Here’s one of those “Drug Discovery of. . .the. . .Future-ure-ure-ure” articles in the popular press. (I need a reverb chamber to make that work property). At The Atlantic, they’re talking with “medical futurists” and coming up with this:
The idea is to combine big data and computer simulations—the kind an engineer might use to make a virtual prototype of a new kind of airplane—to figure out not just what’s wrong with you but to predict which course of treatment is best for you. That’s the focus of Dassault Systèmes, a French software company that’s using broad datasets to create cell-level simulations for all different kinds of patients. In other words, by modeling what has happened to patients like you in previous cases, doctors can better understand what might happen if they try certain treatments for you—taking into consideration your age, your weight, your gender, your blood type, your race, your symptom, any number of other biomarkers. And we’re talking about a level of precision that goes way beyond text books and case studies.
I’m very much of two minds about this sort of thing. On the one hand, the people at Dassault are not fools. They see an opportunity here, and they think that they might have a realistic chance at selling something useful. And it’s absolutely true that this is, broadly, the direction in which medicine is heading. As we learn more about biomarkers and individual biochemistry, we will indeed be trying to zero in on single-patient variations.
But on that ever-present other hand, I don’t think that you want to make anyone think that this is just around the corner, because it’s not. It’s wildly difficult to do this sort of thing, as many have discovered at great expense, and our level of ignorance about human biochemistry is a constant problem. And while tailoring individual patient’s therapies with known drugs is hard enough, it gets really tricky when you talk about evaluating new drugs in the first place:
Charlès and his colleagues believe that a shift to virtual clinical trials—that is, testing new medicines and devices using computer models before or instead of trials in human patients—could make new treatments available more quickly and cheaply. “A new drug, a succesful drug, takes 10 to 12 years to develop and over $1 billion in expenses,” said Max Carnecchia, president of the software company Accelrys, which Dassault Systèmes recently acquired. “But when it is approved by FDA or other government bodies, typically less than 50 percent of patients respond to that therapy or drug.” No treatment is one-size-fits-all, so why spend all that money on a single approach?
Carnecchia calls the shift toward algorithmic clinical trials a “revolution in drug discovery” that will allow for many quick and low-cost simulations based on an endless number of individual cellular models. “Those models start to inform and direct and focus the kinds of clinical trials that have historically been the basis for drug discovery,” Carnecchia told me. “There’s the benefit to drug companies from reduction of cost, but more importantly being able to get these therapies out into the market—whether that’s saving lives or just improving human health—in such a way where you start to know ahead of time whether that patient will actually respond to that therapy.”
Speed the day. The cost of clinical trials, coupled with their low success rate, is eating us alive in this business (and it’s getting worse every year). This is just the sort of thing that could rescue us from the walls that are closing in more tightly all the time. But this talk of shifts and revolutions makes it sound as if this sort of thing is happening right now, which it isn’t. No such simulated clinical trial, one that could serve as the basis for a drug approval, is anywhere near even being proposed. How long before one is, then? If things go really swimmingly, I’d say 20 to 25 years from now, personally, but I’d be glad to hear other estimates.
To be fiar, the article does go on to mentions something like this, but it just says that “it may be a while” before said revolution happens. And you get the impression that what’s most needed is some sort of “cultural shift in medicine toward openness and resource sharing”. I don’t know. . .I find that when people call for big cultural shifts, they’re sometimes diverting attention (even their own attention) from the harder parts of the problem under discussion. Gosh, we’d have this going in no time if people would just open up and change their old-fashioned ways! But in this case, I still don’t see that as the rate-limiting step at all. Pouring on the openness and sharing probably wouldn’t hurt a bit in the quest for understanding human drug responses and individual toxicology, but it’s not going to suddenly open up any blocked-up floodgates, either. We don’t know enough. Pooling our current ignorance can only take us so far.
Remember there are hundreds of billions of dollars waiting to be picked up off the ground by anyone who can do these things. It’s not like there are no incentives to find ways to make clinical trials faster and cheaper. Anything that gives the impression that there’s this one factor (lack of cooperation, too much regulation, Evil Pharma Executives, what have you) holding us back from the new era, well. . .that just might be an oversimplified view of the situation.