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.

(Not)Drugging the Undruggable

Well after spending last week at a Structure-based Drug Discovery conference that ended up being horribly academic and computational (think force fields and not the cool Star Trek kind), I come back to my blogging pile to find this paper. 

In this paper, the authors are looking at HIV Integrase.  HIV integrase has long been considered an "undruggable" target, although I would think that the presence of a marketed drug would kind of kill that perception.  But as we all know, preconceived notions die hard and slowly.  Anyhow, viral evolution necessitates the discovery of next generation integrase inhibitors.  To that end, the authors decided to use GLIDE to dock quinolines to the HIV Integrase-IN−LEDGF/p75 interface because some previous work had shown these molecules to have "been previously explored" for anti-integrase activity.  They quickly found out that 8-hydroxyquinoline made far more favorable contacts (one more hydrogen bond) than quinoline.  

They immediately started testing 8-OH-quinoline fragments for potency (via an Alpha-screen) and found molecules with very low micromolar potency but they do not report LEANs.  [It absolutely should be a requirement for any paper citing itself as "fragment" to include some sort of ligand efficiency metric with its data.] QA, QB, and QC have LEANS of 0.45, 0.41, and 0.37.  Yet, these compounds were cytotoxic.  

To expedite their search for non-cytotoxic, they generated a pharmacophore model.  This was used to screen ~7000 compounds generated from a query of 8-OH-quinoline and found that 5- (typically phenyl) and 7-substituted (typically phenyl) 8-OH-quinolines were the best output.

 In a perfect case of fitting the results to your preconceived notions the authors note: 

Although the pharmacophore identified the neutral form of compounds from the database, we have used the ionized form for the purpose of pharmacophore mapping, as we consider these compounds to be ionized in the context of receptor binding.

Despite adding a tremendous amount of heavy atoms, none of the compounds had activity much better than the original 12 HAC quinoline and were still cytotoxic.  They eventually found that only the 5-substituted-8-OH-quinolines did not have cytotoxicity. However, and please note, the potency is still NOT better than the original fragment hits.  Changing the C5-phenyl for piperazine or piperidine did increase potency (to 0.4uM, 0.35 LEAN) and reduced cytotoxicity.  What about the 8-OH moiety you ask?  Well, they found out that it could be substituted and/or be a thioether.  Of course, these weren't any more potent than the lead fragment and cytotoxicity remained an issue.

Well, to continue in a vein started by Dan, these authors seem to have wasted valuable NIH/NIAID and Campbell Foundation money.  Did they discover inhibitors of HIV Integrase, sure.  Are these useful frameworks?  Proabably not.  Did they establish a robust SAR from which they can move forward?  No.  Their activities floated right around 2 uM, with one or two getting below the uM line.  As I said, your computation is only as good as your experimental follow up.  In this case, it doesn't pass muster.

 

GIGO: pollution of the literature continues

Practical Fragments has repeatedly warned about the dangers of what Jonathan Baell dubbed pan-assay interference compounds, or PAINS (see for example here, here, and here). These are compounds that hit numerous unrelated targets through mechanisms that can charitably be described as “non-druglike”. Regrettably, many people still do not recognize these nuisance compounds for the artifacts they are, and PAINS continue to show up in high-profile fragment libraries. An unintentional illustration of why this is a problem was recently published in J. Med. Chem.

The researchers were interested in STAT3, a popular oncology target. They used a computational approach to extract fragments from reported inhibitors and then recombined them into new molecules, a few of which were made and tested. Unfortunately, some of the previously reported inhibitors were PAINS, and, like HeLa cells contaminating cell cultures, the resulting pathological fragments contaminated this research. The most active molecule out of this exercise, compound 8, is a para-quinone:

Quinones are troublemakers for two main reasons. First, they can nonspecifically react with thiols (see figure), and STAT3 does indeed have several free cysteine residues. Second, quinones are well-known redox-cyclers: they can be reduced and then re-oxidize in air, generating reactive hydrogen peroxide in the process.

The researchers showed that compound 8 is active in several cell assays and a mouse xenograft tumor model, but of course any generic alkylator could also show these effects (mustard gas, anyone?) and hydrogen peroxide is itself an important second messenger. It is impossible to say whether the activity of compound 8 is due to interaction with STAT3 on the basis of the experiments reported in the paper. The only evidence that compound 8 interacts with STAT3 at all comes from a fluorescence-based assay which appears to show 70% inhibition at >100 micromolar compound 8, a concentration far higher than the cell experiments.

In other words, what this paper shows is that a quinone has modest but ill-characterized biological activity. Of course, just because a compound can be a bad actor doesn’t necessarily mean it is behaving as one, but in the case of PAINS it is best to assume guilty until proven innocent. Indeed, a figure in the Supporting Information shows that the compound also inhibits STAT5 phosphorylation, supporting the notion that it acts through multiple mechanisms.

We can do better than this.

I hesitated before writing this post – I don't want to come across as a mean-spirited vigilante – but one of the strengths of science is its self-correcting nature. Researchers should learn to recognize PAINS when they inevitably show up as screening hits. If and when they don’t, editors and reviewers evaluating manuscripts and grants have an obligation to hold them to account.

It is easy to ignore or shrug off sloppy science, perhaps with a cynical chuckle, but papers like this fill me with a mixture of sadness and outrage. This research consumed the time and efforts of four scientists, not to mention scarce funding from the NIH and Alex’s Lemonade Stand Foundation, a charity founded by a young girl who subsequently died of her cancer at the age of eight.

We owe it to society to stop wasting resources chasing artifacts.

Fragments vs DAAO

Fragment-based approaches are often applied to tough targets, such as protein-protein interactions or BACE1, that have stymied more conventional approaches. Although kinases have certainly been the focus of many fragment efforts, other more “traditional” enzymes are sometimes ignored. In a recent paper in J. Med. Chem., Takeshi Hondo, Tatsuya Niimi, and colleagues at Astellas Pharma show that fragments can play a valuable role here too.

The researchers were interested in D-amino acid oxidase (DAAO), a potential schizophrenia target. This enzyme catalyzes the deamination of amino acids such as D-serine, so it is not surprising that very small, fragment-sized molecules can bind to it quite tightly (as indeed we noted here). Recognizing this, the researchers conducted a high-concentration screen of 3500 fragments. One of the more interesting hits was compound 8, which is actually a fragment of a previously reported molecule. With low micromolar potency and just 8 atoms, the fragment has a ligand efficiency of just over 1 kcal mol^-1 atom^-1, one of the highest values I’ve ever seen.

 In addition to its impressive affinity and ligand efficiency, compound 8 induced a conformational change in the protein to open a nearby subpocket, and growing into this pocket led to dramatic improvements in potency. Additional optimization for permeability and brain penetration eventually led to compound 30, with low nanomolar activity in both biochemical and cell-based assays. This compound proved to be selective against a panel of 57 potential off-targets, and was found to be active when dosed orally in a mouse model of schizophrenia.

This is a lovely example of structure-based fragment growing. Although it’s rare to find such a small, potent fragment, examples such as this do support the inclusion of very small fragments in screening libraries.

What you are Missing.

The nice part about blogging is you can post anything you want.  Mostly, we post on interesting (to us at least) papers, news about fragments, conferences, and so on.  We have assiduously avoided being a commercial outfit; what we post is what we believe in and we don't post for cash or barter.  So, we tend to not promote people/companies.  I am so totally breaking that rule right now.  

Many of you probably know Chris Swain, the principal of Cambridge MedChem Consulting.  [Full Disclosure: Chris and I have worked and published together.  I hope that does not diminish your feelings about him.  :-)] If not, this post will introduce you to him and the many wonderful resources he curates, particularly in the FBHG world.  There is so much there, sometimes I forget what he has; in fact, Chris's site goes by the rule, "Why make them buy the milk, give it to them, and the cow too!"  

One of the great resources Chris has is a graphical snapshots of a metric pile of fragment collections.  On Monday, Chris added nPMI (Principal Moment of Inertia) to these snapshots.  As has been discussed some, I (and Justin Bower from the Beatson) think this is the best way to evaluate "3D-arity".  It is interesting to just browse through the snapshots.  Some of the collections that are VERY large do seem to have a good to excellent amount of 3D-arity.  Does this correlate with increased hit rates for certain target classes?  

Well, Chris has thought of that.  He has been collecting the compounds from the literature that are reported as fragment hits.  He has just updated that snapshot with nPMI also.  What do the reported fragment hits tell us?  I would say that the vast majority of the reported fragments are Voldemort Rule compliant.  No surprising.  What I would like to see is a breakdown of fragment property against target type.  This may the part of the cow Chris isn't giving away.  There may be other slices/dices too.  

Trust me, go and spend some time on Chris site.   I won't call it a time waster, but it will suck you in.

Multiple methods find fragments on MEK1, but fluorimetry shines

The tendency of fragments found in one assay to reproduce – or not – in another assay is a frequent topic at Practical Fragments. In a recent paper in Bioorg. Med. Chem. Lett., researchers at Sanofi describe their experience screening the oncology-associated kinase MEK1.

The researchers were interested in the ATP-binding site of MEK1, and they started with a virtual screen (using Glide-SP) of a 10,000 compound library. The top 196 hits were then tested experimentally by differential scanning fluorimetry (DSF) and surface plasmon resonance (SPR), leading to 30 and 44 hits, respectively, with 12 in common. A subsequent biochemical assay of the same 10,000 compound library yielded 106 hits, only 13 of which were in common with the virtual screen. 158 different fragments were identified by one or more of the three experimental methods.

Of 13 hits selected for follow-up experiments, crystallography ultimately yielded structures for 7 of them, of which 5 had been identified in the virtual screen. Interestingly, SPR had only confirmed 2 of these molecules, while DSF had confirmed all of them. Thus, in contrast to some reports, the Sanofi folks are quite sanguine about DSF and advocate using it widely and early in a project (as indeed many people do seem to be doing). The technique is fast and easy, and in this case the researchers were able to run the DSF screen before they had finished developing their biochemical assay.

The paper includes detailed comparisons between virtual screening, DSF, SPR, biochemical, and X-ray approaches, and is well worth examining if you are putting together a screening cascade.

The researchers conclude:

There is no gold-standard method for screening fragments. The general approach is to conduct a primary screen and then follow this up with at least another method to confirm hits, which are subsequently prioritised for structure determination. Different groups adopt different methods based on availability of materials, in-house expertise and prior experiences screening fragments.

In other words, multiple methods can find fragments. Ultimately, you’ll probably find real hits whatever methods you use, as long as you’re careful.

Inhibition in Solution Assay (ISA) – on a surface

As illustrated by our poll, surface plasmon resonance (SPR) is one of the most widely used techniques for finding fragments. However, as commonly practiced, SPR – like all techniques – has drawbacks. For one thing, despite impressive recent advances, it is still not particularly high throughput. Also, the protein is typically immobilized on a sensor chip, and the detection of binders depends on the mass ratio of the ligand to the protein. With larger proteins and smaller fragments, this can quickly push the signal below the noise.

A seemingly simple solution is to reverse the experiment: immobilize the small molecule and add the (comparatively large) protein to get a whopping signal. Indeed, this is the approach that Graffinity (now part of NovAliX) takes, and is conceptually similar to work done at RIKEN. However, both these techniques require dedicated surfaces functionalized with fragments.

In a recent paper in J. Med. Chem., Stefan Geschwindner, Jeffrey Albert, and colleagues at AstraZeneca sought to simplify matters. Their idea is to immobilize a single high-affinity molecule to a chip. Protein in solution should give a good signal when the protein binds to the surface, and adding competitor to the solution should decrease protein binding to the immobilized target compound, thereby reducing the signal. They call this the “inhibition in solution assay”, or ISA.

The researchers used the protein PDE10A as a test case and attached a previously characterized small molecule to the surface; this modified small molecule has an IC₅₀ of 991 nM for the target. They then used two different approaches for detecting interactions, SPR (GE/Biacore) and a 384-well plate-based optical waveguide grating (OWG) from SRU Biosystems. Both formats work and give comparable results for a set of molecules ranging in affinities from 40 nM to 0.5 mM.

One nice feature of this approach is that, as a competition assay, it should only identify molecules that are competitive with a known binder. On the flip side, ISA does require a reasonably potent binder for your protein, and you must be able to modify this molecule such that it can be immobilized to the surface. And of course, there are still problems at high concentrations; the researchers mention that high loading of immobilized small molecule can cause other molecules to stick to the surface. Still, this is an interesting approach that should be easily applied to many systems. I’d be curious to know whether you’ve tried it or a variant, and how it compares to more conventional SPR methods.

Poll results: how small are your fragments?

The results of our most recent poll are in - here are the smallest fragments readers would allow in their library:

 

It looks like the smallest fragment most people would include in their library has a median of 7 or 8 non-hydrogen atoms, just slightly smaller than azaindole. More than 85% of respondents set a minimum size of 5 to 10 heavy atoms, so if we take the Pfizer rule of thumb that each heavy atom averages 13.3 Da, we’re talking 67 to 133 Da for the smallest fragments.

These sound like reasonable limits; slightly smaller molecules start becoming too volatile to handle reliably. Also, as Teddy pointed out, you’ll probably need either very sensitive methods to detect the smallest fragments, or very impressive ligand efficiencies.

Our poll last year asked about the largest fragments, so together these polls set a range of 5 to 20 heavy atoms for typical fragment libraries.

Thanks to the 75 of you who voted in this most recent poll.

A slew of sites for fragments in HIV Reverse Transcriptase

The protein HIV-1 reverse transcriptase (RT) has been something of an Achilles heel for HIV; 13 approved drugs inhibit this enzyme! However, HIV is more adaptable than Achilles, and can develop resistance to drugs, creating a need for new molecules. With this in mind, Eddy Arnold and colleagues at Rutgers University performed an extensive fragment campaign against this target; their work was recently published in J. Med. Chem.

The researchers assembled a library of 775 fragments, 500 from Maybridge and most of the rest from Sigma-Aldrich and Acros. These were combined into 143 pools of 4 to 8 fragments, each at 100 mM in DMSO. Crystals of RT grown with the drug rilpivirine were soaked with each of the pools; rilpivirine stabilizes the protein and yields crystals that diffract to high resolution. The researchers also added 80 mM arginine and 6% trimethylamine N-oxide (TMAO) to the soaking solutions; arginine helped solublize some of the more hydrophopic fragments and improved electron density, while TMAO improved diffraction.

Overall, the researchers found 34 fragments that bound to HIV RT, a hit rate just over 4%. Interestingly, halogenated fragments seemed to give a much higher hit rate: 7 of 29 fluorine-containing fragments produced structures, as did 4 of the 17 brominated fragments and one of the two chlorinated fragments. I don’t recall seeing halogens previously over-represented among fragment hits, though last year we did write about halogen-enriched fragment libraries. The sample sizes reported here are small, but if the findings hold up in other studies, fluorine fetishism may be further justified.

But just as interesting as the composition of the fragment hits is the number of binding sites in the protein: 16, with names ranging from the descriptive (“NNRTI Adjacent” and “Incoming Nucleotide Binding”) to the concise (“399”) to the downright thuggish (“Knuckles”). In the case of three of these sites, some of the fragments also inhibited enzymatic activity.

There is a lot of nice information here, and eight co-crystal structures have been deposited in the protein data bank. Still, I am left a bit dizzy at the sheer number of sites. In fact, one fragment (4-bromopyrazole) bound to all of the 16 sites! What are we to make of this – is this a privileged fragment or a promiscuous binder? And as for the sites with no known functional activity, are these useful? What do you think?

Fragment Events in 2013 and 2014

If you missed Fragments 2013 and the Eighth Annual FBDD there are still a few more fragment events this year, and although we're not quite into summer it's not too early to start marking your calendar for 2014!

2013

June 19-21: CHI’s Thirteenth Annual Structure-Based Drug Design will be held in Boston, with several talks on FBLD.

September 3-5: LibPubMedia Conferences is organizing DrugDesign2013 in Oxford, UK, with a focus on fragment- and ligand-based drug design.

September 23: Teddy and I will be teaching a three-hour short course on FBLD in Boston as part of CHI’s 11th Annual Discovery on Target. 

2014

April 23-25: CHI’s Ninth Annual Fragment-Based Drug Discovery will be held in San Diego. You can read impressions of this year's meeting here and here, last year's meeting here, the 2011 meeting here, and 2010 here.

September 21-24: Finally, FBLD 2014 will be held in Basel, Switzerland. This marks the fifth in an illustrious series of conferences, the last of which was in San Francisco in 2012. I believe this will also be the first major dedicated fragment conference in continental Europe. You can read impressions of FBLD 2010 and FBLD 2009.

Know of anything else? Add it to the comments or let us know!

Fragment Mixes for NMR

The number of fragments in a mixture for NMR screening has been the subject of a poll.  Some people have stated that they go much higher than 10 fragments (of course for 19F it is totally different).  What many people who are interested in doing ligand-observed NMR screening, it is daunting to figure out how to compute the mixes.  This paper addresses the issue.  Unlike many current approaches which use the spectra and then deconvolute them, this approach encodes the spectra into "fingerprints" and uses a Monte Carlo algorithm to minimize signal overlap.  The paper itself delves deeply into a lot of computer-ese gobbledygook (e.g. "the knapsack problem, one of the typical, non-deterministic polynomial time (NP-complete) problems") that I don't find interesting at all.  What I do find interesting is that they are targeting mixtures of 5 fragments. Other than that, they go into serious detail about their algorithm and what version was best.  They worked with 342 fragments from their in-house library.

However, after doing the initial POC on these they did not have a library big enough to test for scalability so they generated some virtual libraries: 500, 1000, 3000, and 5000 fragments.  Similar to discussion going on elsewhere, they clustered their fragments as strongly aromatic, strongly aliphatic, or balanced shown here.  As would be expected, library size and peak distribution did not affect the algorithm, but number of fragments per mixture did. As shown here, for the optimized libraries there is less overlap as you increase the number of fragments per mix (for 5 fragments it was ~0% to about 10-20% for 8-10 fragments).  This is a potentially huge increase in efficiency, simply increasing the number of compounds per mix from 5 (our poll found 5-7 to be the median number in mixes) to 10 would half the number of spectra that need to be acquired; hence lowering the potential cost to companies (especially if they are outsourcing (shameless self-promotion)). 

I have spoken to the authors and while, unlike the Beatson, their tool will not be available online, it is being incorporated into an upcoming release of Mnova's software. [Full disclosure: I have a business relationship with Mestrelab.]  Of the other software available, I believe only AMIX (Bruker) has built in screening tools, but I am not sure entirely as I have never used AMIX.  NMRpipe would be the one to be most likely to also have such tools, but their availability would be based upon the kindness of strangers.  Frankie D (Mr. NMRPipe) is at Agilent (nee Varian) now, so maybe vNMRJ will become more utile.  That last major software package from ACDLabs is not geared to this kind of work AFAIK. Additionally, this approach of course is just as "easily" applied to 19F, which could mean a mean increase of compounds from 10-15 to 25-30 routinely. 

I of course will update this if information on other software becomes available in the comments or via email.

[UPDATE #1: Ben Davis (Vernalis) pointed out CCPN has tools for this.  
Anna Vulpetti (Novartis) points out that python scripts for 19F have been published.
Arvin Moser (ACD) points out that ACD does offer screening tools.]

30% of all Posts...

NOTE: Blogger blew up my post when I published it.  I have fixed what I can.  Blogger keeps on blowing up this post after I edit it.   I have removed what I think may have been causing some of the problems.  This post should be considered in "Wiki-ese" as a fragment.   Thankfully, the summary was unaffected. 

As I recently said, GPCRs are a theme around here, so this post will talk about work published last year by the folks at ZoBio and Heptares.  [In terms of full disclosure, I had a business relationship with ZoBio until recently.]  This work is also on STaRs, the stabilized GPCRs developed by Heptares.  I noted my concerns with this approach here.  These two papers focus on A2A GPCRs, while previous posts here were on A1A, A3A, and B1A GPCRs.

In the ACS Chemical Biology paper, the authors are using TINS to screen an antagonist-stabilized A2AR StAR.   The immobilized protein showed a ~50% greater retention in activity after 5 days at 4C compared to the native protein in membranes (60% vs. 30% binding competency).  So, immobilized stabilized protein is more stable than non-immobilized, non-stabilized protein.  They then took a subset (531 compounds) of the ZoBio fragment library picked for maximal chemical and shape diversity and screened using OmpA as the reference protein. As shown in the bucketing below the vast majority of the compounds cluster around a T/R of 1.  This indicates that they have a slight preference for the target or the reference. The used a T/R cutoff of less than 0 e.g

. aromatic and aliphatics.  Additionally, the use of the logarithmic plot for the bucketing obscures the spread around T/R=1.  I have never seen a discussion from the creators of TINS discussing the relative error of the method and how to best evaluate the screening data.  In this case, they chose a 0.7 cutoff because there appears to be a discontinuity in the data there.
They followed up on these (see Table 1 in the paper) as orthosteric hits by observing if they can inhibit binding of an inverse orthosteric agonist in a radiolabeled assay using WT A2AR in HEK membranes; five fragments inhibited binding by >30% at 500uM (see below). 

This data in conjunction with the TINS data shows the compounds bind reversibly with a 1:1 stoichiometry.  These fragments also inhibited A1AR, which would not be unexpected for such small molecules.  However, 3 of these compounds have poor LEAN values >0.3.  This is particularly poor for GPCR targeting compounds.

Four additional fragments either one or the other of the inverse agonist or agonist used.  The two most potent AM appear to have some subtype specificity (A2AR over A1AR).  When they tried to test these compounds in a cell-based assay, toxicity was observed so no data could be collected.

In summary, the authors show that TINS is productive in finding fragments that bind to GPCRs.  However, they have to rely on standard biochemical assays for follow up.  It would have been nice to see at least one other method used to verify the active fragments, like SPR.  What I really like is that they did the biochemical assays against WT, which does not necessarily alleviate my concerns about screening against a mutant.  I would have really liked to see a comparison of the biochemical data in the STaR and WT.

So, while people say 30% of marketed drugs target GPCRs, I can assure you 30% of all of our posts are not about GPCRs.

Reversibly covalent fragments vs kinases

A big problem with small fragments is that they usually have low affinities for their targets; there is only so much binding energy you can pack into a dozen or so heavy atoms. Indeed, it wasn’t until the rise of sensitive biophysical methods such as NMR that fragment-based lead discovery really took off. But what if you could increase the affinity of fragments themselves?

One way to increase affinity is by introducing a covalent bond between the fragment and the protein: an irreversible covalent bond will, by definition, keep a fragment from ever dissociating from the protein. However, with this type of interaction, it may be difficult to distinguish between fragments with different inherent binding energies: iodoacetamide will alkylate every exposed cysteine residue, even though acetamide itself may have no inherent binding affinity. What you really need is a reversible covalent bond: something just strong enough to improve the affinity for the target, but still allow you to define structure-activity relationships among different fragments. This is the principle behind Tethering, which relies on (reversible) disulfide bonds between fragments and the amino acid cysteine. In a recent communication in J. Am. Chem. Soc., Jack Taunton and coworkers at UCSF apply a different chemistry to discover potent and selective kinase inhibitors.

Among the 518 human protein kinases, there are many non-conserved cysteine residues. Indeed, several advanced clinical candidates target a cysteine found just outside the ATP-binding site of certain kinases. These candidates are potent molecules in their own right, with irreversible covalent “warheads” attached to permanently knock out the kinases.

The UCSF researchers instead started with simple fragments (molecular weights between 96 and 250 Da) found in non-covalent kinase inhibitors. Each fragment was derivatized with a cyanoacrylamide moiety that could form a reversible covalent bond with cysteine residues, and these were screened against three of the eleven kinases that contain a cysteine residue at a certain spot within the active site. Remarkably, all showed activity against at least one of the kinases, though there were very different selectivities. Mutation of the targeted cysteine residue dramatically reduced affinity in all but one case, as did removal of the cyanoacrylamide.

Crystal structures of two fragments bound to the C-terminal domain of the kinase RSK2 were determined. In the case of compound 1, the indazole made the expected interactions to the so-called hinge region of the kinase. Interestingly, though, in the case of fragment 8, the azaindole moiety does not bind to the hinge. Instead, the ketone moiety serves as a hydrogen bond acceptor. Overlaying the two fragments suggested that adding an aromatic substituent to the indazole could improve affinity, a hypothesis that was nicely validated by compound 11. Addition of another small moiety gave compound 12, with improved selectivity over the kinases NEK2 and PLK1.

Compound 12 was tested against a panel of 26 kinases, 12 of which have active-site cysteine residues, and was found to be selective for RSK2 against all but NEK2 and PLK1 (and even then, the compound was more than 40-fold selective for RSK2). Crystallography confirmed the binding mode, complete with covalent bond to the cysteine residue, and mass-spectrometry of the denatured protein confirmed that the covalent bond is reversible.

One of the attractive features of the cyanoacrylamides is that they are quite stable, and in fact compound 12 showed respectable cell-based activity against RSK2 as well as the closely related C-terminal domain of the kinase MSK1, for which no inhibitors had previously been reported.

All in all this is a nice approach that should be broadly applicable not just to kinases but to a wide variety of targets. At least some of this technology has been licensed to Principia Biopharma, so it will be fun to watch this story progress.