FTO revisited: fragment linking this time

Four years ago we highlighted a fragment-merging approach targeting fat mass and obesity-associated protein (FTO), an RNA demethylase implicated in acute myeloid leukemia (AML). In a new J. Med. Chem. paper, Ze Dong, Cai-Guang Yang, and collaborators at University of Chinese Academy of Sciences Beijing describe a fragment linking approach that arrives at a similar outcome.

 

As we noted in 2021, previous research had revealed that meclofenamic acid (MA) binds in the substrate-binding site, near the binding site for the 2-oxoglutarate (2-OG) cofactor. In the new paper, the researchers synthesized an analog of MA which they then linked to analogs of 2-OG through a variety of linkers. Among the best of these was compound 8a, with low micromolar activity in an assay using PAGE (polyacrylamide gel electrophoresis) as well as mid-nanomolar activity in a different type of assay. This is an improvement over the MA analog itself (which was only tested in the PAGE-based assay, results shown in the figure). Compound 8a was also selective for FTO over two related proteins.

 

 

As is often seen in fragment linking, the SAR is quite sharp. The two-carbon linker was critical; lengthening the linker by one methylene or adding a methyl group abolished activity. The double bond in the 2-OG mimetic was also important; the saturated version of this molecule was inactive.

 

Surprisingly, a crystal structure of FTO bound to a molecule closely related to 8a revealed that while the MA moiety bound as expected, the 2-OG analog adopted a different conformation. But inexplicably, according to the experimental section, 2-OG was added to the crystallization solution, which would compete with compound 8a. Indeed, the structure deposited in the protein data bank shows the 2-OG analog N-oxalylglycine bound to the catalytic metal ion.

 

With two carboxylic acid moieties, it is no surprise that compound 8a showed no antiproliferative activity in AML cell lines. However, ester prodrugs did show low micromolar activity. Further characterization of one of these showed changes in protein levels consistent with FTO inhibition. This molecule also caused tumor growth inhibition after intraperitoneal dosing in a mouse xenograft model.

 

Superficially, compound 8a resembles compound 11b from the 2021 paper. Like that molecule it is probably too weak to serve as an ideal chemical probe. That said, with one fewer aromatic ring, compound 8a may be better suited for further optimization.

xLE: solving problems or missing the point?

Ligand efficiency (LE) has been discussed repeatedly and extensively on Practical Fragments, most recently in September. Two criticisms are its dependence on standard state and the observation that larger molecules frequently have lower ligand efficiencies than smaller molecules. In a just-published open-access ACS Med. Chem. Lett. paper, Hongtao Zhao proposes a new metric, xLE, to address these concerns.

 

LE is defined as the negative Gibbs free energy of binding (ΔG) divided by the number of non-hydrogen (or heavy) atoms, and of course ΔG is state-dependent. Standard state assumptions are 298K and 1M concentrations, choices that some people see as arbitrary since few biologically relevant molecules ever achieve concentrations near 1M. To remove the dependence on standard state, Zhao proposes to remove the translational entropy term of the unbound ligand from the free energy calculation.

 

Zhao also addresses the second criticism, that larger molecules often have lower ligand efficiencies. This phenomenon was observed in an (open-access) 1999 paper titled “the maximal affinity of ligands,” which found that, beyond a certain threshold, larger ligands do not have stronger affinities; there are very few femtomolar binders even among the largest small molecules. Thus, Zhao proposes attenuating the size dependence.

 

The new metric, xLE, is defined as follows:

 

xLE = (5.8 + 0.9*ln(M_w) – ΔG)/(a*N^α) - b

Where N is the number of non-hydrogen atoms, α is chosen to reduce size dependence, and a and b are “scaling variables.” He chooses α=0.2, a=10, and b=0.5, with little explanation.

 

To assess performance, Zhao examined nearly 14,000 measured affinities from PDBbind. When plotted by number of atoms, median affinity increased up to about 35 heavy atoms but then leveled off. Median LE values decreased sharply from 6 to 12 heavy atoms and then leveled off somewhere in the 20s. But median xLE values were consistent regardless of ligand size.

 

Zhao also examined LE and xLE changes for 175 successful fragment-to-lead studies from our annual series of J. Med. Chem. perspectives. LE decreased from fragment to lead for 48% of these, but xLE increased for all but a single pair.

 

And this, in my opinion, is a problem.

 

In the seminal 2004 paper, LE was proposed as "a simple ‘ready reckoner’, which could be used to assess the potential of a weak lead to be optimized into a potent, orally bio-available clinical candidate." The metric was particularly important before FBLD was widely accepted, when chemists were even less inclined to work on weak binders.

 

Here is the situation for which LE was devised. Imagine two molecules, compounds 1 and 2. The first has just 12 non-hydrogen atoms, a molecular weight of 160, and a modest 1 mM affinity for a target - similar to some fragments that have yielded clinical compounds. The second is much larger: 38 non-hydrogen atoms, a molecular weight of 500, and 10 µM affinity for the same target. Considering potency alone, compound 2 is the winner.

 

However, the LE for compound 1 is a respectable 0.34 kcal/mol/atom, while the LE for compound 2 is 0.18 kcal/mol/atom. So while a 10 µM HTS hit may initially look appealing, the LE suggests that this is an inefficient binder, and further optimization may require adding too much molecular weight to get to a desired low nanomolar affinity.

 

In contrast, the xLE values for both compounds are nearly identical, 0.38, and so this metric would not help a chemist prioritize which hit to pursue. In other words, xLE does not provide the insight for which LE was created. It might even lead to suboptimal choices. 

 

Moreover, unlike LE, xLE is non-intuitive. And finally, with three scaling or normalization factors, xLE is arguably even more arbitrary than a metric dependent on the widely-accepted definition of standard state.

 

Personally I find the practical applications of xLE limited, but I welcome your thoughts.

Searching monstrously large chemical space with FrankenROCS

Back in 2023 we highlighted a computational fragment linking/merging approach which was used to find high nanomolar inhibitors of the SARS-CoV-2 macrodomain (Mac1), a COVID-19 target. However, those molecules contained carboxylic acids, often associated with poor cell permeability. In a new open-access Sci. Adv. paper, James Fraser and collaborators at UCSF, Relay Therapeutics, Enamine, and Chemspace describe a related approach to find new, non-charged inhibitors.

 

The new approach, called FrankenROCS, “takes pairs of fragments as input to query a database using the rapid overlay of chemical structure (ROCS) method of comparing 3D shape and pharmacophore distribution;” the goal is to find larger molecules that most closely resemble the initial fragment pairs. As with the previous publication, the team started with more than 200 crystallographic fragment hits published in 2021. A set of 7,181 pairs of adjacently-bound fragments were searched against 2.1 million compounds commercially available from Enamine. The top 1000 were inspected, and 39 were purchased and soaked into crystals of Mac1. This led to 10 successful structures, of which AVI-313 did not contain a carboxylic acid. This molecule had weak but measurable activity in an HTRF competition assay.

 

Two million compounds is a lot but pales in comparison to Enamine’s “make-on-demand” REAL space, which at the time this research was done consisted of more than 22 billion molecules. The REAL space molecules are constructed from 960,398 building blocks that can be combined using 143 reactions. We previously described an approach called V-SYNTHES to screen Enamine’s REAL space. FrankenROCS takes a different active-learning approach called Thompson Sampling, which dates back nearly a century.

 

Imagine two sets of 1000 building blocks, R1 and R2, which could be coupled to generate 1,000,000 molecules. Rather than searching all possibilities, each R1 building block is linked to three random R2 building blocks, and each R2 building block is linked to three random R1 building blocks. These are virtually screened, and the R1 or R2 building blocks from those with the highest scoring compounds are used for further iterations. In theory, after tens of thousands of iterations, the best compounds will have been identified.

 

The researchers fed 97 fragment pairs from the 2021 paper into Thompson Sampling FrankenROCS to find molecules that would best overlay with the fragment pairs.  Ultimately 32 compounds were purchased, six of which were successfully crystallized with Mac1. Unfortunately, the most potent was a weaker inhibitor than AVI-313 and contained a carboxylic acid. The researchers speculate that the inability to find better molecules in larger chemical space may have stemmed from limitations of the scoring function, a problem we’ve previously discussed.

 

The researchers returned to focus on AVI 313, making substitutions at multiple positions, ultimately synthesizing 148 compounds, 121 of which could be characterized crystallographically. Importantly, several compounds had low micromolar activity, even without a carboxylic acid. The crystal structures show the binding site to be somewhat flexible, as evidenced by side chain and main chain movements to accommodate some of the binders.

This is a nice, thorough investigation, and the 137 protein-compound crystal structures deposited into the protein data bank provide useful training data for next-generation computational approaches. Moreover, the fact that immeasurably weak fragments can be advanced to low micromolar, ligand-efficient hits is yet another reason for the research community to figure out how to make crystallographic fragment screening data more widely available, as we exhorted here.

Fragments vs RhoDGI2: Towards a chemical probe

Many readers of this blog will be familiar with KRAS, a mutant form of which was successfully targeted a few years ago by a covalent, fragment-derived drug, sotorasib. KRAS is just one member of a large family of molecular switches which are on when bound to GTP and off when bound to GDP. This exchange is facilitated or inhibited by other proteins, including guanine nucleotide dissociation inhibitors (GDIs). GDIs bind to the GDP-form of RAS proteins, keeping them in the off state, but they can also stabilize Ras proteins against proteasomal degradation, keeping them around longer.

 

RhoGDI2 is a GDI that regulates Rho GTPases, which are involved in multiple cell pathways. The biology is complicated though, and RhoGDI2 has been implicated as both a cancer driver and inhibitor. Clearly a chemical probe would be useful. In a new ACS Chem. Biol. paper, Wei He and collaborators at Tsinghua University and University of Science and Technology of China Hefei report the first steps.

 

The story begins with a 2017 paper in Biochim. Biophys. Acta. Gen. Subj. by Ke Ruan (one of the authors of the new paper) and colleagues. A ligand-detected NMR screen of just under 1000 fragments yielded 14 hits, three of which were confirmed by two-dimensional protein-observed NMR. Further experiments suggested these bound in the hydrophobic pocket that binds gerarnylgeranylated Rho GTPases. Compounds 1 and 2, though weak, became the starting points for fragment growing.

 

Borrowing from compounds 1 and 2 and adding a phenyl moiety led to compound 2102, which was crystallographically confirmed to bind in the substrate binding pocket. Further fragment-growing, guided by structure-based design, ultimately led to HR3119, with low micromolar affinity for RhoGDI2 as assessed by surface plasmon resonance (SPR) and isothermal titration calorimetry (ITC). HR3119 has four diastereomers, and those with an R-configuration at the benzylic position (6R) were almost 100-more potent than 6S.

 

HR3119 blocked the interaction of RhoGDI2 with the Rho GTPase Rac1 in cell lysates. (6R)-HR3119 stabilized RhoGDI2 in a cellular thermal shift assay, while (6S)-HR3119 did not. (6R)-HR3119 also decreased migratory activity of a cancer cell line, consistent with the role of RhoGDI2 in actin dynamics. However, (6S)-HR3119 also showed activity in this assay, albeit at a higher concentration, suggesting off-target effects.

 

The biochemical and cell activity are still too weak to nominate (6R)-HR3119 as a chemical probe against RhoGDI2; ideally biochemical activity should be better than 100 nM and cell activity should be better than 1 µM. Nonetheless, this is a good starting point for further optimization, and a nice example of fragment-based lead discovery in academia.