Fragments vs PKC-ι: A*STAR’s second series

Just over a year ago we highlighted work out of A*STAR describing a series of inhibitors for the cancer target protein kinase C iota (PKC-ι). We ended by mentioning that the group had a second undisclosed series. This has now been described in ACS Med. Chem. Lett. by Jacek Kwiatkowski, Alvin Hung, and colleagues.

Compound 1 was among the fragment hits from the high-concentration biochemical screen previously mentioned. Although the researchers did not have a crystal structure, they assumed that the aminopyridine moiety was acting as a hinge binder, which helped them produce a computational model. A simple replacement of the phenyl ring with a pyridyl ring led to compound 2, with a satisfying improvement in potency and ligand efficiency.

As it turned out lots of diverse moieties could be substituted in place of the phenyl, including indoles and phenols. This promiscuity led the researchers to propose that the added heteroatom was making a water-mediated hydrogen bond to the protein; the water could rotate to either accept or donate a hydrogen bond to the ligand. Unfortunately, further growing from this ring did not improve potency.

Returning to their model, the researchers sought to grow from the aminopyridine ring towards a hydrophobic region of the protein. Adding a phenyl group (compound 16) was tolerated, though did not improve the affinity. However, the model suggested that an aspartic acid might be accessible from the phenyl ring, and indeed adding a positively charged piperazine as in compound 19 led to a nearly 100-fold boost in affinity. Unfortunately, the compound’s permeability (measured in a Caco-2 assay) was low, and perhaps because of this it showed only weak antiproliferative activity against hepatocellular carcinoma cells.

Ultimately the researchers were able to solve the cocrystal structure of compound 19 with PKC-ι, which mostly confirmed the model: the aminopyridine interacts with the hinge region, and the second pyridyl moiety likely makes a water-mediated hydrogen-bond with the protein, although the low resolution of the structure makes this somewhat ambiguous. The added piperazine appears to interact with a different aspartic acid than the one targeted.

Although there is more work to be done, it is notable that the researchers were able to optimize a fairly weak fragment to a sub-micromolar compound in the absence of experimental structural information. As they note, “while the empirical SAR remained our ultimate guide in fragment optimization, the model aided the successful design of potent inhibitors.” This is another nice example supporting our 2017 poll results, and recent review, that drug hunters can successfully advance fragments without NMR or crystallography.

SAR by STD: NOT

As noted last week, Practical Fragments has been on something of a crystallography binge. But according to polling, NMR is the most common fragment-finding method. And, according to a different poll, saturation transfer difference (STD) is the most popular NMR technique. Familiarity breeds complacency, and widespread assumptions go untested. A new paper in Front. Chem. by Jonas Aretz and Christoph Rademacher (Max Planck Institute and Freie Universität Berlin) suggests that this is a mistake.

In STD NMR, a protein is saturated by specific electromagnetic pulses, and the resulting magnetization transfers to bound ligands. Assuming that the bound ligands are in rapid equilibrium with ligands free in solution, this “saturation transfer” results in a reduction of NMR signal for the small molecule in the presence of protein compared to no protein. High affinity ligands will remain bound to the protein and thus be missed by STD NMR, but this is usually not relevant in FBLD, where most fragments bind with dissociation constants weaker than 10 µM.

A common assumption with STD NMR is that the strength of an STD signal increases with the affinity of the ligand (again, in affinity ranges between about 10 µM and 10 mM). Indeed, when STD NMR is used as part of a screening cascade, molecules showing the strongest effect are generally prioritized as hits. But is this assumption correct?

To find out, the researchers retrospectively analyzed a fragment screen against langerin, a carbohydrate-binding protein we discussed last year. When they plotted the STD amplification factor against the affinity (measured by SPR) for several dozen fragments, the resulting scatter plot showed no correlation.

Recognizing that experimental errors could obscure a true correlation, the researchers ran virtual STD experiments using COmplete Relaxation and Conformational Exchange MAtrix (CORCEMA) theory. They used well-characterized fragments with published crystal structures and affinities for some dozen diverse proteins. As they conclude, “varying saturation time, receptor size, binding kinetics, and interaction site… there were no conditions in which the STD NMR amplification factor correlated unambiguously with affinity.”

But it gets worse. When the researchers explored the effects of binding kinetics, they found that ligands with slower on-rates or off-rates also had lower STD signals. Several groups have advocated prioritizing compounds with slower-off rates, yet these are the very compounds STD is most likely to miss.

All in all this paper could go some way toward explaining the sometimes poor correlation between different fragment-finding methods.

That said, I’m no NMR spectroscopist, so I’m certainly not as qualified to comment on the importance of this paper as someone like Teddy, who co-wrote this how-to guide for STD NMR. I’d be interested to hear what NMR folks think, and whether we should rethink use of STD. In any case, this work is a useful reminder that skepticism is a scientific virtue.

Crystallographic vs computational fragment screening

Several recent Practical Fragments posts have touched on crystallographic screening: from ultra-high concentration screening of “MiniFrags,” to an extensive analysis of fragment structures in the protein data bank, to an open-source effort to develop new antibiotics. A new paper in Phil. Trans. R. Soc. A by Tom Blundell and collaborators at University of Cambridge, the Diamond Light Source, University of Oxford, and several other institutes provides a useful synthesis and an interesting comparison with computational approaches.

The researchers were interested in the bacterial protein PurC, also known as SAICAR synthetase, which is essential for purine biosynthesis and is sufficiently different from its human orthologue to be an attractive antimicrobial target. The protein has an extended binding site that can accommodate ATP as well as its substrate CAIR and an aspartic acid. Using a traditional screening cascade, 960 fragments were screened at 5 mM in a thermal shift assay, resulting in 43 hits. Each hit was then soaked at 10 mM into crystals of PurC, resulting in 8 bound structures, all of which occupy the ATP-binding pocket. Isothermal titration calorimetry revealed dissociation constants as good as 178 µM, with a ligand efficiency of 0.39 kcal/mol/atom.

Next, the researchers ran a computational screen, Fragment Hotspot Maps. This confirmed the main fragment-binding site. Indeed, the crystallographically-identified fragments even make the hydrogen-bonding interactions predicted by the model. However, the computational approach also identified three other hot spots, two in the active cleft and one on the rear of the protein. There was also a “warm spot” next to the ATP-binding site. Are these real, or computational artifacts?

To address this question, the researchers screened fragments at a much higher concentration at XChem, and processed the data using the PanDDA software we’ve previously described. They screened two libraries of fragments at 30-50 mM: 125 “shapely” fragments and 768 “poised” fragments designed for rapid follow-up chemistry. The 8 hits from the first crystallographic fragment screen were also included. This exercise yielded structures for 35 fragments, 60% of which bound in the ATP-binding site, including all 8 of the previously identified ones. Most of the other fragments bound in shallow pockets or near crystallographic interfaces; only one of the other hot spots predicted computationally had a bound fragment, and that was present at low occupancy. Some hits made new interactions around the ATP-binding site, but none bound in the predicted warm spot. Unfortunately, the proportions of fragment hits coming from the two libraries are not broken out.

So in summary, both computational and crystallographic screening correctly identified the “hottest” hot spot, but each approach also identified additional sites that were not confirmed by the other. The researchers ask, “are these sites truly hot spots… or are they weak binding sites routinely seen in crystals?”

This is indeed the key question, and it would be interesting to see whether other computational approaches – such as FTMap or SWISH – are able to shed light on the matter.

Fragments in the clinic: AZD5991

Venetoclax, the second fragment-based drug to reach the market, binds to and blocks the activity of the anti-apoptotic protein Bcl-2, allowing cancer cells to undergo programmed cell death. The drug is effective in certain cancers such as chronic lymphocytic leukemia and small lymphocytic lymphoma. However, a related protein called Mcl-1 is more important in other types of cancers. Like Bcl-2, it binds and blocks the activity of pro-apoptotic proteins, allowing cancer cells to survive even when Bcl-2 is inactivated. A paper in Nat. Comm. by Alexander Hird and a large group of collaborators (mostly at AstraZeneca) describes a successful effort to target Mcl-1.

Given that the researchers were targeting a protein-protein interaction, they took multiple approaches, including their own fragment-based efforts. They also characterized previously reported molecules, such as those the Fesik group identified using SAR by NMR (which we wrote about in 2013). A crystal structure of one of these revealed a surprise: two copies of compound 1 bound to Mcl-1, which had undergone conformational changes to accommodate the second molecule in an enlarged hydrophobic pocket.

Recognizing the potential synergies of linking these together, the researchers prepared a dimer of a related molecule, but unfortunately the affinity of this much larger molecule was actually worse. However, they wisely isolated and tested a side product, compound 4, and found that this had improved potency. A crystal structure of this molecule bound to Mcl-1 revealed that the pocket had expanded to accommodate the added pyrazole moiety. Since compound 4 adopted a “U-shaped” conformation, the researchers decided to try a macrocyclization strategy to lock this conformation and reduce the entropic penalty of binding. This produced compound 5, and adding a couple more judiciously placed atoms led to AZD5991, with a nearly 300-fold improved affinity. The molecule binds rapidly to Mcl-1 and has a relatively long residence time of about 30 minutes. A crystal structure reveals a close overlay with the initial compound 1 (in cyan).

In addition to picomolar affinity, AZD5991 showed excellent activity in a variety of cancer cell lines dependent on Mcl-1. The compound was tested in mouse and rat xenograft models of multiple myeloma and acute myeloid leukemia and showed complete tumor regression after a single dose. This is all the more remarkable given that AZD5991 is about 25-fold less potent against the mouse version of Mcl-1 than the human version. The molecule was also effective in cell lines resistant to venetoclax, and combining the two molecules caused rapid apoptosis in resistant cell lines. AZD5991 is currently being tested in a phase 1 clinical trial.

This paper holds several lessons. First, the researchers did extensive mechanistic work (beyond the scope of this post to describe) to demonstrate on-target activity. Second, although the initial dimerization strategy was unsuccessful, the researchers turned lemons into lemonade by pursuing a byproduct; we’ve written previously about how even synthetic intermediates are worth testing. Third, the macrocyclization and subsequent optimization is a lovely example of structure-based design and medicinal chemistry. And finally, the fact that the researchers started with a fragment-derived molecule reported by a different group is a testimony to the community nature of science. Last week we highlighted the Open Source Antibiotics initiative, which is actively encouraging others to participate in advancing their early discoveries. Good ideas can come from anywhere, and it takes a lot of them to make a drug.