Who's Doing FBLD, 2014 Version

It's been a while since we updated this list.  The first list had 24 companies, the second 44.  From 2011 to now, what's changed?

I have divided the list into (primarily) providers of services and pharmaceutical companies. This does not mean that that providers don't do their own discovery, or work in a mixed-model.  I have annotated those companies which were not previously on the list.  Please note that this does not necessarily mean they were not doing FBDD previously, they just were not listed.If we missed someone, let us know and we will update the post.

Providers:
Ancorex  (New 2014)
Beactica
Biodesy  (New 2014)
Biofocus(Galapagos)  Acquired by Charles River Laboratories
Biosensor Tools
BioSolveIT
Chemical Computing Group   (New 2014)
Crelux
CrystaX Pharmaceuticals  Acquired by Oryzon Genomics
Domainex   (New 2014)
Emerald BioStructures
Exscientia   (New 2014)
Evotec
Graffinity  Acquired by NovAliX 
iNovacia  Acquired by Kancera
Infarmatik
Intellisyn (New 2014)
IOTA Pharmaceuticals
Kinetic Discovery
MEDIT
Molsoft (New 2014)
Nanotemper (New 2014)
NMR Research  (New 2014)
NovAliX
Pharma Diagnostics
Proteros 
Pyxis Discovery  (Are they still an ongoing concern?)
Red Glead  (New 2014)
Saromics (New 2014)
Schrodinger
Selcia
SensiQ  (New 2014)
Structure Based Design
Viva Biotech  (New 2014)
Zenobia Therapeutics
ZoBio

Companies: 
Abbvie             split from Abbott
Amgen             (New 2014)
Ansaris (previously Locus)
Ariad               (New 2014)
AstraZeneca
Astex              Acquired by Otsuka(2013)
BioLeap
Boehringer Ingelheim
Bristol Myers Squibb
Carmot Therapeutics
Constellation Pharma
Crown Biosciences 
Dart Neuroscience
Eli Lilly
Genentech (Roche)
Genzyme Acquired by Sanofi-Aventis
GlaxoSmithKline
Heptares
Johnson & Johnson 
Merck
Nerviano Medical Sciences
Novartis
Pfizer
Plexxikon    Acquired by Daiichi Sankyo (2011)
Polyphor
Roche
Sprint Bioscience
Takeda California
UCB   (New 2014)
Vernalis
Vertex

**UPDATE** 27Mar2014: added Domainex, UCB
**UPDATE2** 28May2014: added MolSoft, Ancorex

Fragments vs MCL-1, again and again

Last year we highlighted a paper from Stephen Fesik’s group at Vanderbilt in which he used SAR by NMR and fragment merging to identify nanomolar inhibitors of the protein MCL-1, an anti-cancer target that had previously been thought to be impervious to small molecules. In a recent paper in Bioorg. Med. Chem. Lett., Andrew Petros, Chaohong Sun, and other former colleagues of Fesik at AbbVie describe two additional series of inhibitors.

The researchers started with an NMR screen using MCL-1 in which the methyl groups of isoleucine, leucine, valine, and methionine were ¹³C-labeled. Screening this against a library of 17,000 fragments in pools of 30(!) gave dozens of hits, some of which inhibited in a biochemical assay (for aficionados, they assessed binding to the BH3 domain of Noxa using fluorescence polarization as a readout).

Fragment 1 turned out to be fairly potent, though it is super-sized and violates the rule of three. The researchers were unable to get co-crystal structures of any of their fragments bound to MCL-1, but they were able to use NOE-based NMR experiments to develop a model of how fragment 1 might bind. This led them to synthesize a number of analogs such as compound 17, for which they were able to obtain a co-crystal structure with the protein, ultimately leading to the mid-nanomolar compound 24.

Fragment 2 was much less potent than the other fragment but had a considerably higher ligand efficiency. In this case simple modeling suggested growing away from the acidic portion of the molecule, leading to compound 36 (which was characterized crystallographically bound to MCL-1) and the more potent compound 44.

Overlaying the co-crystal structures of compounds 17 (blue) and 36 (red) reveals that they both bind in the same region, where Fesik’s compound 53 (green) also binds. All three molecules place a carboxylic acid in a similar position, but the two more potent molecules thrust a hydrophobic moiety deep into a pocket of the protein. It is tempting to speculate that compound 44, the more potent analog of compound 36, may also take advantage of this pocket.

Andrew Petros presented some of this work at FBLD 2012, so it is nice to see it in print. Though reasonably potent, it is worth keeping in mind that the molecules are also quite lipophilic. Perhaps it is significant that, like the Fesik paper, no cell-based data are presented. Collectively, though, these papers establish that MCL-1 is ligandable. Whether it will be druggable remains an important – and as yet unanswered – question.

PAINS propagation

PAINS, or pan-assay interference compounds, comprise a subject that has cropped up several times here (and here, and here, and here). Although not exclusive to fragments, I wanted to point out a thorough and insightful analysis by Jonathan Baell over at HTSPAINS in which he traces the lineage of a dubious series through paper after paper all the way back to 2001. The assays, models, and mechanistic theories all change, and the molecules keep getting uglier as they devolve from chalcones to bis-benzylidenepiperidones. It’s an entertaining and educational look at sloppy science. He ends with an important point:

People still don’t realize how easy it is to get a biological readout. The more subversive a compound, the more likely this is.

Indeed, even with decent looking molecules it can be difficult to figure out exactly what is going on; with PAINS you may as well start explaining things in terms of phlogiston and humorism.

I can’t help thinking of the late Efraim Racker’s admonishment: “don’t waste clean thinking on dirty enzymes.” Even when they are chemically pure, PAINS molecules are mechanistically dirty. The amount of effort wasted on them boggles the imagination, so keep them out of your libraries!

This is another way to do it.

The key to doing something right is to following the directions.  How closely you follow the directions, or don't follow, can be the difference between brilliance and just a good performance, e.g. cooking.  Sometimes, directions are meant as guidelines, like the Pirate Code or the Voldemort Rule.  Late last year, and blogged about here, I published a paper in Current Protocols on how to prosecute an STD screen.  A recent paper in PLOSOne, shows how someone else runs their screens, but with details on library construction, solubility testing, and more.  What makes this paper of interest is the level of detail that they provide.

Library Design: They assembled a diverse fragment library with the following rules: 110≤ molecular weight ≤350, clogP≤3, number of rotable bonds ≤3, number of hydrogen bond doners ≤3, number of hydrogen bond acceptors ≤3, total polar surface area ≤110, and logS_w (aqueous solubility) ≥ −4.5. 

I am little confused by the figure and what the text says.  In the text, they seem to have relaxed the MW cutoff, but the figure shows that anything not Voldemort Rule compliant is tossed.  They also preferred that the compound has at least one aromatic peak (for easier NMR detection).  They purchased 1008 from Chembridge, solubilized at 200 mM in DMSO-d6 (ease of NMR detection, again) and then tested the solubility at 1 mM in water.  I would have added some salt here, 50 mM, but that is a quibble.  For purity, they claim a low level of impurity (< 15%)!!!  To me, this is a whole lot of impurity.  But, as has been noted here, purity levels vary from library to library.

Solubility Testing:  They then made sure to experimentally test every fragment for solubility.  I can agree more emphatically with this approach.  Bravo!  They go into great detail, which I will not attempt to replicate here, but thanks to open access, they have included the scripts in the supplemental.  Acceptable compounds had > 0.1 mM aqueous solubility.  For me, this is too low, but to each their own.  They ended up with 893 total fragments (89% passed).  The real data I would like to see is how many fail if the cutoff is set at 0.5 mM or higher.  

Pooling: They then describe their pooling strategy.  I like open access articles for a lot of reasons, and tend to overlook small editorial problems (typos, grammar, etc.), but in this case, let me rant.  The authors state in the text that a random mixing of compounds would lead to severe overlap, exemplified in 3a.  To me, it does no such thing. 

Their approach is very similar to the Monte Carlo-based one that has previously been discussed on this blog.  Their final pools contain 10 fragments at 20 mM (I assume in 100 % DMSO-d6). 

Screening: They also acquired the 1H spectrum, STD (-0.7 ppm, > 1 ppm from any methyl), and WaterLOGSY spectrum of every pool for future reference.  This is a very clever approach as the STD should give no signal while the WaterLOGSY should give inverted peaks for all compounds in the pool (when interacting with a target they will be "right-side up").  Again, the figure may show that (I think if you blow up the figure the WaterLOGSY spectra does have peaks) but it is very difficult to see. 

Three of the 90 pools (3.3%) showed peaks in the aromatic region, most likely due to aggregation (they observed precipitation).  I would like to know if those compounds showed STD peaks also had those methyl groups within 1 ppm of the saturation frequency.  I would also like to know if they removed those compounds from the library, or just dealt with it.  For a paper with a great level of detail, it falls flat in this respect.  

Screening is performed at 10uM Target: 500uM ligand and the following parameters: acquisition time of 1 s, 32 dummy scans, and relaxation delay of 0.1 s, followed by a 2 s Gauss pulse train with the irradiation frequency at −0.7 ppm or −50 ppm alternatively. The total acquisition time was 15 minutes with 256 scans.

Screen Analysis: One of the first things they noticed was that there were difference between the reference spectra (plain water) and the screening sample (protein buffer).  They decided they could not automate the entire process and instead just scripted the data processing and display.  Then they confirmed each putative active as a singleton. 

What they are putting together is a "One Size Fits All" process.  I give them credit for doing this, but I think that you cannot find a single NMR-based process for all targets.  In particular, I think they could have used more typical conditions for the reference spectra.  The paper then goes on and discusses their application to targets of interest.  For me, that is irrelevant.  This paper is an excellent companion to the Current Protocol paper, and due to open access, most likely to get far more citations.

Off-rate screening (ORS)

Molecules that dissociate slowly from their target proteins are potentially useful because they can have a long-lasting effect even if they are rapidly cleared from circulation. However, it is next to impossible to predict whether a molecule will dissociate slowly or not. Moreover, the correlation with binding affinity is poor: weak binders generally don’t stay bound to their target for long, but even tight binders often rapidly dissociate. In the early stages of lead discovery most folk are focused on affinity, and it is usually only much later that kinetics enters in. In a new paper in J. Med. Chem., James Murray, Paul Brough, and colleagues at Vernalis introduce a technique that moves kinetics to the front of the line.

The technique, off-rate screening (ORS), relies on surface plasmon resonance (SPR), which is already commonly used to study binding kinetics. The trick here is using SPR to screen products in unpurified reaction mixtures. An initial fragment with known affinity is modified, and products screened for slower dissociation. Of course, the concentration of desired compound is likely to vary from mixture to mixture, but the great thing about looking at compound dissociation is that it is a zero order reaction: it does not depend on concentration. The researchers use mathematical simulations to show that even if the yield is only 5%, a product with a 10-fold slower dissociation rate constant could still be detected. Since off-rates can vary by orders of magnitude, this is not such a high bar.

Of course, simulations are one thing, but how does the technique actually work in practice? The researchers show examples on two targets, one using some of the early compounds for their HSP90 program, the other some of their PIN1 inhibitors. For PIN1, the researchers resynthesized some of the molecules in plastic tubes, which caused leaching of plastic into the reaction mixtures. Nonetheless, for both proteins the dissociation rate constants measured for unpurified reactions were very close to purified molecules, generally differing by less than 30%.

The researchers also tried subjecting compounds to eleven reaction conditions typically used in medicinal chemistry, evaporating the solvent, and testing the products; the idea was to see if the reagents or other components in the reaction mixture would interfere with the assay. Happily in all cases the dissociation rate constants differed by less than 20%, again pointing to the robustness of ORS.

Of course, as with any technique, there are limitations. Since the screening compounds are not purified from their starting materials, the desired products must dissociate sufficiently slowly from the protein to be distinguishable from other components in the reaction mixture; dissociation rate constants greater than about 1.2 s^-1 appear to be challenging. Also, if the starting material itself has a slow dissociation rate from the protein, it may be difficult to differentiate this from a low yield of slowly dissociating product. The researchers note that both cases could be addressed by changing the temperature, either lowering it to slow the dissociation rate constant or raising it to increase it.

All in all this is a nice approach, and it will be interesting to see how widely it catches on.

Flexible fragment linking vs the transcription factor EthR

Transcription factors have a well-earned reputation for being extremely difficult targets. Although the literature is littered with inhibitors of various transcription factors, most of these turn out to be of questionable validity, to put it politely. A recent paper in Biochem. J. by Sachin Surade, Tom Blundell, and collaborators at the University of Cambridge and the Ecole Polytechnique Fédérale de Lausanne-EPFL reports what looks to be the real deal.

The researchers were interested in a protein called EthR, a transcription factor from Mycobacterium tubercuolosis involved in antibiotic resistance. Unlike many other transcription factors, this one contains an allosteric binding pocket known to bind lipophilic molecules. Armed with this knowledge, the researchers performed a thermal shift assay using a library of 1250 fragments at 10 mM each, which resulted in 86 hits that stabilized the protein by at least 1 °C. These were then tested for their ability to disrupt the interaction between EthR and DNA using surface plasmon resonance (SPR), and 45 of them showed greater than 10% inhibition at 0.5 mM. Reassuringly, only 1 of 45 fragments that had shown no stabilization in the thermal shift assay showed more than 10% inhibition here, suggesting that the thermal shift assay had a low false negative rate.

Confirmed hits were characterized by full dose-response curves and soaked into crystals of EthR, resulting in several co-crystal structures. Compound 1 was particularly interesting because two copies of it bound to the central hydrophobic channel, which was only possible due to conformational changes in the protein. Also, although the likely natural ligand of EthR appears to make only hydrophobic contacts to the protein, the carbonyl of compound 1 makes hydrogen bonds. In one of the two bound molecules, the interaction is with an asparagine residue of EthR; in the other, it is with a water molecule.

Swapping the cyclopentyl ring to a phenyl to yield compound 5 gave a slight loss in potency but simplifies subsequent modifications, and crystallography revealed that it binds in the same manner as compound 1. More significantly, linking two molecules of compound 5 via a disulfide bond (compound 9) improved the affinity by more than two orders of magnitude.

Of course, disulfides can react with cysteine residues in a protein – a fact that can be rather useful for finding inhibitors. Thus, it was essential to demonstrate that compound 9 was really binding non-covalently to the protein rather than acting through an unrelated mechanism. Happily, the researchers were able to determine the co-crystal structure of compound 9 bound to EthR, confirming that it binds in the same manner as the two molecules of compound 5, including the two hydrogen bonds. (Unfortunately though, none of the crystal structures appear to be deposited in the protein data bank.)

Compounds 1 and 9 were both tested for their activity to enhance the effect of the antibiotic ethionamide in Mycobacterium tubercuolosis cultures, and both were active, though with similar potencies despite their very different affinities to the isolated protein; it seems likely that the disulfide bond would be reduced in the bacterium. It will be interesting to replace this with a more stable linkage (amides were also tried but did not improve affinity).

One interesting conclusion is that “flexible fragments in the library can lead to a more efficient exploration of chemical space.” This is exemplified by the fact that floppy fragment 1 binds in two somewhat different conformations to the two sites on the protein. Having some flexibility in the early stage of a project can be useful, and another reason not to be too rigid in assembling a fragment library.

FABS-ulous Screening Against Membranes

The blog is running on a 2 hour delay today thanks to the winter storm (we actually got a nothing burger here from it).

If you follow this blog, and actually read what I say, you will know I have a 19F-fetish.  Thus, whenever another paper comes out, I gravitate towards it.  Claudio Dalvit is really one of the primary (if not THE) drivers of 19F NMR screening development.  He has been discussed on this blog often.  Most recently, back in October, when he published an example of n-FABS against a membrane target, FAAH.   Now, he is back with this paper: "Fluorine NMR-based Screening on Cell Membrane Extracts".  I was immediately transported back to my days at Lilly where in our group we came up with the great idea to try to screen (using STD) against crude membrane preps.  I don't remember much but my lab mate being unsuccessful in the end for any number of reasons.  Obviously, the development of a robust, biophysical technique which can be applied to intact cells, cell lysates, or membrane preps would be a significant addition to the entire biophysical toolbox.  Currently, only biochemical assays largely based upon fluorescence can do this.  n-FABS, as decribed previously, relies on the substrate of the target (which is labeled with at least one 19F atom) being converted by target action into product, and thus causing a chemical shift change in the 19F.  This is easily detected by NMR and voila, an assay is born. This work is an extension of the previous work on FAAH and very similar to this work by Brian Stockman.  The 19F chemical shift of substrate and product are easily differentiated and roughly quantitatable:

The proper controls showed that this activity is solely due to the TOI.  What makes this assay so appealing is shown in the next figure:

This figure shows the 1H spectrum of the reaction at 2hr (top) and 24hr (bottom).  There is virtually no difference in this spectrum, indicating that it is impossible to follow the substrate due to large signals from detergents and endogenous protonated signals.  For me this is the key to this.  We all know membrane proteins are hard to do, especially with fragments.  I have always wondered where 19F fits in the biophysical toolbox, especially in light of recent discussions where it presumed that 19F could out perform 1H.  In discussions, I have said that 19F runs circles around 1H when the ligands are highly aliphatic.  Well, this is the converse, and still just as true, when the sample matrix is ugly with "other stuff", in this case the stuff that keeps the target in solution.  One major drawback is that this approach is NOT a binding approach, and thus would be of limited utility against non-enzymatic membrane targets, such as a majority of membrane targets.  In the majority of membrane targets, SPR may be the most robust approach.