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.