How Plants in Space Might Be a Cautionary Tale for Precision Medicine

A version of this originally appeared in the Timmerman Report.

When President Obama announced the Precision Medicine Initiative (PMI) back in 2015, it was a moment not unlike when a pop star drops a new album. We know she’s working on something, but details are scarce until that midnight announcement on iTunes. Then Boom!

This was Precision Medicine’s coming out party, the moment when a sensible, but somewhat obscure biomedical research concept went mainstream. And it’s exciting! As someone who’s been on the Precision Medicine bandwagon for a while now, I’m glad to see the concept getting more attention.

And yet, I’ve found myself worrying that we haven’t learned from the past and once again we’re riding a hype rocket destined to crash and burn like the first (and the second, third, and fourth) attempt at getting that SpaceX booster stage to land on a platform in the Pacific. While we’ve learned an amazing amount about human genetics over the past few decades, there’s much more we still don’t know and understand. While genetics races ahead, we are often still stuck in neutral when it comes to our understanding, much less predicting, the effect of the environment on genes’ effects on phenotype. It’s not easy.

Think, for a second about autoimmune diseases like type 1 diabetes, celiac, or rheumatoid arthritis. We don’t know what causes most of them, or causes them to flare up, but evidence is pointing toward the complex interplay of genes responding to environment. Consider the research on autoimmunity in Finland, compared with that across the border in Russia’s Karelia territory. As Moises Velasquez-Manoff has described, despite having quite homogeneous populations, and a similar geographic environment, the Finnish side sees much higher incidences of autoimmunity and allergies. Finnish scientists are increasingly pointing to genetic variations that have allowed Russians in a less-hygienic environment to avoid some of the autoimmune conditions that have emerged in Finland.

Or consider some provocative cohort studies from Florida, which characterize the timing of diagnosis for inflammatory bowel disease (IBD) in Cuban immigrants. Over time the duration between arrival in the US and diagnosis for IBD has been decreasing. Immigrants who arrived before 1980 had an average time to IBD diagnosis of 31.77 years, whereas immigrant who arrived after 1995 had an average time to diagnosis of 8.30 years. Given that the genetic background of immigrants is expected to be similar over time, the evidence suggests environmental factors.

Changes to the environment, including subtle ones that might seem trivial, sometimes matter.

Basic research often has a hard time justifying its existence in a world that wants immediate payoffs, but this is where it helps to look to basic research for guidance. In specific, we can learn from plants in space. There’s something here for everyone who studies eukaryotes.

I used to work on Arabidopsis thaliana – a genetically complex plant model organism that non-scientists would call a roadside weed. My ears perk up whenever I hear a story about Arabidopsis in the news. A few years back, researchers sent seeds up into space and grew Arabidopsis in microgravity. According to this report, while plant morphology was generally the same, the secondary branches and seed pods grew out perpendicular to the stem. On Earth, branches and pods normally point upwards, making an acute angle with the stem.

Who knew? But more to the point, who would have predicted this? I suspect no one would have guessed. Studies like this in space may help us actually figure out ahead of time, eventually, what effect microgravity will have on the growth pattern of seed pods, not to mention the many other things going on that weren’t or couldn’t be measured. It might also help us confidently predict the phenotypes that wouldn’t be affected. But this will take a lot of time and much better predictive models for gene regulation and phenotypic expression.

Arabidopsis had its genome sequenced back in 2000. It has been the subject of much basic and applied plant biology research—at this point, it’s probably the best studied plant on the planet (sorry maize—you had a good run). Scientists took advantage of the relatively easy transformation methods of Agrobacterium-mediated gene disruption and the ability to do blanket mutagenesis screens to define dozens of developmental and metabolic pathways. Many of those findings were used to improve numerous crop species (something to consider, for those in the US Congress who scoff at spending on basic research). But even after all this investment and research, I don’t think any scientist would suggest we’ve solved Arabidopsis to the point where we can predict everything about how it would grow, develop and otherwise behave when introduced to a new environment. But it’s a simpler model and easier place to start than human biology.

Parenthetically, speaking of unexpected things that happen to organisms in space, I’d suggest you check out this great piece on sex in space by Maggie Koerth-Baker and how the birds and the bees (and the rats) don’t (ahem) function the same in space either.

The heart of Precision Medicine is giving patients tailored treatments based on the molecular fingerprint of their diseases or conditions. There will be better efficacy and fewer side effects of new drugs because the treatment will be more specific. It’s no mystery why the current forefront of Precision Medicine is in cancer, where the strong causal connection between genetics and phenotype means therapeutics tailored for specific mutations in specific oncogenes or other biological processes provide a clear, straightforward path to Precision Medicine. If you have mutation X in gene Y, then take drug Z, which was developed to target that precise pathway.

This simple path works best, however, if the genetic penetrance is nearly complete and is not affected by environmental factors. Outside of cancer there aren’t as many examples of common genetic risk factors that have strong causality, even in combination. When you don’t have that kind of tight causal relationship, Precision Medicine is harder to pull off. The recent Omnigenic model proposed by Evan Boyle, Yang Li, and Jonathan Pritchard, which suggests most genes in a given tissue influence disease susceptibility, and the resulting debate about the applicability and value of GWAS moving forward shows the ongoing evolution in how genomicists and clinicians look at the interplay between our genes and phenotypes. The implication of a model like this is that even when looking only at the genetic side of things, strong causality driven by a small, testable number of variants for many diseases may just not be how biology works.

Adding to this, the plants in space example suggests to me that even as we learn more about genetic contributions and subsequent gene-by-environment interactions, some of that knowledge has an expiration date because of how our environment is changing.

Look at China. Thanks to decades of coal-burning for energy needs, China is currently experiencing a frightening air pollution challenge in some of its larger cities. How is that environmental phenomenon affecting gene expression and penetrance of variants? How will it affect development and gene expression and development of chronic diseases in the future? The airborne irritants and continual exposure might have no effect at all. Or they may have dramatic effects that will render some potential therapeutic pathways more or less effective in that population five, 20, 50 years from now. If, let’s say in a crazy hypothetical situation, which our government is assuring us will never happen, the global temperature was on track to increase by an average of 2 degrees Celsius or more over the next century, with all the cascading environmental changes that would cause, how would our genes respond?

I guess this just reduces down to a call for some circumspection. The very fact that Precision Medicine has entered the common lexicon is a reason for biomedical researchers to be cautious. Overpromising has already happened, but it could get a lot worse. At a time when funding for science is under siege, we don’t need examples at which naysayers can point as instances where scientists promised they’d cure cancer, but didn’t.

The science is already amazing. There are terrific things coming down the biopharma pipelines. And I think we’ll get a handle on gene-by-environment interactions—enough, at least, to meet the goal of creating specific health solutions based in part on each person’s genome for some diseases (although if the late Susan Lindquist’s HSP90 hypothesis turns out to be correct, many bets are off). But health and environment are moving targets. If we want Precision Medicine to be like the successful SpaceX launches, we need to keep an eye on things outside the controlled environments of our labs and clinical trials and do everything we can to embrace and understand the conversation our genes are having with the world outside.

 

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Why Every Biopharma Lab Should Have a 3D Printer (and a Laser Cutter Too)

This article first appeared in the Timmerman Report.

If there’s something most drug development people can agree upon today it’s that the industry needs more valuable new products. Too many drugs seem incremental, and me-too drugs, while providing nuance, flexibility and value within a given drug class, are not by definition innovative, unless your definition of innovative is “like that, but in red.” And that’s why I’d like to propose something that would be simple, cheap and yet also have the potential to unlock creativity on a broad scale.

The biopharma industry should dive into the maker movement and buy up a bunch of 3D printers. Laser cutters too.

For those unfamiliar, a consumer-grade 3D printer is simply a device that, using one of several methods, extrudes plastic in a controlled way to build a three dimensional object. The plastic is cheap (it’s the same stuff LEGOs are made of) and the resulting items can be impressively complex. Look here to get an idea of the kinds of things people make.

Neat, huh?

When I first started learning about 3D printers, the first consumer models were just coming out. They have gone from novelty to ubiquitous in just a few years. They’re at hobby stores and even those strange stores you only ever see in airports. You know, the ones that sell stylized toys for businesspeople who’ve realized at the last minute they forgot to buy the loved ones a gift. But don’t underestimate 3D printers as cheap commodity tools. There may be an advantage in getting them into your lab.

But I’m getting ahead of myself. Why would you want one?

Let’s break up reasons into the practical, the aspirational and the big picture.

On the practical level—have you noticed just how many things in the lab are flimsy pieces of plastic? Test tube holders and racks. Spacers. Gel combs. Which leads to another question: Have you noticed how  a gel comb from Fisher Scientific can cost $77? With a 3D printer and some basic CAD software (there are many cheap and free programs) you could create a comb of whatever dimensions you’d like for a few dollars of plastic and a few hours printing time. Also, there are several online libraries (here’s one. Here’s another) where you can just search for patterns, without having to design items yourself. Like any kind of code, once a pattern is written, it’s there forever to be used and modified, creating exponential levels of creativity and a long tail market for ideas.

And that’s the second reason: aspiration. Using a 3D printer gives people an opportunity to tinker, to design, to grow. It’s been shown for a while that employee engagement is a key factor in increasing the probability of business success. For some workers (I freely admit, not all), the chance to design one’s own tools in the lab could lead to greater engagement in problems and experiments, and the opportunity to think of different ways to approach experiments. Tapping into that creativity, especially among technicians who do the majority of lab work, could be powerful. While the US leads the world in the leeway and freedom it allows technicians, I’ve known many people who work at that level whose talents weren’t fully utilized because there weren’t enough outlets for their thinking.

Last, I don’t know if you’ve noticed but there have been some big picture issues lately with sustainability in the industry. While we’ve got more tools, more smart people, and more money in the industry than ever before, the rate of new drug approvals isn’t keeping pace. And with the new Tweeter-in-chief, it’s unlikely price increases will be able to keep the industry afloat, despite what some commentators say. That means companies need to start thinking outside of the box to come up with more new products. Several striking papers have come out over the past few years about using 3D printing to create various kinds of medical devices such as prosthetics, and even tissues. The 3D printing community has largely been driven by architects and engineers and designers. Biopharma and biomedical researchers ought to be able to figure out the business opportunities.

And I haven’t even gotten to laser cutters, which provide a whole additional way to create new designs and constructs (full disclosure: I know several people at GlowForge, a laser-cutter startup in Seattle) by etching and cutting a wide variety of materials at the micron scale. The potential for combining 3D printers and laser cutters to create innovative microfluidic devices, for example, seems huge.

So here’s my advice: most 3D printers are probably below your purchasing authority. Get one, sneak it in, hide it in your office or maybe on a low shelf near the old copies of Nature that your boss will never throw out, and tell anyone who asks that it’s a broken microwave. And then, when no one’s looking…create!

 

How Distributed R&D Could Spark Entrepreneurship in Biopharma

This piece originally appeared in the Timmerman Report.

Remember the patent cliff and the general lack of new and innovative medicines in the industry pipeline? That was the big story of the past decade in biopharma. It caused a lot of searching for the next best way to organize R&D to improve productivity. One doesn’t hear that quite as often today. There are more innovative drugs both recently approved and moving forward through the pipelines of several biopharma.

The conversation these days has shifted toward drug pricing, and how the public is going to pay for some of these new, exciting drugs (the answer, in some cases, is maybe it can’t).

I don’t think the industry out of the woods yet. One of the main reasons drug prices have become such an issue is because even though there are new, innovative drugs, there aren’t enough of them. At the same time many of the drugs being approved are incrementally better but nevertheless being priced at a premium. And good reporting has made the public more aware of how many of our existing drugs are rising in price on a yearly basis. Especially in a time of little inflation, prices of most goods have not been going up at nearly the rate of pharmaceuticals.

Biopharma sits in a tough place. Analyses suggest the cost of developing a new drug has generally been doubling every nine years, which may be a by-product of some combination of the complexity of biology, our inability to predict which drugs will work, and the “better than the Beatles” problem. The question then is how to overcome these issues and increase the efficiency of developing new, innovative drugs. Without some kind of change, the industry is looking at a very difficult future in which price hikes run headlong into the wall of payers who finally say enough. Then what? Continue reading

Should Basic Lab Experiments Be Blinded to Chip Away at the Reproducibility Problem?

An earlier version of this piece appeared on the Timmerman Report.

Note added 23Feb2016: Also realized that I was highly influenced by Regina Nuzzo’s piece on biases in scientific research (and solutions) in Nature, which has been nicely translated to comic form here.

Some people believe biology is facing a “Reproducibility Crisis.” Reports out of industry and academia have pointed to difficulty in replicating published experiments, and scholars of science have even suggested it may be expected that a majority of published studies might not be true. Even if you don’t think the lack of study replication has risen to the crisis point, what is clear is that lots of experiments and analyses in the literature are hard or sometimes impossible to repeat. I tend to take the view that in general people try their best and that biology is just inherently messy, with lots of variables we can’t control for because we don’t even know they exist. Or, we perform experiments that have been so carefully calibrated for a specific environment that they’re successful only in that time and place, and sometimes even just with that set of hands. Not to mention, on top of that, possible holes in how we train scientists, external pressures to publish or perish, and ever-changing technology.

Still, to keep biomedical research pushing ahead, we need to think about how to bring greater experimental consistency and rigor to the scientific enterprise. A number of people have made thoughtful proposals. Some have called for a clearer and much more rewarding pathway for reporting negative results. Others have created replication consortia to attempt confirmation of key experiments in an orderly and efficient way. I’m impressed by the folks at Retraction Watch and PubPeer who, respectively, call attention to retracted work, and provide a forum for commenting on published work. That encourages rigorous, continual review of the published literature. The idea that publication doesn’t immunize research from further scrutiny appeals to me. Still others have called for teaching scientists how to use statistics with greater skill and appropriateness and nuance. To paraphrase Inigo Montoya in The Princess Bride, “You keep using a p-value cutoff of 0.05. I do not think it means what you think it means.”

To these ideas, I’d like to throw out another thought rooted in behavioral economics and our growing understanding of cognitive biases. Would it help basic research take a lesson from clinical trials and introduce blinding in our experiments? Continue reading