a16z Podcast - Journal Club: Revisiting Eroom's Law

Episode Date: July 5, 2020

Eroom’s Law is Moore’s Law spelled backwards. It’s a term that was coined in a Nature Reviews Drug Discovery article by researchers at Sanford Bernstein and describes the exponential decrease in biopharma research and development efficiency between the 1950s and 2010. Whereas Moore’s describes technologies becoming exponentially faster and cheaper over time, Eroom’s Law describes the trend of drug development becoming exponentially more expensive over time.The article describing Eroom’s Law was published in 2012, and analyzed data up till 2010. That is perhaps ironic as 2010 appears to be an inflection point in the trend. In Breaking Eroom’s Law, the authors analyze the data since 2010 and show that costs appear to have stabilized over the last ten years. But what has contributed to this critical and exciting trend shift? In our conversation, Jorge and Vijay discuss the three causes cited by the authors of the Breaking Eroom’s Law article, their views on what technologies and policies will continue to push costs down, and their opinion on whether Eroom’s Law is broken for good.

Hello, I'm Lauren Richardson, and this is the A16Z Journal Club, our podcast where we cover recent scientific advances, why they matter, and how to take them from proof of principle to practice. For this special holiday weekend edition, we've got something a little bit different. In this snack size episode, A16Z general partners Vijay Ponday and Jorge Condé join me to discuss an opinion piece published in Nature Review's Drug Discovery called Breaking Erem's Law. Erem's Law is Moore's Law spelled backwards. Whereas Moore's Law describes technologies becoming exponentially faster and cheaper over time, Eroom's Law describes the trend of drug development becoming exponentially more and more expensive over time. In breaking Erem's Law, the authors analyzed the data since 2010

and find that costs appear to have stabilized over the last 10 years. But what has contributed to this critical and exciting trend shift? In our conversation, Horstead, Hay and Vijay discuss their views on the technologies and policies impacting this trend and their opinion on whether Eroom's Law is broken for good. We start by discussing the three possible factors for this shift cited in the breaking Eroom's Law article, with the first being, quote, better information. In my mind, there are sort of three vectors that are converging that I think become increasingly important when it comes to impacting productivity and therefore Eeroom's law. The first one is the fact that we can change.

generate an increasingly broad array of data across the various ways in which biology transmits information. So we can do genomics, transcriptomics, we can do proteomics, all of these things at increasing scale. The second point is we can also do them at increasing resolution. We can go now increasingly down to the single cell level, which in many cases is the functional unit of disease at a single cell level. The fact that we can query biology at that level of resolution, I think also makes a big difference. And thirdly, we can take all of these various streams of information and make better sense of them with the use of, among other things, advanced computational capabilities, means that we not only have better information,

we can generate better insights than we could before. And the hope, of course, is that with that we'll be able to, you know, make better decisions. If you want to escape your room's law and escape the flattening of Vroom's law to get to something more Moore's law like the natural way to do it is to do it by engendering a sense of engineering. And that probably will have so many different components. There'll be a huge biology and automation and reproducibility component. You can't engineer something if you can't have reproducible experiments. And so naturally, that's a huge part and there's a lot of working on going on there. And then with all that automated biology, you've got to understand all that data that's coming in.

and machine learning and AI is a very natural connector there. So what I'm hearing you say is that it's not just better information, it's not just more biological data, but it's also better use of this information. And, quote, better use of information is the second factor that the author cite in breaking Erem's law. How will AI and other methods of data interpretation aid in drug development costs? The beauty of current machine learning and current AI is that they're being built to be interpretable, that not only can we get a sense of what the answer is, and hopefully in a way that

has potential to be superior to human beings in many cases, but we can understand why that answer came about. And it's in that interpretability and in that understanding, I think will be even greater advances to come. The other thing, too, is certain things inflate with higher inflation rates than the standard cost of living. Like the cost of buying socks at Target is inflating relatively slowly. But the cost of college, of lawyers, of drugs is actually inflating much higher because it's a different labor pool to the extent that more of this becomes automated, whether we're talking about automated experiments or machine learning and better use of data, when you can automate more and more and more, maybe that also can help shift the curve

because we're using sort of a high-end skilled labor that inflates a much higher rate, much less or in a different way. So in this process of putting all of it together, the end result will be really exciting. Hopefully it would be a great decrease in the cost of drugs, as well as an increase in our ability to understand biology, which itself has this sort of year-over-year advantage. That will hopefully be this great virtuous cycle that will snowball in all of our favors. Yeah, the combination of better information and better use of information is powerful. The third factor listed by the authors is changes to the approval threshold. So there had been this documented progressive lowering of risk tolerance of drug regulatory agencies like the FDA,

which raised the bar in terms of demonstrating safety and efficacy for the introduction of new drugs. Do you two think that this threshold is changing now? I think the pendulum is definitely swinging towards trying to get these drugs in the hands of patients. I think one of the things that helps the cautious regulator dynamic is the fact that increasingly novel drug programs are very targeted. They're targeted from, you know, patient selection standpoint because you're going after a specific mutation or a specific genetic disease. But this whole rise of using biomarkers or other ways to select patients means that you can have a much more targeted development effort, which hopefully has two benefits to it. One is the safety side of things. If you have a better

targeted therapy, you're picking the patients that are most likely to benefit from it, the patients that are unlikely to benefit from it, which means from a cautious regulator problem, you're sort of minimizing risk to those that are least likely to benefit. And number two, on the efficacy side of the equation, to the extent that you're targeting the right thing, you're more likely to have an outsized impact. And so you're likely to see a stronger signal against noise, where a signal, of course, being efficacy, and noise being the potential of things not working or worse, having some sort of toxic or detrimental effect on patients that wouldn't have benefited anyway. So you can hopefully have more thoughtful trial design as a result and move

more quickly as well. The other factor I would say is I think that COVID will have the impact of accelerating the future in terms of modernizing how we think about running the appropriate studies for making sure that we can get therapies to patients safely and effectively, but also quickly. Yeah, and I think we're already seeing advancements in the manner in which trials are done. So there's a huge push to virtual or distributed trials where instead of having to go to a single academic center, there could be doctors throughout the country administering the trial. The technology is emerging and increasingly being embraced that will help us rethink how we do clinical trials in the first place. So we can continuous monitoring of patients, you know, in a decentralized or at-home setting that we can generate a lot more data points more quickly. more reliably for patients.

So I think technology outside of the lab itself, but going out into the community, so to speak, could have a real impact on what the all-in cost to develop a drug looks like going forward. This is kind of like a quiet revolution. And it takes a while for those changes to be seen in something like a Ewan graph. But the things that I think were started

even like eight, 10 years ago, are now very much coming to fruition. So my question is, do you really believe that Ehrim's law is broken? Or is this leveling off that we've seen in the last 10 years, you know, a blip, temporary, or maybe the start of something resembling Moore's law? It definitely looks like there is some major trend, shift. Now, to say that we've shifted from sort of Ehrum's law to something better is unclear. But even just flattening alone would be a major, major accomplishment for the industry. And so how long? long will this flattening last? I wouldn't be shocked if the flattening lasts for five years or 10 years in large part because the things that we're talking about putting into drug design today won't lead to drugs to market for some time. And so there is a little bit of a lag here. These data points we're seeing is talking about the changes that were done five or 10 years ago. And so the

changes that are happening now will only be seen later. The other thing I think it's worth mentioning is there's real debate in terms of whether or not the cost of R&D is calculated correctly. People debate that methodology. There have been alternative approaches that show that the number is actually much lower. I think people pick and choose the data depending on the argument that they're trying to make in many cases. And so if you're the pharmaceutical industry, you make the case that the cost of R&D is incredibly high to justify the pricing for those that are successful. If the cost are much lower, then obviously the prices that the system is forced to bear for therapies becomes less defensible. And so, you know, I think we should also keep in mind,

we look at this paper that there are many, many ways to slice the data in terms of what costs really look like. And in turn, you know, I think it's hard to definitively say in which direction the trends have moved and will move going forward. One thing that is clear is that developing drugs is time consuming, risky, and expensive. That's probably not going to change anytime soon. Too true. Thank you both for joining me on Journal Club this week and for covering these big trends in the space.