Linking lessons on EGFR

From the earliest days of FBLD, fragment linking has been an aspirational goal. Snapping together two low affinity binders to get an additive or even superadditive boost in affinity is conceptually appealing but, as our poll showed, not always easy. In a new open-access J. Med. Chem. paper, Hung Nguyen, David Heppner, and collaborators at The State University of New York at Buffalo and elsewhere propose a general workflow.

 

Epidermal growth factor receptor (EGFR) is a prominent cancer target with multiple approved drugs. It has been studied for decades and thus makes an excellent model system; we wrote about some of Heppner’s work on this protein a couple years ago. As with most kinases, many known ligands bind in the so-called hinge-binding region where the adenine of ATP normally sits. But some ligands have been identified binding to an adjacent pocket, and in a 2024 open-access Comm. Chem. paper the Heppner group reported linking ligands at these two sites.

 

The new paper systematically expands on this work, diving deep into the SAR of compounds binding to the oncogenic L858R/T279M EGFR mutant. Specifically, the researchers vary connecting points as well as the length and composition of the linker.

 

 

As expected, the point of connection can make the difference between improved activity and dead compounds. Linker length matters too, as shown for compounds 4, 5, and 6, where the “Goldilocks” two-carbon linker (compound 5) binds orders of magnitude more tightly than the one- or three-carbon linker. Remarkably, compounds 4 and 6 are less active than one of the initial fragments! Linker composition is also important, but perhaps less than one might expect: the amide-containing linker in compound 8 is only marginally more potent than compounds 9 and 10.

 

The researchers characterize several of their linked molecules both crystallographically and using molecular dynamics. They argue that “maximal flexibility is likely the most important design criterion for linking molecules that occupy adjacent pockets.” That is consistent with this post from 2012 but doesn’t quite fit with the fact that the amide-containing compound 8 is better than either of the (presumably more flexible) compounds 9 and 10.

 

The paper is actually more of a fragment-merging than a fragment linking story: the central phenyl ring is common to both starting compounds, so it is not really possible to assess the extent of additivity. Also, activity is based on inhibition rather than thermodynamic measurements, so it is impossible to say whether the improved affinities of the linked molecules are due to entropic, enthalpic, or some combination of factors.

 

Still, this is a nice contribution to the literature of fragment merging/linking, and the general guidelines are sound: assess multiple connection points and linker lengths before refining the linker itself.

Automating high-throughput NMR

NMR was the first practical method used to identify fragments, described thirty years ago in this famous SAR by NMR paper. That headstart made NMR the go-to method for finding fragments, and as recently as ten years ago it was still in the lead, according to our poll in 2016. But as other methods have become faster and more effective, the relative popularity of NMR has declined, to fourth place in our 2024 poll. Now, a new paper in J. Med. Chem. by Bradley Falk and colleagues at Bristol Myers Squibb reports improvements that might help it move up the ranks.

 

One of the challenges of NMR is that it requires so many steps. Protein-detected NMR requires isotopically labeled protein, but even ligand-detected NMR involves measuring spectra for small molecules in the absence and presence of protein. The specific NMR settings may need to be tweaked for any given molecule, and the spectra are then analyzed, usually manually, to decide whether the molecules are likely binders.

 

The new paper focuses on automating virtually everything in ligand-detected NMR, from sample preparation through NMR runs to analysis. It is meant to be more general than a fragment screen with a curated library where each member is already well understood. First, novel compounds are examined in a band-Selective Optimized Flip Angle Short Transient (SOFAST) NMR experiment, which is particularly sensitive (down to 20 µM ligand). Testing in both DMSO-d₆ and assay buffer provides solubility and quality control data. This is an essential step: as we noted some time ago more than a third of compounds in some vendors’ catalogs fail these quality checks. In addition, a CPMPG SOFAST experiment in assay buffer reveals whether the compounds aggregate, a hazard we wrote about here.

 

Next, an automated workflow funnels appropriate compounds to the next step, in which compounds are assessed for protein binding using CPMG or STD NMR. Four separate samples are tested: low or high concentrations of compound in the absence or presence of protein. Again, automated approaches are used to choose the specific NMR parameters and analyze the results. As the researchers note, “human input is reserved for evaluations that require integrating multiple lines of evidence.” The output places compounds into three bins, “advance to med chem,” “advance with orthogonal confirmation,” or the garbage bin.

 

The researchers describe an example using the model protein human carbonic anhydrase II (hCAII), which we’ve written about previously. The known ligand benzenesulfonamide was tested and passed into the “advance to med chem” bin, despite the fact that, with a K_D of 0.8 µM, it is more potent than most ligands studied by NMR.

 

One thing missing from the paper is a summary of overall time and throughput. The experimental section states that the CPMG-SOFAST experiment itself takes half an hour, and indeed the researchers note that the platform is “instrument-time limited rather than sample preparation- or data analysis-limited.” In terms of absolute throughput, it probably can’t compete with other biophysical methods such as SPR. The automation and automated decision-making is the real advance. In 2019 I satirically imagined a fully autonomous drug discovery engine. As artificial intelligence becomes ever more intelligent, that post may prove prescient. If so, it will feature automated tools such as those described in this paper.