Multiplexing (native) mass spectrometry

Native mass spectrometry (nMS) is one of the less commonly used fragment-finding methods. The approach entails mixing proteins and ligands and gently ionizing them under non-denaturing conditions to look for complexes. As with many other methods, multiple fragments can be screened in a single sample. In a new ACS Med. Chem. paper, Ray Norton and collaborators at Monash University and CSIRO report screening multiple proteins in a single sample.

 

The researchers were interested in fatty acid-binding proteins, or FABPs. As their name suggests, these transporter proteins shuttle lipophilic molecules such as fatty acids around cells. The ten human isoforms are expressed in different tissues and have different functions in metabolic signaling, but their similarity to one another has made finding selective chemical probes difficult. Enter nMS.

 

FABP isoforms 1-5 are the most heavily studied, and these were first assessed individually. They ionized well, though in some cases peaks corresponding to both the native protein and a complex with acetic acid was observed, not surprising given that the buffer contained 50 mM ammonium acetate.

 

Next, all five proteins were mixed together at 10 µM each. All the proteins could still be observed (with or without bound acetate), though some proteins did give stronger signals than others due to differences in ionization efficiency.

 

Adding small molecule WY14643, which the researchers had previously found to bind to FABPs in a fluorescence polarization (FP) assay, led to a more complex spectrum, with peaks corresponding to unbound proteins, proteins bound to WY14643, proteins bound to acetate, and proteins bound to both acetate and WY14643. When WY14643 was added at 10 µM, the selectivity profile was consistent with the FP data. Interestingly though, when ligand was added at the total concentration of all protein isoforms (50 µM), the selectivity profile changed. The researchers suggest this may be due to nonspecific binding at higher ligand concentrations, as has been seen previously for nMS.

 

To explore the generalizability of multiplexing nMS, the researchers turned to more potent (nanomolar) ligands. As with WY14643, these molecules showed good agreement with published selectivity rankings at lower ligand concentrations with some non-specific binding at higher concentrations.

 

When I first wrote about nMS back in 2010, I noted that “the stability of protein-small molecule complexes in native mass spectrometry assays does not necessarily correlate with the (more relevant) solution-phase affinity,” and this fact is investigated in the paper. Careful optimization of the experimental conditions, including ionization voltage and temperature, led to good relative selectivity rankings for a given ligand across the different FABP isoforms but differences in absolute values from those measured by ITC.

 

Another challenge is the fact that the five FABP isoforms tested have similar molecular weights; in one case a ligand complexed with FABP3 was difficult to distinguish from free FABP2. The researchers could solve this by using different protein constructs, such as a hexa-histidine-tagged version of FABP3.

 

Overall this is an interesting approach, and the paper does an excellent job describing the technical details and limitations. Along with protein-observed ¹⁹F NMR, mass spectrometry is a rare experimental technique suitable for screening mixtures of proteins in solution. Indeed, this becomes even easier when screening covalent binders, as seen in this paper from 2003, since there is no need to worry about ligand dissociation during ionization. And with the increasing interest in covalent drugs, the use of MS is only likely to increase.

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.

Fragment-based Drug Discovery Down Under (FBDD-DU) 2024

The end of June brought me to Brisbane for the fifth FBDD-DU Conference, which was meeting for the first time outside Melbourne. This was also my first FBDD-DU conference since 2019, and it was nice to see a wide range of talks from around Australia and beyond. As always, I won’t attempt to be comprehensive, so if you attended, please feel free to add your observations.

 

Techniques

Experimental techniques received considerable attention. Félix Torres (NexMR) described using an inexpensive benchtop NMR that doesn’t require liquid helium. Fragments were screened using photochemically induced dynamic nuclear hyperpolarization (photo-CIDNP). The method is so rapid that it is limited more by sample handling than data collection, and the Torres team is speeding things up using flow technology. Right now photo-CIDNP is still very much DIY, but rumor has it that Bruker may soon launch a photochemical module for their benchtop instrument.

 

We’ve written about high-throughput crystallographic screening at the Diamond Light Source, and synchrotrons around the world are building similar platforms. Kate Smith described integrated systems at the Swiss Light Source which automate crystallization, fragment screening, data collection, and data processing. She also described increasing automation of fragment screening using the free-electron laser (FEL), which we wrote about here. Current throughput is around 40 compounds per day and requires large amounts of protein, but these are still early days.

 

Australia is building their own high-throughput crystallography platform, and various components were described by Roxanne Smith (University of Melbourne), Gautham Balaji (Monash Univesrity), and Yogesh Khandokar (ANSTO-Australian Synchrotron). Watch this space!

 

Speaking of Australia, Nyssa Drinkwater described Compounds Australia, a national repository of more than 2.5 million molecules, including several fragment collections. Members, who can be from outside Australia, can store their own libraries within the facility to ease collaborations with other groups, and they can also access public libraries of compounds, including unusual Antipodean natural product extracts. I was fortunate to be able to visit the facility at Griffith University and can attest that it is easily the equal of those in large pharma.

 

Turning to mass spectrometry, Sally-Ann Poulsen (Griffith University) described covalent library screening against PRMT5, a target we’ve written about here. Sally-Ann is also a pioneer of (conventionally non-covalent) native mass spectrometry, and she described applying this methodology to screen small molecules against RNA.

 

But the star of the conference was SPR, appearing in multiple talks. Long-time readers may recall an instrument made by SensiQ, with its gradient injection capability to accelerate data collection. This is now marketed by Sartorius, and Lauren Hartley-Tassell (Griffith University) described using it to screen a glycoprotein. The larger plumbing in the instrument is less prone to clogging, and Lauren said it can even accommodate screening of whole cells.

 

Anything to accelerate the (sometimes painful) process of advancing fragments is always welcome. As Jason Pun (Monash University) noted, eight of nine targets screened in Martin Scanlon’s group started with fragments having affinities worse than 100 µM. Off-rate screening, an SPR technique we wrote about here, can rapidly identify more potent molecules from crude reaction mixtures, but data processing can be tedious. Jason described new software tools to automate this process, and hopefully he will publish the methodology and code. (An aside: over coffee Yun Shi of Griffith University noted that off-rate screening, or ORS, should really be called off-rate constant screening, which would give the more amusing acronym ORCS.)

 

Targets

Turning to targets, Ben Davis (Vernalis) described a collaboration with Servier to advance oncology target USP7 inhibitors from a literature fragment to a preclinical candidate. Crude reaction mixture screening was used extensively, not just by SPR but even in microsome stability studies. Unfortunately the project ended when on-target toxicology effects emerged, which were perversely more severe in higher animal species than they were in mice.

 

Yun Shi described finding tiny heterocyclic fragments that react with the NAD⁺ cofactor of neurodegenerative target SARM1 in situ to generate a potent inhibitor, as we wrote about here. Yun is using ¹⁹F NMR to follow the base-exchange reaction to identify inhibitors to other glycohydrolases too.

 

Deaths due to E. coli are – somewhat surprisingly – more common than those caused by any other pathogen, and Christina Spry described her work at the National Australian University to discover inhibitors of the essential dephosphocoenzyme A kinase (GPCK) enzyme, which catalyzes the final step in the synthesis of Coenzyme A (CoA). Fragment screening by DSF and NMR identified a weak (K_D=380 µM) binder, and fragment growing has led to a low nanomolar inhibitor that is selective against the human form of the enzyme.

 

Continuing the E. coli theme, several talks discussed efforts against the challenging bacterial virulence target DsbA, a twenty-year campaign in Martin Scanlon’s group at Monash as noted by Yildiz Tasdan. The enzyme has a shallow, hydrophobic active site, but the discovery of fragments binding to a cryptic site and crude-reaction screening by ORS (ORCS?) and affinity-selected mass spectrometry (ASMS) has finally led to molecules with dissociation constants around 1 µM.

 

Finally, in his closing keynote address Alvin Hung, who recently founded NeuroVanda, described a wide range of fragment success stories, many of them covered on Practical Fragments, against targets including pantothenate synthetase, GSK3β, PKC-ι, and MNK1/2. Although structural enablement helped in many cases, Alvin was not rigid about the need for atomic-level details: in response to the question whether he would advance a fragment in the absence of structure, he answered simply, “of course.” Perhaps it's time to redo my poll on this subject.

 

I’ll wrap up here, but if you missed this or earlier events this year there are still a couple more conferences in Boston, and 2025 is already starting to take shape.

Fragment events in 2024 and 2025

The year is half-way done, and we've seen some great events; I'll share my thoughts on FBDD Down Under 2024 next week.

Boston is where it's at in the second half of 2024, and it's not too soon to start planning for 2025.

September 22-25: After a six year hiatus, FBLD 2024 will be held in Boston. This will mark the eighth in an illustrious series of conferences organized by scientists for scientists. You can read impressions of FBLD 2018, FBLD 2016, FBLD 2014, FBLD 2012, FBLD 2010, and FBLD 2009. Early-bird registration ends August 12, so don't delay!

 

September 30 to Oct 3: Autumn is usually a nice time of year in Boston, so stick around to attend CHI’s Twenty-Second Annual Discovery on Target. As the name implies this event is more target-focused than chemistry-focused, but there are always plenty of FBDD-related talks. You can read my impressions of the 2023 meeting here, the 2022 meeting here, the 2021 event here, the 2020 virtual event here, the 2019 event here, and the 2018 event here.

 

Finally, from December 3-5 CHI holds its first-ever Drug Discovery Chemistry Europe in beautiful Barcelona. This will include tracks on lead generation, protein-protein interactions, degraders, and machine learning, with several fragment talks. (Updated July 8.)

 

2025

April 14-17: CHI’s Twentieth Annual Fragment-Based Drug Discovery, the longest-running fragment event, returns as always to San Diego. This is part of the larger Drug Discovery Chemistry meeting. You can read impressions of the 2024 meeting here, the 2023 meeting here, the 2022 event here, the 2021 virtual meeting here, the 2020 virtual meeting here, the 2019 meeting here, the 2018 meeting here, the 2017 meeting here, the 2016 meeting here; the 2015 meeting here, here, and here; the 2014 meeting here and here; the 2013 meeting here and here; the 2012 meeting here; the 2011 meeting here; and 2010 here. 

  

Know of anything else? Please leave a comment or drop me a note.

Fragments vs LTA4H: LipE in action

Three years ago we described the discovery of LYS006, an inhibitor of leukotriene A4 hydrolase (LTA4H) from Novartis currently in phase 2 clinical trials. Companies often pursue multiple chemical series for important targets, and in a recent J. Med. Chem. paper Gebhard Thoma and colleagues describe another fragment-derived lead against LTA4H.

 

A biochemical high-throughput screen yielded compound 2, which is quite potent for a fragment-sized molecule. However, despite good ligand efficiency, the LipE (or LLE) was less impressive due to the high lipophilicity of the fragment. (Note that throughout the paper LipE is calculated based on measured logD rather than logP.) A co-crystal structure revealed that it bound in a similar fashion to other previously characterized LTA4H inhibitors such as compound 1, derived from LYS006 and reported in a J. Med. Chem. paper last year. Adopting elements from these led eventually to compound 12, which though less potent was also much less lipophilic and more soluble while still remaining fragment-sized.

 

 

Continuing to borrow from the rich literature around this target, the researchers added a basic amine group to get to the very potent compound 14. This was metabolically unstable, but further optimization led to compound 3.

 

Compound 3 was profiled extensively in a battery of tests. In addition to good biochemical potency, it showed mid-nanomolar activity in a human whole blood assay and was also active in other assays, including a mouse arthritis model. Other attractive features included a clean profile against a plethora of off-targets, good oral bioavailability in mice, rats, and dogs, and a predicted human oral dose of 40 mg once daily. However, a two week toxicology study in rats and dogs was “slightly less favorable” than compound 1.

 

This is a lovely example of property and structure-guided drug design, and the researchers are refreshingly open about borrowing elements from other molecules, even from outside Novartis. Interestingly, a crystal structure of compound 3 bound to LTA4H revealed that while the overall binding mode was similar to compound 1, which contains the same left-hand portion, the pyrazole and pyridine rings rotated 180º to make different hydrogen-bond interactions. Another reminder that despite our leaps in predictive capability, molecules can still provide many surprises.

Fragments vs MAT2a: a chemical probe

As many of us know all too well, traditional methods to treat cancer often result in severe and even intolerable side effects. An emerging, gentler approach is based on synthetic lethality: targeting a protein that is essential only in certain cancer cells but not in normal cells. One prominent target is MAT2a, one of two human methionine adenosyltransferases. We’ve written previously about AG-270, a fragment-derived MAT2a inhibitor that entered the clinic. AstraZeneca has also pursued this target, as we discussed here. In a new J. Med. Chem. paper, Stephen Atkinson, Sharan Bagal, and their AstraZeneca colleagues describe a new chemical probe.

 

A differential scanning fluorimetry (DSF) screen of about 55,000 compounds at 100 µM, nearly a third of which were fragments, resulted in a healthy 1.5% hit rate. Further DSF as well as biochemical testing ultimately delivered compound 8, which is quite potent for a fragment. A crystal structure of the compound bound to MAT2a demonstrated that it bound in the same allosteric site targeted by other compounds. The methoxy group was pointed towards a couple backbone carbonyl oxygen atoms, and adding a couple fluorine atoms created a weak hydrogen bond donor with a satisfying 50-fold boost in potency.

 

Adding a hydrogen bond acceptor (compound 12) slightly reduced potency but also decreased lipophilicity. Further inspection suggested opportunities for fragment growing, and free energy perturbation (FEP) calculations suggested that adding the methoxyphenyl group of compound 15 would be fruitful. This turned out to be the case, and further optimization led to AZ’9567. The paper provides plenty of meaty medicinal chemistry, with significant efforts focused on reducing lipophilicity and clearance. FEP was used extensively during the design process, and a retrospective analysis found a good correlation between predicted and measured affinity.

 

AZ’9567 was studied in considerable detail. It has excellent oral bioavailability and good pharmacokinetics in both mice and rats. The compound does not significantly inhibit cytochrome P450 enzymes or hERG and is reasonably clean against a panel of 86 off-targets. The main liability is poor solubility, a problem also faced by AG-270. Nonetheless, the AstraZeneca researchers were able to develop a liquid formulation.

 

The paper compares AZ’9567 with AG-270, showing that both compounds are potent in biochemical assays as well as against cell lines in which MAT2a is essential. A mouse xenograft model with AZ’9567 showed considerable and sustained tumor growth reduction.

 

Unfortunately, AG-270 is no longer in clinical development, and there is no mention of a MAT2a inhibitor in the AstraZeneca pipeline. Nonetheless, having a second well-characterized chemical probe will be useful for further characterizing the biology of MAT2a and assessing whether it will be a productive drug target.

Fragments vs CDC14 phosphatases

Practical Fragments has periodically written about protein tyrosine phosphatases (PTPs), which remove phosphate moieties from tyrosine side chains in proteins. Despite decades of attention, progress towards selective inhibitors has been slow due to both the similar active sites and their highly charged nature. A new paper in J. Med. Chem. by Zhong-Yin Zhang and colleagues provides some hope.

 

The researchers were interested in CDC14 phosphatases, so-called dual-specificity phosphatases that can dephosphorylate phosphoserine and phosphothreonine in addition to phosphotyrosine. Two members of this family, hCDC14A and hCDC14B, are widely expressed in humans, but their role in cancer is ambiguous, with some studies suggesting they are oncoproteins while others suggest they may have a protective function. Clearly a chemical probe would be useful.

 

The researchers started by considering non-hydrolyzable phosphotyrosine mimetics, specifically those replacing the central oxygen with a difluoromethyl moiety; we wrote about this bioisostere back in 2013. Eight fragments were made and assessed at 1 mM in aqueous buffer to demonstrate they did not aggregate. They were then tested in functional assays against a panel of ten PTPs, and compound 9 turned out to be quite potent and selective for hCDC14A. Subsequent experiments showed it to have similar activity against hCDC14B, and Lineweaver-Burk plots revealed it to be a competitive inhibitor of both, as expected.

 

Although no hCDC14A structures have been reported, modeling the compound into a published structure of hCDC14B gave some insights into the binding mode and selectivity. In particular, hCDC14B has a larger active site than some other PTPs, thus explaining why the tricyclic compound 9 could fit. Further analysis suggested the possibility of growing the compound towards a hydrophobic pocket, so the researchers synthesized a small set of molecules, of which compound 15 turned out to be the most potent.

 

Compound 15 was tested against 16 PTPs and found to be quite selective against hCDC14A and hCDC14B, with IC₅₀ values 5 µM or worse against the others. Mutagenesis studies in the hydrophobic pocket were consistent with the proposed binding mode. Despite the presence of the highly charged difluorophosphonate moiety, compound 15 showed activity in cells at low micromolar concentration and had some oral bioavailability in mice.

 

Although better cell activity is probably necessary to make a truly useful chemical probe, this is a nice start. Researchers at AbbVie have taken a competitive inhibitor of a different PTP into the clinic, so perhaps we will start to see more successes against these challenging enzymes.

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.

Free computational fragment growing with ChemoDOTS

Back in 2018 we highlighted diversity-oriented target-focused synthesis, or DOTS, a combined computational and experimental method for growing fragments. The computational piece of this has now been turned into a free web server, called ChemoDOTS, and is described in Nucleic Acids Research by Xavier Morelli, Philippe Roche, and colleagues at Aix-Marseille University.

 

To get started, the user draws or uploads the structure of a fragment hit they wish to expand. ChemoDOTS identifies potentially reactive functionalities, such as amine groups. For each functionality, the program also provides compatible reactions, derived from a set of 58 commonly used in industry. The user then chooses one or more reactions of interest, at which point the program generates a list of molecules that could be created by linking the fragment to various building blocks using the selected chemistries. The building blocks themselves consist of 501,542 commercially available molecules from MolPort and 988,112 molecules from Enamine having between 4 and 24 non-hydrogen atoms.

 

The program generates molecules quite rapidly, between 1000-1500 per second. All of these can be downloaded at this point, but ChemoDOTS also allows further processing. Histograms showing molecular weight, cLogP, total polar surface area, the number of hydrogen bond donors and acceptors, and Fsp³ for the library are displayed, and the user can adjust sliders to select molecules having, for example, cLogP between 1 and 3 and 0-2 hydrogen bond donors. Finally, ChemoDOTS generates three dimensional conformers in a ready-to-dock format for each compound.

 

As a retrospective example, the researchers return to the BRD4 case study we wrote about here. Starting from the amine-containing fragment and the sulfonamidation reaction, ChemoDOTS generated 5546 molecules in just 5 seconds, including all 17 of those previously identified.

 

This is a nice approach, and I believe the researchers are correct when they say that to the best of their knowledge “ChemoDOTS is the only freely accessible functional and maintained web server to combine the design of medchem-compatible virtual libraries with an integrated graphical postprocessing analysis.” They plan to continue improving it, for example by adding new commercial building blocks from other sources.

 

If I could make one suggestion, it would be to include new types of chemistries beyond the 58, which came from a paper published in 2011. In particular, C-H bond activation methodologies have made impressive strides in recent years. Adding these is all the more important given that, according to a recent analysis, about 80% of successful fragment-growing campaigns involved growth from a carbon atom. But even in its current form, ChemoDOTS looks to be a useful approach for growing focused chemical libraries around fragment hits. Let us know how it works for you!

Screening MiniFrags by NMR

Small is becoming big. Five years ago we highlighted MiniFrags, consisting of just 5-7 non-hydrogen atoms; FragLites and MicroFrags soon followed. Screening these tiniest of fragments at high concentrations can thoroughly explore hot spots on a protein and identify favorable molecular interactions. But because they are so extraordinarily small, experimental methods for screening them have been mostly limited to crystallography. In a new J. Med. Chem. paper, Annagiulia Favaro and Mattia Sturlese (University of Padova) turn to the most venerable of fragment-finding methods, NMR.

 

The researchers started with the 81 reported MiniFrags and removed those with aqueous solubility less than 250 mM or without protons observable by NMR (such as phosphate). The remaining 69 fragments were dissolved directly in phosphate buffer, mostly at 1 M concentration, though lower solubility fragments were dissolved at 250 mM. Importantly, the pH of each sample was carefully adjusted to 7.1 to ensure that any signals correspond to MiniFrag binding and not to changes in experimental conditions.

 

As a test case, the researchers chose the antiapoptotic target BFL1. This protein is related to BCL2, the target of venetoclax, which was discovered using SAR by NMR. BFL1 has a hydrophobic cleft with five subpockets and has been studied by NMR. Like other BCL2 family members it is a difficult target, as we noted earlier this year.

 

The actual screen was done using chemical shift perturbation (CSP) detected by two-dimensional ¹H-¹⁵N HMQC. Fragments were screened at 100 mM, a 5000-fold excess above the protein concentration. Hits were confirmed at 20 mM (more on that below). As with the library preparation, pH was carefully controlled.

 

At such high ligand concentrations, any impurities could become a problem: a 2% contaminant would be present at 2 mM. To weed these out, the researchers performed WaterLOGSY experiments. These only produce a signal at ligand to protein ratios much lower than 1000 to 1, so any hits could only come from impurities.

 

Even at high concentrations, CSPs caused by weak fragments are small, so the researchers developed an analysis method to identify those that shift more than at least one standard deviation from the average. CSPs can shift in any direction on a two-dimensional map, but any one protein-ligand interaction should shift signals in the same direction. Here is where the 20 mM confirmation experiment comes into play: a “cosine similarity” assesses whether two CSPs are in the same direction and thus likely to be real.

 

Screening BFL1 led to 53 hits, a hit rate of 78%, similar to crystallographic screens of MiniFrags against other targets. Forty percent of MiniFrags bound to multiple sites on the protein; only 11 (16%) bound to a single site. The five subpockets were each liganded by 6-17 MiniFrags. In subsequent experiments, the researchers were also able to measure binding of two different fragments to different pockets simultaneously, akin to SAR by NMR.

 

This is an interesting approach, but while fragments with >5 mM dissociation constants have been advanced to drugs, the utility of a 100 mM binder remains to be seen. That said, the technique could be a boon for understanding protein-ligand interactions, and I look forward to seeing it applied more broadly. In particular, screening the same set of MiniFrags on the same protein by NMR, crystallography, and computational methods could be quite informative.

Fragments in cells, writ large

Earlier this year we highlighted work in which a dozen fragments were screened against cells to look for noncovalent binders across the proteome. A new paper in Science by Georg Winter and collaborators at the Austrian Academy of Sciences, Pfizer, and several other organizations ups the game by more than an order of magnitude, and uses machine learning to make predictions about fragments’ cellular destinations and binding partners. (See also Derek Lowe’s post here.)

 

The researchers started with 407 diverse fully functionalized fragments (FFFs), which as we previously discussed consist of a variable fragment coupled to a photoreactive group and an alkyne moiety that can be used to pull down any bound proteins using click chemistry. These were selected from a larger set of ~6000 FFFs available from Enamine. The FFFs were incubated at 50 µM with intact HEK293T cells, followed by ultraviolet crosslinking.

 

Next, cells were lysed and treated with a biotin-azide probe that reacts with the alkyne on the FFFs. Covalently modified proteins were captured on streptavidin resin and proteolytically digested. Tandem mass tag (TMT) proteomics, which we wrote about here, was used to identify captured proteins. Unlike earlier methods, the researchers did not pinpoint the specific fragment binding sites on proteins.

 

In total the researchers found 2667 proteins bound to one or more fragments, of which ~86% had no reported ligands. Both proteins and ligands varied considerably in promiscuity: some proteins bound to more than half of the FFFs, and some fragments bound to hundreds of proteins, while others bound only a few, or none. To look for specific interactions, the researchers focused on proteins bound by fewer than 10 different ligands.

 

Three protein-ligand interactions were analyzed in some detail: the kinase CDK2 (and other CDK family members), the adapter protein DDB1, and the solute carrier protein SLC29A1. In each case the researchers confirmed the results from their chemoproteomic screens. Follow-up studies with related molecules led to more potent derivatives, with a CDK2 inhibitor showing low micromolar activity in a biochemical assay and an SLC29A1 inhibitor showing micromolar activity in a cell-based assay.

 

The researchers also found patterns in their larger data set. Armed with 47,658 protein-ligand interactions, the researchers were able to use machine learning to start to predict which molecular features were associated with binding. They ranked fragments as promiscuous or nonpromiscuous and built a promiscuity model. Molecules with higher lipophilicity and a greater fraction of aromatic carbon atoms tended to be more promiscuous, but the model could correctly categorize compounds as promiscuous even if they had lower ClogP values, or nonpromiscuous even if they had higher ClogP values.

 

Beyond promiscuity, the researchers used machine learning to predict other behavior, such as subcellular localization. A relatively easy case was to predict which molecules would accumulate in lysosomes; these tended to be hydrophobic basic amines. More impressively, the researchers could predict fragments likely to bind to transmembrane transporters, RNA binding proteins, and even intrinsically disordered proteins. And this is just the start: they hope one day to predict “target proteins from an input chemical structure alone.”

 

Perhaps most exciting, all of the data and models are available for free at Ligand Discovery. You can explore the proteins bound across all 407 fragments, input one or more proteins and find ligands, predict whether any given FFF is likely to be promiscuous or not, and even “build a machine learning model on the fly to predict potential interactions.” 

 

Check it out and let us know your experience.

Covalent fragments vs WRN

Last week Practical Fragments highlighted a covalent clinical compound from Vividion and Roche against the oncology target WRN. Another series of inhibitors against this protein are described in a recent Cancer Discov. paper by Gabriele Picco, Mathew Garnett, and collaborators at the Wellcome Sanger Institute, GSK, IDEAYA, and several academic institutes.

 

As we described in more detail last week, WRN is a synthetic lethal target for microsatellite instability (MSI) cancers. In contrast to the Vividion paper, which started by screening covalent fragments against cell lysates, here the researchers incubated purified WRN protein against each member of their covalent library (at 20 µM for 24 hours at 21 ºC) and analyzed the reactions by intact protein mass spectrometry. The fragment library was based around the methyl acrylate warhead, which, as we discussed a decade ago, has a narrower range of reactivities than more common acrylamides.

 

GSK_WRN1 was one of the prominent hits, with 81% modification. Tryptic digestion revealed that it modified C727, the same cysteine found by the Vividion researchers. Medicinal chemistry led to GSK_WRN3, with sub-micromolar activity in MSI SW48 cells. (Unfortunately no other details on the chemistry are provided; the paper states that these will be written up separately.)

 

GSK_WRN3 or a closely related compound were tested in a battery of assays and found to be inactive against three other helicases, which is not surprising given that C727 is unique to WRN. Chemoproteomic studies in cells also revealed the compound to be quite selective towards WRN vs other proteins. The compounds selectively inhibited MSI cancer cell lines and patient-derived organoids while sparing microsatellite stable (MSS) cell lines and organoids. One of the compounds showed activity in a mouse xenograft model.

 

In a useful public service, the researchers tested two previously reported WRN inhibitors, MIRA-1 and NSC617145, in the same set of several dozen cell lines and found that they were not only ineffective, they lacked selectivity for MSI cells over MSS cells. Although Dr. Saysno might object, I nominate these molecules to be added to the “Unsuitables” bestiary at the Chemical Probes Portal.

 

I do wish more details about the molecules were provided, especially the k_inact/K_i values. It is interesting that GSK_WRN3 bears remarkable structural similarities to VVD-109063. IDEAYA recently announced that their collaboration with GSK has resulted in a development candidate targeting WRN, and it will be fun to see the full story emerge.

Covalent fragments in the clinic: VVD-133214

Back in 2016 we highlighted a paper describing chemoproteomic screening of covalent fragments. That technology formed the basis of Vividion, which was acquired by Bayer in 2021. Now, a paper just published in Nature by Matthew Patricelli, Todd Kinsella, and collaborators at Vividion, Roche, and Universitat Autònoma de Barcelona describes one of the fruits to come from this platform.

 

The work stems from another promising recent approach to find oncology targets, synthetic lethality: searching for proteins that are essential in certain types of cancer cells but dispensable for normal cells, which might mean reduced toxicity. WRN is a DNA helicase that can clean up secondary DNA structures caused by expanded TA-dinucleotide repeats found in cancer cells with microsatellite instability (MSI), which is caused by mutations in DNA repair genes. Previous research had shown that knocking out WRN caused double-stranded DNA breaks and cell death in MSI-high (MSI-H) cancer cells but not normal cells, which do not have so many expanded TA-dinucleotide repeats. This has set off an industry-wide search for WRN inhibitors.

 

The researchers screened several thousand fragment electrophiles against cell lysates and found that some, such as VVD-109063, modified C727 of WRN. Although this cysteine is located some distance from the ATP binding site, functional activity studies with the pure protein found that the molecule was an inhibitor.

 

Optimization of VVD-109063 and related molecules found inconsistencies between results in lysates and intact cells. Some engaged C727 better in intact cells than lysates, others worse. Differences in cell permeability were ruled out by the fact that a cysteine on an unrelated protein was liganded to a similar extent in cell lysates and intact cells. The researchers speculated that, because cell lysates are diluted, they have lower ATP concentrations, and sure enough some molecules were less active in the presence of ATP while others were more active.

 

The team decided to focus on the second class. Optimization ultimately resulted in the clinical candidate VVD-133214. (Unfortunately details are not given; the paper does say these will be provided elsewhere).

 

A crystal structure of VVD-133214 confirmed covalent binding to C727, with the molecule in a hydrophobic pocket in a flexible “hinge region” of the protein. This causes a conformational rearrangement into a “closed” form, which presumably affects the catalytic activity of the helicase. Surprisingly, there are no hydrogen bonds between WRN and VVD-133214. This is highly unusual: a paper we discussed in 2021 found >90% of fragment-derived leads had at least one polar contact.

 

The k_inact/K_i value is reported as being 4848 M^-1s^-1, which is on the low side for clinical-stage irreversible inhibitors. Like sotorasib, its potency seems driven by k_inact, with the K_i being greater than > 15 µM. Consistent with this low inherent affinity, the molecule was inactive against the C727A mutant enzyme.

 

Much of the paper focuses on the biology, which is interesting but beyond the scope of this post. Suffice it to say that VVD-133214 is cytotoxic in MSI-H cells, where it causes G2 arrest and DNA damage, but inactive in microsatellite stable (MSS) cells. Oral dosing led to tumor regression in several MSI-H mouse models, including patient-derived xenografts.

 

This is a nice paper, though I look forward to a full account of the medicinal chemistry. In particular, vinyl sulfones are generally considered quite reactive, and I know of only one other clinical-stage molecule with this warhead. Presumably the cyclopropyl substituent was added at least in part to sterically block access to the electrophile.

 

Also, while the paper refers to VVD-133214 as “clinical-stage,” it appears neither on clinicaltrials.gov nor on Vividion’s website. The Roche website lists RG6457 as a phase 1 WRN covalent inhibitor partnered with Vividion, so perhaps this is the same molecule.

 

The paper ends by mentioning another clinical-stage WRN inhibitor from a different company, this one noncovalent. It notes that “this presents a rare opportunity to compare two small molecule oncology drugs targeting the same protein by different mechanisms,” and that using both could be useful in combating resistance. Practical Fragments wishes luck to these – and other drugs targeting WRN – helping patients quickly.

The limits of published data

Machine learning (or, for investors, artificial intelligence) has received plenty of attention. To be successful you need lots of data. If you’re trying to, say, train a large language model to write limericks you’ve got oodles of text to draw from. But to train a model that can predict binding affinities you need lots of measurements, and they must be accurate. Among public repositories, ChEMBL is one of the most prominent due to its size (>2.4 million compounds) and quality, achieved through manual curation. But even here you need to be cautious, as illustrated in a recent open-access J. Chem. Inf. Model. paper by Gregory Landrum and Sereina Riniker (both at ETH Zurich).

 

The researchers were interested in the consistency of IC₅₀ or K_i values for the same compound against the same target. They downloaded data for >50,000 compounds run at least twice against the same target. Activity data were compared either directly or after “maximal curation,” which entailed removing duplicate measurements from the same paper, removing data against mutant proteins, separating binding vs functional data, and several other quality checks. They used a variety of statistical tests (R², Kendall τ, Cohen’s κ, Matthew’s correlation coefficient) to evaluate how well the data agreed, the simplest being the fraction of pairs where the difference was more than 0.3 or 1 log units, roughly two-fold or ten-fold differences.

 

The results were not encouraging. Looking at IC₅₀ values, 64% of pairs differed by >0.3 log units, and 27% differed by more than 1 log unit. In other words, for more than a quarter of measurements a molecule might test as 100 nM in one assay and >1 µM in another.

 

Of course, as the researchers note, “it is generally not scientifically valid to combine values from different IC₅₀ assays without knowledge of the assay conditions.” For example, the concentration of ATP in a kinase assay can have dramatic effects on the IC₅₀ values for an inhibitor. Surely K_i values should be more comparable. But no, 67% of pairs differed by >0.3 log units and 30% differed by >1!

 

The situation improved for IC₅₀ values using maximal curation, with the fraction of pairs differing by >0.3 and >1 log units dropping to 48% and 13%. However, this came at the expense of throwing away 99% of the data.

 

Surprisingly, using maximal curation data for K_i data actually made the situation worse. Digging into the data, the researchers found that 32 assays reporting K_i values for human carbonic anhydrase I, all from the same corresponding author, include “a significant number of overlapping compounds, with results that are sometimes inconsistent.” Scrubbing these improved the situation, but 38% of pairs still differed by >0.3 log units, and 21% differed by >1 log unit.

 

This is all rather sobering, and suggests there are limits to the quality of available data. As we noted in January there are all kinds of reasons assays can differ even within the same lab. Add in subtle variations in protein activity or buffer conditions and perhaps we should not be too surprised at log-order differences in experimental measurements. And this assumes everyone is trying to do good science: I’m sure sloppy and fraudulent data only make the situation worse. No matter how well we build our computational tools, noisy data will ensure they often differ from reality, whatever that may be.

Detailing hot spots with atomic consensus sites

Practical Fragments has written frequently about hot spots, regions on proteins that are predisposed to bind ligands such as drugs. Determining whether a protein has a hot spot can help prioritize a target for screening, and one of the more established computational approaches to do so is FTMap, which we wrote about most recently just a couple months ago.

 

While FTMap can tell you whether a protein has one or more hot spots, it provides few further details, such as which regions might prefer a hydrogen bond donor or acceptor. This has now been addressed in a new J. Chem. Inf. Mod. paper by Sandor Vajda and collaborators at Boston University, Stony Brook University, and Acpharis. (Diane Joseph-McCarthy presented some of this work at the CHI DDC conference earlier this month.)

 

The original version of FTMap started with a collection of 16 very small molecule probes: these were docked all over a protein, with hot spots being identified as consensus sites where many probes bound. To get more information about each hot spot, the researchers have extended the method – now called E-FTMap – by increasing the number of probes to 119 covering key functional groups. For example, whereas FTMap included dimethyl ether as a probe, E-FTMap also includes 2-methoxypropane, 2-methoxy-2-methylpropane, and tetrahydropyran. If all these probes bind with the oxygen in the same part of the hot spot, this suggests a predilection for a hydrogen bond acceptor, and also provides information about nearby hydrophobic contacts.

 

By using a sufficiently diverse group of virtual probes, E-FTMap is able to more finely detail hot spots, tallying the “atomic consensus sites” within them. This is reminiscent of an approach we wrote about several years ago, though that method used just three different probes.

 

To benchmark E-FTMap, the researchers took 109 fragment-to-lead pairs with published crystallographic information and assessed whether the program could identify interactions that had been experimentally observed. The results were encouraging and far superior to the original version of FTMap. The highest ranked atomic consensus sites generally overlapped with appropriate atoms in fragments and leads. Interestingly, the results for fragments were better than those for leads, and the researchers suggest this is because the fragment “core is responsible for the bulk of the binding free energy in a ligand and that larger ligands bind by forming additional interactions at weaker hot spots that surround the fragment binding site.”

 

Next, E-FTMap was tested against five proteins for which between 31 and 353 fragment-bound crystal structures were available. Here too the program was broadly successful, though some fragments bound regions of the protein that E-FTMap overlooked, particularly in cases where there were conformational changes. This is not surprising given that the program assumes the protein remains rigid. (Other computational approaches such as SWISH, which we wrote about here, are starting to account for protein flexibility.)

 

E-FTMap looks qualitatively at specific atomic interactions, and one question I had was how well the atomic consensus sites matched up with binding affinities of known fragments; perhaps some crystallographically identified fragments bind so weakly one would not expect to find them computationally, as we discussed here and here. This hypothesis might be tested by focusing on comparisons with experimentally characterized fragments with the highest ligand efficiencies.

 

Also, I was struck by the fact that the virtual probes in E-FTMap are roughly the size of MiniFrags or MicroFrags, and I couldn’t help but wonder how well the atomic consensus sites from the virtual screens would correlate with the binding modes of these tiniest of fragments.

 

One nice feature of E-FTMap is that it can be accessed through a simple web server, so if you’re interested in these and other questions you can test it for yourself. If you do, please share your experiences.

Nineteenth Annual Fragment-Based Drug Discovery Meeting

Last week the CHI Drug Discovery Chemistry (DDC) meeting was held in San Diego. This was the largest ever, with more than 900 participants, 95% of whom attended in person, up from 87% last year. I won’t attempt to cover all fourteen tracks but will just touch on some of the main themes.

 

Computational approaches

All four days of the conference featured dedicated sessions on machine learning and artificial intelligence, but since I was in other sessions I don’t know how relevant they were to FBLD. If you attended an interesting talk please let me know so I can watch it on-demand.

 

Among computational talks I did see, Antonina Nazarova (University of Southern California) provided an update on V-SYNTHES, which we first wrote about here. This synthon-based screening approach now covers 36 billion molecules and has been tested against eight different proteins, four of which yielded nanomolar hits when tested experimentally.

 

Computational methods have historically treated proteins as rigid, though many targets are anything but. Diane Joseph-McCarthy (Boston University) described an improvement to the pocket finding approach FTMap, called FTMove, to incorporate molecular dynamics by starting with an ensemble of different crystal structures. A further advance is E-FTMap, which expands the number of virtual probes from 16 to 119 to more finely assess ligandable sites.

 

Benjamin Walters (Genentech) described using protein dynamics to find cryptic pockets using ESP, or Experimental Structure Prediction. In this approach, experimental data from hydrogen-deuterium exchange (HDX) or chemical shift perturbations (CSPs) are used to constrain multiple parallel computational simulations, leading to better models than flexible docking, even for weak fragments.

 

Experimental approaches

Protein-detected NMR was the first practical fragment-finding method, and Steve Fesik (Vanderbilt) described using SAR by NMR to find fragments binding to the papain-like protease of SARS-CoV-2. These have been advanced to molecules with nanomolar affinity and activity in cell-based assays.

 

Andreas Lingel described the new fluorine-containing fragment library built at Novartis and how ¹⁹F NMR was used to generate inhibitors of IL-1β. We wrote about that success last year, noting that the initial fragment hit was “super-sized,” and Andreas confirmed that for trifluoromethyl-containing fragments the upper molecular weight limit was relaxed to 350 Da.

 

Sriram Tyagarajan (Merck) presented a crystallographic screen against the neurodegeneration target TTBK1 which yielded hits at 15 sites. Several potential allosteric sites were identified, but fragment growing and linking were not successful, leading them to a quick (3 month) no-go decision on the protein.

 

Virgil Woods (City University of New York) described using crystallographic screening to find hits against the challenging phosphatase PTP1B both under conventional cryogenic temperatures as well as at room temperature. As we noted about related work, there was a surprisingly poor overlap between the two sets of hits, and some fragments bound in a different manner at different temperatures.

 

Integrating FBDD and DNA-encoded libraries (DEL) for lead generation was the topic of Chaohong Sun’s talk. She noted that of some two dozen targets at AbbVie screened by both methods, 60% found hits from both, 10% found only fragment hits, and 5% found only DEL hits, with a quarter of the targets producing no hits. Hits from both approaches can be combined, as we noted here. Chaohong also noted that for both FBDD and DEL, high quality protein is essential for successful screens.

 

Covalent approaches

Covalent approaches to drug discovery are becoming ever more acceptable as more covalent drugs are approved. Understanding these in depth was the focus of Micah Niphakis (Lundbeck), who characterized 22 approved drugs containing 18 different warheads. The stability in buffer, liver microsomes, and hepatocytes varied dramatically, though more recently approved drugs tended to be more stable. Chemoproteomic studies revealed many off-targets in cells; for example, all the kinase inhibitors tested hit BTK to some extent even when this was not the primary target. The fact that the drugs are (mostly) safe and well-tolerated is a useful reminder that just because we can detect something doesn’t mean it is relevant.

 

Henry Blackwell described building a 12,000-member covalent fragment library at AstraZeneca. Due to the presence of a warhead, they relaxed rule of three parameters, with MW ranging from 250-400 Da and ClogP from 0-4. Henry also discussed the successful use of this library to identify covalent hits against the anticancer target BFL1 that were optimized to k_inact/K_I ~ 7000 M^-1s^-1. This accomplishment is all the more impressive given that screens using ASMS, DSF, ¹⁹F NMR, and SPR had all failed to yield validated hits.

 

We recently wrote about electrophilic MiniFrags, and György Keserű (Research Center for Natural Sciences, Hungary) described screening these against HDAC8 and the main protease from SARS-CoV-2. He also mentioned that the set is available for purchase from Enamine, so you can try it yourself against your favorite target.

 

As covalent modifiers become more common we will see new metrics for characterizing them, as illustrated by Benjamin Horning’s (Vividion) presentation, “Ligand Efficiency Metrics in Covalent Drug Discovery.” He described Ligand Reactivity Efficiency (LRE), defined as pTE₅₀(target, 1 hr) – pTE₅₀(glutathione, 1 hr), where TE is the target (or glutathione) engagement. LRE is analogous to LLE but focused on reactivity rather than lipophilicity. Despite my post last week, the metric could be useful, and I look forward to seeing what Dr. Saysno and friends will make of it.

 

Most covalent modifiers bind to a target and remain intact, but Nir London (Weizmann Institute) has developed Covalent Ligand Directed Release (CoLDR), in which a portion of the small molecule leaves; applications include release of fluorescent or chemiluminescent probes. Useable warheads include α-substituted methacrylamides and sulfamate acetamides.

 

Although more recent covalent drugs have targeted cysteine residues, there is growing interest in other amino acid side chains. Nir mentioned that thio-methacrylate esters can react with lysine residues, thought the kinetics are slow. And Carlo Ballatore (University of California San Diego) described hydroxy-naphthaldehyde fragments that bound reversibly to a lysine on the vascular target KRIT1.

 

Both plenary keynote speakers focused heavily on covalent chemistry. Dan Nomura (UC Berkeley) described using chemoproteomics approaches to find covalent molecules that could inhibit, degrade, or change the cellular localization of myriad proteins.

 

Finally, K. Barry Sharpless (Scripps), one of only five people to have been awarded the Nobel Prize twice, gave a rich description of sulfur (VI) fluoride exchange chemistry (SuFEx), which included drawing chemical structures on a flip chart. He presented the discovery of a fluorosulfate that is bactericidal against multiple resistant forms of Mycobacterium tuberculosis. Interestingly, the molecule works by modifying a catalytic serine residue which then cyclizes to form a β-lactam. His passion for chemistry is obvious, but he also has personal reasons for pursuing the second most deadly infectious disease: his brother died of tuberculosis before effective drugs were developed. And with the rise of extensively drug resistant TB, we’ll need new ones.

 

I’ll end on that note, but please leave comments. And mark your calendar for April 14-17 next year, when DDC returns to San Diego.

Personality tests for molecules

Long-time readers of Practical Fragments will be familiar with various metrics for measuring molecules, such as LE, LLE, and WTF. But these are all hard-edged, numerical constructs. Some folks argue that we should take a softer, more nuanced approach. This call has been heeded by Katharine Bigg and Isabel Myerrors in the form of a “Myerrors-Bigg” Type Indicator, or MBTI.

 

The MBTI consists of a series of questions which rank a molecule into four dimensions: Extroversion/Introversion, Sociable/Nonsociable, Flat/Three-dimensional, and Pretty/Janky. Defining molecules as extroverts may sound strange, but it really just comes down to a question of molecular recognition: we’ve noted that 4-bromopyrazole seems to bind to just about every protein and is thus an Extrovert while other compounds, being Introverts, fall into the category of “dark chemical matter” and never come up in screens.

 

As for the other dimensions, Practical Fragments has written previously about (Non)Sociable fragments as well as Flat fragments. This leads to the last dimension. Claims that beauty is in the eye of the beholder are undermined by the rigorous process of the MBTI, which places molecules such as curcumin squarely in the Janky category while approved drugs are self-evidently Pretty. Thus, toxoflavin is an ESFJ, while sotorasib is an INTP.

 

The utility of the MBTI remains to be established, but this has not stopped companies everywhere from applying it in their acquisition and evaluation processes. And other tests, such as the Decagram of Personality and the Big Six Personality Traits, are also becoming popular. Which do you prefer?

Fragments vs DHODH

Rapidly proliferating cancer cells require a steady supply of nucleic acids, and cutting that off is a potential therapy. The enzyme dihydroorotate dehydrogenase (DHODH), which is important for pyrimidine synthesis, is thus an interesting drug target. In a recent ACS Med. Chem. Lett. paper, Lindsey DeRatt, Scott Kuduk, and colleagues at Janssen describe their approach.

 

The researchers had previously used virtual screening and structure-based drug design to develop compound 1, which is potent in both biochemical and cell-based assays. However, the molecule is highly effluxed by P-glycoprotein, which can limit both oral bioavailability and brain penetration. Thus, they turned to fragments.

 

An SPR screen (about which sadly no details are provided) yielded compound 2, and crystallography revealed that the amide carbonyl makes a similar contact to tyrosine 356 (Y356) as does the carbonyl in the triazolone moiety of compound 1. Merging these led to compound 4, which was considerably more potent than compound 2 but much less so than compound 1. However, further optimization led eventually to compound 25. Although less potent in an enzymatic assay than compound 1, compound 25 is equally effective in cells. It also has excellent pharmacokinetics in mice and – importantly – a considerably lower efflux ratio.

 

Interestingly, when the researchers solved the crystal structure of a related molecule bound to DHODH, they found that the carbonyl no longer interacts with Y356 but is instead flipped 180º and interacts with a different residue. The researchers conclude by stating that they are designing new molecules to reengage Y356, which could further improve potency.

 

Several lessons emerge from this brief paper. First, the flipped urea moiety is another reminder that fragments do not always maintain their orientations, as also seen here, here, and here. Second, information from the fragment was used not to improve potency but rather to address other aspects of an existing lead series, as seen here and here. And finally, one could argue that the only critical feature of the fragment remaining in the final molecule is the NH of the urea. But the fragment did cause the researchers to examine their molecules from a different perspective, resulting in a better series. Perhaps you could call this an example of fragment-assisted drug discovery. As is so often the case, fragments can inspire new ideas that may otherwise be overlooked.