What’s the Role of Experts? A Review of The Death of Expertise and Some Thoughts for Biopharma

This piece originally appeared in The Timmerman Report. 

 

The Death of Expertise by Tom Nichols, 2017, Oxford University Press.

If you’re reading this, chances are you’re an expert or well on your way to becoming one. The Timmerman Report is tailored by content and intent to be valuable to those with the knowledge, experience and interest to make biopharma news worth reading. Experts, in other words.

This isn’t a trivial point: for the vast majority of people—that is, those non-expert in biopharma—news in sites like this one or STAT or Endpoints is as useful as scuba equipment to an octopus. And that’s fine; that’s how our knowledge-based society works. Individuals become experts in specific fields, they take the time and effort to master a specific area and they build up the intellectual framework to enable advances, discoveries and explanations. Specialization underlies the technological, societal and scientific wonders we take for granted today. There are just too many fields of study for any one person to master, the Maesters of a Song of Ice and Fire aside. Divide and conquer isn’t just for Roman governance philosophy; it also makes for progress.

The natural corollary is that we are all affected by what experts outside our field say and do. Lacking a working and academic knowledge of biopharma does not immunize a person from the impact of the kinds of issues, news, and discoveries discussed and reported here. Drug pricing, innovation, access and healthcare quality and affordability have huge impacts on everyone in the US.

And boy, do many of them have opinions about that! Opinions that they hold tighter and higher than the words of experts. Opinions that influence the ways in which they speak, act, think and yes, sometimes, vote.

This growing issue is at the heart of Tom Nichols’ book, The Death of Expertise. Nichols, a professor in National Security Affairs at the Naval War College and adjunct at the Harvard Extension School, is a former Senate aide and an expert in Soviet studies. I first became familiar with his work when, after last year’s US Presidential Election, I started consciously expanding the circle of thinkers I listened to. Like Daniel MacArthur and many others of a more liberal bent, I’ve tried to find and listen to people on the center and right.

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Lessons from PCSK9, and How We Know Where to Go in Drug Discovery

This article first appeared in the Timmerman Report.

What drug development lessons should we take from the PCSK9 story? That might depend on how and why we know what we know.

The recent news about Amgen’s anti-PCSK9 antibody evolocumab (Repatha) and its effects on cardiovascular outcomes—the FOURIER trial—added another fascinating chapter to the story of how human genetics is becoming more entwined with drug development. It also jogged my curiosity once again in some old liberal arts late night dorm-room discussions about epistemology, the theory of how we reach rational belief.

How do we collect biomedical knowledge? How do we know what we know about biology, about the genes and proteins and networks and physiology and other phenotypes that we’ve built into models and hairballs and devilishly detailed flowcharts over the past few centuries?  And why do we have the current body of facts that we do?

Targeting PCSK9 represents the most prominent example of using human genetics to identify new drug targets. There are others in the works (Sclerostin and APOC3 come to mind) and they herald an exciting new period of drug development in which the process will be expedited by the existence and study of humans with variants, functional and non-, in these targets. If you have a human being, or a closely related cohort of people, who have a certain gene mutation that keeps their cholesterol low and doesn’t appear to cause any detrimental health effects, you have a pretty powerful predictive human model for a drug (See TR contributor Robert Plenge’s case for a “Human Knockout Project”). With this kind of biological information in hand, setting priorities for drug discovery gets easier.

But the commentary following the presentation of FOURIER showed many are underwhelmed. As Plenge points out in an excellent blog post, people are disappointed by the relatively modest gains in cardiovascular outcomes and the implications for blockbuster status (or lack thereof) for Evolocumab and Alirocumab, the competing antibody from Regeneron Pharmaceuticals and Sanofi. However, Plenge points out, the drug development process of going from human mutants (over-expressors and, eventually, under-expressors of PCSK9) to a drug worked quite well on many levels. Dosing was improved, pre-clinical models were leveraged for specific hypothesis tests rather than broad (and possibly meaningless) demonstrations of efficacy, and clinical endpoints were informed by human biology.

As a trained geneticist, I love this terrific biological story. But I can’t dismiss the criticisms either, which brings me back to the question of knowledge. Human genetics will provide an orthogonal method of identifying targets, and will make the overall process more efficient. I wonder though: will it be enough to make a real dent in the problems facing the drug development enterprise? Or will it instead end up helping incrementally when we really need quantum leaps to help with clinical success, pricing and curing patients? And I think the answer comes back to epistemology. How and why do we in biology know what we know?

I’m going to focus on gene-centric discovery here. It’s my background and serves as a relevant example given the current drug development paradigm of focusing on specific gene targets. So: here’s a question a bioinformaticist friend and I debated while we were at a former company. Why do we know so much about some genes and so little about others? This was of particular importance because we had been directed to find novel targets. See the catch-22, though? Novel targets by definition have little known about them, and those making decisions were often leery of investing millions of dollars in a target with a skimpy data package. This, by the way, is one big positive when using human genetics and an allelic series, and is highlighted by PCSK9. That gene had been poorly studied before but human genetics allowed a quick ramp-up in understanding of its biology and role.

The problem of novelty in target identification came clear to me as soon as I tried convincing other scientists to consider some novel targets. Here’s a story familiar to anyone who has done ‘omics research. I did a lot of transcriptomics. Invariably, in comparing different tissues, diseases, cells, I generated a list of differentially expressed genes. Often lots and lots of lists. Buzzfeed had nothing on me! Although maybe I would have been more successful taking a page from their sensationalistic style: “You won’t believe the top 10 most differentially expressed genes between inflamed and normal mouse colons (number 3 is a real shocker)!”

In any case, there would be familiar genes and there would be novel genes. When we showed these lists to the biologists with whom we were working, they mostly gravitated to the genes they recognized. I can’t blame them; they knew they’d be asked to justify further work, and having several hundred papers sure makes it easier to build an argument for biological plausibility (Insert your favorite version of the lamplight/car keys story here). The specific question my friend and I debated was: Are known things known and novel things novel because the known things are more important in terms of biological function and therefore will have the greatest likelihood of being good drug targets? Or are they known because of historical accident? Or, the third option, is what we know due to the tools we use? I don’t think this is a binary (trinary?) choice. The reality is surely a mix of all three. But if the first condition is the most predominant, that has some implications on what we can expect human genetics to do for drug development.

Postulate discovery in biology has a bias toward the genes with the largest effect being found first regardless of how one does the looking. To illustrate this, I’ll use an example from Ted Chiang’s amazing novella, “The Story of Your Life,” which was the basis for one of my favorite movies of last year, Arrival. A central theme in these works was how different ways of perceiving reality can nevertheless lead to the same place.

If one throws a ball through the air, where will it land? One can use Newtonian mechanics to describe the arc, rise and fall. Or one can use Lagrangian formulations to see the pathway as the one of minimizing actions the ball must take. Either method predicts the ball ends up in a specific place. My analogy: is our knowledge of genes like that? Would the accumulation of knowledge have looked pretty much the same even if different scientists had been using different tools to study different biological problems because by the nature of our shared evolutionary history certain genes are just more fundamental, important and pleiotropic? (For a fascinating rumination on the same question in chemistry, take a look at Derek Lowe’s piece here).

Contrast this with historical accident. Here I’ll go back to physics and invoke the idea of the many-worlds hypothesis. If we could rewind the clock of time and start again, how different would our history and discovery be? In this interpretation, initial discoveries are at least somewhat random but once they occur, it becomes more likely that knowledge will accrete around those initial discoveries like nacre around a grain of sand in an oyster’s mantle. Initial discoveries have a canalization effect, in other words, and as data and effects of specific genes accumulate, those canals get deeper. As illustrated in my earlier example of showing people lists of genes, there is a natural and understandable gravitation toward adding another pebble to the hill rather than placing a rock on a novel patch of ground.

And then there are tools. I’ve been a biological technologist for much of my career, using technologies like microarrays and, later, next-gen sequencing to speed, enhance and extend experimental approaches. So I know there are questions we could not have easily asked, biological problems we would not have tried to approach without the right tools. I remember the early days of fluorescent microscopy and how much that changed our view of the cell, and of Sanger and Maxam-Gilbert sequencing, when actually decoding the order of nucleotides for a gene became feasible. I also remember friendly-ish debates among the geneticists, biochemists, molecular biologists and cell biologists about the best way to do research, with each approach having specific benefits. This general assertion—that tools help us do more–seems circular and obvious, but the implications are deep. Just as many believe language shapes how we think, tools shape how we measure and construct our pictures of the world. When you have a hammer, and all that.

Circling back to PCSK9 and other human genetics-enabled targets, having an orthogonal target discovery method may not be enough to really push the industry forward if we’ve already found the majority of the most broadly effective drug targets. New targets may be effective but not better than current therapies except perhaps in niche indications. Good for precision medicine, but not so great amid the current pushback on drug prices. On the other hand, if limitations of tools and/or historical accident played the majority role in limiting discovery in the past, many innovative targets may be right around the corner as we sequence more genomes and begin to connect the dots between genetic abnormalities and problematic (or advantageous) phenotypes.

I don’t know the answer, but we’ll get an idea in the next few years as more of these genetics-derived targets make it to the clinic. If it does turn out that genetics helps with process and speed more than innovative leaps, well, that’s still helpful. That would also push us further toward new approaches, new platforms and combinatorial therapies. None of that will be easy, or quicker, or simpler. Just looking at the PD1/PDL1 combinatorial clinical trials landscape might be a preview of how messy this could be.PDl1

Also, if human genetics is orthogonal, it does increase the number of shots on goal a company can make although there are limits on how many targets any company can take to the clinic. It still begs the question, though, of whether those shots will be better or just different. And if it’s the latter, that’s not the solution the industry needs. Unfortunately, like so many techniques and tools that have come before (high throughput screening, anyone?), we just won’t know until we know. As much as people would like it to be so, knowledge in this area just won’t inexorably march onward and upward in a straight line.

 

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!

 

Changing small molecule exclusivity rules as a long-term drug price policy play

This piece originally appeared in the Timmerman Report

We’re entering uncharted waters in the US government. I don’t think it’s hyperbolic to say there will be new regulations, new laws that we’ve never seen before. While I don’t pretend to understand the new administration in any way, I do expect there will be more chaos at the level of policy-making than we’ve seen in decades and one thing that chaos does is it increases the likelihood of extreme outcomes.

So here is one speculative policy idea:

I think we should trade the 12-year exclusivity period from biologics to small molecule drugs. Continue reading

It’s time for biopharma to embrace public health

This piece first appeared in the Timmerman Report.

Some years ago when I was working for a large biopharma, I heard a story. It seems a senior scientific executive had visited and given a seminar in which he described the company’s portfolio of drugs for type 2 diabetes. The company was projecting great uptake and profits. A member of our site raised his hand and said, “But if people just ate less and exercised a little more, they could prevent type 2 diabetes and the market would disappear.”

The answer: “Yeah, but they won’t.”

Harsh! But that executive was right. The Institute for Health Metrics and Evaluation (IHME) recently published a paper in JAMA describing how much different health conditions contribute to private and public health spending in the US. Number one? Diabetes. Following that were heart disease and chronic pain. These are chronic lifestyle diseases with big environmental and behavioral components, and the data make me wonder if there’s an opportunity here for the industry to zig and do some things that, in the long run, may make drug development more sustainable.

I think it’s time for biopharma to get involved in public health. Continue reading

How Valeant, Anthem, and chirping crickets suggest Saunders’ social contract is doomed

This piece originally appeared in the Timmerman Report.

When Allergan CEO Brent Saunders announced his manifesto on drug pricing at Allergan just after Labor Day, he was met with acclaim and approval (some examples here and here). He called for a return to the social contract between biopharma companies and patients. In his view, patients understood in the past that developing drugs was risky and cost a lot of time and money, and therefore patented drugs would be expensive. Drug companies, holding up their end of the social contract, felt an obligation above simple profit-making—that drugs are supposed to keep patients healthy or to get them back to that state. That meant pricing had to take into account the public good, not just profit maximizing, and be reasonable. Moving forward, Saunders announced that, among other things, Allergan would commit to value-based pricing and to limit price increases to no more than single-digit percentage hikes per year.

These are worthy and admirable goals. But I look at other recent events and can’t help feeling his effort is doomed. Continue reading