I can recommend this article on phenotypic drug discovery from the latest Nature Reviews Drug Discovery. (For the nonspecialists in the crowd, there are two broad categories of screening for drug leads. One is “target-directed”, where you have an idea from other studies about what protein or pathway you want to affect, and you set up an assay specifically reading out on that mechanism. And the other is phenotypic screening, where you know what eventual effect you want to see in the cells or the whole organism, and set up your assay to read out on that phenotype, with no biases about how any given hit compound manages to do it).
Overviews like this appear every so often in the literature, but this is a rather comprehensive one, and it’s always worth reading the articles that take into account the latest techniques. As has always been the case, the first key to running a successful phenotypic program is to make sure that you have assays that will translate into real-world success. As I’ve said before, a bad phenotypic screen is the worst of both worlds, an amazing waste of time, because you end up chasing illusory activity and trying to figure out things that didn’t need to be figured out in the first place. But when it’s done right:
The fundamental determinant of the potential success of a PDD effort is the ability of the screening assay to predict the clinical therapeutic response to a drug with a specific mechanism of action. This was described by Scannell and Bosley as the “predictive validity” of a discovery model. Here, we propose the term chain of translatability to describe the presence of a shared mechanistic basis for the disease model, the assay readout and the biology of the disease in humans, as a framework for developing phenotypic screening assays with a greater likelihood of having strong predictive validity.
The classic phenotypic screen has been in a whole (small) animal or via readouts in cell behavior or morphology, but there’s been a lot of work in recent years to see if high-content assays in cells can deliver finer-grained results. “Molecular phenotyping” is an attempt to get a read on entire pathways, distinguishing the various possible assay hits by differences in a whole list of cellular readouts. You’d have to think that this is where the field is going, but it’s not easy. There are so many things to look at that you run the risk of overfitting a model to your data – it’s the classic von Neumann’s elephant problem. Combining this with modern gene-sequencing techniques is also very promising, but adds to that same so-many-variables problem. Advanced cell culture techniques are also clearly the way to go, giving you (potentially) a much more realistic system to test in. The problem there is that progress in assay development can be slow, since the number of potential variations in culture technique is almost limitless, and their effects on the cells (and the validity of the assays derived from them) can be quite difficult to predict.
The review goes on to a good discussion of the strategic considerations in phenotypic screening versus traditional target-directed projects. Phenotypic work, if it’s going to be any good, is often going to take a lot more time and resourcing up front, and an organization has to be committed to that before someone starts getting testy. Another crucial issue is target ID. Every phenotypic program starts out with a plan to identify the targets of any interesting hits they come up with, as well they should. But that’s not always straightforward, or even possible, and management could well lose patience with the whole effort if the screen itself has already taken longer (and been more costly) than they were expecting.
Honestly, if you’re going to run a phenotypic program, you have be prepared, before you start, for the possibility that you may have to go into the clinic without knowing your target or mechanism. Not every organization is ready for that. It takes (for one thing) a lot of confidence in your assay, but if you don’t have that kind of confidence in it anyway, you might want to rethink the idea of using it to drive a phenotypic effort at all (as harsh as that may sound). At the very least, people should be ready to have to make a longer leap of faith than they would ideally want, because it may well come to that. This calculation is going to vary according to the assay, the disease area, the compounds discovered and their activities, and so on, and it will probably not be easy to arrive at a consensus. But the reward for going through all this is the much greater possibility of finding completely new mechanisms of action, and (if things have been done right), the chance to greatly increase the chances for clinical success.
This mode of drug discovery has provided some of the greatest advances in the field, and it has also (under other circumstances) wasted heaps of time and money. Anyone getting into phenotypic drug discovery in hopes of landing in that first category would be well served by this paper, and experienced workers in the field will find it a concise summary of a lot of expensively obtained wisdom. Have a look!