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!

 

How Brexit changed the way I look at biopharma’s reputation problem

This piece originally appeared in the Timmerman Report.

You may have heard something recently about Britain, the European Union (EU), some vote or other, chaos, turmoil, blah, blah, blah…You might also have heard how the presumptive Republican nominee for President of the United States has gotten to that position by identifying a strong thread of anti-establishment, populist sentiment in the US. And you may have heard that biotech and pharma is suffering from a reputation problem.

One of these things is not like the other, right?

I’m not so sure.

That biopharma has a reputation problem isn’t in doubt. The question, though, is how the industry got here. I want to know this because, thinking like many drug developers, I believe that by knowing the cause of a condition a fix can more easily be found.

There have been numerous candidate reasons, and I’m open to the idea that the cause is multifaceted just like it is for many chronic diseases. In the past year alone we’ve had the Martin Shkreli circus, admonishments about drug pricing from political candidates, analyses of how yearly increases in pricing often outstrip inflation, Pfizer pursuing quizzical acquisitions to avoid paying taxes, and companies suing the FDA to prevent generic competition. Biopharma’s problems go further back, as well, and examples of less than exemplary behavior abound. Hey, I was working at Merck when Vioxx was happening.

But Brexit points to something else. While it makes sense to look for behaviors by biopharma for causes for the reputation problem, business doesn’t happen in a vacuum. Political and social trends over the past few years suggest a rejection of elite opinion and earned expertise that is touching many parts of society and culture. Derek Lowe at In the Pipeline had a recent fascinating post on this phenomenon in the context of Right to Try laws (and also delving into Trump and Brexit). As he points out, Right to Try laws sit in that thorny spot where technological knowledge of drug development and Libertarian impulses collide. I can come up with a half dozen reasons why I think Right to Try laws are in general a bad idea, and none of them will sway someone who wants access to an experimental therapy for their dying child. You can see this playing out in the debate about whether eteplirsen should be approved for Duchenne Muscular Dystrophy.

Where did this suspicious, and sometimes hostile, reaction to elites and expert opinion come from? MSNBC anchor Chris Hayes, among others, has posited that over the past several years many people have suffered the effects of broken promises and crippled expectations. If the social contract between elites and the rest of the population is that if the elites (whether they are Democrats or Republicans) are given power, everyone will benefit, then breaking that contract leads to disillusionment and, eventually, rejection. A similar analysis from another part of the political spectrum was made by Charles Murray (H/T to @ScottGottliebMD). One can also point to growing partisanship as, if not causal, at least maintaining and contributing to the diminishment of expert and elite opinion. Unfortunately, there is no precision discrediting. When one calls to question the statements of scientists on specific topics such as global warming or vaccinations, one tars with a broad brush and the whole scientific edifice takes a hit. It’s like those kids with the paint rollers in Splatoon.

From this perspective, the poor reputation of biopharma stems at least in part from larger societal trends in how people perceive expertise. Healthcare is highly complicated and technical, and it’s not a stretch to say it’s associated with the expert and the elite. Taking this perspective has some good and some bad implications for biopharma. On the good side, one can say it’s not (all) the fault of the biopharma companies’ specific actions that the industry’s reputation has suffered. But on the bad side, this makes it that much harder to fix the problem. Better general overall behaviors by companies are a prerequisite for improving biopharma’s rep, but not the final cure.

However, if biopharma is serious about its reputation, and buys into this theory, it could use this perspective in a few ways.

First, it can look at the one industry that is highly expert driven and still has a good reputation: high technology, as represented by companies like Apple and Amazon, among others. I would conjecture that these companies, by taking a very consumer-focused approach and a real dedication to innovation, simply show people repeatedly, several times a day, that they are trustworthy and worth the money. Now, this is hard to do in biopharma where product development cycles are pretty much the diametric opposite of the fail fast, hard and often ethos found in Silicon Valley companies. But the industry can do a better job of explaining its innovative and impactful products, and being honest about when new products have neither—and pricing them accordingly.

The second thing is biopharma could start taking a longer, more societally focused view in how it uses its considerable lobbying muscle. To take one example, many in the US (and Europe) feel betrayed by the obvious effects of globalization on unemployment in some job sectors. Those in favor of globalization routinely argue that everyone benefits from cheaper prices on manufactured goods and also that hundreds of millions of people in the developing world are seeing a substantial increase in their living standards. This is measurably true. It’s also an argument that doesn’t resonate at all with someone who trained and worked as a machinist for fifteen years and lost her job due to outsourcing. There’s an asymmetry in perceived benefit versus experienced insult and loss

Biopharma could push for greater investments in job retraining, in both the public and private sectors, as well as for extensions to programs such as unemployment benefits to allow people the time to get retrained. You might say that this is outside the scope of what biopharma is responsible for, but that’s a self-imposed limit. One of the arguments for why elites and experts have lost their status is that so many organizations seem to be concerned solely with narrowly defined self-interest and shareholder value; not with the workers, customers and society within which they operate.

It’s a problem, figuring out the best way to rehabilitate biopharma’s rep, but it’s a necessary one to solve for the industry’s long term health. The Trump and Sanders campaigns have demonstrated that there are large reservoirs of resentment out there that shouldn’t be ignored. And it’s not hopeless either. Large scale societal change in attitudes can be done. Canada, unlike much of the developed world, has created a culture welcoming of immigrants, and this was accomplished via a long standing, coordinated effort by the Canadian government and others to make openness a core Canadian trait. They persisted and took the long view. If biopharma can spend decades and billions of dollars in dogged pursuit of specific targets (I’m looking at you, amyloid beta), then perhaps it can do the same to try and change the environment in which we all live and work.

 

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

One way to improve clinical trial reporting: a Yelp-style rating system

This piece originally appeared in the Timmerman Report.

STAT recently published an in-depth report about the many research centers that don’t bother to publicly disclose the results of their clinical trials, even though they are required to do so. This follows on a New England Journal of Medicine article back in March that had a similar analysis of the lack of reporting and publication of clinical trial data to clinicaltrials.gov.

Most observers of biomedical research would agree that getting clinical trial data out about what happened in a trial is pretty important, whether the trial succeeded or failed. After all, biomedical translational research is most meaningful when done on human subjects and negative information can be quite informative and useful. Animal models are nice, but translation of results from animals to humans is a spotty proposition at best. We need to know what’s working, and what’s not, to know how to best allocate our research resources and how to treat patients.

The lack of reporting is an embarrassment for research. It’s also understandable, because so far the FDA hasn’t used its authority to punish anyone for delayed reporting. Nobody appears to have lost any research funding because they failed to post trial results in a timely manner. Universities told STAT their researchers were “too busy,” given other constraints on their time, to report their results. So what really seems to be going on is that reporting is prioritized below most other activities in clinical research.

It was interesting and eye-opening that industry fared better than academia in both the STAT story and the NEJM article with respect to how many studies have been reported. Having seen the industry process first-hand, I’d speculate that (at least for positive trials) there’s a much stronger incentive to get data out in public. Successful trial results can create buzz among clinicians and patients, revving up trial enrollment which can then help get a new drug on the market faster, and convince people to use it when it’s available. It may be that in academia the effort of getting trial results in the required format for clinicaltrials.gov is perceived as too much work, relative to the rewards. Academics are naturally going to spend more energy on directly rewarded activities like writing grant proposals and writing peer-reviewed scientific publications that help them win even more grants, promotions, and other accolades. Well okay. If this is the case, then figuring out new incentives may be key.

So what would work? Anyone who participates in a clinical trial is providing time, may be subject to risks and often is asked to provide samples that are biobanked to support future exploratory and translational research. It’s like when people donate to food banks. I’m pretty sure they mean that food to be eaten and not to sit on a shelf. These participants in clinical trials deserve to have their volunteerism rewarded.

This got me thinking about how to empower patients to get more of what they want. Patient-centered research is a buzzword these days, and for good reason. Patients have at times been an afterthought in the biomedical research enterprise. I thought of services like Yelp and Uber and Angie’s List and other peer-to-peer systems that allow users to get information, provide feedback and give ratings to specific providers. And I wondered: could this be a way to apply pressure to clinical trial researchers to improve their reporting? Continue reading

The power law relationship in drug development

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

A few weeks ago a friend and I had the great opportunity to go see Nate Silver speak at the University of Washington. He’s a funny, engaging speaker, and for someone like me who makes his living generating and analyzing data, Silver’s work in sports, politics and other fields has been inspirational.  Much of his talk covered elements of his book, The Signal and the Noise, which I read over a year ago. It was good to get a refresher. One of the elements that particularly struck me this time around, to the point that I took a picture of his slide, was the concept of the power law and its empirical relationship to so many of the phenomena we deal with in life.

Nate Silver graph small

Figure 1: Slide from Nate Silver’s talk demonstrating the power law relationship in business–how often the last 20% of accuracy (or quality or sales or…) comes from the last 80% of effort.

Because I spend way too much time thinking about the business of drug development, I started thinking of how this concept applies to our industry and specifically the problem the industry is facing with creating innovative medicines.

Continue reading