I can strongly recommend this Bruce Booth post at LifeSciVC on computational models in drug discovery. He’s referencing Marc Andreessen’s famous “Why Software Is Eating the World” essay when he titles his “Four Decades of Hacking Biotech and Yet Biology Still Consumes Everything”. To tell you about where Bruce is coming from, I can do no better than to quote an article just as he does. Here we go:
Drug companies know they simply cannot be without these computer techniques. They make drug design more rational. How? By helping scientists learn what is necessary, on the molecular level, to cure the body, then enabling them to tailor-make a drug to do the job… This whole approach is helping us avoid the blind alleys before we even step into the lab… Pharmaceutical firms are familiar with those alleys. Out of every 8,000 compounds the companies screen for medicinal use, only one reaches the market. The computer should help lower those odds … This means that chemists will not be tied up for weeks, sometimes months, painstakingly assembling test drugs that a computer could show to have little chance of working. The potential saving to the pharmaceutical industry: millions of dollars and thousands of man-hours.
This quote is from a Discover magazine article from 1981. And there you have it. Over thirty-five years later, and this promise still hasn’t really been fulfilled. As pointed out in his post, Bruce is no stick-in-the-mud about new technology, and I’m sure that he would absolutely love to realize the savings in time and money that robust computational modeling would provide. But it isn’t there yet. We have useful techniques, we have techniques that can help, but we’re never sure which projects are going to benefit and which techniques might be best to use. I have had the same experience that he mentions – virtually every project I’ve been on in my career has had a computational contribution. But it’s the biology that comes along and overrules everything.
It’s worth highlighting a few examples about biology defeating or obstructing CADD-inspired discovery, though the list of programs could be very very long. T-cell kinase ZAP70 has been attacked by CADD since mid 1990s (here), and yet there are no approved drugs against it. MAPK/p38 is another well-trodden CADD target: dozens of publications out there about CADD success stories against p38 with new and improved binders and the like; yet, clinical development is a veritable graveyard for these programs, as figuring out the safe and effective biology of these projects remains a challenge. Or take renin inhibition – after years of great CADD-enabled discovery, the first program got approved but only to find out in subsequent Phase III that drug development wasn’t kind (see #16 in the FDA’s recent roster of failures).
I also agree very heartily with the recommendations he makes at the end of the post. It’s crucial that the computational folks be integrated as much as possible with the chemists and biologists. This is a terrible place for the “throw it over the wall” procedure; the modelers need to be speaking with the drug discovery team at all times. As tempting as it might be, they also need to be very careful about ruling things out, as opposed to recommending ideas that might work better. We don’t have the horsepower we need, in most cases, to step in and say with confidence that “You shouldn’t work on these compounds at all – our model says that they won’t work”. (And when there is that level of confidence, it’s often something that you didn’t need computation to know, frankly). Always check, if it’s at all possible. Make some compounds – if they really don’t work, it’ll give you more confidence in the model, if nothing else, and if they do, you’ve learned something valuable and interesting.
Read the whole thing; there’s a lot more than I’ve mentioned here. As Bruce takes pains to do, I want to emphasize that I’m not bashing the modelers, either. I’ve worked with a lot of good ones, and the ones who have been best at their job have also, without fail, been the best at not overpromising and realizing what they have the best chance of delivering for the project. And it’s important to remember that computational hardware, software, and techniques are getting better all the time. They’re not getting better as quickly as people thought they would in the 1980s, and they’re not yet where we want to them to be in an ideal world, but the field is advancing every year. Don’t turn your back on it, but don’t fall for the hype that some folks will want to sell you, either. The Silicon Valley types are particularly vulnerable, because they don’t know what biology is like (or how little we understand), and they can be particularly eager to sell you things, too, so be especially wary from that direction.