From fragment to macrocyclic Ras inhibitors

At the Drug Discovery Chemistry meeting last month chemist John Taylor described efforts against the oncology target RAS. This story was recently published in J. Med. Chem. by John, Charles Parry, and a team of some three dozen collaborators at CRUK Scotland Institute, Novartis, and Frederick National Laboratory for Cancer Research.

 

Practical Fragments has highlighted multiple Ras efforts, including the development and approval of sotorasib, which inhibits the G12C mutant of KRAS. Sotorasib binds in the so-called switch II region, next to the site where the nucleotides GDP and GTP bind. Before the discovery of this site, researchers had identified fragments that bind to a different site, switch I-II. 

 

Most of the ligands that bind to either site only inhibit the off-form of Ras proteins, in which the proteins are bound to GDP. One mechanism of resistance for cancer cells is to increase the amount of protein in the active, or GTP-bound state. Thus, the researchers focused on the oncogenic G12D mutant of KRAS bound to a GTP analog and screened it against 656 fragments using SPR. Ligand-detected NMR confirmed five of the hits, including compound 5.

 

Two dimensional ¹H-¹⁵N HSQC NMR revealed that compound 5 binds in the switch I-II pocket; merging this with a literature fragment generated compound 6. SAR studies led to compound 11, which was characterized crystallographically bound to the protein. The structure suggested trying to make a salt bridge with an aspartic acid residue, leading to compound 13, with sub-micromolar affinity for the inactive form of the protein. A crystal structure of a related compound suggested the possibility of macrocylization, and this turned out to be successful, with compound 21 being the most potent. (All values shown here are determined by NMR or SPR on the G12D KRAS mutant bound to either GDP or the GTP analog GMPPMP.)

 

A number of different macrocycles were made and tested, and all of them were more potent against the inactive than the active form of KRAS. Crystal structures suggested that a glutamic acid side chain adopts a conformation in the the GTP-bound form of KRAS that impedes ligand interactions.

 

Interestingly though, building off the molecules in another direction led to the opening of a small subpocket that had not previously been reported in the literature. Exploiting this “interswitch” region led to compound 36, with a nearly 10-fold preference for the active form of KRAS.

 

Most of the macrocycles in both series were able to block nucleotide exchange in a biochemical assay, meaning they could prevent the exchange of GDP for GTP. A few of the compounds were tested in cell-based assays and could block binding between RAF and multiple Ras isoforms, including two mutants of KRAS as well as wild-type KRAS, HRAS, and NRAS.

 

Unfortunately, and not surprisingly given their high polar surface areas, the compounds had low permeability, high efflux, and high clearance in vitro. Mouse studies on one compound confirmed these liabilities in vivo.

 

Although the compounds could not be advanced, this is still a nice fragment to lead story. The fact that a new pocket could be identified despite so much previous effort on this target is a good reminder that no matter how much you know, there is always room for surprises.

Covalent Inhibitor of KRas

So, Ras is big.  We keep on talking about it.  And sometimes we talk about the same work repeatedly.  This recent paper from AZ is a publication of work we have talked about here and here.  This follows on closely to work done by Vanderbilt and Genentech.  Those two papers were done using NMR and this one took a X-ray approach.  The AZ folks were taking a different approach to this PPI: stabilization of the interface.  They took 1160 fragments in pools of 4 and screened against HRas (homolog)-catalytic domain of SOS stable complex.  There were able to identify 3 bindings sites on HRas-SOS (Figure 1):

Figure 1.  HRas-SOS Complex.  HRas (Green), SOS (Blue), A: SOS binding site  (gold) (same as Vanderbilt), B SOS-Hras Interface binding site (Red) (same as Genentech), and C HRas covalent binding site (black).

 

Site A was the same site identified by the Vanderbilt group  Site B was the same as identified as Genentech.  However, the AZ compounds bound to both proteins at the interface.  Their initial hope was to use this site to stabilize the Ras-SOS interface.  Both of the fragments binding to these sites had their affinity determined by TROSY-HSQC NMR.    However, they were not potent enough to elicit a biological effect, which was not unexpected.  After several rounds of chemistry, they were not able to improve these fragments significantly, or even show that they actually stabilized the interface.  

Looking at the growing covalent literature, they hypothesized that an irreversible inhibitor may be the only way to inhibit GTPase activity, especially considering the pM affinity of GTP for Ras.  They identified Cys118R (conserved between HRas and KRas) as a potentially reactive sidechain proximal to the GDP binding site on Ras.  To go after this site covalently, AZ assembled a 400 fragment covalent library (Figure 2) and screened it by mass spectrometry.

Figure 2.  Chemotypes represented in AZ 400 fragment covalent library.

They chose the N-substituted maleimide was deemed "ideal"; other warheads were either insufficiently reactive or overly reactive.  Covalent modification of Cys118R by a fragment partially occludes the nucleotide binding site and potentially prevents the reorganization of the Cys118R loop, thus locking it into the catalytically inactive Ras-SOS complex.  Interestingly, their covalent compounds only inhibited catalytically activity when pre-incubated with Ras-GDP-SOS.  This supports the hypothesis that Cys118R becomes more accessible during SOS-mediated nucleotide exchange.  

This paper brings together several topics which I think are becoming hot: covalent fragments, mass spectrometry, and K-Ras. 

Fragments against PPI Hot Spots

Protein-Protein interactions are important to so many physiological processes.  There is mounting literature examples of utilization of fragments to block PPIs.  In this paper, Rouhana et al. show how they approached the PPI of Arno and ARF1, ADP-ribosylation factor (part of the RAS superfamily). Arno is part of the brefeldin A-resistant GEFs and share a 200 amino acid domain called SEC7.  SEC7 interacts with ARF through insertion of ARF switch regions into hydrophobic regions of SEC7.  This interaction is interesting from a ligand design standpoint is very interesting because it does not involved an alpha-helix inserting into the partner's hydrophobic groove.  Rather SEC7 has a rather large interface denoted by "hot spots". 

The figure shows their "innovative" FBDD strategy.  First, a Voldemort Rule compliant library was screened in silico.  Since in silico screening is not typically used for fragment screening (but becoming more common) they imposed some initial rules: docking site is small (1-2 residues!), hot spots defined by interaction energy (>1kcal/mol from alanine scan), and very strict selection criteria.  3000 fragments from the Chembridge library were screened.  33 molecules were selected and 40 random fragments chosen as negative controls. 

This was followed by a fluorescence assay (2mM fragments) to test their computational results, just as I say you should do.  Promiscuous binders were removed, not by using detergent, but using protein polarization to directly detect interaction with the target.  This seems like over-complexation of an assay, but without knowing the details of system there may be a very good reason for this approach. 

Compounds 1-4 were identifed as inhibitors (35%, 16%, 38%, and 23% inhibition at 2mM respectively) from each of the "hot spots".  I think it is interesting that these compounds were predicted to have affinities of 10uM or better from the docking.  To me, that just illustrates that predicted affinites are rediculous.  Why do people even report them?  Compound 1 had a Kiapp of 3.7mM which is a LEAN of 0.12!  These were then compared to the PAINS list and 3 is "ambiguous".  Compounds 5 and 6 were chosen as negative controls.  SPR confirmed the binding of 1,2, and 4, but at less than stoichiometric binding levels (the assay was run at 250uM).  3 could not be confirmed as a binder.  Does this mean anything for ambiguous PAINS? 

STD NMR was then used to confirm binding.  In a nice departure, they actually talk about conditions they used: 10 and 30uM ARNO with 0.1mM and 1mM compounds at 32 and 12C.  30uM ARNO with 1mM fragments @12C was what worked (33x fold excess fragments). Confirming the SPR, compounds 1, 2, and 4 were shown to bind, while "ambiguous" 3 had some binding. Finally, compounds were soaked with fragments 1, 2 and 4.  This led to crystal structures which could then be used for more model building, compound design, etc.  This led to the following compound (1.61mM KiApp, LEAN = 0.13) (the methoxy derivative of 1) for further analysis:

By and large, this is a well done, thoughtful work.  They really understand how to setup and interpret STD-NMR. However, these compounds are really atom inefficient.  Is that a consequence of the type of interaction they are inhibiting?  As a fragment, there is nothing wrong with it. 

[Quibble: The authors claim that this is an innovative approach, but I am not seeing it.  They claim their in silico screen first then following up by biophysical techniques is the innovation. ] 

Supplemental Information here.

Fragments versus Ras – Part 2

Practical Fragments recently highlighted a paper from Genentech in which researchers there discovered fragments that block the activity of the prominent oncology target Ras. Illustrating just how much interest there is in this protein, Stephen Fesik and colleagues at Vanderbilt University have just reported results of their own work in Angew. Chem. Int. Ed.

Fesik is famous for SAR by NMR, the first truly practical approach to fragment-based lead discovery. In the current work, the researchers also used NMR (HSQC with ¹⁵N-labeled protein) to screen 11,000 fragments, yielding about 140 binders to the GDP-bound form of K-Ras. A number of these were then further characterized crystallographically: of 20 cocrystal structures obtained, all of them were found to bind in the same hydrophobic pocket identified by the Genentech researchers. Fesik and colleagues also noticed a nearby, electronegative cleft, and grew one of their fragments (compound 1) to take advantage of this. This led to compound 12, the most potent compound reported. In addition to binding to the GDP-bound form of K-Ras as assessed by NMR, this compound also inhibited Sos-mediated nucleotide exchange in a functional assay.

Overlaying one of these compounds (blue – similar to compound 12) with the Genentech compound DCAI (red) reveals that while both compounds bind in the same hydrophobic pocket, they make very different contacts.

Of course, it still remains to be determined whether this is a ligandable site on the protein (ie, whether these – or any – molecules can be advanced to high potency). Given the importance of Ras, it’s certain that lots of people are doing their best to find out.

Fragments versus Ras

The protein Ras is one of those cancer targets that’s been around forever and has rebuffed countless attacks by many researchers using multiple strategies. The most obvious ligand pocket is the one where GTP and GDP bind. Unfortunately, these molecules bind with picomolar affinity, and they are present at very high concentrations in cells. To try to find an alternative small molecule binding site, Guowei Fang and colleagues at Genentech took a fragment-based approach, and have reported their results in a recent issue of Proc. Nat. Acad. Sci. USA (as well as at a recent meeting).

The researchers used 1D NMR screening (STD) to screen 3300 fragments in pools against GDP-bound KRas; 240 hits were retested as single compounds and further validated by 2D NMR (HSQC). This resulted in 25 confirmed hits. Surprisingly, all of them appeared to bind to one region of the protein some distance from the GDP-binding site. Subsequent crystallography confirmed that these fragments bind to a small pocket about 250 ų in size. However, there could be more here than meets the eye, as this is a fairly flexible region of the protein, and the pocket changes shape in response to different fragments.

 At least one of the fragments, DCAI, not only binds to Ras, it also inhibits the association of Ras with the protein SOS, thereby blocking nucleotide exchange and Ras activation. Interestingly, this blocking activity was distinct from binding activity; the fragment BZIM has comparable affinity as judged by NMR, yet does not inhibit the interaction with SOS.

Somewhat surprisingly given its low affinity, DCAI is also active in cell-based assays. And although the molecule is still a long, long way from a drug, the results are encouraging. Perhaps a fragment-based approach will finally succeed against this target. Or perhaps Ras will yet again reveal its intransigence.

Seventh Annual Fragment-Based Drug Discovery Meeting

CHI’s annual FBDD meeting took place in San Diego this week, and since this was the first time in a while both Teddy and I have been in the same place we’ve decided to make this a joint post. As with last year this does not aim to be comprehensive.

One of the highlights of the conference was a set of three talks on BACE1 inhibitors from Amgen (Ted Judd), Lilly (David Timm), and Pfizer (Ivan Efremov), the first two of which have been discussed here and here. It’s nice to see fragments playing a pivotal role in delivering advanced leads and – at least in the case of Lilly and Merck – clinical candidates against what has been one of the most difficult drug targets in industry.

Speaking of difficult targets, Till Maurer of Genentech gave a lovely presentation on using NMR-based fragment screening to discover inhibitors of the holy grail of oncology, Ras. They’ve recently published some of this story, which we’ll highlight in an upcoming post.

A common question is ‘how often do you find the same fragment using different methods?’ (see here for an ongoing discussion on LinkedIn). Cynthia Shuman from GE gave a nice case study in which she screened the protein PARP15 against 987 fragments using a Biacore T200. Of the 15 fragments with shapely, well-behaved sensorgrams, 14 were confirmed by NMR. On the other hand, only one of these hits was detected in a differential scanning fluorimetry (thermal melt) assay.

Marcel Verdonk at Astex described general trends from mining in-house and published data. After looking at 43 in-house targets, he found that 8000 compounds had been tested against 2 or more proteins, and after plotting by molecular weight found that, consistent with the original Hannian model, larger compounds are more selective. In a separate analysis of 53 fragments that had been advanced to leads, he found that in most cases the initial fragment maintained roughly the same position and orientation from start to finish.

Rod Hubbard, Teddy and I all ran round-table discussions, but only Teddy kept notes, which are summarized here.

The topic started as a discussion of 2D vs. 3D fragment libraries. In the recent Pfizer fragment build, a group of diverse chemists eyed every compound, and at least five had to agree to each molecule before it went into the library. 

The discussion went briefly to the old fight: Are nitros masked amines or noxious moieties?  The table ended up agreeing that if you would remove the nitro group or change it anyways, why put it in the first place?

We then dove right back into the 2D vs. 3D debate. Kinases seem to love 2D fragments, while other classes of targets seem to NEED 3D fragments. One idea discussed was 3D fragments as complements to 2D fragments. It was mentioned that 3D fragment libraries would need to be MUCH larger to cover equivalent chemical space. I thought the idea that 3D fragments would be exploring “vector” space rather than “chemistry” space would mean that you could go with a much smaller library, if you want to use it for vector space searching. It was also proposed that 2D fragments tend to be much smaller (~150-180Da-ish) and 3D fragments would be, by necessity, bigger (~250 Da).

The topic then changed to SBDD (structure-based drug discovery) as part of FBDD. Most people at the table were of the opinion that they wouldn’t use FBDD (on a normal priority target) without SBDD. And with SBDD, you don’t need 3D fragments to explore vector space since you will have the X-ray to guide you. 

The point was made that 3D fragments also HAVE to be scaffolds which would end up in the final compound. If you are simply using a scaffold to explore vector space without any hope of it ending up in the final product, why bother. [TZ Note: I think this is very shortsighted and people do not understand that targets will most likely NOT have SBDD to guide them, at least in the fragment-based lead generation stage.] 

The question was asked to the table: is FBDD a valid approach in the absence of structure? Three people said yes, the rest (~8) said no. All three who said yes were small company/ CRO people. Everyone agrees if you do go with FBDD without structure you need to have a HEAVY investment in biophysics to characterize the protein and the hits. 

One person asked about low confirmation hits following a fragment screen: the table agreed that this is most likely the result of the library being “wrong”. We finished the discussion by asking if 2D fragments are more pan-class (not targeted to a specific class of targets) and 3D fragments may be more target-focused. The resounding answer was “Who knows, but why would they be?”

As this post is already getting long we’ll stop here, but for those of you who were also at the conference please add your comments. And if you missed this one, there are still several exciting upcoming events this year!