The topic of phenotypic screening has come up around here many times, as indeed it comes up very often in drug discovery. Give your compounds to cells or to animals and look for the effect you want: what could be simpler? Well, a lot of things could, as anyone who's actually done this sort of screening will be glad to tell you, but done right, it's a very powerful technique.
It's also true that a huge amount of industrial effort is going into cancer drug discovery, so you'd think that there would be a natural overlap between these: see if your compounds kill or slow cancer cells, or tumors in an animal, and you're on track, right? But there's a huge disconnect here, and that's the subject of a new paper in Nature Reviews Drug Discovery. (Full disclosure: one of the authors is a former colleague, and I had a chance to look over the manuscript while it was being prepared). Here's the hard part:
Among the factors contributing to the growing interest in phenotypic screening in drug discovery in general is the perception that, by avoiding oversimplified reductionist assumptions regarding molecular targets and instead focusing on functional effects, compounds that are discovered in phenotypic assays may be more likely to show clinical efficacy. However, cancer presents a challenge to this perception as the cell-based models that are typically used in cancer drug discovery are poor surrogates of the actual disease. The definitive test of both target hypotheses and phenotypic models can only be carried out in the clinic. The challenge of cancer drug discovery is to maximize the probability that drugs discovered by either biochemical or phenotypic methods will translate into clinical efficacy and improved disease control.
Good models in living systems, which are vital to any phenotypic drug discovery effort, are very much lacking in oncology. It's not that you can't get plenty of cancer cells to grow in a dish - they'll take over your other cell cultures if they get a chance. But those aren't the cells that you're going to be dealing with in vivo, not any more. Cancer cells tend to be genetically unstable, constantly throwing off mutations, and the in vitro lines are adapted to living in cull culture. That's true even if you implant them back into immune-compromised mice (the xenograft models). The number of drugs that look great in xenograft models and failed out in the real world is too large to count.
So doing pure phenotypic drug discovery against cancer is very difficult - you go down a lot of blind alleys, which is what phenotypic screening is supposed to prevent. The explosion of knowledge about cellular pathways in tumor cells has led to uncountable numbers of target-driven approaches instead, but (as everyone has had a chance to find out), it's rare to find a real-world cancer patient who can be helped by a single-target drug. Gleevec is the example that everyone thinks of, but the cruel truth is that it's the exceptional exception. All those newspaper articles ten years ago that heralded a wonderful era of targeted wonder drugs for cancer? They were wrong.
So what to do? This paper suggests that the answer is a hybrid approach:
For the purpose of this article, we consider ‘pure’ phenotypic screening to be a discovery process that identifies chemical entities that have desirable biological (phenotypic) effects on cells or organisms without having prior knowledge of their biochemical activity or mode of action against a specific molecular target or targets. However, in practice, many phenotypically driven discovery projects are not target-agnostic; conversely, effective target-based discovery relies heavily on phenotypic assays. Determining the causal relationships between target inhibition and phenotypic effects may well open up new and unexpected avenues of cancer biology.
In light of these considerations, we propose that in practice a considerable proportion of cancer drug discovery falls between pure PDD and TDD, in a category that we term ‘mechanism-informed phenotypic drug discovery’ (MIPDD). This category includes inhibitors of known or hypothesized molecular targets that are identified and/or optimized by assessing their effects on a therapeutically relevant phenotype, as well as drug candidates that are identified by their effect on a mechanistically defined phenotype or phenotypic marker and subsequently optimized for a specific target-engagement MOA.
I've heard these referred to as "directed phenotypic screens", and while challenging, it can be a very fruitful way to go. Balancing the two ways of working is the tricky part: you don't want to slack up on the model just so it'll give you results, if those results aren't going to be meaningful. And you don't want to be so dogmatic about your target ideas that you walk away from something that could be useful, but doesn't fit your scheme. If you can keep all these factors in line, you're a real drug discovery scientist, and no mistake.
How hard this is can be seen from the paper's Table 1, where they look over the oncology approvals since 1999, and classify them by what approaches were used for lead discovery and lead optimization. There's a pile of 21 kinase inhibitors (and eight other compounds) over in the box where both phases were driven by inhibition of a known target. And there are ten compounds whose origins were in straight phenotypic screening, with various paths forward after that. But the "mechanism-informed phenotypic screen" category is the shortest list of the three lead discovery approaches: seven compounds, optimized in various ways. (The authors are upfront about the difficulties of assembling this sort of overview - it can be hard to say just what really happened during discovery and development, and we don't have the data on the failures).
Of those 29 pure-target-based drugs, 18 were follow-ons to mechanisms that had already been developed. At this point, you'd expect to hear that the phenotypic assays, by contrast, delivered a lot more new mechanisms. But this isn't the case: 14 follow-ons versus five first-in-class. This really isn't what phenotypic screening is supposed to deliver (and has delivered in the past), and I agree with the paper that this shows how difficult it has been to do real phenotypic discovery in this field. The few assays that translate to the clinic tend to keep discovering the same sorts of things. (And once again, the analogy to antibacterials comes to mind, because that's exactly what happens if you do a straight phenotypic screen for antibacterials. You find the same old stuff. That field, too, has been moving toward hybrid target/phenotypic approaches).
The situation might be changing a bit. If you look at the drugs in the clinic (Phase II and Phase III), as opposed to the older ones that have made it all the way through, there are still a vast pile of target-driven ones (mostly kinase inhibitors). But you can find more examples of phenotypic candidates, and among them an unusually high proportion of outright no-mechanism-known compounds. Those are tricky to develop in this field:
In cases where the efficacy arises from the engagement of a cryptic target (or mechanism) other than the nominally identified one, there is potential for substan- tial downside. One of the driving rationales of targeted discovery in cancer is that patients can be selected by pre- dictive biomarkers. Therefore, if the nominal target is not responsible for the actions of the drug, an incorrect diagnostic hypothesis may result in the selection of patients who will — at best — not derive benefit. For example, multiple clinical trials of the nominal RAF inhibitor sorafenib in melanoma showed no benefit, regardless of the BRAF mutation status. This is consistent with the evidence that the primary target and pharmacodynamic driver of efficacy for sorafenib is actually VEGFR2. The more recent clinical success of the bona fide BRAF inhibitor vemurafenib in melanoma demonstrates that the target hypothesis of BRAF for melanoma was valid.
So, if you're going to do this mechanism-informed phenotypic screening, just how do you go about it? High-content screening techniques are one approach: get as much data as possible about the effects of your compounds, both at the molecular and cellular level (the latter by imaging). Using better cell assays is crucial: make them as realistic as you can (three-dimensional culture, co-culture with other cell types, etc.), and go for cells that are as close to primary tissue as possible. None of this is easy, or cheap, but the engineer's triangle is always in effect ("Fast, Cheap, Good: Pick Any Two").