Don't Read This!

Epigenetics is one of the hot new areas for drug research.  It seems like every company has several targets among the bevy of readers, writers, erasers out there. We have blogged about this previously.  Now, as the field sees more and more players, more and more papers are appearing describing various drug discovery efforts, largely on bromodomains, the readers.   The current state of the art for bromodomain compounds, is shown below.  FBDD approach and led to Cpd 4b of modest potency.   In this paper, the authors aim to diversify the range of scaffolds for bromodomain inhibition.

Cpd 1 is the famous JQ1, cpd 2 from GSK has a very similar scaffold to JQ1, Cpd 3 from Oxford and GSK has the 3,5-dimethylisoxazole scaffold and acts as a AcK mimic (like DMSO), Cpd 4 is from a

This group describes how they put together their fragment library (which is a detail that is often lacking in papers).  Starting from the ZINC database, they applied the following filters: 1. MW < 250 (HAC<18 1="" 2.="" 3.5="" 3.="" 4.="" 5="" bonds="" logp="" rotatable="" u=""><

smallest set of smallest ring<4.  [I have no idea what this last filter means, I have reproduced it faithfully and maybe somebody can tell me what it means in the comments.]  They then clustered the compounds with a Tainomoto cutoff of 0.7 and the center (centroid?) of the cluster was chosen to represent the clusters.  They then applied the "Reality Check" and had a real person look at the compounds and 500 were chosen, with 487 being purchasable.  The authors state that some of their compounds do not obey the Voldemort Rule.  Good for them!  More people should.  They then spend several sentences defending this choice.  Shame on the reviewer who made them.  The properties of their library are presented graphically. 

They started by docking their fragment against the JQ1 structure of BRD4.  All binding conformations were assessed for their interaction with Asn141.  41 Cpds were then put into co-crystallization experiments, they obtained 60 crystals which were diffracted.  Four of these fragments are exemplified below. 

It is satisfying to see that four different moieties bind in the roughly same location with similar orientations.  Compound 8 was chosen for further optimization, but with no explanation.  The end up with Cpd 40a which is a 0.23 uM inhibitor (LEAN = 0.26), compared to > 100uM for the parent fragment.

Cpd 40a

The authors then performed PK testing, reasoning that bad PK here should kill the whole series, while also cautioning that PK does not necessarily extend to all the compounds in a series.  40a (and the parent compound) showed cleaned metabolic stability and stability in human microsomes.  40a also exhibited inhibition of several CYPs at < 50% at 10uM, reinforcing the metabolic stability.  They finally looked at the cellular activity.  They found that compound inhibition is not well correlated with anti-proliferative activity.  Reviewing the compounds used in this assay they found that logP was crucial here, lower logP means better anti-proliferative activity.  PSA did not correlate. 

This is an nice paper, not only for showing the range of chemical diversity that can be identified in this target class, but because it gets to several important questions that are being discussed here and in the LinkedIn group.  Namely, what should your library look like and how to screen it?  I applaud them for not being hide-bound to the Voldemort Rule.  As recently mentioned, there is a difference in how X-ray and NMR view fragments.  X-ray being notably different by requiring high occupancy.  In this field, using X-ray makes a lot of sense because it reduces the likelihood of false positives due to such things as DMSO binding. 

Fragments vs Tankyrases: DSF shines again

The two human tankyrase isoforms, TNKS1 and TNKS2, are members of the PARP family of proteins, which has received considerable attention as a pool of potential anticancer targets. In a recent paper in J. Med. Chem., a team of researchers from Sweden and Singapore use fragment-based methods to discover potent, selective inhibitors of the tankyrases. This is a nice example of FBLD from academia.

The researchers used differential scanning fluorimetry (DSF) to screen 500 fragments at 1 mM concentration each against TNKS2. In this assay, the protein is mixed with a fragment and a fluorescent dye that binds to the denatured form of the protein. When the mixture is heated, fragments that bind the protein should stabilize it against thermal denaturation. Thus, fragment binding can be detected by an increase in melting temperature (which is itself inferred by an increase in fluorescence). As noted previously, people have very different opinions of DSF; some folks swear by it, while others find that it produces too many false positives and negatives.

The present paper is an excellent resource for those wanting to try DSF for themselves; it provides clear experimental details and discussion of some of the things that can go wrong. One recommendation is to validate hits from the initial single-point assay by running dose-response curves over a wide concentration, such as 5-4000 micromolar. Another interesting tip is to add the protease chymotrypsin to the mix; doing so gave cleaner data, presumably by chewing up mis-folded contaminants.

The 500-fragment DSF screen identified two fragments against TNKS1, both of which were characterized crystallographically. The researchers chose to pursue fragment 2 since the structure suggested that this had good vectors for growing. Interestingly, removing the methyl group caused a complete loss in activity – another example of the power of methyl groups, and a sobering reminder of how subtle changes could make the difference between finding a fragment or not.

Fragment 2 was already quite potent, and adding an aryl substituent as in compound 11 further increased activity. Replacing the fluorine with a chlorine was even better, but at the cost of solubility, so the researchers added solubilizing groups and obtained potent, soluble molecules such as compound 17. Compounds 11 and 17 were also soaked into crystals of TNSK2, and the resulting structures overlay nicely with each-other as well as with the structure of the initial fragment.

Dissociation constants and kinetic parameters of the more potent molecules were determined by SPR, and although in general improvements in affinity were driven by decreases in k_off rates, k_on rates started to play a role with the more potent compounds. In another nice vindication for DSF, the thermal shift correlated nicely with both the IC₅₀ and K_d values.

Compounds 11 and 17 bind differently than other PARP inhibitors, so the researchers tested compound 11 against six other PARPs and found it to be quite selective. In fact, it is even 16-fold selective for TNSK2 over TNSK1. While that property may ultimately not be desirable in a therapeutic, it should be useful for exploring the biology.

Overall this is a lovely piece of work, and it does make a good case for the utility of DSF. The fragments identified are quite potent; perhaps the technique really shines at finding these exceptional fragments.

Way back when I was but a young lad, I worked on dual inhibitors of 5-LO and PAF in the lab of T.Y. Shen at UVa.  They were crazy compounds, but as an undergraduate it was a great experience.  We even did our own assays requiring fresh whole blood.  I loved it and learned to HATE HPLC assays at the same time.  People still seem to be doing stuff like that.  Overall, the field of leukotriene antagonists has been pretty successful, but it is littered with debris. 

Prostaglandin E2 (PGE2) is a key mediator in inflammation, pain, fever, atherosclerosis, and tumorigenesis: a veritable collection of the key therapeutics areas for most pharmaceutical companies.  Arachidonic acid (AA) is acted upon by COX1/2, target for a whole pile of drugs, to generate PGH2.  This is then isomerized to PGE2 by microsomal Prostaglandin E2 synthase (mPGES).  Inhibition of mPGES would be expected to only preclude PGE2 and thus eliminate many of the adverse side effects of COX inhibitors.  As would be expected, mPGES inhibition is not new and several inhibitors are in the clinic.  So, we happen upon this paper: a recent academic effort at a potentially rich field.   The authors begin the explanation of their approach with this: 

To date, there is no real three-dimensional X-ray crystal structure of mPGES-1 in the apo form or with an inhibitor bound with exception of electron crystallographic structure complexed with glutathione in its closed state (PDB code:3DWW).

Their strategy is to replace glutathione with non-peptidomimetics via fragments based upon this pharmacophore model (3DWW): two negative ionizable, one HBD, and one HBA

 They hypothesized their molecule thusly:

The choice of the triazole is to leverage click chemistry to generate it, while the sulfonamide has been shown to a "privileged" fragment in other drugs. Prior to synthesis, the decided to calculate the binding energies (with three decimal place precision!).

The authors then tested the compounds in a biochemical assay.  The first thing that they noticed is that n=2 for the linker is better than n =1 or 3 (50% increase in potency).  Compound 6f (R1=R2=phenyl) was the most potent (1.1 uM).  However at 29 HAC, its LEAN is 0.21 and would be considered lousy.  It can be argued that lipases and such are going to have relatively inefficient compounds due to the highly hydrophobic nature of the active site. [I completely ignored their discussion of the calculations and modeling, because honestly, does it really matter?] Compound 6f showed ~1000x mPGES-1 selectivity over COX1 and no COX2 activity.  They then (much to Dan's delight I am sure) tested for activity with and without detergent.  Not surprisingly, in the presence of detergent, compound 6f lost significant amount (75), but not all of its activity.  The authors very honestly say:

Therefore, compound 6f may be judged as a partial nuisance inhibitor
of mPGES-1 instead of true mPGES-1 inhibitor.

 I would have like them to have tested ALL of their compounds in the presence of detergent to begin with.  This smacks of something that was added in after initial review and honestly really makes the paper uninteresting.  I would really like to know if 6f would still have the outstanding potency from the compounds made, or if some other compound would have aggregated less and thus been of interest.   I would also argue that calling this paper "fragment-based" is a stretch.  As Dan just pointed out, some target classes may just need larger compounds to hit them. 

The rule of three at ten

One of the rewards of following a field for years is being able to revisit classic papers to see how they’ve held up. Two years ago we re-examined molecular complexity. This year marks the tenth anniversary of the publication of the “rule of three”, and Harren Jhoti and colleagues from Astex have marked the occasion with a brief but trenchant letter in Nature Reviews Drug Discovery.

Think back, if you will, to 2003. Abbott researchers had published their seminal SAR by NMR paper seven years previously, but fragment-based efforts were still widely scattered, with each organization more or less figuring things out on its own. It was in this primordial environment that Jhoti and colleagues published a short “discussion forum” in Drug Discovery Today. It framed its premise as a question (A ‘rule of three’ for fragment-based lead discovery?) and suggested that fragments have the following characteristics:

• Molecular weight (MW) < 300
• ClogP ≤ 3
• # of hydrogen bond donors ≤ 3
• # of hydrogen bond acceptors ≤ 3

In the new publication, the researchers note that most of the focus has been on the first two critera. Indeed, as has been pointed out, there is some ambiguity as to how one defines hydrogen bond donors and acceptors.

Since proposing the Rule of Three, Astex has been moving towards ever smaller compounds; the majority of their fragments now have fewer than 17 non-hydrogen atoms, with a molecular weight < 230 Da. One consequence is that the other properties automatically fall into line: a quick search of ~100,000 compounds with ≤ 16 heavy atoms reveals that 86% have ClogP ≤ 3, while out of 370,000 compounds with MW ≤ 300, only 72% have ClogP ≤ 3.

This push towards smallness has been questioned, particularly in the context of protein-protein interactions, where some have suggested that larger fragments may be required. Jhoti (and others) counter with two arguments.

On a theoretical level, all proteins are made up of amino acids, so there shouldn’t be anything special about protein-protein interactions:

Fragments are – or should be – simple enough to probe the basic architecture of all proteins yet have sufficient complexity to allow them to be elaborated into lead compounds.

On a practical level, after screening more than 30 targets, the researchers find that many fragments that hit protein-protein interactions also hit other targets.

The researchers are in favor of “three-dimensional” fragments, but not at the cost of increased size. They note that the perception that fragment libraries are dominated by “flat molecules” may be distorted by the fact that many fragment success stories (including nearly half of clinical-stage compounds) involve kinases, which have a predilection for planar adenine-like fragments. That said, they acknowledge that many fragment libraries are sub-optimal, leading to heartbreak during optimization. As they note with restraint, “not all fragment libraries are alike.”

Finally, there is a nice analysis of what to do with fragments that don’t reproduce in orthogonal assays. They typically observe 30%-40% correlation between fragment hits from ligand-observed NMR and X-ray crystallography, but note that this isn’t bad given that an NMR hit can be detected at just 5% binding, while crystallography typically needs at least 70% occupancy. Thus, NMR can detect fragments with solubilities less than their dissociation constants, which is unlikely in the case of crystallography. Although it is reassuring when multiple techniques confirm, the danger is that:

This strategy implicitly places a reliance on the least sensitive technique. This is of particular concern as the most potent fragment is often not the best starting point for hit-to-lead chemistry.

Not surprisingly to those familiar with Astex, the researchers put a premium on crystallographic information.

Closing with the rule of three, I think part of what bothers some folks is the notion of “rules” in general; nature has never read an issue of Nature, and drug discovery will never be as reductionist as physics is. Indeed, the researchers acknowledge that the rule of three “is just a guideline that should not be overemphasized.” The rule of three is a play on Chris Lipinski’s (equally contentious) rule of five; perhaps the “guideline of three” would have been less controversial. But the spirited discussion ensuing over the years has generated light as well as heat, which the authors welcome:

We trust that our comments, some of which are deliberately provocative, on these many facets of FBDD will generate active discussion and might assist in improving the success of this approach for the broader drug discovery community.

The comments are open for those who would like to continue the discussion here!

Fragments vs MDM2 – retrospectively

Protein-protein interactions (PPIs) are some of the tougher targets in drug discovery: they often have relatively flat interfaces that lack obvious small-molecule binding sites. But despite large surface areas, these interfaces often have smaller hot spots, and fragment-based approaches have succeeded against targets where traditional screening approaches have failed. In a paper in this month's issue of ACS Med. Chem. Lett., David Fry and colleagues at Roche describe a retrospective fragment study against a classic PPI (this story was also presented at the CHI conference earlier this year).

Molecules that disrupt the interaction between p53 and MDM2 have been sought since the 1990s as potential anti-cancer agents. The first such inhibitor to enter the clinic was RG7112, one of the so-called nutlins developed at the Roche Nutley site, which is sadly in the long process of closing. Although this series of compounds originated from a high-throughput screen, the researchers were interested to know whether they could have been discovered from fragments. To do so, they deconstructed RG7112 into a series of smaller pieces.

RG7112 is a “tripod-like” molecule with three hydrophobic moieties (blue, red, and green, below) that bind to subpockets on MDM2, as well as a polar “cap” (pink) that projects into solvent but is nonetheless important for activity. The researchers were unable to detect the binding of any of these individual fragments by either SPR or two-dimensional (HSQC) NMR, suggesting that each moiety alone was too weak, so the researchers tried lopping off one or two pieces from RG7112. In all cases, pared-down molecules containing three out of the four moieties led to molecules with affinities in the 14 to 1000 micromolar range. The tipping-point seemed to be when they kept just two of the four component fragments: in two cases the resulting molecules showed no detectable binding, while in two other cases they did. Compound 5, in fact, was a decent hit by any measure, and crystallography revealed that this molecule binds in the same manner as RG7112.

Interestingly, compound 5 makes interactions with two adjacent subpockets, including a deep hole that seems to be critical for all MDM2 binders; p53 inserts the indole moiety of a tryptophan into this site.

However, compound 5 does teeter on the edge of what could be called a fragment: with a molecular weight of 305 it falls just outside the Rule of Three (sorry Teddy!), and with 20 non-hydrogen atoms it sits right on the border of what most people seem to include in their libraries. The researchers suggest that larger fragments may be necessary for tackling PPIs, though they recognize that doing so will increase the number of fragments necessary to sample chemical space.

This is a beautiful, thorough study, but I do worry about super-sizing fragments. There are now multiple success stories of finding and advancing fragments against PPIs, including such “teflon-targets” as Mcl-1. Perhaps the nutlins would not have been discovered starting from small fragments, but with hundreds of billions of possibilities there are certainly other, even more ligand-efficient leads out there waiting to be discovered.

Fragments vs Chymase: swapping out the grease

One of the useful features of fragments is that they can give you unexpected answers to difficult questions. That’s true not just at the beginning of a drug discovery campaign but even after leads have been identified. A nice example of this was published recently in J. Med. Chem. by Steven Taylor and colleagues at Boehringer Ingelheim, and was also presented earlier this year at Fragments 2013.

The researchers were interested in a serine protease called chymase, a potential target for cardiovascular diseases. An initial medicinal chemistry effort had arrived at compound 1, a reasonably potent molecule that unfortunately produced reactive metabolites at the benzothiophene moiety. Crystallography revealed that this moiety binds in the hydrophobic S1 pocket of the enzyme. Traditional SAR thus focused on replacing this moiety with other lipophilic groups.

However, the researchers recognized that their fragment collection offered the potential to discover quite different – and less lipophlilic – replacements, as the average clogP of the fragment collection is 1.48. A thousand fragments were screened, and compound 2 was found to be a weak but ligand-efficient hit. Surprisingly given its relatively polar nature, X-ray crystallography revealed that this fragment bound in the S1 pocket, albeit in a somewhat different manner than the benzothiophene moiety.

Linking this fragment to a close analog of the starting molecule yielded compound 12, and further optimization led to compound 15, with nanomolar potency and improved selectivity against another serine protease, cathepsin G. The crystal structure of compound 15 (blue) bound to chymase was determined. The comparison to compound 1 (red) reveals a number of differences that help to explain the improved selectivity.

In earlier days FBLD was often ignored by project teams once they had identified potent leads. This paper is an elegant example of how an empiricial fragment screening approach can impact a reasonably advanced program by delivering new and unanticipated chemical matter. I suspect we will see more and more examples of this type of fragment-assisted drug discovery.

Tool Discovery Done Right

I have been on a bit of a jag lately pummeling academic "Drug discovery".  Dan recently hopped on, in his much more circumspect manner.  My big problem is calling something drug discovery that is not.  I may come off as harshly anti-academic; I am not.  I am pro-good science and even more for the right things in the proper place.  I think this one is one of those.  In this paper, the authors describe their use of fragments to find inhibitors of Mycobacteria.  Thiolactomycin is a natural product of beta-ketoacyl acpM synthase (KAS) from M. tuberculosis, but exhibits broad spectrum anti-biotic activity. While it has rather low potency against M. tuberculosis KAS (200uM, 0.26 LEAN), it has favorable physicochemical properties, low cytotoxicity, high bioavailability, and activity in animal infection models.  This makes it an excellent target for optimization, and considering the activity and lack of selectivity, lots of it!

The authors decided to use inter-ligand NOE to guide their efforts.  This is an approach that has been used previously, and in a case of cosmic togetherness, against a different M. tuberculosis target.  Amzingly, that work is NOT cited in this paper.   As pointed out, iLOE has some issues, aggregation and so on.  The authors do not seem worried by this and instead are interested in utilizing selective iLOE.  One thing the authors do NOT do is include any detergent in their samples.  They synthesized PK940 and used this for the NMR studies.  

They were unable to detect iLOEs with mixing times shorter than 500 ms (that's long!) using the standard 2D NOESY, so they used a selective experiment.  This allowed them to shorten the mixing time, increase sensitivity, and solve some other NMR based problems.  [N.B. They had to use 900 MHz to get good enough separation for some of the methyl resonances.  I guess you have to justify that humongous tin can somehow.]  This was able to generate the needed NOE data to put into a model to give them an idea on how to make compounds, suggesting elaboration of the thiolactone at the 3 or 4 position (A below).

They started to explore these hypotheses, and found that activities were all within 2x fold of the original.  So, unlike others, they decided this was flat SAR. Although they do make some conjectures that would seem to be easily testable (FOLLOW UP PAPER?). None of the compounds were significantly better than the lead and many in fact lost the "slow onset" binding that is considered important for good PD.  There is nothing really ground breaking here, but what I like about this paper is where it was published Journal of Biological Chemistry, not Journal of Medicinal Chemistry.  This is where these types of papers belong.  As I keep on saying, your computation is only as good as your follow up.  In this case, the computation is to support the NMR and not the other way around.

Fragments vs BACE1: again, and sometimes unintentionally

If I had to pick one target as a poster child for fragment-based lead discovery, it would probably be BACE1, a hot but controversial enzyme implicated in Alzheimer’s disease. Indeed, several inhibitors that have entered the clinic trace their origins to fragments (though unfortunately LY2886721 was just dropped due to liver problems). Four recent papers give a good flavor for where things stand.

The first paper, published in Bioorg. Med. Chem. Lett. by Thomas J. Woltering and colleagues at Roche, is not really a fragment story. Rather, it describes how a high-throughput screen against BACE1 identified a fragment-sized hit that had first been synthesized at Roche in the 1970s as part of an analgesic program. Despite relatively low affinity, compound 1 had good ligand efficiency, and its similarity to other reported BACE1 inhibitors suggested how to grow into the so-called S3 pocket of the enzyme. At the same time, the cyclic amidine “head group” was modified to try to modulate the pKa of the molecule and thus improve the pharmaceutical properties, leading ultimately to compound 12, with good biochemical and cell potency and marginal activity in a mouse model.

In a related J. Med.Chem. paper by Hans Hilpert and colleagues, the Roche researchers further optimized this series of molecules, most notably by introducing electron-withdrawing fluorine substituents around the cyclic amidine ring to further tweak its basicity. Compound 89 was potent, exhibited good pharmacokinetics, and showed impressive target modulation in both mice and rats. The authors state that “a compound from this chemical class is currently undergoing clinical evaluation.” Thus, even though this program did not start explicitly from fragment screening, an initial fragment hit ultimately led to a clinical candidate.

The third paper, in Curr. Opin. Chem. Biol., is much more fragment-centric. In this brief but lucid review, Merck researchers Andrew Stamford and Corey Strickland describe how FBLD has played an integral role in developing BACE1 inhibitors and highlight several successful examples; given that MK-8931 is the most advanced clinical candidate for BACE1, they know of what they write. They note that:

Key elements of successful fragment based drug discovery (FBDD) approaches targeting BACE1 have been the use of X-ray co-crystal structures to design optimal starting points for subsequent optimization and an emphasis on ligand efficiency (LE) rather than affinity to drive the discovery of drug-like, brain penetrant inhibitors.

And if this just whets your appetite, check out the 25 page review in a recent issue of J. Med. Chem. by Suresh Singh and colleagues at Vitae Pharmaceuticals, which contains more than 100 chemical structures of BACE1 inhibitors. The review is certainly not limited to fragment-derived molecules, but it does note that these are superior to the peptidic inhibitors discovered using more traditional approaches.