Rare diseases – remember years ago, back when those were a case of market failure? When companies were reluctant to work on them because the market size was guaranteed to be small and you’d have to charge, like, a hundred thousand or more a year to make the whole idea financially viable? Which wasn’t going to happen, until it did? And now there are rare-disease programs all over the place. (I last wrote about this here).
You can look at this several ways, depending on what you think of the drug industry, and it’s no sin to have more than one of these opinions in your head simultaneously. You can see this as the market discovering the price for such therapies – as it turns out, patients (well, their insurance companies) will actually pay the six-figure price tags necessary for such programs to be worthwhile to a for-profit company, and once that became clear, such companies began to populate that space. Market failure no more! But it’s also fair to say that many of these companies have realized that if those insurance plans will shell out $100,000 for such drugs, then they’ll probably shell out $150,000. Or $250,000 – try it and see! After all, it’s not that many patients – no one insurance plan is going to get hammered on too badly, and at those prices you can afford compassionate-use exemptions, too. The place where all those patients (and their payments) congregate is on the receiving end of the money, a situation that has probably led to (and is surely leading to) unsustainable pricing situations on the whole.
But another reason that companies have headed into this space is the way that human genetics have advanced. We now have so many more sequences, of such higher quality, across such broader populations that it’s possible to draw conclusions from the data that would have been unacceptably shaky before. Not only do you get to charge a lot for your drug – should you make a drug – but you can have a better idea that it’s going to work in the clinic. And “having a better idea that it’s going to work in the clinic” is (make no mistake) one of the most cherished goals of drug research. We get slammed 80 to 90% of the time in clinical trials. It’s horrendous, and we’re spending more and more money just to experience even that level of success. If you can improve that failure rate, either through avoiding toxicity or through more confidence in a new drug’s mechanism of action, billions of dollars are sitting out here for you to scoop them up.
Thus rare genetic-based diseases. Because with those, you can (1) generally identify your patients and make sure that the right people are in the clinical trial. You’d think that would be pretty easy, but go try it with (say) Alzheimer’s. And you can (2) have a much clearer picture of what your disease target does in humans by looking at natural genetic variation. This ranges (in many cases) across the whole spectrum from mild impairment to total loss-of-function, and it can be a big red blinking arrow sign telling you that Yes, This Is the Problem That Causes the Disease. You might think that would be not such an issue, either, but hey, try it with Alzheimer’s again. Or depression. Or osteoarthritis, or lupus, or even Type II diabetes. We know many things about those diseases, and we can (sometimes) interrupt the trouble at various points and in various ways, but reaching down to the ultimate first cause is something else again.
But even that part is fraught with difficulties, as this article will make clear. Here’s the take-home:
Long story short: there is no doubt that if you’re developing a drug against gene X, then knowing the effect of human loss-of-function variants in gene X is incredibly valuable. Yet at the same time, there is no simple formula or algorithm for what pattern of LoF variants makes a gene a safe target or an unsafe target. In fact, there isn’t even any complex formula or algorithm. The best we can do is deep curation and consideration to each individual gene and drug.
Why should it be that tricky? Well, there are problems in making sure that your gene of interest really has been picked up correctly as a loss-of-function in the datasets. But once you’re past that, you’re faced with the same problem you have in the animal knockouts that we’ve had available for many years now. Developmental effects do not have to be the same as pharmacological effects. An inhibitor drug may well affect a protein target’s activities in different ways than the loss-of-function mutants do, and you have to dig into the situation to figure that out. Giving such a drug to an already-developed creature may well affect things differently than such a mutation present from the very beginning does. And so on.
So you get the full spectrum of possible confounding variables. There are genes whose knockouts are embryonic lethal that nonetheless represent viable protein targets for inhibitors. There are genes whose knockouts look almost perfectly normal that nonetheless represent viable protein targets for inhibitors – and everything in between. And there are a range of reasons for both of those situations to exist. The article I just linked to goes into deep and very useful detail on this; I strongly recommend it to anyone thinking about these issues. It’s especially aimed towards the idea of predicting target-based tox trouble ahead of time by looking at genes that seem to be intolerant of loss-of-function in the human population, but even there:
All the above points make it clear that even if LoF variants are incredibly informative for drug discovery, and I think they are, we’re never going to have a plug-and-play formula. We are never going to be able to say, for every X% your gene is depleted for LoF, your probability of adverse events goes up by Y%. And we are certainly never going to have a rule, like don’t develop drugs against targets with >Z% depletion of LoF variation.
And it goes into detail about just what you can do to mine real insights under these conditions – you will not be surprised to learn that it involves a fair amount of work, as so many useful things do. That doesn’t mean you shouldn’t do it, of course. You most certainly should, because if things work out you will indeed have a far better idea of what your target is like and what you can expect from a drug for it. But it will not be the work of a moment, so look out for any breezy confidence you might come across in these efforts and act accordingly.