All active kinase states are similar but...

CDK2 bound to inhibitor staurosporine (PDB code: 1aq1)

…inactive kinase states are different in their own ways. This admittedly awkward rephrasing of Tolstoy's quote came to my mind as I read this new report on differences between active and inactive states of kinases as revealed by differential interactions with inhibitors. 

The paper provides a good indication of why kinases continue to provide such enduring objects of interest for drug designers. Ever since imatinib (Gleevec) opened the floodgates to kinase drugs and quenched the widespread belief that it might well be impossible to hit particular kinases selectively, researchers have realized that kinase inhibitors may work either by targeting the active as well as the inactive states of their proteins. One intriguing observation emerging from the last few years is that inhibitors targeting inactive, monomeric states of kinases seem to provide better selectivity than those targeting phosphorylated, active states. In this study the authors (from Oxford, Padua and Newcastle) interrogate this phenomenon specifically for cyclin-dependent kinases (CDKs).

CDKs are prime regulators of the cell cycle and their discovery led to a Nobel Prize a few years ago. Over the last few years there have been several attempts to target these key proteins, not surprisingly especially in the oncology field; I worked on a rather successful kinase inhibitor project myself at one point. CDKs are rather typical kinases, having low activity when not bound to a cyclin but becoming active and ready to phosphorylate their substrates when cyclin-bound. What the authors did was to study the binding of a diverse set of inhibitors to a set of CDKs ranging from CDK2 to CDK9 by differential scanning fluorimetry (DSF). DSF can provide information on binding affinity of inhibitors by way of the melting temperature Tm. Measuring the Tm values for binding to different kinases can thus give you an idea of inhibitor selectivity.

The main observation from the study is that there is much more discrimination in the Tm values and therefore the binding affinities of the inhibitors when they are bound to the monomeric state than when they are bound to active, cyclin-bound states. Interestingly the study also finds that the same CDK in different states provides more discrimination in inhibitor binding compared to different CDKs in the same state. There is thus plenty of scope in targeting the same CDK based on its different states. In addition, binding to the monomeric state of specific cyclins will be a better bet for engineering selectivity than binding to the active, cyclin-bound states.

There are clearly significant structural differences between the inactive and active states of CDKs, mainly related to the movement of the so-called alphaC-helix. This study promisingly indicates that structure-based drug design experts can potentially target such structural features of the binding sites in the inactive states and design more selective drugs.

Met/VEGFR/FGFR inhibitors by VS

This one comes from a NCI group. Starting with a database of 3.5 million compounds, 175 prospective candidates were finally selected for inhibition of Met kinase by virtual screening based on filtering by size, log P, polarity, h-bond donors and acceptors etc. The 175 compounds were docked by GOLD and 70 were selected on the basis of force-field calculated protein-ligand interaction energies (always a risky endeavor, but for a similar series, errors may cancel). For paring down the compound set, an empirical pharmacophore based on the usual kinase interactions (eg. h-bonding to hinge residues and h-bonding to a crucial Tyr to keep the kinase locked in the inactive conformation) was used.

The 70 compounds bought from vendors were assayed in cell-free Met assays as well as HGF (hepatocyte growth factor) induced Met activation. 3 compounds were chosen with modest Met inhibition IC50 values of 0.6 and 40 µM. The IC50s for the intact cells were 30, 50 and 30 µM. In spite of this less than stellar inhibition performance, the three were tested for the inhibition of other kinases and found to inhibit VEGFR and FGFR to similar extents. Since the single-kinase inhibition paradigm seems to be called into question these days, one might as well diversify. The different inhibition values were rationalized on the basis of hydrogen bonding and other interactions (or the lack thereof) by docking. Wisely, docking and docking scores were not used for judging binding affinity, just the orientations of the compounds in the active sites.

The flavone-like looks of the compounds make me quite suspicious about PK and Tox. Only time will tell. But Met is emerging as a valuable target.

Reference:
Megan L. Peach, Nelly Tan, Sarah J. Choyke, Alessio Giubellino, Gagani Athauda, Terrence R. Burke, Jr., Marc C. Nicklaus and Donald P. Bottaro (2009)
Directed Discovery of Agents Targeting the Met Tyrosine Kinase Domain by Virtual Screening
J. Med. Chem ASAP

Selective vs Multitargeted Kinase Inhibitors: Still in the Stone Age

In the conference on kinase inhibitors I attended recently, there was a panel discussion on the second morning (why do these discussions have to start at 7:30 a.m.?) about the utility of selective vs multi- target directed inhibitors. The conventional wisdom has been that selective inhibitors- or any selective drugs for that matter- are best, since off-target effects can cause toxicity. The fight against cancer has largely been about finding selective and therefore safe drugs that hit targets only in cancer cells. It is a measure of how less we have accomplished in cancer therapy in spite of the countless amounts of dollars spent that we still are far from rationally designing reliable, selective and safe cancer drugs.

The discussion we had did not end in any consensus. While selective drugs may clearly be good in certain cases, there are cases in which drugs designed for selectivity ended up promoting their action by being non-selective and targeting multiple targets, but only in retrospect. Gleevec, the revolutionary drug for treating chronic myeloid leukemia, is a classic example. Initially supposed to be a "magic bullet" that targeted only a mutated kinase named Bcr-Abl in cancer cells, Gleevec later turned out to also potently target two other kinases, c-Kit and PDGFR. Interestingly these two targets are valuable targets in cancer therapy of two other cancers, renal cell carcinoma of the kidneys and glioblastoma of the brain.

In any case, the consensus was that we are still far away from designing drugs for a specifically chosen subset of targets. Something like staurosporine that hits almost every kinase out there is going to be undoubtedly gratuitously toxic. But inhibitors hitting a very specific subset of kinases could target a few crucial choke-points in disease pathways, thus serving as valuable drugs. But we are still far from rationally designing such inhibitors. Indeed, in the first place we don't even know what specific subset of kinases to hit for treating a particular disease. First comes target validation, then modulation. Most of the specific subset targeting kinase inhibitors seem to be discovered only in retrospect. In my own project where we are trying to target only one kinase selectively, we are now being skeptical about whether the beneficial effects we are observing are due to multi-target binding.

The other unrelated point we discussed was whether anybody knew kinase inhibitors which were near clinical trial phase completion for areas other than oncology. The silence around the table spoke for itself.

The bottom line is; as far as targeting specific subsets of kinases with inhibitors or even knowing which specific subset to target is concerned, we are still in the Stone Age of kinase drug discovery. The drugs which we have are largely still stone and tree branches. We have a long way to go before discovering tools and bronze.

In the next post, I will talk about a recent effort that overcame the rational multi-kinase inhibitor design for two very different kinases. It points the way forward.

Break

I am in Boston for a kinase inhibitors conference this week, so I may not be able to blog, except when I want to complain about some kinase inhibitor speaker. Enjoy the fall.

"The name's Inhibitor. Kinase Inhibitor"

Since we were on the topic of kinases a few days back, it is worth pointing out a recent paper by Alex Aronov, Mark Murcko and others at Vertex on kinase-likeness.

One of the most common questions asked by medicinal chemists is about how to identify "privileged scaffolds", either generally among molecules that could bind to proteins, or among a subset of molecules. It would be useful indeed to identify, for example, common scaffolds among kinase inhibitors, which could help to pick out putative kinase inhibitors from a large dataset of molecules.

Aronov's group sets out to do just this. They do a statistical study in which they identify rings and linkers that are commonly found among kinase inhibitors. For this, they looked at three different databases. The important one was a database of molecules obtained from GVK Biosciences, which contains inhibitors from J. Med. Chem. published between 1959-2003. Naturally, there is a preponderance of kinase inhibitor-like molecules in the later publications. As controls, they also used two other databases which were underrepresented in kinase inhibitors.

Now any kinase lover worth his or her salt should not be surprised at their database-fishing results- they find that amino (NH) linkers are among the most common, along with rings containing one or two nitrogens. Cyano groups are also pretty common in side chains. They find that biaryl amines are extremely common among kinase inhibitors, another not very surprising conclusion. Anilines, pyridines and pyrimidines as well as pyrroles are also better represented among kinase inhibitors.

But the authors do something more valuable and generally applicable. After making these observations, they come up with what they call the "2-0 rule" for kinase likeness prediction. The rule needs putative kinase inhibitors to have an aromatic pyridine like (N) or pyrrole like (NH) nitrogen, or an aniline NH and/or a cyano group. Simply put, the rule for kinase-likeness is represented as:

∑ (N aromatic) + ∑ (NH aromatic) > 2
OR
∑ (Ar-NH) + ∑ (R-CN) > 0

When they applied this simple rule to the GVK database, they found that four out of five kinases inhibitors were flagged with this rule. In case of the kinase-inhibitor underrepresented database obviously, there were very few compounds that emerged through this rule.

The paper also went one step further and documented the locations in the ATP-binding pocket of kinases where these commonly identified fragments fit and found most probable locations for the fragments. However, it should be remembered that allosteric kinase inhibitors can have some of the most valuable functions for selectively targeting kinases, and this rule may not help in identifying them. But in any case, not too many allosteric inhibitors are known relative to ATP-site binding inhibitors.

Such a rule, while not completely exhaustive, can serve some valuable purposes. For one thing as the authors demonstrate, it can be used to fish out kinase inhibitor-like molecule hits/leads from a database of existing drugs. This is of course a well-known strategy, to use a drug prescribed for one ailment to treat a different one, and one of the big advantages of this is that because the drug has been on the market its PK/ADME-TOX properties are already well-established. But it's not easy to do in general, and it's nice to have a structural rule that could weed out such drugs and "redeploy" them for targeting kinases. In this case, the fished-out drugs that originally bound to totally different targets did show broad-spectrum kinase activity experimentally.

In general, such a rule can be used both ways. More commonly, it can be used to fish out kinase inhibitors. But in a more novel application, it can be used to exclude kinase inhibitors and then screen other moelcules for novel scaffolds, albeit with a lower hit rate.

There is a caveat in this study which the authors note; the scaffolds for kinase inhibitors probably also reflect the ease of access to these inhibitors through existing synthetic chemistry. For example, one of the reasons anilines and biarylanilines are predominant among kinase inhibitors is because they are easy to synthesize through coupling reactions like Suzuki coupling. Ready availability of starting materials also drives the structures of kinase inhibitors. Thus, the existing structures should not be taken as representative of the most commonly possible scaffolds for kinase inhibition. The search should certainly continue.

Aronov, A.M., McClain, B., Moody, C.S., Murcko, M.A. (2008). Kinase-likeness and Kinase-Privileged Fragments: Toward Virtual Polypharmacology. Journal of Medicinal Chemistry DOI: 10.1021/jm701021b

Water-inhibitor synergy in CDKs

An interesting paper published in the J. Chem. Inf. Mod. investigates the role of water molecules in CDK2 and CDK5 active sites. The authors use MD simulations to simulate water and inhibitors (in this case the well-known indirubin analogs) in the active site of the CDKs. The interesting observation they make is that in some cases, the inhibitor "recruits" a water molecule to form a bridged interaction with an atom in the protein, thus achieving the "correct" binding mode. In other cases, blockage of the active site by a loop prevents this water grabbing trick.

The study also highlights how it can make a big difference between using monomeric CDK and CDK bound to a cyclin when simulating nib ("nib": inhibitor...apparently new yippie slang) binding. In this case, the difference is in the water recruitment.

Again, one is reminded of the many subtle factors, including water interactions, that dictate differences between nib binding to different but similar kinases.

Questions for the kinase biologists

I am working on a kinase inhibitor design project and I realised that there are some key questions that we need to get answered from the biologists before we can rationalize the selectivity of various kinase inhibitors for a given binding site. I also realised that these questions need to be answered for many other kinds of protein-inhibitor interactions.

1. Whenever we get different IC50 data for two inhibitors, we immediately try to look at binding interactions that may be different for the two moelcules to rationalize this observation. But as I have alluded before, it's not the IC50 but the Ki that's really to do with different binding interactions. The Ki and IC50 are related by an equation that includes both the Km value of ATP and the concentration of ATP in the two experiments, or in general, these two parameters for the natural binding substrate for the protein. Only if these two are the same for both inhibitor experiments is the IC50=Ki. So make sure you confirm this. Otherwise, extrapolate and calculate the new IC50s based on identical values for these parameters. Then rationalize the IC50s based on binding interactions.

2. For many kinases, three events are absolutely essential for activation:
a. Phosphorylation of one Ser, Thr or Tyr residue,
b. Binding of ATP (duh), and
c. Dephosphorylation of another Ser, Thr or Tyr residue.
Think of it like a logic gate. IF the answers to all a. b. and c. are YES, THEN the kinase will be activated and proceed to perform its function. (I got this from Alberts et al.'s Molecular Biology of the Cell)
In the assays that are run, it is important to know (and not very easy to always determine as I have been told) whether the necessary residue is phosphorylated or not. For one thing, inclusion of this knowledge in your docking and modeling can naturally make a big difference. And secondly, depending on the state of phosphorylation, you can think of different modes of inhibition for your inhibitor (eg. ATP blocking + substrate blocking).

As usual, it's important to know what the biologists are doing. They don't know the nuances of modeling/crystallography and you don't know the nuances of their assays. But it's important for both camps to think of questions which the other camp should answer that will affect their own work.