Fragments versus Ras

The protein Ras is one of those cancer targets that’s been around forever and has rebuffed countless attacks by many researchers using multiple strategies. The most obvious ligand pocket is the one where GTP and GDP bind. Unfortunately, these molecules bind with picomolar affinity, and they are present at very high concentrations in cells. To try to find an alternative small molecule binding site, Guowei Fang and colleagues at Genentech took a fragment-based approach, and have reported their results in a recent issue of Proc. Nat. Acad. Sci. USA (as well as at a recent meeting).

The researchers used 1D NMR screening (STD) to screen 3300 fragments in pools against GDP-bound KRas; 240 hits were retested as single compounds and further validated by 2D NMR (HSQC). This resulted in 25 confirmed hits. Surprisingly, all of them appeared to bind to one region of the protein some distance from the GDP-binding site. Subsequent crystallography confirmed that these fragments bind to a small pocket about 250 ų in size. However, there could be more here than meets the eye, as this is a fairly flexible region of the protein, and the pocket changes shape in response to different fragments.

 At least one of the fragments, DCAI, not only binds to Ras, it also inhibits the association of Ras with the protein SOS, thereby blocking nucleotide exchange and Ras activation. Interestingly, this blocking activity was distinct from binding activity; the fragment BZIM has comparable affinity as judged by NMR, yet does not inhibit the interaction with SOS.

Somewhat surprisingly given its low affinity, DCAI is also active in cell-based assays. And although the molecule is still a long, long way from a drug, the results are encouraging. Perhaps a fragment-based approach will finally succeed against this target. Or perhaps Ras will yet again reveal its intransigence.

Fragment linking leads to nanomolar leads for LDHA

The metabolic changes seen in cancer cells were first observed decades ago, but only recently have companies gotten serious about exploiting these changes for new therapies. One potential target is the enzyme lactate dehydrogenase, in particular the LDHA isoform. In a paper recently published in J. Med. Chem., Richard Ward and colleagues at AstraZeneca describe a fragment linking strategy to generate nanomolar leads for this enzyme.

An internal HTS campaign gave a hit rate of about 1%. Worse, none of these hits confirmed in ligand-observed NMR assays, and follow-up studies suggested that the activity was often caused by heavy metal impurities, particularly silver. (This is yet another form of false positive of which to beware.)

When HTS doesn’t succeed, fragments start looking more attractive, so the researchers used their NMR assay to screen about 1000 fragments in pools of 6, with each fragment present at 0.2-0.4 mM. This resulted in 44 hits; of the 27 of these chosen for follow-up 13 gave quantifiable binding, with affinities ranging between 0.3-4.2 mM. Happily, some of these (such as compound 12, below) could be soaked into crystals of LDHA, and structure determination showed these fragments bound in a pocket that normally binds the adenine portion of NADH. An SPR assay was developed and used to screen 350 analogs of some of these hits, and although some improvement in potency could be seen, ligand efficiencies remained more or less the same. (All Kd values in the figure below are taken from SPR data.)

The active site of LDHA is quite long, and so the researchers sought to span its length to obtain decent affinity. The adenine pocket where the identified fragments bind is located some distance away from the site where LDHA’s product lactate binds, and so linking fragments from both pockets would generate molecules spanning the desired region. Finding fragments that bind in the lactate pocket posed a challenge, however, as none of the first set of fragment appeared to bind there. Because lactate is negatively charged, the researchers assembled a specially-tailored 450 fragment library with a high proportion of acids and screened compounds at 2.5 mM using SPR. This screen resulted in 40 hits, and although many of them were nonspecific (they also bound to denatured LDHA!) some hits were specific, including compound 20.

A crystal structure revealed that fragment 20 bound, as expected, some distance from fragment 12, so the researchers generated libraries around both fragments to try to help bridge the gap. About 150 analogs were made around compound 12 and about 70 analogs were made around compound 20, resulting in compounds 24 and 25. Although not necessarily more potent than the initial fragments, crystallography revealed portions of these that were positioned more closely to one another, and linking them to form compound 26 gave a very satisfying boost in potency. This was actually the first linked compound made, and it was also the first compound to show activity in the enzymatic assay. Subsequent optimization was able to drive the potency down to low nanomolar (compound 34). Not surprisingly, the acidic nature of the compounds precluded cellular activity, but some diester derivatives showed sub-micromolar activity.

This is a thorough and engaging account of how fragment-based methods can tackle a difficult target. Although the compounds still need work, they represent good starting points for further optimization and for better exploring the validity of LDHA as a cancer target.

And a Reviewer Responds

A recent paper in J. Med. Chem was reviewed on this blog.  It was also noted by In the Pipeline as one of the worst papers ever published in that journal in a long time.  Today, both ITP and PF received an email from one of the reviewers of the paper, Dr. Elizabeth Howell. ITP has a post about this and a correspondence from the main author of the paper here.  I am reproducing her entire email to us below:

Dear Teddy Zartler and Derek Lowe,

I was made aware of your blogs on the Bastien et al. paper (April 18^th, How do these things get published? &April 17^th, Sometimes your compound sucks). I am an enzymologist and I was one of the reviewers of the paper. I would like to respond to your comments.

I have worked on DHFR since the 1980s. You may perhaps be unaware, but there are 2 different types of DHFRs. The chromosomal DHFR is the target of many drug design efforts and is the more well-known enzyme. A protein conferring resistance to trimethoprim, typified by R67 DHFR, was identified in the 1970s. This R-plasmid encoded DHFR (a type II DHFR) has an entirely different scaffold than the chromosomal DHFRs. I wonder if you thought the Bastien et al. paper was describing drug design efforts for the chromosomal DHFR and this misunderstanding is at the root of your concern? For example, Dr. Zettler [Ed: Zartler] used the term “mutant DHFR” in his post. Is an apple the same as a mutant orange? To me, R67 DHFR and chromosomal DHFR are like apples and oranges. While these 2 DHFRs catalyze the same reaction, they have different protein folds, different oligomerization states, different active sites (R67 has a single active site pore while chromosomal DHFR has a typical binding cleft) and the enzymes use different transition states (endo vs exo).

FYI, I am attaching a pdf file [Ed:shown below as two jpgs] showing the 2 different structures and a Table comparing the binding constants and active site volumes of the 2 enzymes (emailed to Drs. Lowe & Zartler).

A few observations about R67 DHFR (I am happy to provide citations supporting these data if requested):

●R67 DHFR is a homotetramer and each monomer has a fold that is related to an SH3 domain.

●The R67 tetramer has exact 222 symmetry and a SINGLE active site pore.

●For each cofactor (or substrate) binding site in R67 DHFR, there must be four related sites from the symmetry. However a total of 2 ligands bind per pore, either 2 substrates, 2 cofactors or 1 molecule of each. Only the latter is productive and results in catalysis.

●The classical chromosomal DHFR inhibitors (trimethoprim & methotrexate) do not inhibit R67 DHFR very well (K_i values of 150mM and &500mM respectively).

The above observations (& others) lead to the hypothesis that R67 DHFR has a promiscuous binding surface that can accommodate both ligands. The 222 symmetry imposed on the single active site pore of R67 DHFR is utilized by Bastien et al. in their drug design process as they construct "dimeric" ligands. Again, I wonder if the bloggers did not realize a different DHFR was targeted, as for example, Dr. Lowe says “The authors string these things together into huge dimeric molecules, apparently because they think that this is a good idea, but they get no data to support this hypothesis at all.” The hypothesis of Bastien et al regarding symmetry related binding sites comes from the previous crystal structures of the R plasmid enzyme, by stoichiometries derived from time resolved fluorescence and ITC binding studies and by the ability of R67 to catalyze a transhydrogenase reaction (albeit weakly).

With regard to the bloggers comment on electon density interpretation, It is the 222 symmetry that makes it difficult to deconvolute electron density of crystal structures describing bound complexes in R67 DHFR , please see the attached figure of the symmetry problem in this enzyme.

In regards to my review of the paper, I read the paper carefully and wrote 2 pages of comments in my initial review. I also read the revised manuscript. In my opinion, the paper was a first step in the design of an inhibitor that would target this unusual binding site and have some specificity for the R67 DHFR vs. the chromosomal DHFR. To my knowledge, this is the first drug design effort to target R67 DHFR activity. I stand by my decision, the paper is worthy of publication.

Sincerely, Liz Howell, Professor, Biochemistry, Cellular & Molecular Biology Dept., University of TN, Knoxville, TN

Comments

Dan and I have been talking about the blog here at the CHI Drug Discovery conference we are both attending. The blog was born of a night having beers here five years ago. We were wondering why we barely ever get comments (and if you exclude Peter Kenny the total asymptotically approaching zero). If you just look at the blog we look like two cranks writing a blog no one reads (and Dan would be the crazier one because he writes more of the posts). However, our web traffic is good and constantly increasing. We have wondered if it is because people don't want to comment from work (where Big Brother is always watching). Or if people have nothing to say (I find that hard to believe). So, what can we do to get you all to comment more?
Dan's latest post on  the FBDD conference is a good example.  I KNOW many of your were there, but no one has anything to add?

Seventh Annual Fragment-Based Drug Discovery Meeting

CHI’s annual FBDD meeting took place in San Diego this week, and since this was the first time in a while both Teddy and I have been in the same place we’ve decided to make this a joint post. As with last year this does not aim to be comprehensive.

One of the highlights of the conference was a set of three talks on BACE1 inhibitors from Amgen (Ted Judd), Lilly (David Timm), and Pfizer (Ivan Efremov), the first two of which have been discussed here and here. It’s nice to see fragments playing a pivotal role in delivering advanced leads and – at least in the case of Lilly and Merck – clinical candidates against what has been one of the most difficult drug targets in industry.

Speaking of difficult targets, Till Maurer of Genentech gave a lovely presentation on using NMR-based fragment screening to discover inhibitors of the holy grail of oncology, Ras. They’ve recently published some of this story, which we’ll highlight in an upcoming post.

A common question is ‘how often do you find the same fragment using different methods?’ (see here for an ongoing discussion on LinkedIn). Cynthia Shuman from GE gave a nice case study in which she screened the protein PARP15 against 987 fragments using a Biacore T200. Of the 15 fragments with shapely, well-behaved sensorgrams, 14 were confirmed by NMR. On the other hand, only one of these hits was detected in a differential scanning fluorimetry (thermal melt) assay.

Marcel Verdonk at Astex described general trends from mining in-house and published data. After looking at 43 in-house targets, he found that 8000 compounds had been tested against 2 or more proteins, and after plotting by molecular weight found that, consistent with the original Hannian model, larger compounds are more selective. In a separate analysis of 53 fragments that had been advanced to leads, he found that in most cases the initial fragment maintained roughly the same position and orientation from start to finish.

Rod Hubbard, Teddy and I all ran round-table discussions, but only Teddy kept notes, which are summarized here.

The topic started as a discussion of 2D vs. 3D fragment libraries. In the recent Pfizer fragment build, a group of diverse chemists eyed every compound, and at least five had to agree to each molecule before it went into the library. 

The discussion went briefly to the old fight: Are nitros masked amines or noxious moieties?  The table ended up agreeing that if you would remove the nitro group or change it anyways, why put it in the first place?

We then dove right back into the 2D vs. 3D debate. Kinases seem to love 2D fragments, while other classes of targets seem to NEED 3D fragments. One idea discussed was 3D fragments as complements to 2D fragments. It was mentioned that 3D fragment libraries would need to be MUCH larger to cover equivalent chemical space. I thought the idea that 3D fragments would be exploring “vector” space rather than “chemistry” space would mean that you could go with a much smaller library, if you want to use it for vector space searching. It was also proposed that 2D fragments tend to be much smaller (~150-180Da-ish) and 3D fragments would be, by necessity, bigger (~250 Da).

The topic then changed to SBDD (structure-based drug discovery) as part of FBDD. Most people at the table were of the opinion that they wouldn’t use FBDD (on a normal priority target) without SBDD. And with SBDD, you don’t need 3D fragments to explore vector space since you will have the X-ray to guide you. 

The point was made that 3D fragments also HAVE to be scaffolds which would end up in the final compound. If you are simply using a scaffold to explore vector space without any hope of it ending up in the final product, why bother. [TZ Note: I think this is very shortsighted and people do not understand that targets will most likely NOT have SBDD to guide them, at least in the fragment-based lead generation stage.] 

The question was asked to the table: is FBDD a valid approach in the absence of structure? Three people said yes, the rest (~8) said no. All three who said yes were small company/ CRO people. Everyone agrees if you do go with FBDD without structure you need to have a HEAVY investment in biophysics to characterize the protein and the hits. 

One person asked about low confirmation hits following a fragment screen: the table agreed that this is most likely the result of the library being “wrong”. We finished the discussion by asking if 2D fragments are more pan-class (not targeted to a specific class of targets) and 3D fragments may be more target-focused. The resounding answer was “Who knows, but why would they be?”

As this post is already getting long we’ll stop here, but for those of you who were also at the conference please add your comments. And if you missed this one, there are still several exciting upcoming events this year!

Sometimes Your Compound Sucks

Folks, let me wax grouchy. But, first, let me explain. My kids (10 and 7) play sports. I try to inculcate them with a sense of fairplay and sportsmanship. From their first game, I have always said to them: two rules to playing sports. 1. Never blame the refs (umps). 2. Never blame the equipment. It has gotten me through a lot of losses, hurt feelings, and it generally makes me feel like a good dad. I then take the money I am putting away for when they need therapy later in life for all the bad things I will/have done and buy beer. Trust me, this will come into play in at the end.
I have been trying to figure out how to review this paper from Bastien et al. It hasn't been easy. The kinder gentler version of Teddy is just not a hit. Go figure. This paper is aimed at delivering J. Med Chem. quality data derived from fragments on a mutant DHFR. DHFR is a long time drug target and is currently used in many approved therapies.

The paper all started out so well, with description of the fragment library, you know, fragments, "generally comprise[d} cycles -- often aromatic-- and were generally of an elongated structure." Seriously, in J. Med. Chem., they described their fragment library that way. There is no supplementary information.
With this excellently characterized fragment library, the screened all 100 compounds (what a herculean effort) and found 7 compounds in the high micromolar to millimolar range.
This is what they found...oh, and no inhibition in the biochemical assay for follow up compounds 1a, 4a,b,c,5a, and 6a. The depth of the SAR startled me. They did extensive SAR which led to this:
Now, starting from fragments which had pretty decent ligand efficiency, there were able to generate these horrendous monsters, with absolutely revolting LE. They then point out that these are "poorly optimized compounds." DUH.
They then did docking of the molecules and it shows that these symmetrical molecules for a U shaped molecule and suggest that intermolecular stacking may occur in solution. They then point out that this may be due to limitation in the docking software. Then tried to soak (9) into crystals, but there was a change in the active site pore electron density. And, again another excuse, "The pore lies on the crystallographic symmetry axes, combined with the fact that the ligand did not appear to be present at full occupancy, it prove impossible to interpret the electron density." YET, in the very next sentence, seriously the very next one, "From what could be seen, the density was not consistent with the U-shaped conformation of compound 9 as suggested by the docking results, but the poor quality of the electron density PRECLUDES DRAWING CONCLUSIONS [emphasis mine] on the actual bound conformation of the inhibitor."
So, now at this point, my brain literally explodes like this:
One more highly quotable line is: "Notwithstanding the precise mode of binding, symmetry appears to play a key role in binding in selectivity. " I assure you all I am NOT making this up.

There may be some redeeming social value in the paper, but I give up in finding it. Bastien et al., stop blaming the refs and the equipment!!!

Library design, search methods, and applications of fragment-based drug design

This is the title of a new book, edited by Rachelle Bienstock, which comes out of two symposia she organized at recent ACS Meetings (one of which is summarized here).

The book starts with an overview of fragment-based drug design by Bienstock. This is a thorough summary of the talks in both symposia, including those that did not end up as full chapters. This chapter also includes a useful table of available software relevant for FBLD.

The longest section of the book is devoted to designing and searching fragment libraries. In chapter 2, Dimitar Hristozov and colleagues at Eli Lilly and the University of Sheffield describe an algorithm for the de novo design of new molecules based on known reactions. Chapter 3, by John Badger of DeltaG Technologies, addresses the question of how to design libraries for crystallographic screening, with some emphasis on software. Chapter 4, by Ammar Abdo and Naomie Salim at the University Teknologi Malaysia, describes a “Bayesian interference network” for virtual screening as an alternative to conventional similarity searching. And chapter 5, by François Moriaud and colleagues at MEDIT, describes the researchers’ mining of the protein data bank (PDB) to understand the relationship between ligands and their protein pockets. This information is used for generating bioiosteric replacements and to generate new compound libraries for specific targets.

The next section of the book is focused on docking. In chapter 6, Zsolt Zsoldos of SimBioSys describes their high-speed eHiTS (electronic High Throughput Screening) engine for docking fragments. This chapter delves deeply into a statistical scoring function, and should be of particular interest for the mathematically-inclined. Chapter 7, by Peter Kolb at UCSF, discusses DAIM (Decomposition and Identification of Molecules), a program designed to break larger molecules into fragments, as well as computational methods for docking these derived fragments. He describes the use of this software to discover high affinity ligands for the kinase EphB4 and other targets.

The last section is devoted to fragment growing and linking. Chapter 8, by Eugene Shakhnovich and colleagues at Harvard, discusses FOG (Fragment Optimized Growth), which grows molecules by adding fragments such that the resulting molecules resemble those in a training set. This allows one to focus on regions of chemical space that are believed to be particularly productive or drug-like. And finally, in chapter 9 Zenobia founder Vicki Nienaber describes how fragment-based approaches are ideally suited for discovering drugs targeting the central nervous system.

By my count this marks the fifth book completely dedicated to fragment-based lead discovery, but its focus on computational methods still sets it apart from the others. That’s the fun thing about fragments: there’s something for everyone.

FBLD 2012 - registration now open

We are delighted to announce that registration is now open for the FBLD 2012 meeting to be held in San Francisco from 23rd to 26th September this year. Please visit the conference site and click through to registration.

This promises to be THE major fragment-based discovery conference of the past two years (you can read impressions of FBLD 2010 and FBLD 2009). A great list of speakers has already confirmed, and we still have plenty of room for more talks and posters. We plan to have some themed sessions around particular targets and approaches - these will be announced on the website as they are arranged.

In addition to great science, we have worked hard to keep the costs down, both for registration and hotel.

So - visit the site http://www.fbldconference.org.

Early bird rates expire on the 1st July 2012.

For a complete list of fragment-based events please see here.

Looking forward to seeing you in San Francisco!

Universal fragments

Third world diseases are popular academic drug targets, perhaps none more so than malaria. In a recent paper in Bio-Medicinal Chemical Letters, researchers from the University of Durak report their efforts to discover inhibitors of falcipain-1, a cysteine protease important for the parasite that causes malaria.

The researchers performed a functional screen of a small fragment library. Among many hits, the most potent were compounds 1 and 2, both with high ligand efficiencies. When these were linked together the resulting compound 3 had an improved potency, and adding a small substituent to yield compound 4 gave a sub-micromolar inhibitor.

Remarkably, not only was compound 4 potent against falcipain-1, it was also potent against several other important disease targets. In recognition of this pan-potency, the researchers have named compound 4 “hitinane.” Whether or not this discovery will lead to a drug, it undoubtedly will fuel many papers, patents, and grant proposals.

Fragment docking: promiscuous but good enough?

Computational docking of fragments can be difficult, partly because the energetic differences between possible binding modes can be so small that it’s impossible to select the best one. In a recent paper in J. Med. Chem. Andrew Good and colleagues at Genzyme ask whether docking results are nonetheless good enough to act on.

The researchers were interested in the kinase Pim-1; we’ve previously highlighted their success using SPR-discovered fragments to generate nanomolar inhibitors of this target. In the current paper, they use the program Glide to virtually screen 13,888 fragment-sized molecules from their general collection against Pim-1. About 3% (462) of these were tested in a functional assay at 125 micromolar concentration, resulting in 46 fragments with IC50 better than 100 micromolar. Five diverse representatives of these fragments were then soaked into crystals of Pim-1, and their structures were compared with those from docking.

Overall, only two of the fragments showed a good correlation between the in silico and crystallographic models (rmsd 1.0 Å or better). However, two of the “failures” do make key hydrogen-bond interactions seen in the crystal structures, though some of the hydrophobic interactions are quite different.

As the researchers note:

Fragments that do not fill their binding pocket can exhibit promiscuous hydrophobic interactions due to the lack of steric constraints imposed on them by the boundaries of said pocket. As a result, docking modes that disagree with an observed crystal structure but maintain key crystallographically observed hydrogen bonds still have potential value in ligand design and optimization.

This seems reasonable, and is consistent with the notion that polar interactions are more directional – and thus perhaps easier to correctly dock – than more generic hydrophobic interactions.

But there may also be something more fundamental going on: the assumption seems to be that the observed crystallographic structures are definitive, but is this an oversimplification? After all, a crystallographically-derived model seldom provides more than one binding mode (though a notable exception led to the first approved fragment-based drug). Perhaps the reason it’s so difficult to score docked fragments is that fragments really can assume multiple binding modes, and our insistence on one single best model is the problem. If this is true, docking models are telling us more about reality than we are giving them credit for. NMR-based models are often presented as an ensemble of structures; perhaps the same should be done for docking? At any rate, NMR studies on Pim-1 with these fragments could prove interesting.

Practical Application of NMR

I am sometimes harsh on academic papers, especially those that purport to describe drug discovery. However, Isabelle Krimm and colleagues have continued their excellent work, previously discussed on this blog in a this paper. This paper reads like a "How To" on prosecuting an NMR-based screen. In this work, they have two goals in mind: studying fragments interacting with targets with multiple hotspots and determining the utility of fragments for allostery.

Glycogen phosphorylase is an interesting system to work in: it has an active site and six regulatory sites, including an allosteric site with a variety of positive homotropic and negative heterotropic effects between the various sites. They took 19 known inhibitors that bind to the active site (1-6), inhibitor site (7-9), allosteric site (10-12), and the "new" allosteric site (13-15) and deconstructed them.
Then they screened these fragments against GPa and GPb using both STD and WaterLOGSY. A compound was only deemed a hit if it was observed to bind via both methods. I find this approach very interesting. STD works via NOE from the saturated target to the bound compound. WaterLOGSY works via NOE from bound water to the compound. Each experiment has advantages and disadvantages, but are they truly orthogonal experiments. As a third experiment, the authors use transfer NOE to confirm binding. I would expect to see at least one truly orthogonal method to confirm binding, such as SPR. They then used competition screening against known inhibitors to bucket their fragments based upon the site they are binding to.

While I don't think the results here are not similar to results achieved in industry many times over, this is an excellent paper that shows the power of NMR in screening and how to apply that to drive answers to target validation and compound bucketing.

This paper leads I think to interesting academic musings. Does ontogeny recapitulate phylogeny for compounds derived from fragments? Does it matter if the fragment is 3D or not? Is there a floor below which a fragment will not bind? Does this floor move if you are using 3D fragments vs. highly planar ones?

LLE vs LELP

Besides ligand efficiency (LE), a slew of other metrics has been proposed to help evaluate what compounds to take forward in drug discovery. As seen in our poll and a recent round-table discussion, lipophilic ligand efficiency (LLE) is quite popular:

LLE = pIC₅₀ (or pK_i) – ClogP (or logD)

However, because this metric is not size-adjusted, it is not particularly useful for evaluating fragments, which often have low potency. In contrast, the metric LELP accounts for size:

LELP = logP / LE (where LE = ligand efficiency)

In a recent issue of J. Med. Chem., György Keserű and colleagues evaluate how these two metrics compare in a variety of settings.

The authors examine eight different compound sets: fragment hits and derived leads, HTS hits and derived leads, leads that subsequently became drugs (ie, “successful leads”), development candidates, compounds that entered phase II trials, and drugs on the market. Not surprisingly, drugs and phase II compounds had better LLE and LELP scores than other molecules. Also not surprisingly, fragments scored misleadingly poorly on the basis of LLE but well on the basis of LELP. What is perhaps unexpected, though, is that LELP was better at identifying successful leads than was LLE. Moreover, when compounds were evaluated for pharmacokinetic and safety parameters, LELP was more effective at predicting problems than was LLE. The authors state:

In summary, evaluation of pharmacokinetic and safety parameters revealed that LELP has benefits over LLE, as compounds with acceptable in vitro ADMET profiles are discriminated from compounds with significant liabilities.

Despite these potential advantages, LELP doesn’t seem to be widely used, perhaps because it is less intuitive than some of the other metrics. Indeed, it would be interesting to see these studies repeated using LLE_AT, which also takes lipophilicity into account but has the same scale as LE.

Fragment-based drug discovery and X-ray crystallography

I’m holding in my hands a book of this title, edited by Thomas Davies and Marko Hyvönen and published this year as part of Springer’s Topics in Current Chemistry series. I believe this is the fourth book entirely devoted to fragment-based drug discovery, which shows both the vitality and rapid development of the field.

The book starts with an introduction to fragment-based drug discovery by me. If you’re new to the field, this chapter should serve as a self-contained summary.

In the next chapter Thomas Davies and Ian Tickle describe how FBDD is practiced at Astex, paying particular attention to the use of X-ray crystallography. Notably, researchers from this company “do not consider a fragment hit to be ‘validated’ and suitable as a starting point for medicinal chemistry until it has been observed to bind by crystallography.” This chapter also contains a nice analysis of fragment library design and a couple case studies, including the discovery of the clinical-stage CDK2 inhibitor AT7519.

Rod Hubbard and colleagues at Vernalis and the University of York next describe their efforts to discover Hsp90 inhibitors using a combination of virtual and fragment screening. We’ve covered some of this before (here and here), but it’s nice to see the full story.

The next chapter also focuses heavily on a single target: Daniel Wyss and colleagues at Merck describe their success in discovering BACE inhibitors. This chapter also includes an excellent review of NMR methods for finding fragments.

Michael Hennig and colleagues at Roche (Basel) contrast the various biophysical methods used to discover fragments, with a heavy emphasis on SPR. Crystallography is also covered, in particular co-crystallization of fragments with protein. Co-crystallization is more time-consuming than soaking fragments into preformed crystals, so compound prioritization techniques such as SPR are especially useful.

One of the most promising applications of fragment-based methods is tackling tough targets such as protein-protein interactions, the subject of a chapter by Marko Hyvönen and colleagues at the University of Cambridge. The chapter contains a nice discussion of energetics and hot spots as well as a detailed analysis of methods to find fragments which complements some of the other chapters.

Eddy Arnold and colleagues at Rutgers discuss the use of crystallographic fragment screening against two HIV-1 targets, HIV protease and HIV reverse transcriptase (RT). We’ve previously discussed the former here. In the case of RT, fragments were soaked into crystals in the presence of a high affinity inhibitor, effectively blocking its binding site from fragments. More than 30 fragments were identified binding to multiple other sites on the protein – one fragment bound at 11 distinct sites! Interestingly, the fragments were enriched for halogen-containing molecules. Several also had functional activity with respectable ligand efficiencies. The authors also discuss other published fragment work on HIV RT.

Finally, Didier Rognan at the University of Strasbourg discusses computational approaches to library design, binding site determination, and predicting druggability. Fragment docking is extensively covered, along with a discussion of what factors contribute to success. It seems that docking is particularly good at identifying negatively charged, relatively buried fragments that make similar hydrogen bonds as the substrate. De novo ligand design, both the successes and challenges, is also covered.

Like last year’s book, all the chapters in this one are published online, but it is worth getting a bound copy as it is nicely put together, with color figures liberally integrated throughout rather than banished to plates at the back.

Molecular Medicine Tri-Con 2012

Molecular Medicine Tri-Con has just ended in San Francisco, and although it is one of those massive conferences with a very wide scope, from bioinformatics to stem cells, there were some talks relevant to FBLD.

Teddy recently summarized how fragment-based approaches have been used to develop bromodomain inhibitors at GlaxoSmithKline, and they’re certainly not alone: Mark Bunnage of Pfizer described how they used fragment-based methods to discover nanomolar inhibitors of BRD4 that are active in cells. This work was done in collaboration with the Structural Genomics Consortium (SGC), and one well-characterized compound, PFI-1, is being released as a chemical probe to the worldwide research community with no intellectual property entanglements.

There was a heavy emphasis on metrics, and as we saw in the poll last year ligand efficiency and LLE (sometimes also called LipE) seem to be dominant. However, the latter metric is used more for advanced leads than for fragments. Siegfried Reich from the Lilly Biotechnology Center (neé SGX) said of LLE that it is less important where you start than where you end up, though starting from a very polar fragment gives you the luxury of adding lipophilicity during optimization.

Slight changes to fragments can often cause them to bind in different orientations, and the same fragment may bind differently in closely related proteins, but Siegfried argued these multiple orientations could be advantageous by providing multiple opportunities for optimization. Siegfried also mentioned that deconstruction of HTS hits to fragments has been successful at identifying fragments with high ligand-efficiency that could subsequently be optimized to new series.

Richard Law of Evotec gave as clear account as possible of fragment molecular orbital (FMO) calculations, a high-level quantum mechanical method for understanding protein-ligand interactions. Although quantum mechanical calculations are notorious for taking hours, days, or even weeks to run, the calculations can be done much more efficiently by breaking larger molecules into fragments.

Richard also kept thorough notes at a break-out discussion I moderated, and was kind enough to share them; I’ve also made a few additions. There were nine people from both large and small organizations, all but one from industry.

Screening technologies

• As we’ve seen here, SPR seems to be the most common fragment-finding technique; in his presentation, Walter Huber said that it is the primary screening method at Roche.
• "Reverse SPR" - the Graffinity/Novalix technology we’ve discussed previously, has been applied to over 100 targets. It is reversed because the small molecules are immobilised on the chip, in multiple orientations to present different moieties to the target protein. This is also a larger library (~25,000).
• NMR was not being used as much as SPR, and no one at the table was using 19F NMR, though its use does appear to be growing, and compound suppliers are coming out with 19F fragment libraries.
• FCS++ is being used at Evotec; it has the advantage of being high-throughout but still highly sensitive and therefore accommodates a larger than average fragment library (~20,000).

3-D fragments

• There is increasing desire for sp3 (3-dimensional) fragments and a move away from planar fragments, though one participant had seen chemists shy away from too many chiral centers.
• Advantage of more vectors for SBDD, and additional solubility versus otherwise equivalent flat compounds.
• Despite these advantages, often planar fragments yield more hits - likely because planar compounds are more likely to form dispersive/non-specific interactions, whereas sp3-fragments must form very specific H-bond interactions in order to bind. Does sp3 therefore also enrich for enthalpic binders?

Use of indices
LE was used by everyone at the table, whereas LipE/LLE were not used until lead compound stage. BEI/SEI and other metrics were not really used.

FBLD vs HTS
Fragment screening is being used on most programs at many companies, in parallel with HTS and virtual screens. A subset of targets is addressed only with fragment screening either because of target-specific information or specific requirements to lower costs of screening.

Fragment screening of GPCRs
There was very limited experience using FBLD on G-protein coupled receptors, though one company is trying to use nanodiscs to stabilize GPCRs for fragment screening. As more crystal structures are solved people may become more comfortable tackling this target class.

Finally, there was widespread agreement that fragments are ideal for designing in the desired physico-chemical properties of molecules as fragments are developed. The parent fragment is not biasing, and could often be med-chemed away. Richard Law offered the analogy of a rock-climber:

An HTS gets you halfway up the cliff, but the route to the top may not be from where you are, or may just be too difficult to find from that position. Whereas a fragment hit is at the base of cliff but enables you to see and select the exact route to the top that you need.

If you attended the break-out discussion or the meeting please comment on your impressions, and if you missed this conference don’t worry – there are many more great events coming up throughout the year.

Upcoming Webinar on NMR in DD

I wanted to make everyone aware of an upcoming webinar: "You are smarter than you think: Applications of NMR in Hit-to-Lead Discovery". Here are the key learning points:

• Application of NMR in Fragment Screening using both target and ligand based methods
• Utilization of NMR Data, e.g. epitope mapping, in Hit to Lead efforts
• Structure determination of compounds by NMR

What I would love to have from the readers of this blog is suggestions as to what particular topics they feel would be most important to learn about. Obviously, the first two topics are broad and I wanted to give the readers here a chance to help set the specifics that will be discussed. Leave a comment or send me an email directly.

Slow-off, albeit tight, fragments

Practical Fragments recently discussed binding kinetics, and that got me wondering whether any fragments have slow off-rates. Turns out some do: a January 2012 review of protein-ligand energetics and kinetics in Drug Discovery Today by Sara Núñez and colleagues at Abbott summarized a paper published last October in Eur. J. Med. Chem. by Jos Lange et al. In it, Lange and colleagues extensively characterize six inhibitors of the enzyme D-amino acid oxidase (DAAO), a potential target for schizophrenia.

The researchers use biochemical assays, surface plasmon resonance (SPR), and isothermal titration calorimetry (ITC) to characterize the thermodynamics and kinetics of their inhibitors binding to DAAO. Although all six molecules are fragment-sized, these are not your typical fragments: the weakest has a Kd better than 1 micromolar, and all have ligand efficiencies of 0.79 kcal/mol/atom or better! Three of them are shown below, along with their dissociation constants (determined by ITC) and their dissociation rate constants (determined by SPR).

One interesting aspect of the kinetics is that compound 6 dissociates from the enzyme roughly 50-fold more slowly than compound 3, even though it binds only about 3-fold more tightly. As an interesting aside, compound 1 has a slower on-rate than any of the other molecules, a phenomenon the researchers attribute to tautomerization around the pyrazole.

The researchers go on to measure a number of other properties of these molecules, including Log P, pKa, thermodynamic and kinetic solubility, cell membrane permeability, and in vivo pharmacokinetics. There is a tremendous amount of data here, and it’s a lot of fun to dig into.

With a predicted half-life of more than 2 hours, compound 6 certainly classifies as a slow-off fragment. So is it a better drug? Well, it’s not orally bioavailable, in contrast to some of the other compounds, and it has faster clearance in rodents. Unfortunately the researchers do not report in vivo target modulation, but one has to assume that a schizophrenia drug would need to be oral. Chemists can optimize affinity, thermodynamics, kinetics, and drug-like properties all we want, but the body still has the final say.