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DBL%20Hendrix%20small.png College chemistry, 1983

Derek Lowe The 2002 Model

Dbl%20new%20portrait%20B%26W.png After 10 years of blogging. . .

Derek Lowe, an Arkansan by birth, got his BA from Hendrix College and his PhD in organic chemistry from Duke before spending time in Germany on a Humboldt Fellowship on his post-doc. He's worked for several major pharmaceutical companies since 1989 on drug discovery projects against schizophrenia, Alzheimer's, diabetes, osteoporosis and other diseases. To contact Derek email him directly: derekb.lowe@gmail.com Twitter: Dereklowe

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July 24, 2014

Phenotypic Assays in Cancer Drug Discovery

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Posted by Derek

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").

Comments (22) + TrackBacks (0) | Category: Cancer | Drug Assays | Drug Development


COMMENTS

1. SP on July 24, 2014 8:52 AM writes...

They note that reporter gene assays fall in this class, and those are pretty simple to set up and have been common for years. Make a reporter for Myc or Ras or your favorite oncogene expression and find something that knocks it down through whatever unknown mechanism, chromatin this or repressor that. Although as usual the simpler it is the more likely you are to fool yourself.

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2. Neo on July 24, 2014 8:53 AM writes...

I still get surprised when an experienced medicinal chemist say things like "...a real-world cancer patient who can be helped by a SINGLE-TARGET drug". Do you really think that you can explain the efficacy of a drug by looking only at its intended primary target? How are these mechanistically-guided approaches going to work if you only have (very) incomplete information about the polypharmacology of the drug?

This paper looks very much like target-based DD trying to rebrand itself...

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3. Anonymous on July 24, 2014 8:58 AM writes...

3-D cell cultures are the way of the future in phenotypic screening. Much better correlation to actual tumors will be observed.

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4. a. nonymaus on July 24, 2014 9:46 AM writes...

The genetic drift of cancer cell lines away from their host has caused me to adopt the opinion that there has been a speciation event. We've done it, we've become gods, V. Frankenstein, et al. eat your hearts out and gaze upon Homo hela. As novel model organisms, our creations may have value, but they are definitely not the tumours that we started with. Maybe in time, they'll make nice pets.

What I don't get about this paper is why the same problem doesn't arise in antiviral or other infectious disease research. Bacteria certainly mutate and modify their gene expression in response to their environment or because it's Thursday and you need a more stressful weekend. Viruses, especially retroviruses such as HIV, are regular mutation-factories. Certainly there is the same issue that a Petri dish full of medium is different from the inside of my lungs whether the target of the screen is pneumonia or lung cancer. Is it that the bacteria are typically fewer cell divisions removed from the clinical isolate? If so, is there some sort of shortage of excised cancer tissue to get fresh, more-relevant cells from?

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5. Anon2 on July 24, 2014 10:11 AM writes...

Can you please elaborate on this? :
"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"

Are the authors saying that no one looked at VEGFR2 prior to it entering the clinic? (I find that hard to believe)
Or are they saying that that no one compared this drug in BRAF mutant mice and saw a black and white difference between it and the WT?
Surely there is more to this story?

Unrelated, I've always felt a big issue with cancer research has been the cell lines. Decreased funding has labs skimping on the science without them realizing it. I invite you to talk with an academic lab (the postdocs and grad students, not the PI) about where they got their cell lines. Most are "borrowed" from neighboring labs, with contamination along the weigh (cell type and mycoplasm). Very few people are culturing them from mice, the clinic, or buying them from ATCC. When I was at MDACC we actually had an issue getting them from the clinic because the surgeon/pathologists would demand to be listed on grants and papers otherwise "No tumor for you!" *soup nazi voice*

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6. SP on July 24, 2014 10:14 AM writes...

"Certainly there is the same issue that a Petri dish full of medium is different from the inside of my lungs whether the target of the screen is pneumonia or lung cancer."
A well known problem in TB discovery.

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7. HT on July 24, 2014 11:09 AM writes...

@4: Actually these are very common occurrences in MB research. My co-workers have previously tried to replicate some key work on bacterial pathogenesis reported in high IF journals. 1/3 of time, we couldn't reproduce the results, and later found other labs failed to do so too. Another 1/3 of the time, it works, but only with the specific strain and under the exact same conditions, which mean that the results couldn't be generalized and are not applicable to the clinical situation. Naturally, negative reports that contradict prior published results will not be published.

On the other hand, it can work both ways: a number of antifungal and antimycobacterial drugs (not just TB, BTW) have poor to no activity when tested in vitro, whether using agar culture, broth dilution or cellular models that simulated in vivo conditions. However, they demonstrated clear efficacy in animal models and in the clinic. Don't ask me how they were first brought to the clinic though ...

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8. johnnyboy on July 24, 2014 11:38 AM writes...

Sorry to be nihilistic here, but the more I know about cancer and its therapeutics, the more I feel that except for few specific indications, oncology drug development is a mostly futile endeavour. Finding a drug that prolongs life for an average of 5 months might make billions for a biotech, but in the grand scheme of disease it's a pretty puny advance. In my opinion, the really significant advances against cancer will not come from drug treatments, but from early detection and diagnostics. Trying to treat a malignancy after metastasis has occurred is essentially closing the barn door months after the horse has bolted.

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9. Argon on July 24, 2014 12:17 PM writes...

"Give your compounds to cells or to animals and look for the effect you want: what could be simpler?"

Give them to plants, insects or fungi and that's what you call the main, early screening operation for herbicides, insecticides and fungicides in the crop protection field. SAR is a pain but at least the model system *is* the target organism in many of those assays.

For oncology I think it's somewhat driven by the 'search for your lost car keys at night under a lamp post', best-of-the-mostly-terrible options paradigm.

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10. Argon on July 24, 2014 12:19 PM writes...

Anonymous writes: "3-D cell cultures are the way of the future in phenotypic screening. Much better correlation to actual tumors will be observed."

Certainly, if the 3D cultures are actual humans...

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11. Anonymous on July 24, 2014 1:58 PM writes...

"Good models in living systems, which are vital to any phenotypic drug discovery effort, are very much lacking in oncology."

Actually, this is true for all therapeutic areas, except for antibiotics, where we actually do have pretty good models. Anyone who develops a truly predictive animal model for any chronic disease will get an all-expenses-paid trip to Stockholm...

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12. emjeff on July 24, 2014 2:01 PM writes...

#8, You may be right on an abstract level, but what if the patient is your spouse or your mother? Then, 5 months is worth a lot.

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13. RTW on July 24, 2014 3:55 PM writes...

@12 correct! And if you can string a few iterations of 5 months together to get you a year and a half or more? Any incremental advance may make it possible that someone could get to the time point when something new and radical become available. Perhaps one of the new immunotherapy treatments that are starting to show remarkable prolonged therapeutic advances in certain cancers.

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14. DrSnowboard on July 24, 2014 5:16 PM writes...

You could argue that a confluence of target-based arrogance (magic bullet obsession ) and model / cell line limitations have hampered oncology drug research. Contrast antivirals. you test on the actual virus (admittedly replicating in a host cel in a test tube or a surrogate replicon) and there is an experience (clinical) that says 2 or 3 molecule combinations are necessary to avoid resistance.

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15. Anonymous on July 24, 2014 7:22 PM writes...

How can the paper claim that PDD has not worked so well when it hasn't looked at the actual success rates as a % of all attempts, including failures? What if there haven't been many PDD successes simply because pharma has ignored PDD while pursuing its obsession with TDD?

And what the hell is target-guided PDD supposed to be anyway, apart from just a buzzword or fad, like "big data" (woooh!) to make a name for the author?

This is a load of crap.

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16. Anonymous on July 24, 2014 7:39 PM writes...

@12: "You may be right on an abstract level, but what if the patient is your spouse or your mother? Then, 5 months is worth a lot."

Sorry, but I'd rather take the inheritance *without* the deduction of another 5 months medical bills.

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17. Dana on July 24, 2014 10:26 PM writes...

@9 As a complete numpy, why though were things different in the treatment of some childhood cancers like leukaemia? I thought there the initial advances, in the seventies, were really tiny, with less than 1% of the treated kids showing any even tiny increase in survival, while now the actual curing rates of are quite high. Are grown-up cancers just fundamentally different?

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18. anon-ymous(e) on July 25, 2014 6:17 AM writes...

11. Anonymous - "Anyone who develops a truly predictive animal model for any chronic disease will get an all-expenses-paid trip to Stockholm..."

The db/db mouse for type 2 diabetes. I know of NO anti-hyperglycemic agent that works in this model that doesn't also work in man to lower blood glucose.

Please contact me soon to pay for my arrangements - Sweden is lovely this time of year.....

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19. Not Important on July 25, 2014 8:32 AM writes...

@17 Yes, in general cancer in adults is different from cancer in children because in adults the cells have transitioned into maintain-and-replace mode vs. the growth mode exhibited in children. This is why pediatric cancer has been likened to fighting a brush-fire during a drought.

Permalink to Comment

20. John Moffat on July 25, 2014 2:22 PM writes...

This is (mostly) the sort of discussions that we were trying to promote with this paper.

@2 I think we're in agreement, explaining the efficacy of a drug by it's action on a single target - certainly. Expecting that single-target drug to have great single-agent efficacy, no, and that's increasingly a straw man argument. - Combination of targeted agents or polypharmacology - either way, it pays to know what your drug is doing at a molecular level.
Rebranding TDD ? never really thought of my work as a 'brand'

@4 "is there some sort of shortage of excised cancer tissue to get fresh, more-relevant cells from?"
- for basic research and compound MOA studies, yes. - for new lead discovery screens, Hell Yes.

@5 "Are the authors saying that no one looked at VEGFR2 prior to it entering the clinic? (I find that hard to believe)"
- Nope, never said that
"Or are they saying that that no one compared this drug in BRAF mutant mice and saw a black and white difference between it and the WT?"
- I wasn't there, I just know they launched trials in BRAF melanoma anyway.

@14 "You could argue that a confluence of target-based arrogance (magic bullet obsession ) and model / cell line limitations have hampered oncology drug research. Contrast antivirals..."
- Disregarding the crack about arrogance,I totally agree. The fact that you can't do with cancer what you can with virusss was pretty much the point of the paper.

@15 Thanks, appreciate your deeply-thought out and well reasoned anonymous response. (Do we know each other?) Now I'm going back to work trying to find a useful cancer drug by whatever methods work best and you can carry on being angry.

Permalink to Comment

21. exchem on July 29, 2014 9:34 AM writes...

MIPDD - "inhibitors of known or hypothesized molecular targets that are identified and/or optimized by assessing their effects on a therapeutically relevant phenotype"

I've heard this kind of thing before but can't see how this is any different from "pure" TDD. Has anyone really tried to optimize a drug without assessing its effects on a (supposedly) therapeutically relevant phenotype as early as possible on the critical path? Seems to me there are people around who've convinced themselves this is what TDD was like, but I've been around a while working on TDD programs and never seen it.

Permalink to Comment

22. Anonymous on July 30, 2014 11:10 AM writes...

First, I'm a big fan of phenotypic screening. Also, what does it mean to take a targeted approach? Most often, targeted approaches are discussed relative to some sort of signaling pathway. However, cancer metabolism has regained a loooooot of attention. Metabolic targets can often be quite profoundly different than a signaling target--metabolic bottlenecks exist with no known ways around them, in contrast to signaling targets where there are many ways around a signaling cascade blockage that produce robust signaling networks.

Also, for years now we've talked about the huge, huge differences in screening based on 2-D culture vs. 3-D. I've seen papers such as this one where HIF-1, mTOR, and AMPK have been shown to be no longer important in contributing to malignant phenotypes once you move to 3D culture:

http://www.ncbi.nlm.nih.gov/pubmed/24316969

But how much attention and papers have been published on HIF1, mTOR, and AMPK in cancer? There are many, many reasons for disconnects between in vitro studies and in vivo how about starting with 3D?

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