Twentieth Annual Fragment-Based Drug Discovery Meeting

Last week’s CHI Drug Discovery Chemistry (DDC) meeting was held as usual in San Diego. More than 850 people attended, 96% in person, with 70% from industry and 28% from outside the US. I personally attended more than three dozen talks over the four days and will just touch on some broad themes.

 

Noncovalent approaches

Steve Fesik (Vanderbilt) gave two talks, the first of which was focused on “FBDD tips for success.” This opinionated and entertaining romp revealed lessons learned across several projects on difficult targets such as KRAS. Another holy grail oncology target is MYC, which is largely disordered. A two-dimensional NMR screen against the protein failed to yield any hits, but a screen of the MYC:MAX heterodimer provided hits which have been optimized to high nanomolar potency and are able to block DNA binding.

 

The second talk was focused on E3 ligases, a target class Steve has been pursuing for the past decade. Steve is particularly interested in E3 ligases such as CBL-C, TRAF4, and KLHL12 that are differentially expressed in certain tissues. In the case of KLHL12, which is not found in heart tissue, an NMR-based screen led to fragment hits that were ultimately optimized to mid-nanomolar binders and could be turned into bivalent degraders for Bcl-xL and β-catenin.

 

When asked about his second favorite fragment-finding method after protein-detected NMR, Steve mentioned SPR. The throughput for SPR has historically been modest, but John Quinn (Genentech) described the new Carterra Ultra, which is capable of screening 96 proteins simultaneously while retaining good sensitivity. John screened 3000 fragments at 500 µM against multiple proteins in just two weeks, which provided an immediate assessment of both protein ligandability and fragment selectivity. Interestingly, and in contrast to some other analyses, shapelier fragments had similar hit rates to flatter fragments.

 

Several talks focused on fragment-to-lead success stories, some of which we’ve covered on Practical Fragments, such as RIP2 kinase inhibitors that started from flat fragments and were evolved to more three-dimensional leads as described by Mark Elban (GSK). John Taylor discussed pan-RAS inhibitors discovered at Cancer Research Horizons, the subject of an upcoming post. Andrew Judd (AbbVie) described the discovery of ABBV-973, a potent STING agonist that could be useful for certain types of cancer. And Justyna Sikorska described the discovery of a non-covalent WRN inhibitor at Merck. This is a nice complement to Vividion’s covalent WRN inhibitor, which we wrote about here and which was presented by Shota Kikuchi. Interestingly, structural biology was not enabled until late in this project.

 

One of the earliest arguments for fragment linking was the concept of avidity, and this underlies the basis of a technology discussed by Tom Kodadek and Isuru Jayalath at University of Florida Scripps. The idea is to immobilize fragments onto TentaGel beads, each the size of a red blood cell. These can be screened against multivalent proteins using either simple plate-based assays or FACS, the idea being that even if an individual protein-ligand interaction is weak, a multimeric protein can interact with several ligands on a single bead for enhanced binding. The researchers validated the concept with streptavidin, and also used it to find millimolar binders to the proteasome subunit Rpn13.

 

Last year we wrote about using photoaffinity crosslinking with fully functionalized fragments (FFFs) to identify non-covalent ligands to thousands of proteins in cells, and this was the subject of several talks. Chris Parker (Scripps) has mapped more than 7000 binding sites and described the discovery of an inhibitor against the inflammatory target SLC15A4. Interestingly, the molecule binds what appears to be a disordered region, though Chris speculated that it adopts a more defined structure in cells.

 

Belharra has gone all in on using FFFs, and Jarrett Remsberg and Andrew Wang described the construction of a diverse >11,000-membered FFF library, 88% of which consists of enantiomers. This has been screened against 13 different oncology and immunology cell lines to identify enantioselective or chemoselective hits against >4000 proteins including STAT3, IRF3, and AR.

 

Covalent approaches

The FFF approach uses covalent bond formation to trap a noncovalent ligand, but of course covalent ligands are all the rage these days, as we noted just last week. Dan Nomura (UC Berkeley) described the identification of stereoselective covalent ligands against a disordered region of cMYC that seem to work by destabilizing the protein in cells. Similarly, covalent ligands against the largely disordered AR-V7 also seem to destabilize the protein. It will be interesting to explore the mechanism of these molecules to see whether the proteins are more ordered inside cells.

 

Jin Wang (Baylor College of Medicine) described a chemoproteomic approach called Fragment Probe Protein Enrichment (FraPPE) which entails linking covalent fragments to a desthiobiotin tag. Labeled proteins are then pulled down, proteolyzed, and analyzed by mass spectrometry. In contrast, competition methods such as those described last year pull down labeled peptides after proteolysis. The advantage of FraPPE is that it can capture multiple peptides from each pulled-down protein, leading to fewer false negatives.

 

Of course, not every application of covalent discovery involves chemoproteomics. Joe Patel, who co-organized FBLD 2016, described the Nexo Therapeutics platform. They’ve built from scratch a library of >12,000 fragments, a third of which contain stereocenters. Each member is rule-of-three compliant before adding the warhead, meaning that the final molecules can be larger, which as we noted earlier this month is probably a good idea. To date Nexo has successfully screened more than a dozen targets using intact protein mass spectrometry.

 

The Nexo library targets not only cysteines but other residues as well, and Maurizio Pellecchia (UC Riverside) described using sulfonyl fluorides and fluorosulfates to target histidine residues. He and his group screened a library of 600 fluorosulfate-containing fragments (MW 250-350 Da) against the oncology target MCL1 and found several that stabilized the protein towards thermal denaturation. Crystallography confirmed covalent bond formation.

 

Most covalent fragments are electrophilic so that they can react with nucleophilic protein residues, but as we noted in 2022 it is possible to do the reverse. Megan Matthews (University of Pennsylvania) described how she used chemoproteomics to discover the mechanism of action for hydralazine, a drug that has been used since 1949 to treat hypertension. This fragment-sized (MW 160 Da!) molecule irreversibly alkylates a histidine residue within the active site of the enzyme ADO, a target that has also been implicated in gliobastoma.

 

Plenary Keynotes

The approval of the covalent BTK inhibitor ibrutinib in 2013 arguably marks the start of the modern era of covalent drug discovery, and Chris Helal described Biogen’s efforts against this target using reversible inhibitors, irreversible inhibitors, and degraders. Chris traced the origin of their phase-2 BIIB091 to a collaboration with Sunesis that used Tethering, so perhaps we should include this molecule in our list of fragment-derived clinical compounds.

 

Phil Baran of Scripps, who last spoke at the conference in 2020, gave the secondary plenary keynote. After stating that “medicinal chemists are the backbone of society,” he then detailed multiple examples of how they’ve been doing things wrong. Fortunately, he provided useful chemistry solutions, with “useful” defined as reactions that are operationally simple, have wide scope, and require only readily available reagents. Rather than deploying tedious protecting group installations and deprotections, Phil uses radical chemistry to directly generate carbon-carbon bonds between or within complicated molecules. His goal is to make the chemistry so simple and practical as to be boring, and he illustrated the point by showing his teenage daughter successfully running a reaction.

 

I’ll end here, but please leave comments. And mark your calendar for April 13-16 next year, when DDC returns to San Diego.

Do covalent fragments need to be larger?

A few months ago we highlighted work out of AstraZeneca detailing how to build a covalent fragment library. One of the design features was including larger molecules beyond the traditional rule of three (Ro3) criteria. A new open-access paper in J. Med. Chem. by György Keserű and collaborators at the HUN-REN- Research Centre for Natural Sciences and the Weizmann Institute of Science explores “size-dependent target engagement of covalent probes.”

 

The paper starts with a theoretical discussion of covalent inhibitors, focusing on the classic two-step mechanism in which binding of a ligand to a protein is followed by covalent bond formation. These steps are characterized by the inhibition constant (K_I) and the inactivation rate constant (k_inact). The most appropriate way to assess an irreversible covalent inhibitor is by the ratio k_inact/K_I, as we discussed last year.

 

A two-step mechanism is not the only possibility: the researchers also consider a three-step model in which binding of the ligand is followed by a second step, deprotonation of the amino acid nucleophile, before the final bond-forming step.

 

Fragments typically have lower affinities than lead-size or drug-size molecules, and thus k_inact will usually need to be higher for smaller molecules in order to see significant protein labeling. Simulations in which K_I is held constant show that at the high micromolar affinities often seen for fragments, protein modification requires either long incubation times or high reactivities. In addition to these simulations, the researchers also reanalyze publications we’ve previously covered such as this and this to argue that “reactivity contributes to labeling when the effects of other factors cancel out.”

 

Next, the researchers examine the kinase BTK and the oncology target KRAS, both of which have been successfully drugged with covalent molecules, ibrutinib and adagrasib, respectively. They trimmed back these molecules to smaller lead-like and fragment-like molecules and found that while some lead-sized molecules could still label the proteins, this was not the case for the fragment-sized molecules. From this they conclude that “fragment-sized covalent agents do not offer smooth optimization and are not ideal starting points.”

 

Two examples do not a trend make, but the researchers point to other examples in the literature. In 2020 we noted the larger size of covalent KRAS hits, and Vividion’s WRN inhibitor also started from a molecule with a molecular weight of 337 Da, while GSK’s starting point weighs in at 312 Da. The AstraZeneca library we mentioned at the start of this post yielded a hit against BFL1 that also just missed the Ro3 cutoff, coming in at 302 Da.

 

That said, there are counterexamples. Just last month we highlighted a covalent fragment hit that fits comfortably within the rule of three. Fragment-sized covalent hits can be found, but don’t expect them to be common. The alternative approach, screening lead-like compounds, will also likely require screening more compounds due to lower coverage of chemical space. Either way, libraries containing more molecules are likely to be beneficial for finding covalent starting points.

SAR by TR-HT-SAXS

Well that’s an acronym soup! SAR by NMR was the first practical fragment-finding method, and over the years Practical Fragments has covered lots of other techniques. Small-angle X-ray scattering, or SAXS, has not been among them. As the name suggests, this technique uses X-rays, typically produced at a synchrotron. However, unlike conventional crystallography, it doesn’t require crystalline material. Instead, proteins in solution are analyzed to provide information on their size and shape. The resolution is too low to assess small molecule binding, but suitable for observing dimerization or changes in conformation.

 

Time-resolved SAXS, or TR-SAXS, examines SAXS over time in response to a trigger. For example, you can rapidly add a ligand to a protein and watch for changes in conformation. And HT simply means high throughput. A recent Nature Chemical Biology paper from Chris Brosey, John Tainer, and collaborators at the University of Texas MD Anderson Center, Lawrence Berkeley National Laboratory, University of California Santa Cruz, and University of Arkansas for Medical Sciences Little Rock describes structure-activity relationships by time-resolved high throughput small-angle X-ray scattering (TR-HT-SAXS).

 

The researchers were interested in apoptosis-inducing factor (AIF), a mitochondrial protein with potential implications for cancer and other diseases. AIF normally exists as a monomer in complex with an FAD cofactor. Binding of NADH causes reduction of FAD to FADH^- and concomitant dimerization of the protein. Could fragments do the same, allowing dimerization on demand?

 

A library of 2500 fragments purchased from Life Chemicals was screened at 0.75-1.5 mM against the AIF-FAD complex using differential scanning fluorimetry (DSF), and those that raised or lowered the temperature by more than 1.7 ºC were further characterized by microscale thermophoresis (MST). This led to 32 binders and 7 negative controls, or molecules that did not confirm either by DSF or MST. (Side note: although many people discount compounds that give negative thermal shifts, the natural ligand NADH lowers the melting temperature of AIF by a whopping 10.8 ºC.)

 

Next, the fragment binders and negative controls were screened at 0.5-1 mM by TR-SAXS. Intense X-rays cause reduction of the FAD cofactor, but in the absence of NADH or other ligands the AIF protein remains monomeric. However, some fragments did cause dimerization of the protein during TR-SAXS. Interestingly, these fragments were structurally related to one another. Subsequent crystallography revealed that they bind where NADH normally binds and make some of the same interactions to induce protein dimerization. The paper includes much more detailed characterization, including mutagenesis, spectroscopic, and protein crosslinking experiments to further understand the mechanism.

 

TR-SAXS is an interesting addition to our toolbox of biophysical methods suitable for fragment screening. It does have some disadvantages, such as the need for large amounts of protein at high concentrations: 67 µM in this case. Also, the “HT” may be somewhat aspirational, with a current throughput of 100-200 compounds per synchrotron shift. Finally, the technique is probably best suited to well-characterized proteins where SAXS data can be carefully modeled. With these limitations in mind, it will be fun to see how generally TR-SAXS finds fragments that alter the conformation and multimerization of proteins.

Throwing the kitchen sink at IL-1β

Last year we highlighted a paper out of Novartis describing a fragment-to-lead story for interleukin-1 beta (IL-1β), a pro-inflammatory cytokine implicated in numerous diseases. The approved antibody drug canakinumab targets IL-1β, but a small molecule would provide easier oral dosing as well as better access to tissues such as the central nervous system. A new paper in J. Med. Chem. by Anna Vulpetti, Konstanze Hurth, and their Novartis colleagues describes the multiple approaches they've taken. (Anna also presented this work at Fragments 2024.)

 

The paper starts by summarizing the fragment work we described here. Notably, of nearly 4000 fragments screened, only a single super-sized fragment was validated, and it was quite weak. The researchers were able to optimize this to a molecule that inhibits binding of IL-1β to its receptor with an IC₅₀ = 1.1 µM.

 

Starting from the initial fragment hit, the researchers performed virtual screens to find alternative binders. Of 281 selected for testing by ¹⁹F NMR or TR-FRET, two hits were obtained, one with an affinity of around 230 µM and the other worse than 1 mM. These molecules were similar to each other, and merging them led to a 43 µM binder. All molecules exceeded conventional fragment size, with the smallest containing 24 non-hydrogen atoms. We’ve previously discussed the possible need for larger fragments for difficult targets such as protein-protein interactions.

 

In addition to FBLD, the researchers also performed DNA-encoded library (DEL) screens using 15 libraries containing >1.6 billion molecules. This led to one family of hits, one member of which inhibited binding of IL-1β to its receptor with an IC₅₀ = 8.3 µM. This molecule contains an aldehyde moiety, a reversible covalent electrophile. Subsequent experiments confirmed that the aldehyde reacts with a lysine residue on IL-1β, and the researchers were able to improve the potency to 1.2 µM. This molecule is even larger than the hit derived from fragments, with >50 non-hydrogen atoms. Interestingly, the molecule binds at a different site on the protein from the initial fragment hit.

 

Finally, the researchers screened a library of macrocyclic peptides in an mRNA display system. The macrocycles consisted of 10-14 amino acid residues, and the library was impressively large, containing “<10¹³ unique cyclic peptides.” This effort yielded a 14 µM inhibitor. Strikingly, crystallography revealed that the molecule binds at a site distinct from either the fragment- or DEL-derived hits.

 

This paper is a tour de force addressing a difficult target. Although the researchers conclude that the protein is “ligandable,” the physicochemical properties of all the hits will need to be improved, along with the affinities, in order to make useful chemical probes, let alone drugs. On the other hand, the fact that the ligands bind to different sites and yet can all inhibit the protein-protein interaction is encouraging, offering multiple opportunities for optimization.

Spacial Scores: new metrics for measuring molecular complexity

Molecular complexity is one of the theoretical underpinnings for fragment-based drug discovery. Mike Hann and colleagues proposed two decades ago that very simple molecules may not have enough features to bind tightly to any proteins, whereas highly functionalized molecules may have extraneous spinach that keeps them from binding to any proteins. Fragments, being small and thus less complex, are in a sweet spot: just complex enough.

 

But what does it mean for one molecule to be more complex than another? Most chemists would agree that pyridine is more complex than methane, but is it more complex than benzene? To decide, you need a numerical metric, and there are plenty to choose from. The problem, as we discussed in 2017, is that they don’t correlate with one another, so it is not clear which one(s) to choose. In a new (open access) J. Med. Chem. paper, Adrian Krzyzanowski, Herbert Waldmann and colleagues at the Max Planck Institute Dortmund have provided another. (Derek Lowe also recently covered this paper.)

 

The researchers propose the Spacial Score, or SPS. This is calculated based on four molecular parameters for each atom in a given molecule. The term h is dependent on atom hybridization: 1 for sp-, 2 for sp²-, 3 for sp³-hybrized atoms, and 4 for all others. Stereogenic centers are assigned an s value of 2, while all other atoms are assigned a value of 1. Atoms that are part of non-aromatic rings are also assigned an r value of 2; those that are part of an aromatic ring or linear chain are set to 1. Finally, the n score is set to the number of heavy-atom neighbors.

 

For each atom in a molecule, h is multiplied by s, r, and n². The SPS is calculated by summing the individual scores for all the atoms in a molecule. Because there is no upper limit, and because it is nice to be able to compare molecules of the same size, the researchers also define the nSPS, or normalized SPS, which is simply the SPS divided by the number of non-hydrogen atoms in the molecule. Although SPS can be calculated manually, the process is tedious and the researchers have kindly provided code to automate the process. Having defined SPS, the researchers compare it to other molecular complexity metrics, including the simple fraction  of sp³ carbons in a molecule, Fsp³, which we wrote about in 2009. 

 

The researchers next calculated nSPS for four sets of molecules including drugs, a screening library from Enamine, natural products, and so-called “dark chemical matter,” library compounds that have not hit in numerous screens. The results are equivocal. For example, the nSPS for dark chemical matter is very similar to that for drugs. On the other hand, natural products tend to have higher nSPS scores than drugs, as expected. Interestingly, the average nSPS score for compounds in the GDB-17 database, consisting of theoretical molecules having up to 17 atoms, is also quite high.

 

The researchers assessed whether nSPS correlated with biological properties, and found that compounds with lower nSPS tended to have lower potencies against fewer proteins, as predicted by theory. That said, this analysis was based on binning compounds into a small number of categories, and as Pete Kenny has repeatedly warned, this can lead to spurious trends.

 

The same issue of J. Med. Chem. carries an analysis of the paper by Tudor Oprea and Cristian Bologa, both at University of New Mexico. This contextualizes the work and confirms that drugs do not seem to be getting more complex over time, as measured by nSPS. This may seem odd, though Oprea and Cristian note that by “normalizing” for size, nSPS misses the increasing molecular weight of drugs.

 

This observation also raises other questions, such as the fact that SPS explicitly excludes element identity. Coming back to benzene and pyridine, both have identical SPS and nSPS, which does not seem chemically intuitive. One could quibble more: why square the value of n in the calculation of SPS? Why allow s to be only 1 and 2, as opposed to 1 and 5?

 

In the end I did enjoy reading this paper, and I do think having some metric of molecular complexity might be valuable. I’m just not sure where SPS will fit in with all the existing and conflicting metrics, and how such metrics can lead to practical applications.

Fragment screening on a benchtop NMR

Practical Fragments has been on an NMR theme for the last two weeks, and this post continues that trend. One of the main barriers to entry for NMR methods is the instrument itself: not only are the machines large, requiring a good size room, the price starts at several hundred thousand dollars. Then there is the maintenance, which includes regular refills of liquid helium, which is both costly and often scarce. And if the helium runs out, your precious superconducting magnet “quenches”, which looks like this.

 

Large magnets such as those in 600 MHz instruments are unlikely to change until room temperature superconductors become a reality. Less powerful permanent magnets are available though, and you can purchase a benchtop 80 MHz machine for less than $100,000. But the low sensitivity requires very high concentrations of sample, too high for fragment screening. Unless, that is, you could increase the sensitivity. This has now been described in a new (open-access) Angew. Chem. Int. Ed. paper by Felix Torres, Roland Riek, and collaborators at the Swiss Federal Institute of Technology, Bruker, and NexMR.

 

The somewhat complicated method is called photochemically induced dynamic nuclear polarization (photo-CIDNP), which we wrote about in June. As the name suggests, this involves light excitation of a photosensitizer molecule which can then increase sensitivity for detecting other small molecules, particularly when they are not bound to proteins. Weirdly and fortuitously, photo-CIDNP theory predicts that polarization transfer is actually higher at lower magnetic fields, making it ideal for benchtop NMR.

 

The researchers first tested three fragments, each at 500 µM, using 25 µM fluorescein as the photosensitizer. Just 3 minutes of measurements each gave very clear spectra after light irradiation at 450 nm. In the absence of light it would take between 22 hours and 10 years to achieve comparable signal-to-noise enhancement.

 

Next, the researchers screened 32 fragments from their custom-designed "NMhare1.0 library” we previously described, which contains molecules suitable for photo-CIDNP. As before they used the protein PIN1 (at 10 µM) and collected data for 3 minutes per sample. Six compounds had reduced polarization in the presence of protein, four of which had been previously detected as binders and validated using a 600 MHz NMR. Of the two new hits, one confirmed using protein-detected NMR while the other did not.

 

To explore the limits of sensitivity, the researchers conducted a series of experiments lowering the concentrations of protein and small molecules. One of the compounds could be detected at concentrations as low as 250 nM and quantified at 1 µM in just 3 minutes. At 50 µM this compound clearly showed binding to 5 µM protein, despite having an affinity in the low millimolar range.

 

This is a fun paper, and I particularly like the fact that it expands fragment screening to an instrument previously not thought to be suitable. As we wrote previously, one limitation of photo-CIDNP is that only some molecules are able to be photo-sensitized. A solution would be to find one such ligand and then run a displacement assay to see whether a second ligand could compete with it, akin to what has been done for fluorine NMR. I look forward to seeing how this technique develops.

Poll results: fragment libraries in 2023

Our latest poll on fragment libraries suggests the field is settling into some standard practices. The poll ran from April 9 through May 26. Of the 59 participants, all but one answered all the questions (there was one skip for the last question). This is slightly down from previous years; perhaps people are sick of internet polls? We also don’t know how many organizations the respondents represent; it is possible several people voted from one company or university, which might skew the results. Nonetheless, we think this survey gives a reasonable snapshot of how people construct and maintain fragment libraries.

 

Our first question asked about library size, and the results are similar to when we last asked this question in 2018, with the average library having between 1001 and 2000 fragments.

 

 

Next, we asked about the size of fragments themselves, specifically the minimum and maximum number of non-hydrogen atoms allowed in a fragment. The minimum hasn’t really changed from 2018, averaging 7-8 heavy atoms. However, the fraction of respondents who include the tiniest fragments has doubled (albeit from a low number), perhaps due to increasing interest in MiniFrags and MicroFrags.

 

Unlike in 2018, the maximum size of fragments seems to be bimodal, with some folks drawing the line at 15-16 heavy atoms (consistent with this analysis from Astex) while others allow larger fragments. It will be interesting to see whether this bifurcation represents a true shift, though even fragments with 22 heavy atoms are likely to be under 300 Da, consistent with the rule of three, which is twenty years old this year.

 

 

We then asked about the presence of chiral molecules. There was little change from 2017, with most respondents stating that they have racemic compounds in their library, though there was a slight increase in the number of respondents excluding chiral fragments.

 

 

A new question for this poll asked whether synthetic tractability was considered at the outset of library design. This was a consideration for 85% of people who took the poll; more than a third said they considered progressability for every fragment in the library.

 

 

 

 

 

 

 

 

 

Finally, we asked about library storage conditions. As was the case when we asked this question nine years ago, more than two-fifths of respondents said they store their library at -20 ˚C. However, the fraction of respondents who store their library at room temperature dropped, while those who store their libraries at -80 ˚C increased.

 

 

 

 

 

Although some changes are noticeable over the years, it seems that best practices have been established and widely adopted in fragment library design. What do you think – does anything surprise you?