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The Landscape of Kinase Inhibitors

I’ve been meaning to link to this article, which is the best overview I know of for kinase inhibitors. The authors (a large multicenter team led out of Munich) characterize 243 (!) kinase inhibitors that have made it into human trials across a very wide range of the known kinase enzymes, and the result is a mass of data that’s finally available in one place. (I should also note that the authors have incorporated the data into their online open-access ProteomicsDB tool). Kinase inhibitors get tested against other kinases as they’re developed, but not against lists this long (and not under the same conditions, as you start to compare compounds from different organizations against each other).

Many clinical KIs (kinase inhibitors) are claimed to be potent and selective; however, this is often not the case, resulting in failure of clinical trials and obstacles with laboratory research. Assessing selectivity of a compound for a target or target class is not a trivial undertaking, because the full range of targets (and their cellular expression levels or concentrations) is often unknown and the complete compound dose range is rarely measured. All KIs in our study were profiled in a dose-dependent manner and at near thermodynamic equilibrium in cellular lysates. Thus, this large body of binding data enabled the development of a new selectivity metric termed CATDS (concentration- and target-dependent selectivity) that goes beyond previously published selectivity scores (351921) in that it also captures aspects of target engagement and drug MoA.

About 10 to 20% of the 243 are quite selective, including Tykerb (lapatinib) and rabusertib. but the scores decrease pretty smoothly down to compounds that are just not selective at all, such as Rydapt (midostaurin) and XL-228. You’ll note that there are both approved and unapproved drugs at both ends of the scale – selectivity really doesn’t, in the end, correlate with clinical usefulness as much as we’d like to imagine it does. Rabusertib, for example, is an extraordinarily selective chk2 inhibitor, but guess what? Lilly has given up on it after failures in the clinic, from every indication, because that’s just not enough to show benefits in the real world. It’s also worth noting that some of the compounds in this study are listed at, but turn out not to be as selective as previously thought (!)

The group tried profiling across mechanistic classes – for example, Type I inhibitors (which target the enzyme’s active conformation) versus Type II inhibitors (which hit inactive ones), but those two really didn’t show much of a selectivity difference across the numerous compound examples. The covalent kinase inhibitors (a smaller set) are more selective, but still not perfect. Some of them do hit other kinases, just without the covalent “warhead” coming into play (after all, they have to fit into an active site for the covalent modification to occur). So it’s difficult to generalize.

As for off-target effects in general:

As expected, the vast majority of compounds interacted with protein/lipid kinases, but our study also revealed binding to seven metabolic kinases, 19 other nucleotide binders, five FAD (flavin adenine dinucleotide) binders, and the heme-binding enzyme FECH (ferrochelatase) (Fig. 3A and table S2). These unanticipated interactions not only may lead to desired consequences but also can represent mechanisms of drug toxicity. A survey of the scientific and patent literature (using PubMed, SciFinder, or ChEMBL) revealed that many of the 243 drugs investigated in this study are surprisingly poorly characterized with regard to their target space or bioactivities.

Exactly (and there are many more details on off-target binding to other proteins with ATP binding sites, I should add. Update: see here for a review on these things). And that’s even as the authors note that there are over 110,000 papers in PubMed and over 47,000 patents and patent applications in Scifinder on kinase inhibitors. There’s a power-law distribution, as you might expect – half of those papers are on just five compound! At the other end of the scale, although everything apparently shows up in the patent literature, there are 17 kinase inhibitors that have been into human trials that still have no publications on them in PubMed. But that still leaves you with a lot of literature to cover in between (with a lot of gaps) which is why I’m glad that this new paper exists.

That’s how, for example, we know about Tafinlar (dabrafenib). It’s in the literature as a selective BRAF inhibitor, but that may not be the case:

The kinobeads data showed that the drug is a multikinase inhibitor with ~30 submicromolar targets (fig. S5B). Kinase activity assays confirmed potent inhibition of several SRC family members, and there was no apparent difference in selectivity between the three RAF family members. Moreover, wild-type (WT) BRAF and the V600E mutation for which the drug is used in the treatment of melanoma were equally well inhibited (fig. S5, C to F).

It’s not alone. As mentioned above, there’s nothing wrong with polypharmacology per se in this area, but everyone should know what the real situation is. Claiming selectivity seems to be an artifact of the selectivity-is-good mindset that all of us tend to have, but you know what’s really good? Clinical efficacy and safety. Your chances for the latter are probably improved if your kinase inhibitor is not a blunderbuss that blasts the kinome to shreds, of course, but your chances for the former are not necessarily improved by exquisite targeting. Either way, the real selectivity data need to be out there.

The paper suggests a number of older candidates for re-evaluation on more recently appreciated kinase targets, either as drugs or as starting points for new programs, and the paper suggests several specific examples of approved compounds that may well have until-now-unevaluated new indications (such as cabozantinib in FLT3-ITD–stratified AML patients). Back upstream, the authors also show how the data can be used to try to profile entire pathways in cellular assays:

One key challenge in drug discovery is to assess whether a drug molecule engages a target or associated pathway in a cell. The present resource allowed us to explore this in a novel way by analyzing the phosphoproteome of cancer cells in response to KI treatment and by integrating this information with the target spectrum of the drug(s) used. To illustrate this concept, the phosphoproteomes of BT-474 cells after treatment with the EGFR/HER2 inhibitors lapatinib, afatinib, canertinib, dacomitinib, and sapitinib were determined to a depth of ~15,000 phosphorylation sites (fig. S8C and table S9). The analysis revealed a surprisingly large number of statistically significantly regulated phosphorylation events for each drug. . .

The five drugs mentioned have about 211 protein phosphorylation events in common in that particular cell line, which tells you a lot about the EGFR/HER2 network – but on the other side, they all have somewhat different selectivity profiles against other kinases in those same cells, so you can learn from what they have in common and from the places where they differ as well.

So anyone who’s at all interested in the kinase inhibitor world needs to look over this paper. And anyone who might want a reminder of (on one side) just how messy and complex things are, or (on the other side) how many interesting opportunities remain out there, should have a look, too. This is state-of-the-art stuff.


20 comments on “The Landscape of Kinase Inhibitors”

  1. Janex says:

    When working in kinases we tried to aim for slightly dirty. The highly selective compounds weren’t efficacious. Our theory was that there was so much redundancy built in that a single inhibited target was simply bypassed. And on the other side compounds which were unselective were toxic for obvious reasons.

    1. Barry says:

      Is there any good reason to believe that kinases which are alike in their binding sites are closely related in their signaling roles in cancer? Or is this just a rationalization for pushing a compound into the clinic because it looked hard to get any more selectivity?
      Cancers are heterogeneous, and even a single cancer cell will often have activated multiple redundant parallel signaling pathways. But hitting two or more of those might need a drug that binds two or more very different binding sites, rather than two which are very similar.

      1. Janex says:

        It depends on the pathway. Frankly a LOT of luck is involved.

    2. Chris Phoenix says:

      I wonder whether overly selective inhibitors are too easy for the cells to evolve to get around? A tiny function-preserving tweak to the receptor, and the inhibitor stops working.

      1. Janex says:

        You don’t see this as much in the model systems used pre-clinically. But it shows up a lot in the clinic.

  2. marklar says:

    Don’t have access to the article.
    Agree with Janex. Selectivity was pushed hard in the project I was a part of (at least in the beginning), but efficacy seemed to favor compounds that hit both the upstream and downstream (related) kinases. I thought that kinase selectivity should be taken with a grain of salt because of the inherent differences in Km of ATP. It’s hard to get selectivity when targeting the ATP binding site when you have a 3-4 log difference in Km between kinases (assuming targeting the kinase having the weakest ATP Km). From what I can remember, I believe that only 1 concentration of ATP was used in all of the assays. I figured if selectivity was crap, but the compound showed potency/efficacy then so what…move it forward. That is what eventually happened–funny how project philosophy can change to fit the observations.
    Also, pet peeve: Isn’t ‘thermodynamic equilibrium’ redundant?

    1. marklar says:

      should be: ‘targeting the kinase having the strongest ATP Km’

    2. ab says:

      Project philosophy SHOULD change to fit the observations, right?

      1. marklar says:

        Yes, I agree that philosophy should change. However, it shouldn’t take nearly 3 years for things to (kind of) change which is what happened in my case.

  3. david lilienfeld says:

    What ever became of the Ambit Tree?

  4. Marcin says:

    Obviously authors must have purchased those 243 or so compounds, but have they tested them for identity and purity?
    It would seem reasonable considering serious concerns on identity and/or purity of the commercially available “chemical probes”

    1. Marcin says:

      Geez!! I did not realize I would be such a buzzkill

    2. Scott says:

      One would hope so, but I remember one ‘Big Data’ paper that didn’t (and got ripped to shreds for not doing so).

  5. Researchfella says:

    Some of the compound data sets in this paper seem very odd, e.g. JAK inhibitors such as baricitinib that don’t show any JAK target engagement at 1 uM. And there are other examples. I don’t know enough about the assay methodology – is it well established and reliable? A paper like this would be fabulous, if the data were solid.

    1. Tom says:

      The cells in which the compounds were tested, probably do not express high enough levels of JAK1.

  6. tangent says:

    “Your chances for the [safety] are probably improved if your kinase inhibitor is not a blunderbuss that blasts the kinome to shreds, of course.”

    Is this known? Is inhibiting a single kinase actually a single effect, or is the regulatory network coupled enough that it’s simply nothing of the sort?

    It’s mathematically reasonable that the single effects would project back into kinase space as “inhibit this, inhibit that 2x, …” Or, horribly for us, would require time-varying kinase inhibitions.

  7. Covailment says:

    Covalent kinase inhbitors, along with other covalent drugs ( eg. Serine hydrolase drugs in particular) are not more selective, they just appear to be under the conditions of the assay. Put one radioactive version of these things in a mouse for a set of dosings, and then take out tissue, and it will light up like A-bomb fallout. Only a matter of time until a few of the serine hydrolase drugs have clinical trial side effect profiles that look like sarin gas.

    1. Pharmacologist says:

      Actually not at all in some cases. Have seen radiolabel ADME of covalent compounds (even early stage) that had lower tissue retention in rats than approved non-covalent drugs. But it is obviously an inherent risk for any compound (parent or metabolite) having potential to react.
      The covalent drugs “gain” in selectivity when their PD extends beyond their exposure.

  8. “you know what’s really good? Clinical efficacy and safety…”
    Indeed. However, if you don’t have a MOA in a single, pithy sentence, many big Pharma companies and VCs that rely on old school med chem experts don’t want to know. No matter what your molecule looks like, if it has clinical activity and an excellent safety profile, it should be of interest, no? Well…. as it happens, many potential big Pharma partners and VCs are not interested. From personal experience, touting a highly active and very safe molecule with a multifaceted MOA resulted in little traction because it didn’t fit into the “X kinase” or other already established box. I would implore Your Readers to look beyond mechanism. To understand that biology is complicated. To accept that truly innovative molecules may not look like you thought they should. And that a MOA can be complex, because by Jove, biology is complex!

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