily reflect those of Novo Nordisk.

Last week I posted on how our measurements of defense in baseball have become a lot more sophisticated, and how that gave me hope for the evaluation of innovation.  If baseball, one of the most tradition-bound of US sports can adopt to new metrics, surely business can too.

I was reminded of this with the publication of a recent article about the National Health Service (NHS) in the United Kingdom and their plan to publicize the surgical success rates of clinicians across their country.  Surgeons in eight different specialities will have their mortality rates for specific procedures, length of hospital stays post surgery, and other elements published in tables for anyone to access.  The first group to have this information released is vascular surgeons.

A fascinating aspect of how this is being done is that publication of one’s rates is voluntary, but if a surgeon chooses not to have his or her rates published, that surgeon will be named.  It’s not quite putting people into stocks in the public square, but it is definitely a form of public shaming meant to increase participation.

Nevertheless, six surgeons have opted out and been named.  Game theory might predict these are surgeons on the low end of the measured metrics, who are taking a calculated risk that the negatives associated with not publishing their rates are less than the negatives that would come with disclosure of their rates.  But that’s not the case.  The NHS has stated that none of these surgeons lie outside the normal range for the reported metrics.

Instead, these doctors are protesting that the metrics are not measuring the right things.   They suggest the metrics don’t take into account the subtleties involved in surgical cases, how procedure names alone don’t properly capture how difficult or easy a procedure might be for a given patient.  Are there comorbidities?  Is a patient in generally poor health?  Is a surgeon one who specializes in tricky, difficult cases which would therefore lead to a lower success rate even though the surgeon him or herself might be highly skilled and effective?  Could these metrics scare new surgeons away from performing more difficult procedures?

This echoes the debate about defense in baseball, and whether standard metrics such as fielding percentage are the best for measuring defensive ability, or if more elaborate measures better reflect reality.

Still, while I agree with the viewpoint that we should always try to improve metrics, I also think the NHS is doing the right thing.  I think in this case the proper analogy might be baseball defense back at the time before the invention of fielding percentage.  In the practice of medicine world-wide there is a surprising lack of information about measures like success rates and efficacy.  As Sir Bruce Keogh said to the BBC: “This has been done nowhere else in the world, and I think it represents a very significant step.”  To take another quote from the article, Professor Ben Bridgewater commented, “We’ve been collecting data on cardiac surgery since 1996 and we’ve been publishing it at individual surgeon level since 2005, and what we’ve seen associated with that is big improvements in quality: the mortality rates in cardiac surgery today are about a third of what they were ten years ago.” That which we don’t measure, we can’t improve.

In the US, that idea is becoming more prominent.  Recent articles in Time and the New York Times have highlighted how transparency is lacking in the United States healthcare system, and the Obama Administration’s emphasis on comparative effectiveness is another thrust in that direction.  What the NHS is doing is a great model and a great start, and I hope they continue to both make these aspects of healthcare more transparent and work to refine their metrics so that they accurately reflect the difficulty of practicing good medicine.

Cheetahs hunting and the quantified self

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Who doesn’t love cheetahs?  A young person of my acquaintance went so far as to spend a large portion of her time, at a certain age, cavorting on all fours and yipping and chirping like a cheetah.  And of course we all know that cheetahs are the fastest land animals, and that’s how they catch their prey, by outrunning them.

Only that’s wrong.

Yes cheetahs are wicked fast, reaching about 60 miles per hour, but a recent report in Nature has shown, via novel monitoring techniques, that maneuverability and deceleration skills are the keys to successful hunting.  The researchers designed a new type of monitoring collar that included GPS and accelerometers.  No word on whether the collars also allowed cheetahs to play Words with Friends.

This report highlights the things we can learn as we get better and better at measuring.  Conventional wisdom may remain or be turned on its head, and either outcome is fine.  The key is that we have a better  basis upon which to understand that wisdom, that we don’t take things for granted, that we question our assumptions.

The cheetah collars also point to how we can gather so much more data on individuals, whether furry or bipedal (or both), than we ever could before.  I’ve recently been made aware of the quantified self movement (HT @bkolko), and what they hope to do is in line with what was done with these cheetahs.  Take individual monitoring and data gathering to new heights.  No, it won’t involve tracking collars (unless, you know, that’s your thing).  But it will involve using technology to measure what previously we could only guess at, and enable decision making and research in new and powerful ways.

Gastric bypass surgery and the ever expanding world of GxE interactions

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All opinions are my own and do not necessarily reflect those of Novo Nordisk.

An early publication article from the Proceedings of the National Academy of Sciences reports the fascinating finding that children born to mothers before and after gastric bypass surgery show differences in the expression of  genes involved in, among other things, glucose metabolism and immune function.  The study is small, with only 50 children evenly split between cohorts born to moms before and after gastric bypass, but if it replicates, it’s another piece of evidence  showing how the environment influences the way our genes function.

Epigenetics has been a hot topic in genetics research for a while now.  It’s clear that DNA methylation changes over time and within an individual and can affect gene expression.  Studies in a number of institutions such as Washington State University in the lab of Michael Skinner have shown that changes can even persist through multiple generations (in rats, at least).  The PNAS report adds another twist in that the gene by environment interaction arose due to a change in maternal health induced by surgery.

There are a lot of implications to this, including the rather theoretical one of whether this knowledge would induce more potential mothers to undergo gastric bypass surgery, and also practical ones of whether weight loss alone without surgery or via, for example, a lap band, would have the same effect.  But the one I wonder about is what this might imply for drug development.

While many genetic variations are known to affect disease risk and progression, and drug metabolism, there has been considerable debate on how to use such data.  In many cases, such as with the majority of Genome Wide Association Study hits, the relative risk of discovered variants have been statistically significant but small.  However, as we have seen with Amgen’s purchase of DeCode, drug development companies are keen to use genetic information to help inform their drug development efforts, to find an edge.

In this PNAS report, however, I see a flag of caution.  I applaud the efforts of Amgen and other companies taking these risks, but this report of possible epigenetic effects following maternal surgery also points out how much we’re still discovering about basic human biology, how much we still don’t know about the diseases we study.  Understanding Gene by Environment interactions is, I think, one of the key factors we deal with in developing drugs, and not one to ignore.  And yet, it feels currently like one of those “unknown knowns,” the things we willfully decide not to think about, even though we know it’s there.

A Genomics Researcher’s Take on the Global Health Metrics Conference 2013

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All opinions are my own and do not necessarily reflect those of Novo Nordisk

Over the past three days I had the opportunity to attend the Global Health Metrics Conference here in Seattle.  This is not my field; I’m a genomics researcher working in biomedical research and drug development, but I’ve also been curious about what’s going on in the area of public and global health.  This seemed like a good place to get a crash course.  The Lancet has kindly published all the abstracts and I wanted to give my impressions of what I heard.

First takeaway:  I was surprised and intrigued by how many parallels I saw between the work I do (primarily transcriptomics and genomics) and the work I saw reported.  Sure, global health researchers use surveys rather than high throughput sequencing, and gather data on nations rather than patients, and deal with the complexities of culture and government instead of human biology, and work in the public sphere as opposed to the private, and use a completely different vocabulary than I do, but other than that it was really similar.  So similar I put together this table:

Biomedical Genomics Research Global Health  Metrics
Increasing amount and types of data Yup
Biomarkers Indicators
Growing emphasis on efficacy measurements ditto
Lots of Acronyms, NIH, AMA, EULAR, ADME GBD, DALYs, CDVS, USAID
Struggle to understand what tissue, cell, analyte to measure Struggle to characterize the right metric to demonstrate effects/efficacy
Gene X Environment interactions poorly understood Local environment effects beginning to be captured
Personalized medicine Nation specific solutions
Noisy data, lots of unknowns Maybe even noisier data and, yeah, unknowns
More focus on longitudinal studies Already there

And so on.  I’ll elaborate on a few more below.  Another immediate takeaway:  I wasn’t even aware of the Institute for Health Metrics and Evaluation (sorry guys).  Now that I am, it’s a place I’d like to visit.

One thing that really impressed me was the work that IHME has put into making the Global Burden of Disease survey lucid, simple and accessible.  The data presentation by Kyle Foreman and Peter Speyer (@Peterspeyer) was terrific.  Not so much for any specific piece of data (although the trends and findings are all pretty fascinating), but rather for their demonstration of the power of dynamic presentation and facile web-based tools.  Static powerpoint charts are clearly so last decade.  Anyone wanting to check out their presentation can go here, or even better just go directly to the site.  As a scientist who also works with large, multifactorial datasets, I know the struggle to condense that data into a usable, comprehensible form.  I think Peter and Kyle have done a great job, and I also like the potential crowdsourcing aspect of it.  As I’ve commented on before, crowdsourcing methods, whether via games or other techniques, have a real potential to fully utilize large datasets and also to solve big problems.

Of the many talks I heard, a few I’ll highlight, just for the specific points I took away.  On the first day, Tanya Marchant showed interesting and cautionary data about making sure that what you’re measuring really measures what you think you’re measuring.  In this case, measuring the presence of skilled birthing assistants as a proxy for maternal care during childbirth turns out to be incomplete because of other factors such as availability of basic medical supplies.  Reminds me of debates over things like how best to measure drug efficacy in clinical trials–for example, response versus progression free survival in oncology.

Joseph Dieleman presented his work on looking on the effects of external aid to developing nations for health.  In a perfect world, external aid would just be added to pre-existing health expenditures, and after aid expired, local governments would maintain spending at pre-aid levels, or even higher.  Well, turns out this isn’t always the way this happens.  Aid comes in, local health budget gets shifted “temporarily,” but temporarily turns to permanently when the external aid leaves.  One of the thoughts that went through my head during this conference was to remember the law of unintended consequences.

I enjoyed Michael Wolfson‘s talk on functional health status.  Coming from an industry that really likes it’s tried and true measures like HDL/LDL levels, the concept of looking holistically at factors relating to actually feeling good was a nice contrast, and food for thought.

Bruce Hollingsworth had a great quote in his part, “People need incentives to provide accurate data.”  Yeah.  Tell me about it.  In transcriptomics it’s been a mantra for years that “Garbage in, garbage out,” in terms of incoming biological sample integrity and resulting data quality.  From what I saw, the data you can get trying to measure Global Health is maybe even noisier than the kinds of data that I normally deal with.  My main conjecture for why all hope is not lost due to data quality in Global Health is that GH researchers are able to bias the indicators they sample towards things with (hopefully) real meaning, else they would be adrift in a sea of not very useful data.  Maybe they feel that way anyway?  Bruce also made the point that there are external factors, again, which influence health.  Even people who know where to go for the best treatment may not because the facility is too far away.  Location, location, location.

Speaking of garbage (but not in a bad way), David Phillip‘s talk later that day referred to the problem of trying to extract useful data out of vital health records full of things like garbage codes.  That is, causes of death that are supremely unhelpful from a public health perspective, such as (I’m exaggerating here) death by lack of life.  His work on extracting useful proportions from this data based on the overall data distribution reminded me of imputation techniques that are used in genomics.

There were many more engaging talks, and I also had great conversations at lunch with different people. I suppose I shouldn’t be surprised by the similarities.  I think many research fields these days are converging on a similar emphasis on big data, analytics, efficacy, and finding the right metrics.  I also appreciated the long view shown by so many of these programs.  One of the drawbacks of private industry is the prevalence, often, of the short term view.  I could wish we had the decades-long commitment shown by various Global Health initiatives.

The aspect I find daunting in Global Health is how much uncertainty that community is dealing with, which greatly affects efficacy and efficiency.  An intervention might be exactly the right one when viewed in isolation, but can be so easily derailed by external factors.  Like biology, like baseball, it seems the key thing is to find the metrics that at least tell you that you made the change you hoped for, with the understanding that what happens at the end is so often, unfortunately, out of our control.