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.

 

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

No, CRISPR-Cas won’t save the day for ag biotech

You want to know how to drive a scientist crazy? Insist that you believe something that’s not supported by current scientific evidence. Tell her vaccines cause autism, or creationism is just as valid a theory as evolution, or that climate change isn’t really happening, I mean, after all, a monster blizzard hit Washington DC this January! Global warming, pssh…

There’s an old episode of Friends that did a good job of showing how this kind of conversation goes. Phoebe professes not to believe in evolution and Ross, a paleontologist, keeps trying to convince her that evolution is real using scientific evidence and logic. He grows increasingly frustrated and insistent as she continues to deny the basis of his life’s work, finally losing it when she goads him into admitting (like a good scientist) that even theories like evolution are not immune from questioning and testing.

We train scientists to carefully generate, weigh and use evidence. To no one’s surprise, this leads many scientists to generalize and think that in all matters having to do with the physical world we all should and of course will follow the evidence. Yes, sometimes that leads to unpopular ideas, and sometimes the ideas change as the weight of evidence changes. This training can make scientists kind of boring at cocktail parties. Still, the overall scientific process keeps moving forward and it’s because of this reliance on evidence.

But many people (including, at times, even some scientists) don’t always think the same way about things in the physical world. And that’s why I’m pessimistic that CRISPR-Cas technology will peacefully resolve the Genetically Modified Organism (GMO) debate. Continue reading

Google Knows What’s in Your Inbox, But It Shouldn’t Get Your Genome Without Consent

Originally posted in the Timmerman Report

I once had an idea for a science fiction story where everyone was paranoid about their genetic information getting out because of a misguided belief that genes equal destiny and that the burden of privacy is all on the individual. People would wear protective suits and carefully guard against leaving any iota of tissue out in public—not a single follicle or skin flake. All to prevent anyone else—potential employers, rivals, even potential lovers—finding out information about their genes.

I planned the story as a satire, taking our current world where Precision Medicine and cheap genome sequencing and not-quite-as-cheap genome interpretation are real things, and extrapolating to an absurdity. I wanted to highlight the kinds of more realistic challenges we might face as we learn more about our genes and face increasing questions about privacy and access to health care services. Of course, I thought this was completely speculative; I’d just be building a straw man story to make a point. I knew something this extreme would never really happen.

But maybe I was wrong. Continue reading

Baseball, Bayes, Fisher and the problem of the well-trained mind

One of the neat things about the people in the baseball research community is how willing many of them are to continually question the status quo. Maybe it’s because sabermetrics is itself a relatively new field, and so there’s a humility there. Assumptions always, always need to be questioned.

Case in point: a great post by Ken Arneson entitled “10 things I believe about baseball without evidence.” He uses the latest failure of the Oakland A’s in the recent MLB playoffs to highlight areas of baseball we still don’t understand, and for which we may not even be asking the right questions. Why, for example, haven’t the A’s advanced to the World Series for decades despite fielding good and often great teams? Yes there’s luck and randomness, but at some point the weight of the evidence encourages you to take a second look. Otherwise, you become as dogmatic as those who still point to RBIs as the measure of the quality of a baseball batter. Which they are not.

One of the thought-provoking things Arneson brings up is the question of whether the tools we use shape the way we study phenomena–really, the way we think–and therefore unconsciously limit the kinds of questions we choose to ask. His example is the use of SQL in creating queries and the inherent assumptions of that datatype that objects within a SQL database are individual events with no precedence or dependence upon others. And yet, as he points out, the act of hitting a baseball is an ongoing dialog between pitcher and batter. Prior events, we believe, have a strong influence on the outcome. Arneson draws an analogy to linguistic relativity, the hypothesis that the language a person speaks influences aspects of her cognition.

So let me examine this concept in the context of another area of inquiry–biological research–and ask whether something similar might be affecting (and limiting) the kinds of experiments we do and the questions we ask.

Continue reading

Could pro sports lead us to wellness?

Comment From Bill
St. Louis is being hindered in the stretch drive by some kind of GI bug passing through (so to speak) the team. Reports have as many as 15 guys down with it at once. That seems a lot, but given the way a baseball clubhouse works, my question is why don’t we see more of that? Answering that baseball players are fanatically interested in sanitation and hygiene ain’t gonna cut it, I don’t think…

12:10
Dave Cameron: They have access to a lot of drugs.

–comment from a chat at Fangraphs, September 24, 2014

So this comment caught my eye. Ever since I began following sites like BaseballProspectus.com and Fangraphs.com, and reading things like Moneyball, I’ve found myself thinking about efficiency and unappreciated or unexplored resources in different situations.

I realize this was a throwaway line in a baseball chat. But it piqued my interest because it seems to point out something that’s maybe underappreciated and understudied about how sports teams go about their business–specifically, the kinds of things they do to keep their athletes healthy.

My question is, does this represent a potential source of “Found Research” data that could help the rest of us reach wellness? Continue reading

The potential for “Found Research” in fecal transplant treatments

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

A few days ago the New York Times ran a nice article discussing a recent test of whether fecal transplants can be done using a pill format delivery system. The research, reported (and free, no less!) in the Journal of the American Medical Association, was peformed by physicians at Massachusetts General Hospital who had formulated human feces in an encapsulated pill format to see if that would be effective as a kind of fecal transplant. Fecal transplants  appear to overcome infections by Clostridium difficile in patients. However, the conventional method for providing a fecal transplant is to deliver a liquid slurry either nasopharyngeally or via an enema-like procedure, neither of which is easily scalable. Also, yuck.

The current work, in which 14 of 20 patients responded to initial treatments using the poop pills, and an additional 4 responded the second time around, provided a proof of concept that a frozen, pill format delivery system may be a workable alternative to the current standard.

But as I was reading this article, I was struck by another thought. Are we missing a great opportunity for research into the interplay between the microbiome and human physiology?

Continue reading