Identifying ligand-binding pockets in RNA, computationally and experimentally

Most drugs bind to proteins, but RNA provides many interesting targets. Unfortunately, finding drug-like small molecules that bind to RNA is difficult. A new paper in Proc. Nat. Acad. Sci. USA from Kevin Weeks and colleagues at University of North Carolina Chapel Hill provides tools to do so.

 

RNA presents several challenges for drug discovery. First, there are far fewer high-resolution structures than there are for proteins. This is in part due to the second challenge: RNA strands are often wriggly, able to form multiple conformations. And finally, RNA is highly charged and more polar than most proteins, so there are fewer opportunities for the hydrophobic interactions that often provide significant affinity in protein-ligand complexes.

 

These challenges have not deterred intrepid investigators: Practical Fragments first wrote about targeting RNA with fragments way back in 2009. However, examples of high-affinity ligands remain elusive, and in 2023 I wondered whether “most RNA is truly undruggable.”

 

The latest paper leaves me more optimistic. It describes a computational approach to find small-molecule binding sites in RNA. The researchers started with an open-source tool called fpocket, which was built for proteins. The fpocket program places virtual spheres all around a biomolecule, where each sphere contacts the center of four atoms. The size of each sphere depends on local curvature, and clusters of spheres define pockets.

 

To benchmark fpocket on RNA, the researchers first constructed a curated database of drug-like ligands bound to RNA. Of 538 RNA-ligand structures solved at the fairly low bar of ˂ 3.5 Å resolution, only 48 ligands were deemed drug-like by the quantitative estimate of drug-likeness (QED) score. (Although the QED score may be overly restrictive, and many approved drugs have low QED scores, setting a strict threshold means that any pockets identified are likely to be particularly attractive.)

 

Using default (protein-appropriate) parameters, fpocket identified just 63% of known ligand-binding sites in RNA, vs 83% for proteins. Worse, many predicted RNA pockets probably aren’t actually ligandable because they are too exposed to solvent. By tweaking parameters, the researchers improved performance of the program for RNA to 92%, and they also identified several attractive pockets that had previously been missed.

 

When the researchers applied the reparametrized program, redubbed fpocketR, to two bacterial ribosomes, they found several dozen pockets in each, including known antibiotic-binding sites. To assess whether the new pockets could bind fragments, they used an experimental approach called Frag-MaP, which uses fully functionalized fragment (FFF) probes containing a variable fragment, a photoreactive diazirine, and an alkyne. Treating bacterial cells with these FFF probes in the presence of UV light crosslinks them to nearby RNA. Crosslinked probes can then be isolated using click chemistry with the alkyne, and RNA sequencing reveals the sites of modification. Impressively, 89% of ligand binding sites found in the Frag-MaP experiments were predicted by fpocketR.

 

In another validation experiment, fpocketR identified pockets where 7 out of 17 antibiotics bind to bacterial ribosomes. Notably, all but one of the undetected pockets bind antibiotics such as aminoglycosides that don’t appear conventionally drug-like and indeed are not orally bioavailable.

 

Continuing to apply fpocketR to more RNAs led to the identification of dozens of new pockets. Interestingly, most of these pockets occur in complex RNA structures, such as multi-helix junctions or pseudoknots, rather than simpler structures such as bulges and consecutive loops. This could explain the paucity of fragment hits in a study we highlighted in 2023, which focused on simple loops.

 

Now that we know where to find attractive ligand-binding pockets in RNA, hopefully we will be more successful finding high-affinity ligands.

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.

Fragments finger the BPTF PHD Finger

Plant homeodomain (PHD) fingers, despite their name, are found in nearly 300 human proteins. They are small (50-80 amino acid) domains that typically recognize post-translational modifications such as trimethylated lysine residues in histones. The PHD finger in BPTF is implicated in certain types of acute myeloid leukemia. However, because of the large number of PHD fingers as well as their small binding sites, few attempts have been made to develop corresponding chemical probes. (Indeed, the only mention of them on Practical Fragments was in 2014.) In a just-published ACS Med. Chem. Lett. paper, William Pomerantz and collaborators at University of Minnesota and St. Jude Children’s Research Hospital report the first steps.

 

The researchers started by screening a library of 1056 fragments (from Life Chemicals) against the BPTF PHD finger using ligand-observed (¹H CPMG) NMR. Fragments were at 100 µM in pools of up to five. This gave a preliminary hit rate of 5.7%, but only ten compounds (<1%) reproduced when compounds were repurchased and retested individually.

 

These ten fragments were next tested by SPR (at 400 µM), which confirmed six of them. Also, all ten CPMG hits were tested in an AlphaScreen assay in which they competed with a known peptide binder. This confirmed nine, including the six that confirmed by SPR.

 

Interestingly, the most potent fragment in the AlphaScreen assay was the starting point for the KRAS inhibitor we highlighted last year. However, this fragment did not show binding to the BPTF PHD finger by SPR, and the researchers had identified the 2-aminothophene substructure as a hit against an unrelated protein. Whether this fragment is privileged or pathological may be context dependent.

 

This and the top three fragments that confirmed in all assays were used as starting points for SAR by catalog, and a handful of analogs were purchased. The researchers also resynthesized two of the compounds. Oddly, resynthesized F2 turned out to be three-fold more active in the AlphaScreen assay than the commercial material. One analog, compound F2.7, showed mid-micromolar activity.

 

 

Docking and two-dimensional protein-observed (¹H,¹⁵N HSQC) NMR experiments suggest that most of the fragments bind in the “aromatic cage” which normally recognizes methylated lysine residues, but F2 may bind in an adjacent region. Both subpockets were also identified as being ligandable using the program FTMap.

This paper is a nice example of using orthogonal methods to find and carefully validate fragments against an underexplored class of targets. The researchers conclude by stating that “these hits are suitable for further SAR optimization and development into future methyl lysine reader chemical probes.” I look forward to seeing more publications.

RSC Medicinal Chemistry special FBDD issue

The Royal Society of Chemistry puts out RSC Med. Chem., and last year they asked David Rees (Astex), Anna Hirsch (Helmholtz Institute for Pharmaceutical Research Saarland), and me whether a special themed issue on FBDD would be useful for the community. Naturally we said yes, and the results have now been published. You can read our introduction here.

 

Unlike olden days, when special issues were bound between covers, this is a virtual special issue, with papers published over a period of several months. Indeed, we already wrote about two of them last year: one on combining DNA-encoded libraries (DEL) with FBLD and one on inhibitors of PRMT5/MTA. (Both of these were also topics at the CHI FBDD meeting earlier this month.) In the next few paragraphs we highlight the rest.

 

AstraZeneca has been doing FBDD since 2002, and has gained hard-won wisdom, some of which was shared in a 2016 review we wrote about here. After years of screening, their fragment library had started to deteriorate, so they rebuilt it entirely, as described by Simon Lucas and colleagues. Some of the starting fragments came from their previous library, but they also considered molecules from their larger collection. Rather than focusing on the rule of three, they developed their own multiparameter optimization function, “FragScore,” which incorporates logD_7.4, heavy atom count, number of rotatable bonds, and number of hydrogen bond donors. All compounds were inspected to make sure they would be synthetically tractable, and quality was assessed by SPR, NMR, redox activity, and solubility. The final set consists of 2741 fragments, with a subset of 1152 maximally diverse and attractive fragments for ligandability assessments or screening hard-to-make proteins. They also gathered 16,806 near neighbors for hit follow-up. So far the effort has paid off, with all four of the targets screened thus far yielding progressible hits. If you’re building or renovating a fragment library, you should read this paper.

 

Continuing on the theme of libraries, Bradley Doak, Martin Scanlon, and colleagues at Monash University describe their “MicroFrag” library, a set of 91 tiny (5-8 non-hydrogen atom) compounds similar to MiniFrags and FragLites. A crystallographic screen (at 1 M concentration!) of the MicroFrag library against the difficult E. coli target DsbA yielded a 52% hit rate, compared with a 2% hit rate with a conventional fragment library. Importantly, the MicroFrag screen identified the two main hot spots previously discovered from the conventional fragment library, along with ten others that may be less actionable. Interestingly, a crystallographic screen of 15 organic solvents at even higher concentrations (50-80%) was less informative: the primary hot spot did not distinguish itself from others. In the case of MicroFrags, not only did this hotspot bind the largest number of fragments, but all the molecular interactions seen for larger fragments were observed.

 

Fluorine NMR takes advantage of its own specialized library, the subject of a paper by Chojiro Kojima (Osaka University), Midori Takimoto-Kamimura (CBI Research Institute) and collaborators from several institutions. The researchers describe the construction of a 220-member library divided into pools of 10-21 compounds. This library was screened against four diverse proteins, yielding between 3 and 16 hits. The three hits against FKBP were characterized in more detail, including two-dimensional NMR and isothermal titration calorimetry. The researchers also discuss using ¹⁹F STD experiments to determine the binding mode of bound fragments.

 

Fluorine is not the only halogen of interest for library design. We’ve previously described the halogen-enriched fragment library (HEFLib, here and here), which consists of chlorine, bromine, and iodine-containing molecules. Frank Boeckler and collaborators at Eberhard Karls Universität Tübingen and the Max Planck Institute describe screening this library against the Y220C mutant of p53 in an expansion of work they first described back in 2012. Of 14 hits identified by thermal shift or STD NMR, ten confirmed by two-dimensional ¹H-¹⁵N-HSQC NMR. Four of these bound in the cleft created by the Y220C oncogenic mutation. Two other fragments turned out to be covalent binders, though they reacted with more than one cysteine residue. Although all the fragments have low affinities, they could potentially serve as starting points for optimization.

 

An ongoing debate is whether there is an advantage to screening more “three dimensional” fragments as opposed to planar aromatic fragments. If your taste tends towards the former, the synthetic chemistry can get tricky. According to an analysis we highlighted last year, the piperidine ring is the third most common scaffold found in drugs. Now, Peter O’Brien (University of York) and an international group of collaborators report efficient synthetic routes to all 20 cis- and trans-piperidines substituted with a methyl group and a methyl ester. A virtual library of 80 compounds in which the secondary amine is capped with simple substituents such as methyl or acetyl groups was found to be quite shapely, particularly compared with the disubstituted pyridyl starting materials. Moreover, the fragments are still reasonably sized, with no more than 15 non-hydrogen atoms and ClogP values < 2.

 

Machine learning is gaining prominence everywhere, not least in drug discovery. In 2021 we highlighted an “autoencoder” designed for constructing fragment libraries biased towards “privileged” fragments more likely to generate hits. However, the method required considerable programming savvy. Now Angelo Pugliese (BioAscent) and collaborators at the Beatson Institute have implemented their model in the open-source KNIME platform, making it accessible to a wider range of researchers. As an example they use the method to construct a GPCR-focused fragment library, with the structures of all the members provided in the supporting information.

 

On the subject of fragment libraries, please make sure to vote in our 6-question poll on library design (right side of page; you may need to scroll up).

 

Not all the papers in this special issue involve library design. Marko Hyvönen, David Spring, and collaborators at University of Cambridge and National University of Singapore describe allosteric inhibitors of the kinase CK2α, which has been implicated in cancer cell survival. We highlighted some of their work against this target in 2017, in which they used fragment linking to find high nanomolar inhibitors of the enzyme. In the new paper, the researchers describe additional fragment binders at the so-called αD pocket, distant from the ATP-binding site. Virtual screening for analogs led to a fragment with mid-micromolar activity in biochemical and cell assays, and fragment merging led to low micromolar inhibitors.

 

This is a nice collection of papers, and for those of you without easy literature access make sure to check them out soon: for the next six months all of them are free to read after free RSC registration. Enjoy!

FragLites and PepLites meet bromodomains

The last two Practical Fragments posts focused on bromodomains, epigenetic readers that recognize acetylated lysine residues. Today’s post could thus be considered part of a trilogy, though the focus is less on bromodomains themselves than a specific type of fragment library.

 

In 2019 we highlighted FragLites, small fragments containing pairs of hydrogen bond acceptors and/or donors along with a bromine or iodine atom. FragLites were designed to assess ligandability as well as identify what types of interactions would be favorable at various sites. The original test protein was the kinase CDK2. In an open-access paper published late last year in J. Med. Chem. by Martin Noble, Michael Waring, and colleagues at Newcastle University, FragLites are screened against two members of the bromodomain family.

 

The first bromodomain (BD1) of BRD4 is considered highly ligandable, with multiple inhibitors disclosed (see for example here). In contrast, ATAD2, a bromodomain in another subfamily, is more challenging, in part because it lacks a hydrophobic region useful for increasing affinity for small molecules. Thirty-three FragLites were individually soaked at 50 mM into crystals of either bromodomain. The halogen atom on each FragLite facilitates analysis by anomalous dispersion, allowing more sensitive detection of low-occupancy binders. This, along with Pan-Dataset Density Analysis (PanDDA), was used to identify specific protein-ligand “binding events.”

 

In total, 26 binding events at five sites were identified for BRD4; four ligands bound at more than one site. Of these, 17 FragLites bound at the orthosteric site of BRD4 (which recognizes N-acetyl lysine). In contrast, ATAD2 displayed 16 binding events total over seven sites; only three bound at the orthosteric site, consistent with its lower ligandability. ATAD2 had previously been screened crystallographically against the 776-membered DSI-poised fragment library, and this effort also identified seven ligand-binding sites, six of which were common to those discovered here, suggesting that the small FragLite set is able to identify most pockets.

 

As far as specific types of interactions, the average FragLite made 1.1 hydrogen bond, suggesting that the second donor or acceptor is often not engaged. In contrast, the bromine or iodine atom makes protein contacts in 33 of 42 binding events. In half a dozen cases no hydrogen bond to the protein was observed, with the primary interaction being a halogen bond.

 

The FragLites are small, relatively “flat” aromatic molecules, but of course most proteins interact with other proteins. To try to explore such interactions, the researchers developed a library of “PepLites:” N-terminally acetylated amino acid residues with a C-terminal bromopropargyl group. These were also screened crystallographically against the two bromodomains and produced considerably lower hit rates, with six bound to BRD4 (all at the orthosteric site) and nine bound to ATAD2 (of which five bound to the orthosteric site). Reassuringly, the N-acetylated lysine PepLite bound to both proteins in a similar manner as seen in larger peptides.

 

The researchers conclude that FragLites and PepLites “represent highly valuable components of a larger crystallographic screen, and we anticipate that this is where they will fit into most drug discovery programs.” Indeed, this is already happening; last year we wrote about how FragLites were screened against the bromodomain PHIP2 as part of a larger screen, and I was surprised this paper was not mentioned here. Laudably, all the atomic coordinates have been deposited in the Protein Data Bank, so folks are able to do their own analyses.

 

As FragLites and PepLites are screened against ever more targets, it will be fun to see what they can teach us about intermolecular interactions and starting points for new leads.

Fifteenth Annual Fragment-Based Drug Discovery Meeting

What a strange year for conferences! Everything fragment-related in the first half of the year was canceled or rescheduled. And when it turned out that postponing Drug Discovery Chemistry (DDC) from April to August was overly optimistic, Cambridge Healthtech Institute went virtual for the first time, with the meeting running last week from August 25-28. Although I was skeptical, in the end the meeting was quite successful, with more than 600 attendees – roughly three-quarters last year’s in-person attendance. 

 

To accommodate multiple time zones each of the parallel tracks was shorter. Days typically began around 10:00 AM EDT, which was 7:00 for me in San Francisco but midnight for folks in Melbourne. Despite this inconvenience, 40% of participants this year came from outside the US, up from 30% last year.

Most of the talks were pre-recorded, which meant that speakers could answer questions in real-time in a chat box. More importantly, the talks are available to attendees for a year, which means you can watch relevant talks from other tracks, thus relieving the FOMO inevitably experienced in meetings of this sort. It also means you can replay particularly relevant talks to your colleagues, though I wonder if this also makes speakers wary about disclosing the freshest information. The on-demand access to posters is especially useful, as it is too easy to overlook these in the usual melee. The organizers also did a nice job of trying to foster the feel of an in-person meeting, with multiple live Q&A panels, breakout sessions, and other interactive events.

There are drawbacks, chief among them the lack of spontaneous and serendipitous meetings that are one of the main benefits of in-person conferences. And of course there were technical snafus. Most talk slots were only 20 minutes, but some of the pre-recorded talks went as long as 28 minutes, forcing attendees to choose between missing part of one talk or another (or a live Q&A). Although you can fast-forward, it would be nice if you could also speed up playback speeds. And on at least one occasion the wrong talk was played, though I was able to go back and watch the correct one later.

But enough about process, what about the event itself? With dozens of talks over four days I can’t be exhaustive, so please add your thoughts to the comments.

Faster, cheaper, better.

This describes one consistent theme. Frank von Delft (Diamond Light Source) gave an update on their high-throughput crystallographic screening, which has led to > 3000 fragment hits on > 150 targets since 2016. We’ve written previously about their efforts against COVID-19, which have led to three separate sub-micromolar lead series against the viral protease M^Pro. This rapid progress has been enabled by crowdsourcing across more than 30 separate groups, but Frank is also moving toward crystallographic screening of crude reaction mixtures, similar to the REFiL approach described by Beatrice Chiew (Monash University, see here for a longer description). Frank hopes to make good compounds a commodity, with a 5-year vision of achieving “biologically relevant potency routinely, cheaply (£10k) and quickly (weeks).” It’s an audacious goal, and we’ll check back in 2025 to see how far the field has come.

Key to the success of these types of approaches is what plenary speaker Phil Baran (Scripps) calls “boring chemistry” that consistently works in multiple contexts. To Phil, inventing chemistry that becomes boring is a great compliment, and he showed examples of running interesting transformations in tea, beer, and wine. As the name of this blog suggests, I have a soft spot for this sort of thing, and wholeheartedly agree with his statement that “you can’t give a Nature paper to a cancer patient.”

Covalent fragments (or not)

Covalent drug discovery was also a major theme, with John McCarter (Amgen) and Matt Marx (Mirati) describing discovery of two covalent clinical compounds against an oncogenic mutant form of KRAS (see here). Dom Esposito (Frederick National Laboratory) is also pursuing KRAS using covalent Tethering. They have prepared 91 cysteine mutants and screened 18 of them against nearly 1200 disulfide-containing fragments, yielding a plethora of hits.

Alexander Statsyuk (University of Houston), Maurizio Pellecchia (UC Riverside), and Nir London (Weizmann Institute) all also discussed covalent lead discovery. Maurizio has been targeting lysine residues using sulfonyl fluorides and fluorosulfates; for the latter warhead he has been able to show reasonable pharmacokinetics in rodents. We’ve previously discussed some of Nir’s work, but here he described a nice case study against the challenging anti-cancer target Pin1 which led to potent and surprisingly selective chloroacetamides with activity in mice.

Interestingly, while chloroacetamides were the main class of M^Pro fragment hits, the three most advanced lead series Frank mentioned are all non-covalent. There were plenty of other nice non-covalent fragment-based success stories too, including potent selective inhibitors of the lipid kinase Vps34 (Jenny Viklund, Sprint Biosciences) and selective inhibitors of one kringle domain of apolipoprotein(a) (Jenny Sandmark, AstraZeneca).

Methods

As always, there were many talks on methods, especially during the Fifth Annual Biophysics Friday track. John Quinn (Genentech) provided a state-of-the-art update on SPR, and mentioned that they are able to screen 3000 fragments in a day or two using the Biacore 8K. This allows them to assess ligandability for new targets, and the ligandability score is predictive for how well the target performs in HTS or DEL screening.

Amit Gupta described NanoTemper’s new Dianthus instrument, which relies on the temperature-related intensity change (TRIC) of a fluorophore bound to a protein. This is similar to their MST approach though it appears to be higher-throughput, and a paper benchmarking these techniques against DSF and SPR should be coming out later this year.

Also on the subject of thermal shifts, Justin Hall (Pfizer) gave a provocative presentation on using these to determine ligand affinities. He noticed a correlation in his own research, but the prevailing wisdom held that irreversible thermal denaturation (as seen for most proteins) would not provide thermodynamic parameters. Nonetheless, perhaps because proteins are fundamentally similar (consisting as they do of chains of 20-odd amino acid residues), the temperature-dependent Arrhenius functions and activation energies of unfolding are also similar, and thus for heating rates of 4°C/minute and 100 µM ligand one can extract dissociation constants. However, he did mention that this approach is restricted to reasonably tight ligands (K_D < 20 µM). Also, if a ligand binds to the unfolded state of the protein, or to multiple sites, all bets are off.

In the interest of time I’ll stop here, but if you’d like to experience a virtual conference yourself, there will be a number of good FBLD talks at Discovery on Target next month, and I hope to “see” you there. But I especially hope that in-person conferences will resume next year once our industry – and competent governments – get COVID-19 under control.

PhABits: photoaffinity-based fragment screening

Three years ago we highlighted work out of Ben Cravatt’s lab describing “fully-functionalized fragments” that – in addition to a variable portion – contain a photoreactive diazirine moiety and an alkyne moiety. These were incubated with cells and irradiated with UV light to crosslink the fragments to bound proteins. The alkyne was then used in click chemistry to isolate and identify the bound proteins. Cell-based screening is not for the faint of heart, but as demonstrated in a paper recently posted on ChemRxiv by Jacob Bush and collaborators at GlaxoSmithKline and University of Strathclyde, the functionalized fragments can also be used in biophysical screening. (Emma Grant presented a nice poster on some of this work at FBLD 2018.)

A small library of 556 fragments, rebranded as PhotoAffinity Bits (or PhABits), was synthesized by coupling the alkyne- and diazirine-containing carboxylic acid with a diverse set of amines (each with < 16 heavy atoms). These were then screened at 200 µM against six pure recombinant proteins, irradiated with UV light, and analyzed using intact protein mass spectrometry as in Tethering and other forms of covalent FBLD. Hit rates varied tremendously, from less than 3% for myoglobin to 47% for lysozyme. It would be interesting to see whether this approach, like other fragment finding methods, is able to assess protein ligandability.

Most of the PhABits did not react with the proteins tested, though 58 crosslinked to at least four, and 10 crosslinked to all six. For one of the proteins screened, the bromodomain BRD4-BD1, a known high-affinity ligand could compete 68 of the 89 fragment hits, suggesting a specific interaction at the acetyl lysine pocket. Of the 21 fragments that were not competed, 19 bound to at least three other proteins. Interestingly, the physicochemical properties and solubilities of these fragments were not notably different from the rest, and the researchers speculate that their non-specificity may be due to a longer-lived reactive intermediate generated after UV irradiation.

Several of the BRD4-BD1 fragments were confirmed as binders using a TR-FRET assay, some with low micromolar affinities, though the tighter ones tended to contain known bromodomain binding motifs such as isoxazoles. A couple of these were successfully used to generate PROTACs, as suggested here. Protein digestion and LC-MS/MS sequencing revealed that the fragments crosslinked residues near the acetyl lysine binding site, and this binding mode was confirmed using X-ray crystallography for one of the fragments.

In addition to BRD4-BD1, another target the researchers highlight is KRAS4B^G12D. Of the 11 unique hits, some resembled previously reported molecules, and LC-MS/MS studies suggested that they do in fact bind in the same pocket. Competition studies confirmed this, and the resulting IC₅₀ values were similar to those previously determined using HSQC NMR.

As the researchers point out, this photoaffinity-based screening approach is limited to homogenous proteins that are suitable for mass spectrometry. Also, the crosslinking efficiency is not necessarily related to the affinity of the fragment. Still, this is an interesting approach to both find fragments and identify their binding sites. It will be fun to see how it develops.

Fragments find flexibility in fascin 1

Protein flexibility can be both an opportunity and a barrier – quite literally, when a solid wall of protein seems to block opportunities for fragment growing. But like secret doorways, protein domains can yawn open to expose tunnels and cavities. An example of this was published earlier this year in Bioorg. Med. Chem. Lett. by Stuart Francis and collaborators at the CRUK Beatson Institute.

The researchers were interested in fascin 1, which increases the invasiveness of multiple cancers by helping pack filamentous actin into bundles important for cell migration. The team began their search for an inhibitor by performing a surface plasmon resonance (SPR) screen of 1050 fragments, generating an impressive 53 hits. Although a number of these were reported to bind to multiple sites on the protein, only one is discussed.

Compound 1 binds between two domains of the protein in a pocket that does not exist in unbound fascin. However, the fact that the pocket completely envelopes the fragment “hampered attempts to develop the series.” Fortunately, the researchers were following the patent literature, and when they characterized compound 2 (not a fragment, and reported by a different group), they discovered that while it binds in the same pocket as compound 1, additional conformational changes occur to accommodate the larger molecule.

Next, the researchers looked for analogs of compound 2 and also performed a virtual screen against the enlarged pocket. Of 110 commercial compounds tested, three gave dissociation constants better than 100 µM, including compound 3. The researchers recognized that compound 3 lacks the halogens found on both the original fragment and compound 2, and by adding these they were able to improve the affinity more than ten-fold. Further optimization ultimately led to BDP-13176, with mid-nanomolar affinity by SPR and ITC as well as activity in a functional assay. Although the molecule has reasonable solubility and stability against liver microsomes, it has low permeability and high efflux.

This is a nice structure-based design story, and while the fragment did provide some information about the binding site, one could argue that the real breakthrough came with determining the binding mode of compound 2. Indeed, without this information, it would have been all too easy to assume that the pocket was not ligandable. This is an important reminder that crystal structures usually only reveal one form of a protein. The system is also a good test case for modelers who want to see how their algorithms perform against a dynamic protein. Breakthroughs are often unexpected, and it is always worth making a few compounds that don’t look like they’ll fit.

Review of 2018 reviews

As 2018 recedes into history, we are using this last post of the year to do what we have done since 2012 – review notable events along with reviews we didn’t previously cover.

This was a busy year for meetings, starting in January with a FragNet event in Barcelona, then moving to San Diego in April for the annual CHI FBDD meeting. Boston saw an embarrassment of riches, from the first US-based NovAliX meeting, to a symposium on FBDD at the Fall ACS meeting, followed closely by a number of relevant talks at CHI’s Discovery on Target. Finally, the tenth anniversary of the renowned FBLD meeting returned to San Diego. Look for a schedule of 2019 events later this month.

If meetings were abundant, the same can be said for reviews.

Lead optimization

Writing in J. Med. Chem., Dean Brown and Jonas Boström (AstraZeneca) asked “where do recent small molecule clinical development candidates come from?” For three quarters of the 66 molecules published in J. Med. Chem. in 2016 and 2017 the answer is from known compounds or HTS, though fragments accounted for four examples. Although average molecular weight increased during lead optimization, lipophilicity did not, suggesting the importance of this parameter.

The importance of keeping lipophilicity in check is also emphasized by Robert Young (GlaxoSmithKline) and Paul Leeson (Paul Leeson Consulting) in a massive J. Med. Chem. treatise on lead optimization. Buttressed with dozens of examples, including several from FBLD, they show that the final molecule is usually among the most efficient (in terms of LE and LLE) in a given series, even when metrics were not explicitly used by the project team. Perhaps with pedants like Dr. Saysno in mind, they also emphasize the complexity of drug discovery, and note that “seeking optimum efficiencies and physicochemical properties are guiding principles and not rules.”

Lipophilic ligand efficiency (LLE) is also the focus of a paper in Bioorg. Med. Chem. by James Scott (AstraZeneca) and Michael Waring (Newcastle University). This is based largely on personal experiences and provides lots of helpful tips. Importantly, the researchers note that calculated lipophilicity values can differ dramatically from measured values, and go so far as to say that “this variation is sufficient to render LLEs derived from calculated values meaningless.”

Turning wholly to fragments, Chris Johnson and collaborators (including yours truly) from Astex, Carmot, Vrije Universiteit Amsterdam, and Novartis have published an analysis in J. Med. Chem. of fragment-to-lead success stories from last year. This review, the third in a series, also summarizes all 85 examples published between 2015 and 2017, confirming and expanding some of the trends we mentioned last year.

Targets

Two reviews focus on specific target classes. Bas Lamoree and Rod Hubbard (University of York) cover antibiotics in SLAS Discovery. After a nice, concise review of fragment-finding methods, the researchers discuss a number of case studies, many of which will be familiar to regular readers of this blog, including an early example of whole-cell screening.

David Bailey and collaborators from IOTA and University of Cambridge discuss cyclic nucleotide phosphodiesterases (PDEs) in J. Med. Chem. The researchers provide a good overview of the field, including mining the open database ChEMBL for fragment-sized inhibitors. As they point out, the first inhibitors discovered for these cell-signaling enzymes were fragment-sized, so it is no surprise that FBLD has been fruitful – see here for an example from earlier this year. Interestingly though, although at least six fragment-sized PDE inhibitor drugs have been approved, none of these were actually discovered using FBLD.

PDEs are an example of “ligandable” targets, for which small molecule modulators are readily discovered. In Drug Discovery Today, Sinisa Vukovic and David Huggins (University of Cambridge) discuss ligandability “in terms of the balance between effort and reward.” They use a published database of protein-ligand affinities to develop a metric, LIG_exp, for experimental ligandability, and also describe their computational metric, Solvaware, which is based on identifying clusters of water molecules binding weakly to a protein. Comparisons with experimental data and with other predictive metrics, such as FTMap, reveal that while the computational methods are useful, there is still room for improvement.

We have previously written about how target-guided synthesis methods such as dynamic combinatorial chemistry have – despite decades of research – yielded few truly novel, drug-like ligands. Is this because the targets chosen were simply not ligandable? In J. Med. Chem., Anna Hirsch and collaborators at the University of Groningen, the Helmholtz Institute for Pharmaceutical Research, and Saarland University review some (though by no means all) published examples and examine their computationally determined ligandability scores. There seems to be no difference between these targets and a set of traditional drug targets.

Finding fragments

Crystallography continues to be a key tool for FBLD: as we noted in the review of the 2017 literature, 21 of the 30 examples made use of a crystal structure of either the starting fragment or an analog, and only 3 projects didn’t use crystallography at all. That said, FBLD is possible without crystallography, as illustrated through multiple examples in a Cell Chem. Biol. review by Wolfgang Jahnke (Novartis), Ben Davis (Vernalis), and me (Carmot).

In the absence of a crystal structure, NMR is best suited for providing structural information, and this is the subject of a review in Molecules by Barak Akabayov and colleagues at Ben-Gurion University of the Negev. The researchers provide a nice summary of NMR screening methods and success stories within a broader history of FBLD. They also include an extensive list of fragment library providers as well as a discussion of virtual screening.

Speaking of virtual screening, three reviews cover this topic. In Methods Mol. Biol., Durai Sundar and colleagues at Indian Institute of Technology Delhi touch on a number of computational approaches for de novo ligand design, though the lack of structures sometimes makes it challenging to read. A broader, more visually appealing review is published in AAPS Journal by Yuemin Bian and Xiang-Qun Xie at University of Pittsburgh. In addition to an overview and case studies, the researchers also provide a nice table summarizing 15 different computational programs. One of these, SEED, is a main focus of a review in Eur. J. Med. Chem. by Jean-Rémy Marchand and Amedeo Caflisch (University of Zürich). The researchers describe how this docking program can be combined with X-ray crystallography (SEED2XR) to rapidly identify fragments; we highlighted an example with a bromodomain. Their ALTA protocol uses SEED to generate larger, more potent molecules, as we described for the kinase EphB4. The researchers note that together these protocols have led to about 200 protein-ligand crystal structures deposited in the PDB over the past five years.

Rounding out methods, Sten Ohlson and Minh-Dao Duong-Thi (Nanyang Technological University) provide a detailed how-to guide in Methods for performing weak affinity chromatography, and how this can be combined with mass spectrometry (WAC-MS), as we noted last year.

Chemistry

One drawback of some computational approaches for fragment optimization is that they do not consider synthetic accessibility. In Mol. Inform., Philippe Roche, Xavier Morelli, and collaborators at Aix-Marseille University and Institut Paoli-Calmettes focus on hit to lead approaches that do, and provide a handy table summarizing nearly a dozen computational methods. We highlighted one from the authors, DOTS, earlier this year.

DOTS is an example of using DOS, or diversity-oriented synthesis. In Front. Chem., David Spring and colleagues at University of Cambridge review recent applications of DOS for generating new fragments, some of which we recently highlighted. Only a couple examples of successfully screening these new fragments are described, but the authors note that this is likely to increase as virtual library screening continues to advance.

Perhaps the most productive fragment of all time is 7-azaindole, the origin of three fragment-derived clinical compounds. (The moiety appears in both approved FBLD-derived drugs, vemurafenib and venetoclax.) Takayuki Irie and Masaaki Sawa of Carna Biosciences devote their attention to this little bicycle in Chem. Pharm. Bull. The researchers count six clinical kinase inhibitors that contain 7-azaindole (not all from FBLD) as well as more than 100,000 disclosed compounds containing the fragment. More than 90 kinases have been targeted by molecules containing 7-azaindole, and the paper provides a list of 70 PDB structures of 37 different kinases bound to molecules containing the moiety.

Finally, in J. Med. Chem., Brian Raymer and Samit Bhattacharya (Pfizer) survey the universe of “lead-like” drugs. Among the most highly prescribed small molecule drugs, 36% have molecular weights below 300 Da. Only 28 of 174 drugs approved between 2011 and 2017 fall into this category, consistent with the increasing size of newer drugs. The researchers discuss 16 recently approved drugs, and find that 13 have very high ligand efficiencies (at least 0.4 kcal mol^-1 per heavy atom). As noted above, optimization often entails adding molecular weight by growing or linking, and the researchers suggest that alternative strategies such as conformational restriction and truncation also be investigated.

And with that, Practical Fragments wishes you a happy new year. Thanks for reading some of our 686 posts over the past decade plus, and please keep the comments coming!

Sixteenth Annual Discovery on Target

CHI’s Discovery on Target took place in Boston last week. With >1300 attendees from over two dozen countries, this is the older, larger cousin of the San Diego DDC meeting; at some points ten tracks were running simultaneously. Although more heavily focused on biology, there were still plenty of talks of interest to fragment folks.

Michael Shultz (Novartis) provocatively asked “do we need to change the definition of drug-like properties?” Long-time readers will recall that his earlier papers on ligand efficiency led to considerable debate, which seems to have been settled to everyone’s satisfaction with the exception of Dr. Saysno.

His new study, which has just published in J. Med. Chem., analyzes the molecular properties of all 750 oral drugs approved in the US between 1900 and 2017. Contrary to what strict rule of five advocates might expect, the molecular weight has increased over the past couple decades, as has the number of hydrogen bond acceptors. In contrast, the number of hydrogen bond donors (#HBD) has remained constant, suggesting that this may be more important for oral bioavailability. (Indeed, #HBD is the only Lipinski rule not broken by venetoclax.) Although Shultz did not examine “three dimensionality,” he laudably includes all the raw data – including SMILES – in the supporting information. This will be a useful resource for data-driven debates.

Molecular properties are carefully considered by Ashley Adams, who discussed the four fragment libraries used at AbbVie. The first is a 4000-member “rule of three” compliant library. For tougher targets, a 9000-member Ro3.5 library is available, as is a specialized fluorine library for ¹⁹F NMR (2000 members) and a 1000-member “biophysics” library, in which all compounds are less than 200 Da. Fragment optimization is often challenging, and since the C-H bond is most common but perhaps least explored, the AbbVie database is annotated with references on C-H bond activation relevant to each fragment.

Anil Padyana spoke about the metabolic enzymes being targeted at Agios. As we mentioned recently, these are very difficult targets, so the researchers often use parallel (as opposed to nested) screening using different techniques to minimize false negatives. Anil also described an interesting SPR assay in which fragments were introduced to the protein after the addition of an activating substrate.

High-quality protein constructs are essential for any fragment screen, and Jan Schultz described ZoBio’s technology for generating these. The company’s “protein domain trapping” approach entails high-throughput generation and screening of tens or hundreds of thousands of truncations of a given protein and rapidly selecting stable, high-expressing, and active variants.

Trevor Perrior mentioned that Domainex has a similar technology, which has been able to produce soluble protein domains in 90% of its attempts. Trevor also described a separate project in which a 656-fragment compound library was screened using SPR against the enzyme RAS. They found fragments that bind in a previously discovered site but, unlike the earlier work, the Domainex researchers were able to optimize these to nanomolar inhibitors.

Another success story was presented by Dean Brown (AstraZeneca), who described a collaboration with Heptares to discover inhibitors of protease-activated receptor 2 (PAR2). As the name suggests, this GPCR is activated when a protease cleaves the N-terminus, allowing the remaining N-terminal residues to fold back and activate the GPCR. The researchers used a stabilized form of PAR2 in an SPR screen of 4000 fragments and obtained >100 binders in multiple series. This led to AZ8838, which blocks signaling by binding in an allosteric pocket. It also has a slow off-rate, which is often an attractive feature – particularly in the context of intramolecular activation.

A number of talks were focused on protein degraders such as PROTACs (PROteolysis-TArgeting Chimeras). These are generally two-part molecules connected by a linker: one part binds to a target of interest, while the other engages the cellular degradation machinery to destroy the target. As Shanique Alabi, a graduate student in Craig Crews's lab at Yale demonstrated, the molecules are catalytic – a single PROTAC molecule can cause the destruction of multiple copies of a target protein. This “event-driven” pharmacology is thus different from most historical drugs, which are “occupancy-driven.” Is there a role for fragments?

One of the strengths of FBLD is that if a ligandable site exists, it can be found. As Astex demonstrated, the majority of proteins seem to have secondary sites, away from the active site. Although some of these may be allosteric, others probably have no functional activity, particularly in the case of protein-protein interactions where secondary sites may be located some distance from the interface. The power of degraders is that non-functional sites can be made functional. The power of FBLD is that it can find small-molecule binding sites, which could then be used as anchoring sites for one side of a degrader. Watch this space!

Industrializing native MS: hundreds of fragments against dozens of targets

Native mass spectrometry (MS) is a direct binding assay in which fragment binding to a target is detected when the complex is ionized and “weighed” in high vacuum. The technique is less commonly used than others, and there is some debate as to how well it works. A paper just published in ACS Infect. Dis. by Ronald Quinn and collaborators at Griffith University, the University of Washington, and the University of Toronto provides some encouraging data.

To demonstrate just how high-throughput native MS could be, the researchers started with 79 different proteins. These were all from Plasmodium falciparum, one of the main organisms that causes malaria. The proteins were chosen based on their size (< 50 kDa, for easier MS analysis) and likely importance for the parasite. Of these, 62 gave a good signal-to-noise ratio by native MS and were screened.

The researchers used an existing fragment library of 643 natural products; we highlighted an earlier version of this library in 2013. Of these, 602 molecules met the strict criteria defined in that design, with MW < 250 Da but with other properties more relaxed than rule of three guidelines. The library also contained significantly fewer aromatic rings than conventional fragment libraries and was more “three dimensional,” as assessed both by PMI and Fsp³.

Fragments were screened in pools of 8 at 5-400 µM each, with protein present at 1-20 µM; final ratios were 5:1 to 20:1. Hits were judged qualitatively as strong, medium, or weak, and the researchers estimate that strong and medium binders have dissociation constants < 100 µM.

Just over half of the proteins (32) had at least one hit, and a total of 96 fragments came up as hits. Importantly, many of these were selective: 48 fragments bound just one target, while another 18 bound just two (fragments that hit more than 6 proteins were considered promiscuous and excluded from further analysis).

Similarly to what has been done with NMR and thermal-shift assays, the researchers suggest that native MS can be used to assess ligandability. This is an appealing suggestion, though the researchers do not correlate MS-assessed ligandability with other methods such as SPR or high-throughput screens.

Conventionally, the next step would be to confirm binding with orthogonal techniques. Instead, the researchers took the rather bold move of testing fragment hits against the parasite directly. Remarkably, 79 of the fragments were active at 100 µM, with 13 having IC₅₀ values < 45 µM.

A major strength of this paper is the disclosure of all the hits against all the targets. Not only does this allow others to confirm the results, it also provides starting points for further studies. So what do the fragments look like? Many of them are somewhat PAINful – we previously mentioned the promiscuity of one of their compounds, securinine. Although this molecule only hits two proteins in their panel, previous research has found that native MS can give high false-negative rates. Moreover, even if a molecule is truly inactive against a few dozen proteins, that doesn't mean it won’t hit many of the thousands of other proteins in a live protozoan.

Ultimately I would take any of these molecules with a huge dose of caution. That said, there are lots of interesting molecular structures in here, so if you’re looking to jump-start a program against malaria while exploring new chemistry, it may be worth digging into the data.

Assessing ligandability by thermal scanning

Ligandability refers to the ability to find small-molecule leads against a target. A protein might be ligandable but not druggable if, for example, potent inhibitors of the target do not affect a disease state. But knowing in advance whether a target is ligandable can be useful, both to decide whether to embark on a campaign and to plan the resources it will likely require. Fragment screens by NMR have been shown to be good predictors of ligandability, but not everyone has access to this technology. Computational methods (such as FTMap) are also useful, but require a structure of the target. In a recent paper in J. Med. Chem., Stefan Geschwindner and colleagues at AstraZeneca describe high-throughput thermal scanning (HTTS) for assessing ligandability.

Thermal scanning (alternately called, as the researchers note, thermal shift, differential scanning fluorimetry (DSF), or thermofluor) relies on the preferential binding of a fluorescent dye to protein that is heat-denatured. Since ligands generally stabilize a protein against denaturation, an increase in melting temperature (Tm) is taken as an indication of binding. The assays can be plate-based and thus very fast.

The researchers chose 16 diverse targets (mostly enzymes) and screened their 763-ligandability fragment set (described here) at 1 mM by HTTS. Hits were defined as compounds that increased  thermal stability at least 3-fold above the standard deviation of controls. Targets were then categorized as follows:

Low ligandability: hit rate < 1.5%

Medium ligandability: hit rate between 1.5 and 4.5%

High ligandability: hit rate > 4.5%

Nine targets ranked low, and all of these failed high throughput screening (HTS), while 5 out of the 7 targets ranked medium or high by HTTS yielded useful HTS hits. Of course, failure in an HTS does not preclude target advancement by other means – including FBLD. Ultimately all but three targets (including all of those ranked medium or high and 6 of 9 ranked low) went on to enter hit-to-lead optimization programs.

Encouragingly, HTTS and NMR agreed perfectly for low and high ligandability targets, but NMR assigned three targets as medium where HTTS assigned them as low. The researchers thus set out to increase the sensitivity of HTTS.

It turns out that entropically-driven binders tend to cause greater thermal shifts than enthalpically driven binders. The observation that most fragments bind largely enthalpically, and with low affinity too, makes them particularly challenging to detect. To try to shift the balance, the researchers repeated the HTTS assay for three of the low-scoring targets in D₂O instead of H₂O, which enhances entropic interactions at the expense of enthalpic interactions. Indeed, all three targets showed enhanced hit rates, and two moved from low to medium ligandability.

Another way to improve sensitivity of a thermal shift assay is to add urea, which destabilizes proteins by lowering the unfolding enthalpy. Adding non-denaturing amounts of urea (0.8 to 2.4 M concentration) to the three low-scoring targets above did indeed increase the hit rate for two of them.

One interesting tidbit is the observation that particularly stable targets, with unfolding temperatures >70 °C, tend to produce lower hit rates in HTTS than less stable targets. This could account for the very different experiences people have had with the technique.

This is a nice paper, and the approach may be worth implementing, as the researchers note has already happened at AstraZeneca. Although HTTS is unlikely to ever be as robust as SPR, NMR, or crystallography, it is hard to beat the low cost and high speed.

Lead Generation: Methods, Strategies, and Case Studies

Lead generation refers to that point in drug discovery when initial screening hits against a target are wrought into compelling chemical matter. This chemical matter is often plagued with deficiencies in terms of potency, pharmacokinetics, or novelty, yet it provides a starting point for further optimization. This is the subject of a massive (800+ pages!) new two-volume work edited by Jörg Holenz (GlaxoSmithKline, formerly AstraZeneca) as part of Wiley’s Methods and Principles in Medicinal Chemistry series. Readers of this blog will not be surprised to find that fragments play a major role; indeed, the molecule on the cover of the book came out of FBLD. I won’t attempt to summarize all 25 chapters here, but will simply highlight those most relevant to FBLD.

Mike Hann (GlaxoSmithKline) sets the stage in chapter 1 by briefly describing the characteristics of successful leads. He emphasizes the importance of physicochemical properties and avoiding molecular obesity, and how judicious use of metrics can help navigate away from perilous chemical space. He also summarizes internal programs that again demonstrate that fragment-derived leads tend to be smaller and less lipophilic than those from other lead discovery techniques.

In chapter 3, Udo Bauer (AstraZeneca) and Alex Breeze (University of Leeds) discuss the concept of ligandability – the ability of a target to bind to a small molecule with high affinity. Fragments are ideally suited for assessing ligandability, and the researchers briefly describe fragment-based experimental and computational approaches to do so. They also include a nice 11-point summary of factors to consider when starting lead generation on a new target, ranging from the presence of small-molecule binding sites to the number of patent applications.

Chapter 6, by Ivan Efremov (Pfizer) and me, is entirely about fragment-based lead generation. I'm undoubtedly biased, but I think it provides a self-contained and fairly detailed guide to FBLD, including topics such as screening methods, hit validation, metrics, hit optimization, fragment growing vs fragment linking, and case studies on vemurafenib, BACE, MMP-2, LDHA, venetoclax, MCL-1, and GPCRs.

Helmut Buschmann and colleagues at RD&C Research, Development, and Consulting, focus in chapter 9 on optimizing side effects of known molecules to develop new drugs, but they also discuss some interesting older work reporting that 418 of 1386 drugs contain other drugs as internal fragments.

Chapter 12, by Dean Brown (AstraZeneca), is devoted to the hit-to-lead stage, and much of his advice is applicable to FBLD. Dean also includes a fantastic metaphor to illustrate the size of chemical space: "if a typical corporate screening collection were to fit on a postcard, the rest of the earth is the amount of available drug-like space." This assumes a million-compound library and a conservative estimate of 10²³ drug-sized molecules, so if anything it is an understatement.

Molecular recognition is critical for both FBLD and lead generation in general, and this is the topic Thorsten Nowak (C4X Discovery Holdings) tackles in chapter 13. He covers key areas such as thermodynamics, emphasizing the importance of enthalpy while acknowledging the difficulty of prospectively using thermodynamic data. The role of water and halogen bonds are covered, along with some freakishly high ligand efficiency values. There are a couple errors: one paper is categorized as using dynamic combinatorial chemistry when in fact it actually used static libraries, and Tethering is confused with Chemotype Evolution, but overall there's lots of good stuff here.

Biophysical methods are covered in chapter 14, by Stefan Geschwindner (AstraZeneca). These include NMR, SPR, ITC, thermal shift assays, native mass spectrometry, microscale thermophoresis, and more.

Chapter 16, by Ken Page and colleagues at AstraZeneca, discusses "lead quality." This often entails various metrics, from simple ones such as ligand efficiency and LLE to more complicated attempts to predict clinical dosages. Although it is easy to poke fun at metrics, most thoughtful scientists find them useful for making sense of the reams of data generated in lead optimization campaigns.

Chapter 17, by Steven Wesolowski and Dean Brown (both AstraZeneca), is arguably the most entertaining. Entitled "The strategies and politics of successful design, make, test, and analyze (DMTA) cycles in lead generation," it is replete with pithy quotes and even an original (and highly geeky) cartoon. Along with multiple examples, the chapter formulates plenty of questions to consider during lead optimization, and ends with a particularly relevant quote by Billings Learned Hand: “Life is made up of a series of judgments on insufficient data, and if we waited to run down all our doubts, it would flow past us.”

In chapter 23, Sven Ruf and colleagues at Sanofi-Aventis Deutschland describe a success story generating leads against cathepsin A, a target for cardiovascular disease. HTS yielded three different chemical series with sub-micromolar activities, each with different liabilities. Crystallography revealed their binding modes, and this allowed the team to mix and match fragments across the different series to generate a molecule that ultimately went into the clinic. Although this may not be classic FBLD, it does seem to be a good case of using concepts from the field, or fragment-assisted drug discovery.

A similar, if less directed, approach is the subject of chapter 25, the last in the book. Pravin Iyer and Manoranjan Panda (both AstraZeneca) describe "fragmentation enumeration," in which known drugs or clinical candidates are fragmented into component fragments and recombined. On some level the fragments themselves are likely to be privileged; the researchers cite the famous quote by Sir James Black that "the most fruitful basis of the discovery of a new drug is to start with an old drug." Most of the work is computational, although one molecule derived from the approach has encouraging cellular activity against Mycobacterium tuberculosis.

There's far more to this book than could be listed even in this relatively long post, including multiple case studies, so for those of you who are interested in lead generation definitely check it out!