When you get down to it, one of the biggest problems in drug discovery is that there is (in most cases) no alternative but doing things the hard way. If you want to find out if your drug is going to work for a given disease, there’s no other way to be sure than to give it to a bunch of people with the disease, under expensively controlled conditions, and watch them carefully (certainly for weeks, sometimes for years) to see what happens. Want to know if a new compound is going to be toxic? You certainly do – but the only way to do that is to give it to a bunch of animals, at varying doses and for varying periods. While that should get rid of a lot of bad actors, it still won’t eliminate the human-specific tox that might be lurking out there. For that, you still have to give it to people – or, if you’re really unlucky and there’s a bad but low-incidence effect, you get to wait until the drug’s actually been on the market for a year or two.
Even if you just want to know what a compound’s likely in vivo profile will be (ADME: absorption, distribution, metabolism, excretion), there’s only so far you can get without doing the animal experiments. In vitro liver assays (microsomes, hepatocytes) have helped, but there’s a lot more to ADME than the liver, important though it is. We’d really like to be able to sort compounds out an an earlier stage and sort out what the structural features are that help or hurt a given series of analogs, but there’s a limit to how many full pharmacokinetic workups a project can get done.
Enter computation and modeling. There have been many, many attempts at an <i>in silico</i> solution to this problem over the years, but it’s safe to say that none of them are solid enough to base real decisions on yet. Never once have I heard a project leader say “Hey folks, the ADME model rank-orders all the compounds like this, so we don’t have to worry about running these in rodents any time soon”. Doesn’t happen – no one is going to draw any actionable conclusions until the animal numbers are in. The best you can do is with the M component. If your compound is shredded by liver microsomes, you probably do have a real problem, but good microsomal stability still doesn’t tell you anything much about its absorption, etc.
Here’s a new paper taking a crack at this problem, and it’ll be interesting to see how it’s received. The authors have a new computational approach (graph-based signatures), and to their credit, they’ve started a web server to let anyone try it out who wishes. (They note that no information is retained by said server, but that assurance, I have to say, is still not enough for anyone inside the industry to put any important structures into it – you can get fired for that kind of thing). What someone in industry can do, though, is put a bunch of already-disclosed structures through it, ones whose ADME behavior has already been characterized, to see how it does.
I’ll reserve judgment until I see some more data of this sort. The program uses the graph-based approach in addition to the traditional ones (physical properties, databases of structural features), and those latter ones are what we don’t trust enough already. So the key question is what the new algorithm adds to what we already have. The authors claim that it “achieved a performance as good as or better than similar methods currently available”, and specifically cite improvements in prediction of rat toxicity, P-glycoprotein inhibition, and inhibition of several CYP metabolic enzymes, among others. Of all the things on their list, the rat tox is to me the most interesting, because many of the others can be achieved by reasonably high-throughput assays, or (in the case of the Caco-2 permeability assay) are themselves not-always-reliable models of the real situation. A computational model of an in vitro model is not going to do anyone any good, but a reliable model of something like whole-animal tox could. We’ll see how this one performs!