Fragment-based events in 2009 and 2010 (and calls for abstracts)

We’re in the last quarter of 2009, and I know of just one more event this year involving fragments:

October 13: The Life Science Regional Technology Symposium will be held in Somerset, NJ, and Dr. Teddy Z. will be one of several excellent speakers.

2010

Next year is starting to take shape nicely, and two events have put out calls for abstracts, so if you have something interesting to present, now’s your chance!

February 3-5: Cambridge Healthtech Institute’s 17th International Molecular Medicine Tri-Conference will be held in my beautiful city of San Francisco, with a track on medicinal chemistry that will have some fragment talks, and a short course on “Fragment-Inspired Medicinal Chemistry” on February 2.

March 21-25: The spring ACS meeting will also be held in San Francisco. There will be a symposium on “Fragment Based Drug Design: Novel Approaches and Success Stories,” and Rachelle Bienstock at the FBDD LinkedIn site has put out a call for abstracts, due October 19.

April 20-25: The Keystone Symposium on computer-aided drug design will take place in brisk Whistler, British Columbia. Although not exclusively devoted to fragments, the schedule shows several talks on the topic.

April 27-28: Cambridge Healthtech Institute’s Fifth Annual Fragment-Based Drug Discovery will be held in summery San Diego. This conference has also put out a call for speakers, with a deadline of October 16.

Know of anything else? Organizing a fragment event? Let us know and we’ll get the word out.

Looks can be deceiving: Getting misled by crystal structures - part 2

Last year we highlighted a paper that touched on some of the ways crystal structures can mislead, and a theme of FBLD 2009 was how dubious data can derail modeling efforts. Now, Jens Erik Nielsen and colleagues at University College Dublin add to the discussion by showing how the crystal lattice can potentially distort protein-ligand interactions. Their paper in J. Med. Chem. provides an analysis of the prevalence of two common structural artifacts, plus a practical tool for detecting them.

The first problem the authors consider is that some ligands make “crystal contacts.” Because a crystal is made up of a three-dimensional lattice of proteins packed together, a ligand bound near the surface of one protein may be in close contact with another protein in the crystal (a nonbiological “symmetry mate”); this contact occurs only in the context of a crystal and could distort how the ligand binds to its (true) partner protein.

The second, related problem is that water molecules that appear in the crystal structure can form bridges between a ligand and its nonbiological symmetry mate.

The authors examined a set of 1300 protein-ligand crystal structures with noncovalently bound ligands and experimentally measured binding affinities (PDBbind Database). Of these, 36% of ligands showed crystal contacts, and a similar number (37%) had crystal-related water bridges.

This doesn’t mean that all of these structures are misleading: the researchers note that “it is entirely possible that crystal contacts in some cases do not perturb the geometry of a protein-ligand complex whatsoever.” However, removing these structures before running docking experiments did improve the results.

The tricky thing about these structural artifacts is that they are often invisible, even when suspected. Most non-crystallographers focus on just on a single protein-ligand complex and don’t consider the crystal lattice when examining a crystal structure. Happily, Nielsen and colleagues have constructed a simple online tool (LIGCRYST) that can evaluate structures from the pdb to search for these types of problems. Although I’m not a crystallographer, I found it quite easy to use.

Hopefully modelers will increasingly take crystal contacts into account, and the next time you examine a structure from the pdb, you may want to give it a quick run through LIGCRYST.

FBLD 2009

Fragment-based Lead Discovery Conference 2009 just concluded in York, UK; it was the second in what will hopefully be a continuing series. With more than two dozen talks and as many posters spread over three days, most of them very high quality, it is impossible to summarize even the highlights (and I don’t want to scoop pending publications). Instead I’ll just jot down a few impressions.

On the broad topic of why FBLD is useful, an interesting shift in emphasis seems to have occurred. A few years ago a key argument in favor of fragments was getting compounds to the clinic faster, but there is now a greater focus on quality over speed. In summarizing over a decade of fragment work at Abbott, Phil Hajduk noted that FBLD hits consistently bind more efficiently than those from HTS. Similarly, Chris Murray of Astex noted that, among their five clinical candidates (four of which target kinases), the average ClogP was 1.7 (vs 4.1 for a set of 45 reported orally active kinase inhibitors), while the average molecular weight was 390 (vs 457).

One theme that differentiated this meeting from others was a strong focus on modeling: an entire day was devoted to sessions on “fragments, scoring functions and docking” and “design from fragments.” This concluded with a lively round table discussion, chaired by Vernalis’ James Davidson, titled “Chemistry challenging modeling.” But challenges didn’t only come from chemists: one prominent modeler noted that there have been no fundamentally new approaches to modeling in the past two decades; another asked why, despite the number of interesting new chemistries out there, so many modelers restrict themselves to the same old standbys such as amide bonds.

Part of the problem with modeling, of course, is separating hits from noise: true hits often show up near – but not at – the top of a ranked list, so how does one decide what is worth pursuing? Phil Hajduk discussed the use of “Belief Theory”, in which the similarity of an unknown molecule to a known active is used to evaluate the unknown.

Another problem is the quality of primary data: As Hajduk noted, “no one takes experimental error into account” when predicting ligand binding, and a recent analysis suggests that over-fitting data is a substantial problem with many computational approaches. This is all the more problematic when the data are not just noisy but spurious; Practical Fragments has noted the problem of aggregation, and UCSF’s Brian Shoichet emphasized this point, noting that 85-95% of hits from a high-throughput screen could be artifacts, while 85-100% of what remains could also be bogus. He did note, though, that fragments are less problematic in this regard than larger molecules. And Genentech’s Tony Giannetti, Vernalis’ James Murray, and others illustrated how surface plasmon resonance is effective at weeding out bad actors.

Getting better data will clearly be essential to getting better models, but one essential category, the forces involved in protein-small molecule interactions, is still poorly understood. Gerhard Klebe of the University of Marburg presented a detailed and elegant set of experiments exploring the effects of chemical structure on the enthalpy and entropy of binding to the protein thrombin. He emphasized that desolvation of fragments from water is critical, and only possible if compensated by strong interactions with the protein. This also implies that you want fragments that have low desolvation penalties as well as high solubilities – a tricky balancing act.

FBLD 2009 was held barely six months after Fragments 2009, and it is a testament to the vibrancy of the field that both conferences managed to be so successful and exciting while sharing very few speakers in common.

For the other two hundred plus attendees at the conference, what were some of your impressions?

Upcoming Fragment Talks

There is an upcoming conference with an extraordinary FBDD lineup. :-)
Don Huddler from GSK will be talking about SPR in fragment screening.
Bill Metzler from BMS will be talking about the uses of biophysical methods and structural information for hit prioritization.
I will be talking about how to put together an integrated FBDD paradigm.
There is one more talk of the TBD variety, but I think it will be a very nice complement to these other three.
Please come out and see what the state of the art is.

Who’s doing FBDD?

Lots of companies are using FBDD, but aside from big pharma it’s not always easy to find them. As a public service we have started a running list with live links. This first installment is taken largely from a nice review by Wendy Warr in the JCAMD special issue we highlighted; we’ve removed companies that have been bought or ceased working in FBDD.

Astex Therapeutics
Beactica
BioLeap
BioSolveIT
Carmot Therapeutics
Crystax Pharmaceuticals
deCODE Chemistry and Biostructures
Evotec
Graffinity Pharmaceuticals
IOTA Pharmaceuticals
Locus Pharmaceuticals
MEDIT
Plexxikon
Proteros Fragments
Pyxis Discovery
Structure Based Design
Vernalis
Zenobia Therapeutics
ZoBio

I’m sure there are plenty of omissions; put them in the comments and we’ll add them in the next update.

BioLeap leaps into collaborations

Pennsylvania-based BioLeap, which uses computational FBDD, has just signed a deal with GlaxoSmithKline to work on “difficult” targets. The announcement came September 8, just a month after BioLeap started a collaboration with Lycera on autoimmune disorders. I haven’t personally seen any talks or papers out of BioLeap, but there have certainly been plenty of improvements in computational chemistry applied to FBDD recently (see here, here, and here), and given the lag between discovery and disclosure there are likely many new developments.

This is also the second fragment deal that GSK has done in the past month; we already noted their collaboration with Vernalis.

What do you think? Does this flurry of new deals signify increasing use of FBDD?

Journal of Computer-Aided Molecular Design Special FBDD Issue

Our friends over at FBDD-Literature have already highlighted this, but it bears repeating that the entire August issue of J. Comp. Aid. Mol. Des. is devoted to FBDD. For aficionados of all things silicon, there are articles on computational chemistry applied to FBDD generally as well as on more specific topics such as MCSS, NovoBench, FTMap, and two papers on Glide (here and here).

But don’t be put off by the name of the journal: with 14 articles covering close to 200 pages, there is something here for almost everyone, even for those whose interest in computers ends at using them to read this blog! A brief editorial outlines the challenges of FBDD, and a longer introductory piece gives an overview of the field. Several articles focus largely on specific targets such as p38alpha, heparanase, and Eg5, while one is devoted to assessing druggability.

Finally, two articles address the important topic of designing fragment libraries, one from the perspective of big pharma (nicely summarized here), the other from biotech.

Destructible ligands

Crystallography-based methods of fragment screening often rely on growing many crystals of a protein and soaking these in fragment-containing buffers. But how do you get biologically relevant crystals in the first place? Many proteins adopt a variety of different conformations in solution, and their freedom of movement is constrained once they are forced into a crystal lattice. Crystallizing the protein in a state that is relevant for binding ligands often means co-crystallizing them in the presence of a known ligand. In fact, some proteins are so disordered on their own that the only way you can get them to crystallize at all is by adding a small molecule. In many cases, these “co-crystals” can then be soaked in a solution containing new ligands; the existing ligands will diffuse out of the crystal, making room for new ligands. Unfortunately, in some cases the original molecule binds so tightly that it can’t be forced out. Two recent papers in J. Am. Chem. Soc. provide a clever solution.

Both papers focus on the major histocompatibility complex (MHC) Class I proteins. These proteins bind 8-11 amino acid intracellular peptides and present them on the cell surface, allowing passing T cells to survey the contents of cells for viruses, bacteria, or other nasties and, when appropriate, eliminate the infected cells. As might be expected given their function, the MHC proteins are quite promiscuous in which peptides they bind to, frustrating a general understanding of the molecular recognition. Moreover, crystallography is complicated by the fact that MHC class I proteins do not crystallize in the absence of a bound ligand.

In the first paper, Anastassis Perrakis, Ton Schumacher, and colleagues at the Netherlands Cancer Institute designed a 9-amino acid "conditional" peptide ligand for MHC that contains two internal photosensitive nitrophenyl substituents. They were able to crystallize this in complex with MHC and solve the structure. When they exposed these crystals to UV-light, the nitrophenyl groups caused the peptide to break apart into into three pieces. Interestingly, structural characterization after this exposure revealed that while the central portion of the peptide was gone, the two end bits were still bound to MHC. However, the researchers were able to successfully replace these remnants with new, full length peptides derived from HIV and avian flu proteins by soaking the crystals for just a few hours in buffer containing the new peptides. The resulting structures were identical with previously determined structures, even revealing some side-chain movement. A second paper from Ton Schumacher, Huib Ovaa, and colleagues reports a similar strategy, this time using diol-containing peptides and mild chemical cleavage with sodium periodate rather than UV-light, although in this case the reaction is done in solution rather than in crystals.

This seems like an interesting approach for tackling peptide-binding proteins, and possibly even small-molecule binding proteins, though this would require more effort to design destructible ligands.

Avoiding will-o’-the-wisps: aggregation artifacts in activity assays

The phenomenon of aggregation is the drug hunter’s quicksand. A prerequisite for using biochemical assays to study fragments – or any low-affinity molecules – is an ability to sort activity from artifact. Many small molecules, even bona fide drugs, form aggregates in aqueous solution, and these aggregates can non-specifically interfere with biochemical assays. There are several ways to expose these promiscuous inhibitors (see list below), but even with vigilance, researchers can inadvertently stumble onto a route lit by will-o’-the-wisps. The most recent issue of J. Med. Chem. provides a particularly insidious example from Brian Shoichet, Adam Renslo, and colleagues at UCSF.

The researchers were looking for noncovalent inhibitors of cruzain, a popular protease target for Chagas’ disease. After a virtual screen of commercial lead-like compounds, 17 molecules were purchased and tested in enzymatic assays, and compound 1 (below) inhibited cruzain, albeit weakly. However, the compound looked like the real deal: it showed no time-dependence; it was active in the presence of detergent; and Lineweaver-Burk plots revealed that it was mechanistically competitive.

The researchers thus turned to medicinal chemistry, replacing the ester group of compound 1 with an oxadiazole bioisostere and swapping the aryl group for a substituted pyrazole, ultimately arriving at molecules such as compound 21, more than two orders of magnitude more potent than the starting molecule.



So far, so standard: similar stories appear every week in J. Med. Chem., Bioorg. Med. Chem. Lett., ChemMedChem, and other journals, and it would not have been surprising to see this published with a title like “Discovery of a high affinity inhibitor of cruzain.” Only in this case, the researchers became suspicious: most of the molecules were not active against the targeted protozoa, and many of the dose-response curves had unusually steep Hill slopes, a tell-tale sign of aggregation. Looking more closely at their protocol, the researchers also realized that the concentration of non-ionic detergent in their assays was ten-fold lower than they had thought. D’oh!

A series of tests confirmed that, despite interpretable and rationalizable SAR, the series had been optimized for aggregation-based inhibition: compound 21, with an IC50 of 200 nM in buffer containing 0.001% of the detergent Triton X-100, showed no inhibition whatsoever in 0.01% Triton X-100. The compound also inhibited AmpC beta-lactamase, an enzyme particularly sensitive to aggregators, and this inhibition could be reversed with detergent. Finally, dynamic light scattering (DLS) revealed the presence of particles (or aggregates) in aqueous solutions of compound 21.

But the tale gets even more twisted. Some of the aggregators show legitimate, competitive binding to cruzain under high-detergent conditions, albeit at much higher concentrations (with IC50s above 40 micromolar). Conversely, compound 1 actually shows noncompetitive behavior in low-detergent conditions, though again only at fairly high concentrations. In other words, promiscuous inhibitors can behave legitimately under sufficiently stringent conditions, and legitimate inhibitors can behave promiscuously under less stringent conditions.

What’s especially sobering is how easy this promiscuity would have been to overlook: many molecules with good activity in biochemical assays don’t show any effects in cells, and it is easy to ignore steep slopes in inhibition assays. How many of those “Discovery of a high affinity inhibitor of Hot Target X” papers actually report promiscuous inhibitors? The authors, who have been researching this problem for a long time, end on a justifiably paranoid note:

The cautionary contribution of this study is to point out that even within a clear SAR series, one is never entirely free from the concern that non-stoichiometric, artifactual mechanisms are contributing to the inhibition one observes.

This is a serious problem, both for the researchers doing the original work and for anyone trying to follow up on the results. But one can take precautions, summarized below and described more fully here:

• Add non-ionic detergent to the assay (Triton-X 100, Tween-20, CHAPS, others)
• Increase protein concentration – this should have no effect on genuine binders (within limits)
• Characterize the mechanism of inhibition (competitive, noncompetitive, or uncompetitive): competitive inhibitors are normally not promiscuous
• Centrifuge your samples and retest them – this can sometimes remove aggregators
• Examine your samples with DLS or flow cytometry – aggregators can sometimes be directly observed as 50-1000 nm particles
• Look closely at your dose-response curve - unusually steep slopes can signal aggregation

And of course, biophysical methods such as SPR, NMR, and X-ray crystallography can provide more information than biochemical assays and reveal stoichiometric (and – in the case of SPR – superstoichiometric) binding.

Difficulty sorting true low-affinity binders from false positives stymied fragment-based approaches for decades, and in fact the nature of promiscuous inhibition caused by aggregation wasn’t even characterized until earlier this century. We now have techniques to sort deceptive aggregation from true but faint affinity. Let’s make sure these tools are consistently used.

DCC and FBDD

Dynamic combinatorial chemistry (DCC) has grown alongside of and often intersected with FBDD. In a recent issue of Angewandte Chemie, Jörg Rademann and colleagues at the Leibniz Institute of Molecular Pharmacology describe the latest example.

Put simply, DCC generates new molecules with some desired property by allowing smaller molecules to assemble reversibly under selection pressure. If the selection pressure is binding to a protein target and the molecules undergoing reactions are fragments, DCC can be used for FBDD. As we previously noted, Huc and Lehn published one of the earliest demonstrations of this. DCC has also been used at a few companies, including Astex and Sunesis, and even formed the basis of the (sadly) short-lived Therascope.

Rademann’s approach, "dynamic ligation screening", is based on labeling one fragment with a fluorescent probe and then screening it in a fluorescence polarization assay with other test fragments. If the labeled fragment binds competitively with a test fragment, this implies that the two fragments bind to the same site. However, if the fluorescence polarization signal increases in the presence of the test fragment (indicating increased binding of the labeled fragment), this suggests that the test fragment and the labeled fragment are binding cooperatively.

The researchers applied dynamic ligation screening to the protease caspase-3, a key mediator of apoptosis relevant for many diseases. As their labeled “fragment,” they chose a high-affinity tetrapeptide containing an alpha-ketoaldehyde: the ketone interacts covalently with the catalytic cysteine of the enzyme, while the aldehyde can form imines with amine-containing fragments. Interestingly, this strategy selects for fragments that bind in the S1’ subsite of the enzyme, which has not received as much attention as the tetrapeptide binding sites S1-S4.

A fluorescently labeled version of the tetrapeptide was screened against a library of 7,397 fragments, of which 4,019 contained primary amines. Of these, 78 fragments caused a decrease in the fluorescence polarization signal, suggesting that they compete with the tetrapeptide for binding. These were tested in an enzymatic assay: 21 of them were active at 10 micromolar concentrations, and four had Ki values from 3.1 to 5.5 micromolar; these four molecules have electrophilic carbons, making it likely that they bind to the catalytic cysteine residue.

Of greater interest, 176 fragments were cooperative, increasing the fluorescence polarization (FP) of the labeled tetrapeptide fragment by at least 20%. 50 of these were tested in an enzymatic assay, with the amine shown below emerging as the most potent FP enhancer and a Ki of 120 micromolar alone. A series of experiments guided by mathematical modeling suggested that the protein was templating the formation of an imine bond between the aldehyde of the tetrapeptide and the amine. Moreover, the reduced (amine) version of this conjugate exhibits a very high affinity for caspase-3, with a Ki of 80 picomolar.



Of course, affinity is not everything: with a molecular weight of 767 Da and a clearly peptidic nature, the pharmaceutical properties of this molecule, and even its cell activity, are questionable.

This study is reminiscent of some work we did at Sunesis, using caspase-3 to template the assembly of a non-peptidic inhibitor using Tethering. In that case we built molecules in the S1-S4 pockets, but did not do much work to extend into the S1’ pocket. It would be interesting to see if the fragment Rademann and colleagues discovered also boosts the potency of the molecules we identified.

For dynamic ligation screening to be general it needs to surmount at least two major potential limitations. First, it remains to be seen whether the technique will work with actual fragments, which are likely to have far lower affinities than the 25 nM tetrapeptide used in this study. Second, cooperative binding of the fragments does not translate to synergy in the final molecule: the conjugate has a lower ligand efficiency than either of the fragments, despite the apparent cooperativity of the two fragments binding to the target. This could be because the conjugate contains an amine, whereas the two fragments in solution presumably were linked by an imine; the differences in geometry and chemical nature between these two moieties are profound, and one could imagine that many amine-linked compounds would not be selected as imines, and vice versa.

Still, this is an interesting approach to tackle the long-standing challenge of linking fragments, and it will be fun to watch for new developments.

Fragments of Life shut down LTA4H

A couple months ago we highlighted research suggesting that natural products are a fruitful field for finding fragments. One company, deCODE, has taken this idea very seriously, and has constructed their fragment library based largely on molecules (or close analogs) that actually appear in nature. Their strategy is described in detail in the most recent issue of J. Med. Chem.

The “fragments of life” (FOL) screening library consists of three sets of molecules:

• 218 “molecules of life,” which are known metabolites from some living organism
• 666 synthetic derivatives and isosteres of known metabolites
• 445 synthetic biaryl molecules, which mimic peptide turns (biaryls have also previously been reported to be privileged pharmacophores)

This gives, in total, a 1329-fragment screening set. Naturally, given their origin, some of the fragments are slightly unusual, including the dipeptide bestatin and the trendy resveratrol. However, with the exception of a somewhat higher polar surface area, the molecules conform to rule of 3 guidelines, with an average molecular weight of 182.5 and ClogP of 0.96. All fragments are soluble up to 50 mM in methanol, and in fact stocks are made in this solvent rather than the more conventional DMSO.

The fragment library was tested against Leukotriene A4 Hydrolase (LTA4H), an enzyme with two functions: it has an aminopeptidase activity whose biological relevance is unknown, and an epoxide hydrolase activity that converts leukotriene A4 to the inflammatory leukotriene B4, which is implicated in heart disease and inflammation. Both activities map to a single active-site, a long cleft containing a catalytic zinc.

About 200 of these fragments were screened by soaking crystals of LTA4H in pools containing 8 compounds. Although all compounds in a given pool were structurally diverse, in some cases electron density was ambiguous, necessitating subsequent soaks of individual fragments to confirm hits. Ultimately 13 fragments were found to bind LTA4H, a hit rate of 6%. These fragments were tested in functional assays and found to have IC50s as good as 178 nM for bestatin, though the next best was mid-micromolar. Most of the fragments bound in the active site, although one fragment bound on the surface of the enzyme. Considerable structural data are presented in the paper, and all the structures have been deposited in the protein data bank.

Interestingly, the researchers also found that some of the fragments only appeared to bind when they were soaked in the presence of another fragment, bestatin. Bestatin also caused the binding mode of another fragment to shift compared to its binding mode without bestatin.

Based on the crystal structures available, some of the fragments were elaborated to provide more potent inhibitors, increasing affinity by some four orders of magnitude, as well as improving ligand efficiency (see figure). Crystallography revealed that these more potent compounds bind in a similar fashion to the fragments.

Compounds 14 and 18 also bind in a similar manner to DG-051, which has recently completed phase IIa clinical trials. There is apparently another manuscript in the works focused exclusively on this molecule. We look forward to reading the full story.

Hsp90 and fragments – part 2: NVP-BEP800/VER-82576

Proving again that Hsp90 is tailor-made for fragment-based approaches, the latest issue of J. Med. Chem. has a thorough article describing the development of an anti-cancer candidate targeting this protein. The researchers, mostly from Vernalis but also from Novartis and the Institute of Cancer Research, used a combination of fragment-based methods, computational screening, and medicinal chemistry. This is likely to be representative of a coming wave of reports in which a fragment approach supplements other techniques (or vice versa).

The fragment effort started with a library of fragments grouped into pools of 10-12 each and screened using three different NMR methods (saturation transfer difference, water-LOGSY, and T2 relaxation filtered 1D). Compounds were tested in the presence and absence of PU3, a compound known to bind to the ATP site of Hsp90. Of the 1351 fragments tested, 59 of them (4.4%) were confirmed in all three NMR experiments and competed with PU3. Interestingly, a further 158 compounds were found to bind to Hsp90 but could not be displaced by PU3, suggesting that they bind outside of the ATP binding site; these were not pursued.

Two of the fragments identified, along with their IC50s in a fluorescence polarization (FP) assay, are 10 and 11 (see figure – click to see larger image). These fragments were also characterized crystallographically. An interesting aside is that the binding mode of fragment 10 differed depending on whether it was soaked into Hsp90 crystals or co-crystallized with the protein. (This is reminiscent of the different binding modes, both productive, observed for two fragments using crystallography versus NMR on Hsp90 by researchers at Abbott.)

In addition to the fragment work on Hsp90, a virtual screen of 700,000 (non-fragment) compounds was conducted, leading to the purchase of 719 commercially available molecules that were tested in the FP assay. Two hits, 12 and 13, are shown in the figure and were also characterized crystallographically.



With these SAR and X-ray crystal structures in hand, the researchers added elements from the larger compounds (a phenyl from 12 and 13 or the amide from 13) to their fragments to generate the more potent fragments 14 and 15. Further structural examination led to the 2-amino-thienopyrimidine scaffold 16, which formed the basis for subsequent optimization.

Appending a phenyl group onto compound 16 led to the expected boost in potency, with the dichloro compound 21e having good biochemical as well as cell-based activity. Addition of a solubilizing group led ultimately to NVP-BEP800/VER-82576, which, in addition to potent biochemical and cell activity, also showed tumor regression in mice following once-daily oral dosing, along with pharmacodynamic biomarkers consistent with Hsp90 inhibition. This compound also showed good antiproliferative effects in a number of human cancer cell lines. Crystallographic characterization revealed that the molecule binds in a manner consistent with the previous structures (and with the co-crystallized structure of fragment 10).

This could be seen as a nice example of what has been dubbed fragment-assisted drug discovery: fragments were not the sole drivers of the project, but they did play an important role in guiding the overall strategy to develop optimal molecules.

In related news, Vernalis just announced a multimillion dollar collaboration with GlaxoSmithKline around an undisclosed cancer target – another indication that fragment-based approaches have not just scientific value, but monetary value as well.

Fragment-based conferences in 2009 and 2010

Hard to believe, but 2009 is more than halfway over. As far as I know there is only one more event this year focused primarily on fragments, but it’s a biggie, and conferences are already being planned for 2010. Here’s what might be of interest over the next few months.

August 16-20: The fall ACS meeting this year will be held in sweltering Washington, DC. Although there are no sessions devoted exclusively to fragments, a number of FBLD talks and posters are sprinkled throughout the conference.

September 21-23: Much-anticipated FBLD 2009 will be held in historic York, UK. There will also be a one-day workshop on September 20 to provide an overview of fragment-based drug discovery for newcomers to the field. The draft schedule for the conference has just been released (pdf) – looks like a great lineup, so if you missed the earlier conferences this year, don’t miss this one!

2010
February 3-5: CHI’s Molecular Medicine Tri-Conference is being held in the beautiful city of San Francisco, with a track on medicinal chemistry that will probably have some fragment talks, and a fragment workshop on February 2.

March 21-25: The spring ACS meeting will be in foggy (but beautiful) San Francisco. No schedule or link yet.

April 20-25: The Keystone Symposium on computer-aided drug design will take place in brisk Whistler, British Columbia. Although not exclusively devoted to fragments, the schedule shows plenty of talks on FBLD.

April 26: The CHI fragment-conference will be held in summery San Diego. No web link yet, but I’ll have this when it becomes available.

As always, let us know if we’ve missed anything and we’ll get the word out!

Hsp90 and fragments

Some targets seem particularly amenable to fragment-based approaches. Protein kinases are one example. Another is the N-terminal ATP binding domain of Hsp90, a widely pursued anti-cancer target: at least two fragment-derived compounds against this target are currently in the clinic. At FBLD 2008 in San Diego last year, so many talks discussed this protein that it became a running gag (one speaker promised at the outset not to talk about it, then slipped in a few slides). A recent paper in ChemMedChem provides a particularly clear example of a multidisciplinary fragment-growing approach against this target.

The researchers, mostly from Evotec, started with a high-concentration biochemical displacement assay to screen 20,000 fragments against Hsp90. A relatively potent aminopyrimidine (compound 1, below) was characterized crystallographically, and this structure was then used to run a virtual screen of 3.8 million commercially available molecules using the program GOLD 3.0.1. Some of the resulting hits were purchased and tested, including compound 3, which showed sub-micromolar biochemical activity but no cell-based activity. Subsequent modeling and medicinal chemistry led to compound 19, which, in addition to mid-nanomolar biochemical activity, also displayed submicromolar cell activity in A549 and HCT116 cancer cell lines.


In addition to compound 1, a number of other fragments containing the aminopyrimidine substructure were also identified as hits. This moiety seems to be a privileged pharmacophore for Hsp90: for a fun read, check out this 2007 paper from researchers at Abbott, in which fragments are linked together in a couple different ways as well as grown. As in the more recent paper, the protein displays a remarkable degree of flexibility to accommodate small molecule binders.

Guest Blogger: Darren Begley Hidden Pool Response

Folks, as you know (or may not) we have invited the reading community of Practical Fragments to guest blog. In response to this post we got this response from Darren.

If there's a hidden pool of FBDD talent, it is most likely in industry, not in academia. Fragment-based approaches are occasionally mentioned in courses or lectures by professors who want to appear up-to-date. But the only folks actually doing fragment-based work (as distinct from structure-based methods) are in a few key labs, all led by PIs with prior industry experience.

If you look at the 2008 FBLD Conference poster session, almost half of the presenters were from a single academic lab. The rest were largely virtual docking, traditional medicinal chemistry, or people from industry. One of the conference organizers told me that there were less than a handful of us graduate students in attendance; compare that to attendees at any given Gordon Conference. So I believe there are "puddles" of FBDD here and there, but not what I would call a vast resource.

That said, it seems the skills one needs to do FBDD can be acquired by other means in academia (ie. structural biology PhD, synthetic chemistry training, etc.). But if companies are looking to hire PhDs well-versed in fragment-based methodologies, there is currently not a huge group being freshly minted each year at commencement.

[Ed Note: I also think it is interesting that the industrial types that go to industry fair not well in terms of grants and such (my impression talking to the ones I know). But, I would be interested in hearing from those types also.]

Is FBDD a FADD?

Two reviews in the July issue of Drug Discovery Today provide an update on the state of FBDD.

The first, from researchers at the VU University, Amsterdam, and IOTA Pharmaceuticals, discusses 23 examples. Many of these have been reviewed elsewhere, but the paper also describes some studies that are unpublished or just reported at meetings. It’s a nice, thorough introduction to the field, and the organization of the review, by institution, gives a flavor of the diversity of approaches.

The second review, from researchers at Astex Therapeutics, provides a historical perspective and clinical focus. There are also useful tables of commercial suppliers of fragments as well as FBDD-derived compounds that have made it into clinical development.

In an accompanying editorial, Mark Whittaker of Evotec asks whether fragment-based drug discovery (FBDD) should really be called fragment-assisted drug discovery (FADD):

This is more than just a difference in semantics, but is, in fact, a broader question of when and how to apply fragment approaches to lead generation, either on their own or in concert with other hit finding techniques.

He goes on to explain that although fragment-based methods can be used by themselves to generate leads, they can also be complementary to other approaches to assess target druggability or focus later hit-finding. This conclusion is consistent with Practical Fragments’ latest poll, in which 85% of respondents reported that, far from being a fad, FBDD (or, if you like, FADD) is integrated in the hit finding stage at their company.

Fragments in Japan

We missed this meeting in our last events list, but Daisuke Tanaka of Dainippon Sumitomo Pharma reports on LinkedIn that:

On June 22, FBDD researchers from 11 Japanese pharmas/biotechs got together in hot and humid Tokyo. This one-day meeting, hosted by a crystallography-based CRO PharmAxess (www.pharmaxess.com) and an in silico-based CRO PharmaDesign (www.pharmadesign.co.jp/eng), started in the morning with reviews of benefits and techniques of FBDD, and then culminated in the afternoon with enthusiastic discussions on a non-confidential basis. The aim of this unofficial meeting was to share experienced problem-and-solution cases while carrying out FBDD, rather than reporting success stories in a conference fashion. Finally, it was adopted unanimously that the meeting should be held periodically once or twice a year.

Daisuke is organizing the next meeting – we’ll post the date and location as soon as we know (and everyone please contact us about other upcoming fragment events).

It’s great to see the field becoming more collaborative and international. Although the symposium proposals for Pacifichem 2010 are already set, perhaps a FBDD track at Pacifichem 2015 will give the fragment community an excuse to get together in Hawaii!

Fragments for sleeping sickness don’t lie still

In fragment-based drug discovery, the binding mode of the initial fragment often remains constant during the course of optimization (see AT9283 and AT7519 from Astex). But this isn't always true. An intriguing counterexample has recently been published in J. Med. Chem.

Ruth Brenk and colleagues at the University of Dundee were interested in pteridine reductase 1 (PTR1), an enzyme from Trypanosoma brucei, the protozoan that causes sleeping sickness. They used the program DOCK 3.5.54 (which has been successfully used for fragment-docking) to screen 26,084 commercially available fragments against the crystal structure of PTR1. After a variety of computational and manual filters were applied, the researchers purchased and tested 45 compounds in an enzymatic assay. Of these, 10 fragments inhibited PTR1 at least 30% at 100 micromolar concentration, the most potent of which was compound 4 (below).



Removing the chlorine atom to generate compound 5 resulted in a dramatic loss in activity, while adding the dichlorobenzyl moiety caused a similarly large boost in activity (compound 9). The researchers were able to characterize the binding mode of each of these molecules crystallographically, and it turns out that, despite sharing a common aminobenzimidazole core, they all bind in very different fashions.

The initial compound 4 binds in two orientations, one of which closely resembles the binding mode predicted from the computational screen, with hydrogen bonds between the fragment and the enzyme cofactor NADP+. Compound 5 makes indirect (water-mediated) hydrogen bonds with the cofactor, while compound 9 binds in a completely different manner some distance from the cofactor.

Brenk and colleagues observed a hydrophobic pocket near compound 9 which they exploited to generate the low nanomolar compound 12; crystallography confirmed this binds in a similar fashion to compound 9. This molecule also displayed impressive selectivity against the potential off-target dihydrofolate reductase. Unfortunately, despite the promising biochemical activity of compound 12, it displays only modest activity against T. brucei in cell culture.

This study illustrates two important points. First, it can be hazardous to assume that even very closely related molecules, such as 4 and 5, bind in the same manner. Second, because of this, one should not adhere too slavishly to models, even those based on crystal structures. The binding modes of compounds 4 and 5 would not accommodate the dichlorobenzyl moiety, and yet this addition provided a sizable boost in potency. Sometimes it pays to make substitutions even where you wouldn’t expect them to make sense, especially where the changes are easy to make.

Fragments of the future - part 3 (977 million and counting)

A few weeks ago we gave a passing nod to work by Jean-Louis Reymond, who with colleagues enumerated all possible compounds with up to 11 C, F, N, and O atoms. In a new JACS Communication with Lorenz Blum, he has now expanded this analysis to molecules containing up to 13 non-hydrogen atoms.

The new dataset, GDB-13, contains 977,468,314 molecules containing carbon, oxygen, and nitrogen atoms (as well as hydrogens, of course). Unlike its predecessor it excludes fluorine, but it happily adds chlorine as an aromatic substituent as well as sulfur in heterocycles or in sulfones, sulfonamides, or thioureas. To speed calculation (which still required the equivalent of 4.5 years of CPU time), a few other simplifications were made to limit the number of heteroatoms in a given structure.

The resulting collection, while huge, is thus obviously incomplete: about two-thirds of 619,675 molecules that contain up to 13 atoms and are reported in a variety of databases do not appear in GDB-13. And GDB-13 has many unconventional structures – over half of the molecules contain one or more three- or four-membered rings.

Still, there is lots of neat stuff here: for example, 804,153 structural isomers of aspirin, and 18,371,393 structural isomers of mexiletine! And since 45.1% of the new molecules are rule-of-three compliant, there are hundreds of millions of virgin fragments just waiting to be made – and tested.

Fragments vs RNA

One of the promises of fragment-based methods is that they can tackle “hard” targets such as protein-protein interactions and nucleic acids. But going after these unconventional targets may require new libraries. A new paper in J. Med. Chem. sets out to do just this for RNA.

There are a few previous reports of discovering fragments that bind to RNA and their subsequent optimization; Ibis Therapeutics was particularly active in this field a few years back. In the current study, Fareed Aboul-ela and coworkers at Louisiana State University started by analyzing 120 known RNA-binding ligands and comparing these to known drugs and publicly available compounds. A variety of computationally derived physicochemical descriptors failed to differentiate the RNA binders from other molecules, but the authors note that:

This result does not preclude the likelihood that a finite set of chemical moieties constitute a “privileged” RNA binding set. The special properties of these functionalities may be too subtle or complex to detect using standard descriptors.

Following up on this hypothesis, the authors computationally “cleaved” their RNA binders to generate a set of fragments, and then purchased just over a hundred of these. These were then screened using four different NMR experiments to see if any bound to a 27-residue oligonucleotide derived from E. coli 16S rRNA, an important antibiotic target. Happily, five fragments were identified as binding to the RNA target, two of which had not previously been identified in the literature.

Whether these fragments can be advanced to high affinity binders, and whether the library will be generally useful against RNA, remain open questions. But one nice feature of this paper is the complete list of fragments tested provided in the supporting information. This list will allow other researchers to easily assemble their own screening set and test its utility. And if it proves useful, perhaps it will one day be sold by one of the commercial suppliers of fragments.

Fragments in cells

A couple months ago I considered writing an April Fools’ post on screening fragments in vivo. A recent paper in J. Med. Chem. reports something similar, only it’s no joke.

Alexander Shekhtman and colleagues at SUNY Albany have developed a method they call “screening of small molecule interactor library by using in-cell NMR”, or SMILI-NMR. The process starts by overexpressing two proteins within cells (E. coli, in this case). If the proteins are sequentially expressed, one of them can be selectively labeled with NMR-active isotopes. To test their system, the researchers overexpressed the model proteins FKBP and FRB. These proteins interact only weakly by themselves, but in the presence of the small molecule rapamycin they form a high affinity complex. By performing NMR on the cells, the researchers could observe changes in NMR peaks corresponding to formation of the ternary complex inside the cells when rapamycin was added. They could also do competition studies: adding the small molecule ascomycin to this complex causes a change in the NMR peaks corresponding to the rapamycin being competed away by the ascomycin.

The next step was to look for new molecules that would modulate the interaction between FKBP and FRB, and the researchers chose a library of 289 dipeptides, which are actively transported into cells. The dipeptides were mostly fragment-sized, ranging from a low molecular weight of 132 (Gly-Gly) to a high of 390 (Trp-Trp). The dipeptides were screened in pools (organized in a matrix) and then deconvoluted to identify the most active molecules. Interestingly, none of the molecules caused discrete changes to the NMR spectra as observed with rapamycin or ascomycin, but several caused some of the NMR peaks to disappear and the remaining peaks to broaden dramatically. The most potent compound was Ala-Glu (MW 218), which caused this phenomenon at 5 mM concentration. The authors interpret this effect as being caused by the formation of a large complex consisting of many molecules of FKBP, FRB, and Ala-Glu. Interestingly, although ascomycin could reverse the effect of Ala-Glu, rapamycin could not.

The dipeptide Ala-Glu also behaved similarly to rapamycin in yeast cells: both molecules prevented growth by yeast expressing FKBP, while having no effect on yeast lacking FKBP. This was attributed to both molecules facilitating complex formation between FKBP and FRB within yeast.

The Ala-Glu “fragment” has some issues (ClogP = -4, for example); it would be interesting to see how some of the original FKBP fragments discovered at Abbott behave in this assay. And although not everyone has access to a 700 MHz NMR with a cryoprobe, this is an intriguing approach for studying protein-protein interactions in a very biologically relevant milieu.

Fragments of the future - part 2 (The intersection of chemical and biological space)

In a previous post about heterocycles that appear chemically feasible but have not been reported, we wondered whether these molecules would show biological activity. The structure of biologically relevant chemical space – that fraction of possible molecules that will exhibit some biological effect – is of great interest, but as yet unknown. Brian Shoichet and coworkers at UCSF have just published a thought-provoking analysis in Nature Chemical Biology that is also relevant to developing new fragment libraries. 

The researchers ask why it is that HTS collections of a million or so compounds, vanishingly small in comparison to the roughly 1,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000 possible small drug-sized molecules, nonetheless so often succeed in identifying hits. A lovely paper by Tobias Fink and Jean-Louis Reymond had previously computationally enumerated all possible compounds with up to 11 C, N, O, and F atoms. Of these 26,429,328 molecules, 25,810 are commercially available. 

Shoichet and colleagues compared the structures of these compounds with the structures of metabolites and natural products (all of which have by definition been processed by at least one protein) and found that the commercially available compounds were much more similar to natural products and metabolites than were non-commercially available compounds. Indeed, the more similar a molecule is to a known natural product or metabolite, the more likely that it is available for purchase; 2918 of the commercially available compounds are in fact natural products or metabolites. 

The bias also increases exponentially with molecular size: a random 11-atom commercial compound is almost 1000-times more likely to resemble a natural product or metabolite than is a non-commercially available molecule, whereas the bias is only about 2-fold for 6-atom molecules. Similar results were observed with other libraries. 

The authors conclude that: A major reason why the screening of synthetic compounds ever finds notable hits is that our libraries are biased toward the sort of molecules that proteins have evolved to recognize.  

This resemblance is reasonable. After all, most commercially available compounds are ultimately derived from naturally occurring starting materials, so their similarity to natural products isn’t surprising. Moreover, historically much of chemistry was devoted to natural product synthesis, so many of the intermediates built up over the years resemble natural products. And of course, once you learn how to do chemistry on one moiety, you will tend to stick with it unless you have a good reason to do otherwise; each heterocycle behaves (often frustratingly) differently, so if a natural-product-like molecule does the job, why look for trouble? 

But does this “biogenic bias” mean that the rest of chemical space is a biological desert? Not necessarily. I can imagine at least two alternative models of chemical-biological diversity space. 

Let’s call one model “lamp posts in dark fields.” Consider a vast field of some crop that can only be harvested by night. There are lights scattered haphazardly throughout the field. One might expect that the crops immediately under the lamp posts would be harvested more intensively than crops in darker parts of the field, even if other areas are equally productive. In this scenario, the lamp posts reveal natural products and similar molecules, but much – or even most – of (unlit) chemical space may also be biologically active, it just hasn’t been sampled yet. 

Another possibility is the “oil-field model.” As we are all too aware, petroleum is distributed very unevenly across the globe. In some areas, such as Texas, oil was easy to find and easy to extract. In others, such as the deep ocean or the high arctic, oil is harder to find and more technically demanding to access. In this scenario, there are vast pockets of chemical space that are relevant to biology, they just haven’t been identified (let alone accessed) yet. 

These are fun, speculative questions, but the paper provides some practical data. Specifically, 83% of core ring scaffolds found in natural products are absent from commercial libraries. In fragment or lead-sized molecules of MW < 350 with less than three stereocenters, 1891 rings scaffolds found in natural products are not commercially available. These could be useful additions to fragment libraries, and the paper lists 18 examples. 

In fact, at least one company, deCODE, is explicitly enriching its fragment collection with molecules based on natural products and metabolites. This might be a good strategy. After all, even if the “lamp posts in dark fields” model is correct, there are plenty of brightly illuminated, unharvested chemotypes. At least for now, picking these may be more productive than venturing into the twilight-zone of uncharted chemical space.

Hidden Talent Pool

I have a question for the followes/readers here. Is there a hidden talent pool of recent Ph.Ds who have training in FBDD and can immediately walk into a pharmaceutical company? I am aware of two companies that had openings for FBDD people. Both companies wanted only freshly minted Ph.Ds or just post-doctoral experience. There is a big and deep (and obviously more expensive) pool of FBDD out there who would jump for these jobs.

I was also under the impression that industrial post-docs were largely a thing of the past. So, two questions: 1. are there academic programs/labs who are specifically training NMR-FBDD people, and more in general FBDD practitioners at all? 2. If not, are jobs like these a way for management to dip their toe in the water without actually resourcing FBDD efforts?

Fragments of the future

We recently provided a list of suppliers of commercial fragment libraries. A problem with buying compounds from these sources, of course, is that all your competitors can buy the same compounds. And while talented medicinal chemists are adept at turning common fragments into novel clinical candidates, it’s awfully nice to start with fresh fragments (see here for examples of both).

Researchers at UCB Celltech have recently taken this to an interesting extreme: they’ve computationally enumerated all neutral mono- and bicyclic 5 and 6 membered heteroaromatic rings containing carbon, nitrogen, oxygen, sulfur, and hydrogen. The resulting VEHICLe (virtual exploratory heterocylic library) is a set of 24,847 ring systems, of which only 1701 have been reported.

Of course, as the authors note, many of the remaining molecules “are outlandish and would obviously be either very difficult or impossible to make.” To address this, they used a machine learning approach to gauge synthetic tractability. This resulted in over 3000 molecules, some of which look quite reasonable:



Interestingly, the researchers estimate that only 5 to 10 of these heterocycles are being made each year, which leaves hundreds of virgin synthetic targets.

Are these a rich source of new fragments? Or, as the authors also speculate, do many of these lie outside biological activity space?

Size Doesn't Matter...Complexity Does...or does it?

With 16 days left to vote (Use you franchise!!!) 17 of 20 respondents have said that FBDD is integrated in the hit finding stage at their companies. WOW! and Awesome!
Lorenzo Williams posted a discussion topic on one of the LinkedIn groups (I of course forget which one) asking how many chemists does your company use for going from Hit to Lead. Below are the answers he shared with me...

Aurigene 8 FTEs
Arqule 1-5FTEs
Roche up to 10-13 FTEs
Bayer 1-10 FTEs
Shanghai Medicilon 6 FTEs
Chiron/Novartis 4-6 FTEs

Of course, the proviso was that it all depends on the complexity of the project. My question to this group, since we know so many of your have integrated FBDD in the hit finding stage, is: "Are FBDD based hits resourced differently than non FBDD hits?"

Commercial fragments – part 2

Teddy recently observed that it’s not the size of your library that matters, but how you use it. But how do you get a fragment library in the first place? You can of course build one up from scratch, but it may be easier to just buy one. Last year we put out a call for sources of commercial fragments and received some good comments. There are now some new suppliers of which we’ve recently become aware, so an updated list follows.

Enamine
1190 fragments w. strict “Rule of 3”
11,717 fragment extension set

Iota Pharmaceuticals
Focused on fragment-based discovery
1500 fragments available for purchase
4000 additional fragments in collaboration with Vitas-M

Life Chemicals
22,000 fragments w. MW < 300, clogP < 3
9000 fragments with rotatable bond, PSA, HBA limits

Maybridge
30,000 fragment library (MW < 350)
1000 fragments w. “Rule of 3” and solubility > 1mM
1500 Br- and 5300 F- containing fragments

Zenobia Therapeutics
352 very small fragments (Avg. MW 155)
Verified solubility at 200 mM

Does anyone have any experience using any of these? Are there others we’re missing? Let us know or post comments – anonymously if need be!

It's Not How Big it Is, It's How You Use It.

Boys will be boys, won't they. There is some gene buried on the Y chromosome that makes them think if something works as is, the bigger you make it the more success you will have. I bring this up because as we all know, many companies are slow to pick up FBDD; it is typically the "Hail Mary" pass, so success is slow in coming, as is uptake of the method as a primary tool. But once there is success, everybody jumps on the bandwagon. So, then the "crappy" little library you had needs to be increased in size (because obviously that's why it wasn't deliver superdrugs before); thank god for the medchemists!!!

So, what is the optimal size of a fragment library.
This is a table put together by the Good Folks® at Evotec for this fabulous book, with some added data bny the Editor of said Tome. What does this show us? FBDD libraries range all over in size. They obviously know something. What do they know?
No matter your screening paradigm, fragment hits need to be confirmed by an orthogonal method. So, even though you can blow through 50-100k compounds by a biochemical screen, you are still limited by your ability to confirm the hits by NMR or SPR or whatever.
If you don't think you need orthogonal methods (and really you do?) then your fragments need to be whistle-clean, because even low level impurities screened at 1-10mM can negatively impact the results of the screen. So, you think you have 10k-50k whistle-clean compounds?
Then, comes this [Shoichet and Austin (J Med Chem. 2008 Apr 24;51(8):2502-11)]. Aggregators!!!
Egads, it's like the Vikings crashing across the North Sea. So, to filter them out, you still need an orthogonal method(s).
So, creating massive (relatively) fragment libraries because you can screen them biochemically cheaply and quickly, doesn't buy you anything, because you still need to confirm them (typically by the same methods you chose not to use in the first place).

In conclusion, it's not the size, but how you use it.

Fragment linking: too strained, too flexible, or too positive?

One of the most rewarding fragment-based outcomes is to link two fragments together and get a pop in activity, as predicted by William Jencks almost three decades ago. Unfortunately, all too frequently linking two fragments gives a less than additive boost in binding energies, and often it doesn’t work at all. A new paper from James Stivers and colleagues published in Nature Chemical Biology investigates why.

The authors had previously discovered bipartite inhibitors of the DNA repair enzyme uracil DNA glycosylase (UNG): one piece of the inhibitor was fixed as the substrate uracil, and the other piece was chosen empirically from a library of fragments; the two fragments were each attached by rigid oxime connectors to a flexible linker. A crystal structure of one of these inhibitors bound to UNG showed that the uracil fragment does indeed bind in the uracil-binding pocket, while the other fragment binds in a nearby phosphodiester-binding pocket. However, the linker exhibited an unusual kink, suggesting it might be strained. The researchers have now followed up on this observation to explore the effects of varying the linker.

Four of the molecules made and tested are shown below; these vary only in how fragments connect to the linker. Importantly, the authors were able to determine co-crystal structures of all of these molecules bound to UNG. The most potent molecule, MA1, has a (flexible) secondary amine connection to the uracil fragment and a (rigid) oxime connection to the benzoate fragment; the linker appears unstrained in the crystal structure. In contrast, although the two fragments of DO bind in the same manner as the two fragments of MA1, the linker assumes the apparently strained “kinked” conformation previously observed, and the molecule binds with a thirty-fold lower affinity. In the case of the other two molecules in this set, the uracil fragment still binds as expected, but the benzoate is either not visible in the electron density (MA2) or binds to a nearby molecule of UNG in the crystal (DA). Both of these molecules are weak inhibitors, with IC50s comparable to that of uracil connected to the linkers alone, without the benzoate fragment (700-750 micromolar).



The authors argue that the greater linker flexibility in MA1 compared to DO reduces linker strain when the two fragments assume their optimal positions. They also suggest that the effect of the linker on the tighter-interacting uracil fragment will be less pronounced than on the more “loosely interacting” benzoate fragment; uracil’s specific binding interactions are able to overcome linker strain, while the weaker benzoate requires more precise positioning. In other words, the flexible connector to the uracil and the rigid connector to the benzoate give a Goldilocks-type situation for MA1.

This is reasonable. But there may be more than strain and flexibility at work here. In particular, introducing a (positively charged) secondary amine near the benzoate may have a considerable electrostatic effect on binding. The authors consider this possibility unlikely, and provide some evidence against it, but given that this enzyme binds to negatively charged DNA, I can’t dismiss it. Although the linkers are solvent-exposed, the electrostatic surface of UNG is quite positive, and in fact the benzoate linkage is not far from a histidine residue. It would be interesting to see whether replacement of the oxime linkages with similarly uncharged ethers or methylenes has the same effects as the amines.

Still, this type of systematic analysis is a valuable experimental addition to the field of fragments. I hope someone will do high-level computational modeling on these complexes to try to further dissect the origins of the differences in affinities.

More on DOCKing fragments and sampling chemical space

A few weeks ago, we highlighted a paper from Brian Shoichet’s group at UCSF demonstrating that computational screening could successfully identify fragments binding to a protein target, and that the binding modes predicted were actually observed experimentally. A companion paper just published online in PNAS now extends these results, and also beautifully illustrates that it is possible to cover much more chemical space with fragments than with lead-like molecules.

Denise Teotico, Shoichet, and colleagues used the program DOCK 3.5.54 to screen 137,639 fragments against AmpC beta-lactamase, a bacterial protein responsible for antibiotic resistance. The protein had previously been the target of HTS and computational screens of drug-like like molecules. The computational screens had modest success rates (2-7%), but the HTS screen was a total bust: of the more than 1200 hits from the 70,000+ compound screening collection, more than 95% of these turned out to be false positives, mostly aggregators, with just a few dozen true inhibitors, all of which turned out to be covalent (irreversible).

In contrast, of the 48 high-scoring fragments that were experimentally tested, 23 had Ki values better than 10 mM, for a hit rate of 48%. The authors also assessed potential for false negatives by choosing 20 random fragments and testing these for inhibition; only one showed inhibition (with a Ki value of 3.1 mM), and this molecule had scored in the top 5% of docked fragments.

The paper presents a fascinating empirical test of the Hannian chemical complexity hypothesis. Starting with the 23 active fragments, the researchers calculated how many lead-like molecules (up to 25 non-hydrogen atoms) could contain these fragments. Of the roughly 47,000,000,000 to 430,000,000,000 possible lead-like molecules, only 675 are commercially available. By repeating this analysis with fragment-sized molecules (up to 17 non-hydrogen atoms), the size of the haystack was reduced by six orders of magnitude: only about 10,000 possible molecules contain these fragments, of which 93 are commercially available. Moreover, many of the active fragments represent unique chemotypes not previously observed in AmpC inhibitors. As the authors note:

The chances of discovering interesting chemotypes for biological targets is many orders of magnitude higher when targeting molecules in the fragment weight range than even at slightly higher size ranges.

But, as the paper asks, “are the docking predictions right for the right reasons?” The researchers solved the crystal structures of 8 fragments bound to AmpC. Four of these reproduced the docking predictions well, two were somewhat different, and two were way off. In these last two cases, the protein itself adopted different conformations than had been used in the docking studies.

Protein conformational flexibility is remarkably common, and likely to be a persistent difficulty for computational methods. Clearly, current computational methods can’t identify all possibilities, particularly with fluxional proteins. Still, especially with relatively rigid proteins, computational fragment-screening may reveal chemotypes that HTS won’t.

A notable feature of the fragments is their relatively poor ligand efficiency: with one unusual exception (a phosphinate), all of the active fragments have ligand efficiencies less than 0.3 (kcal/mol)/atom. AmpC has a large, open active site, and the authors suggest that the failure of other hit-ID methods against this target may reflect issues such as solubility.

It remains to be seen whether these fragments can be advanced to low nanomolar inhibitors, but at least fragment-screening has provided many new starting points. And the paper demonstrates, once again, that triaging a fragment set computationally can be an effective means for concentrating the needles in a haystack.

Updated Again: Fragment Events in 2009

It seems the fragment calendar was front-loaded this year, but there are still a few upcoming events.

June 8-9: GTCbio is holding its “Fourth Assay Development and Screening Technologies” conference in San Francisco, and there is one session on fragment-based screening. (Full disclosure: I’ll be speaking at that one - stop by and introduce yourself!)

June 26: Select Biosciences has a “Fragment-Based Lead Discovery Summit” in London. It’s only one day, but there are a number of great speakers.

September 21-23: Last but much-anticipated, FBLD 2009 will be held in York, UK.

Regarding previous events, RSC’s Fragments 2009 has been covered both here as well as on FBDD-Lit. There is also an excellent eBriefing of the NYAS symposium on molecular diversity that can be found here.

As always, let us know if we’ve missed anything and we’ll get the word out.

LELP fragments reach their potential

In last month’s issue of Nature Reviews Drug Discovery, György Keserü of Gedeon Richter and Gergely Makara of Merck published a thought-provoking analysis of recent trends in lead discovery. Their results illustrate the potential of fragment-based methods, but also point out the still sizable gap from current practice.

The authors assembled a database of 335 hit-lead pairs derived from high-throughput screening (HTS) that were published between 2000 and 2007. They also assembled a database of 84 non-HTS hit-lead pairs published between 2000 and February 2008, consisting of fragment-based, virtual screening, natural product, and miscellaneous examples. They then compared properties – such as potency, molecular mass, logP, logS (a calculated measure of solubility), and ligand efficiency – of the initial hits with the resulting leads.

The results for HTS hits are not pretty: the lipophilicity as assessed by logP was considerably higher on average for HTS hits than for hits from any other methods, and this only increased as the hits were progressed to leads. The same goes for (in)solubility (as measured by logS). Even more alarming, the average properties of even the hits are worse than those of a collection of 541 approved drugs.

Fragment hits start out with the lowest lipophilicity and highest predicted solubility, but during hit-to-lead optimization these properties deteriorate to the point where they are similar on average to leads derived from HTS. Also surprisingly, the ligand efficiency of fragment hits and leads are, if anything, lower than their HTS counterparts, contrary to expectations. Even the average molecular weight of fragment-derived leads is not much lower than HTS-derived leads.

So what’s going wrong? The authors point out that, at most larger companies, fragment-based approaches are often only attempted after HTS has failed, suggesting that the targets tackled by fragment-based methods may be inherently more difficult. But they also suggest that in the early stages of hit-to-lead optimization the primary measure of success is how many compounds are delivered to lead optimization, which could encourage rapid hit expansion with simple chemistries to rapidly boost potency by adding grease, leading to more hydrophobic, less soluble molecules that will ultimately struggle in the clinic.

The authors suggest a new metric, ligand-efficiency-dependent lipophilicity, or LELP, to help avoid this trap:

LELP = (log P / LE)

Since a desirable logP range is between 0 and 3, and a desirable ligand efficiency is above 0.4, one should strive for LELP values between 0 and 7.5. There are already lots of metrics out there for evaluating molecules: see, for example, discussions of %LE, antibacterial efficiency, and fit quality (also here and here). Is a new one really necessary? Perhaps, if it gets people to focus on non-lipophilic means of increasing potency.

The authors end on a positive note for fragments:

Bearing in mind the sampling of chemical space, hit properties and synthetic accessibility, we consider that fragment hits are the optimum starting points for lead discovery and optimization.

There is, however, a burden on the team transforming a fragment hit into a viable lead: it is important to focus not merely on improving potency, but on maintaining as many of the fragment-like properties that make fragments attractive starting points in the first place. Although this goes without saying, analyses like this one suggest that it still needs to be said – and heard.

Book Review on FBDD Book

There is a review just publish ASAP in JACS of the Zartler and Shapiro edited book on FBDD. Overall, it is a nice review (Thanks to Andrew and Phil). There is one error (chapters 4-11 are EIGHT chapters, not seven) and one critique that I want to address.

Although there is a paragraph in Chapter 3 covering X-ray methods, which are mentioned in the introductory chapters, it would have been nice to have an entire chapter dedicated to these methods as several groups in industry have applied them successfully.

Mike and I made a conscious choice NOT to include any chapters on X-ray. We thought that of all the methods for FBDD X-ray has been done; there was nothing new to the field that our book could contribute. The focus of this book was on practical applications and newer techniques. There are a plethora of nice reviews out there, X-ray focused companies, and three chapters entirely or mostly about using X-ray in the Jahnke and Erlanson book.

What are the feelings of others? Did we swing and miss by leaving that topic out of the book or is X-ray the "mature" FBDD method? I would argue (and did in my editorial choice) that there is little left to say about X-ray that hasn't already been said.

[Update]: Zartler and Shapiro:Amazon.com Sales Rank: #1,595,845 in Books.

Jahnke and Erlanson: Amazon.com Sales Rank: #1,238,437 in Books

Nuclear Magnetic Crystallography part II

In what can only be seens as a cosmic convergence, a second paper has appeared on NMR-X-ray hybridization. This one is a collaboration from Medivir, The University of Florence, and Bruker Biospin. This method is aimed at generating structural information for a family of related proteins (in this case MMPs). The authors argue that the cost of 13C and 15N labeling is so low that such samples should be readily available, making this method widely applicable. The thrust of their method is the use of X-filtered NOESY spectroscopy to generate distance constraints, then use Autodock to determine binding. I expect that this will get covered on our FriendBlog, the FBDD-Lit Blog, so I won't go into many details.
Instead I would like to make this a discussion of the perceived value of methods such as this to the FBDD community. I, despite being an NMR jock by trade, don't feel that labeled protein methods, give enough bang for the buck, compared to ligand-based methods. Will methods such as described Isaksson et al. change that cost-benefit analysis?
What do other people think about the value and the proper role of NMR?

Nuclear Magnetic Crystallography

There is often a competition, explicit or implied, between NMR spectrometrists and X-ray crystallographers. A new technique merges the best of both worlds

Researchers at the University of Shutka, Russia, have constructed a unique NMR spectrometer with a hole bored all the way through the magnet and probe, perpendicular to the main sample cavity. This hole allows the researchers to send an X-ray beam directly into a crystal mounted in a specially designed sample chamber, allowing them to screen for compound binding by NMR while simultaneously obtaining crystal structures. Of course, due to the solid state (crystalline) form of the protein, the researchers can’t actually detect the protein itself by NMR, but by mounting the crystal in a flow cell and testing pools of fragments, they can use target-based NMR to determine which fragments bind to the protein. Once they find a fragment that binds, they can then immediately obtain the crystal structure. The researchers are planning to bring their NMR to a synchrotron to have access to a brighter X-ray source.

Will the technique become widely accepted? If so, this could be the start of a beautiful friendship.