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 (3, 5, 19–21) 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 chemicalprobes.org, 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.
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.