MIP and MDP

Dan and I are were at the CHI Discovery on Target meeting last week.  It is highly focused on target validation and early stage hit generation.  This is NOT a chemistry conference, although there were plenty of chemists and chemistry talks; the target audience is biologists.  As such, it was a great arena to be teaching about fragments and educating a whole different phyla of FBDD consumers.  It was also nice to meet people and have them say, "Oh, I love the blog."  Of course, I just say, try commenting, that's manna to bloggers.  The nice thing is people generally understand FBHG, unfortunately I think they generally misunderstand it.  Why?

I think part of the problem is that the Most Impactful Papers (MIPs) in the field are also the Most Destructive Papers (MDP) in the field.  So, what are the MIP for this field?  For me, the criteria are pretty straight forward: one or two papers that are seminal to understanding the field.  As you may already be guessing, my list of MIP intersects my MDP.  

Most Impactful Papers: 

The Rosebowl of Fragment Papers: SAR by NMR.  This is the paper that showed that NMR was not bound by doing structures, but was a viable screening paradigm.  It started the whole "Fragment" thing. 

• The Rationale Behind it All: The Leach and Hann Molecular Complexity paper.  If I had one paper to give to someone to explain why you should use fragments, this is it.  The first three graphs should be in every introduction to FBHG. 
• The Voldemort Rule: The Rule of Three paper.  This paper has defined what a fragment is for a decade. 
• Fragments get a name (that's never used): Fragonomics.  OK, self-referencing is not cool, so this should really be Dan's paper, the first review on FBDD. 
• Pfizer lifts the curtain: Pfizer's fragment library paper. I love this paper because it gives a great overview of how Big Pharma put its fragment library together (think laser pointers!). 

This is obviously a very short and incomplete list, and totally my opinion.  Let me know what you think MIP are in the comments.

So, what are "destructive" papers?  Those are the papers that require me to spend a lot of time explaining why what people understand is not really a good general, practical approach.  

Most Destructive Papers:

• I think THE most destructive paper is also the most impactful: SAR by NMR.  How can that be you say?  Easy.  Because of this paper, the vast majority of people who "have heard" of fragments think that you need to label protein to do use NMR for fragments.  While, target-based screening is really powerful, I don't think it should be the first thought for screening, but is more impactful on active follow up.  I can here the counterarguments coming, but WAIT there's more.  They used linking, rather than growing.  I think most people would agree that this is the "Serendipity" approach.  I think this territory is well trod on this blog.  Lastly, warheads. 
• While it had its place the Rule of Three paper has also become destructive.  This is a "hot" topic, but my main problem is the slavish devotion to an empirical "Rule".

What are your thoughts?  

I am also cross-posting this on my site http://www.quantumtessera.com/325/ and the LI group to see if maybe a different venue will generate more comments.

Programming Note

Dan and I will be co-teaching our award-winning (maybe not, but I bet our mother's think we are special) FBDD Short Course at CHI's Discovery On Target meeting.  It was standing room only in San Diego (probably because there was a shortage of chairs of something), but I bet you could squeeze in if you asked nicely.  If you are in Boston (Go Red Sox!), drop us a note, it would be great to catch up. 

Fragments vs wild-type GPCRs by SPR

Membrane proteins such as G-protein coupled receptors (GPCRs) represent a large fraction of drug targets. These are mostly overlooked by the fragment community, for two reasons. First, assays for low affinity binders are difficult to develop. Second, the proteins usually lack structural information useful for advancing fragment hits. Earlier this year Heptares provided a lovely solution to both problems by generating stabilized mutant GPCRs, which could be screened using surface plasmon resonance (SPR) and characterized crystallographically. In a new paper in ACS Med. Chem. Lett., Iva Navratilova, Andrew Hopkins, Robert Lefkowitz, and a multinational team at the University of Dundee, Duke University, and the University of North Carolina Chapel Hill report using SPR to screen fragments against a wild-type GPCR.

The researchers chose the human β2 adrenergic receptor, which has served as a model GPCR for a variety of biological and biophysical studies. They expressed this with a His₁₀ tag on the C-terminus and used a conventional nickel chip to immobilize the protein in the presence of detergent. The immobilized protein was able to bind to a known agonist and antagonist with dissociation constants similar to those reported in the literature, suggesting that it was folded correctly.

Next, 656 fragments were screened against the protein at 50 micromolar each. Using a surface containing β2 adrenergic receptor blocked with a known high-affinity, slowly dissociating agonist as a reference, the researchers looked for fragments that bound selectively to the surface containing the unblocked protein. A total of 81 fragments were then examined more closely in dose-response curves, yielding five confirmed hits, with dissociation constants ranging from 17 nM to 22 micromolar.

All five of these hits were tested in a conventional radioligand competition assay, confirming their binding. Interestingly, four of the five ligands were N-arylpiperazines, a class of molecules that the Heptares team also found as ligands for the β1 adrenergic receptor. When tested against this GPCR most were not selective, but one did show some selectivity for β2 adrenergic receptor against a panel of 27 GPCRs.

The fragment hits were then tested for activity in a cell-based assay, and all of them inhibited signaling. This illustrates a general complication with binding (versus functional) assays: with simple enzymes, once you’ve found a binder, it is probably either an inhibitor or has no effect. With GPCRs, a binder could be an agonist, an antagonist, a partial agonist, an inverse agonist, a neutral antagonist, or something else entirely; you need to go into cells quickly to figure out what you’ve got.

I do wonder whether it would be possible to screen at higher concentrations to look for weaker ligands, particularly for more challenging GPCRs for which no small molecule ligands are known. Still, the fact that SPR works as well as it does for a native GPCR is quite impressive. I suspect that we’ll see more and more fragment screening by SPR on membrane proteins. Whether folks will be comfortable optimizing fragment hits in the absence of high-resolution structures, though, remains to be seen.

Rule of five versus rule of three

Metrics (such as ligand efficiency) and rules (such as the rule of three) seem to be some of the more controversial topics around here. If you aren’t experiencing metric-fatigue, it’s worth checking out a recent (and free!) “Ask the Experts” feature at Future Med. Chem., in which four prominent scientists weigh in on the utility of the rules of five and three.

Monash University’s Jonathan Baell (of PAINS fame) notes that, as of early 2013, the original 1997 Lipinski et. al. rule of five paper (and the 2001 reprint) had been cited more than 4600 times! Baell holds that, of the properties covered by the rules – molecular weight, lipophilicity, number of hydrogen-bond donors (HBD), and number of hydrogen-bond acceptors (HBA) – the property lipophilicity is probably the most important. Although he agrees that rules can be too strictly applied, he also asks:

What sum value is represented by the dead-end investment that the world never saw because of application of a Ro5 mentality?

I think this is a good, often-overlooked point. It is easy to find examples of drugs that violate the rule of five or programs that were killed by rule-bound managers with limited vision, but, as GlaxoSmithKline’s Paul Leeson says, “there is massive unexplored chemical space within the Ro5, which is available to innovative chemists.” Why not put much of the focus here?

Of course, readers of Practical Fragments are probably thinking as much about the rule of three as the rule of five, and one of the main criticisms of that rule, particularly by Pete Kenny, has been the fact that it is not clear how to define hydrogen-bond acceptors: do you count all nitrogen and oxygen atoms, including for example an amide –NH? I think the common-sense answer would be no, and Miles Congreve, the first author on the original rule of three paper, seems to agree. He also notes that the number of hydrogen bond acceptors seems to be less important in general than the number of hydrogen bond donors, which is negatively correlated with solubility, permeability, and bioavailability.

Given last year’s poll on the maximum size of fragments people allow in their libraries, it looks like most people are already capping molecular weight well below 300 Da, which skews the other parameters toward rule of three space. That said, Congreve does warn that commercial fragment libraries “contain too many compounds that are close to 300 Da, rather than containing a distribution of compounds in the range of 100 – 300 Da,” a statement borne out by by Chris Swain’s analyses. Of course, the larger you get, the more possibilities there are, and the optimal property distribution of a fragment library is still a matter of debate.

Ultimately I think many people will agree with Leeson, who says that “there are probably sufficient metrics in the literature today,” and with Celerino Abad-Zapatero, who notes that “additional rules will not be the answer in the long run.” On this note I promise no more posts on metrics or rules – for at least a month!

More thoughts on the Astex-Otsuka marriage

Teddy already highlighted the planned $866 million acquisition of Astex by Otsuka, and I thought I’d add a bit of context. Astex Therapeutics was founded in 1999, just three years after publication of the Abbott SAR by NMR paper that arguably launched widespread interest in fragment-based lead discovery. From the outset, Astex focused heavily on crystallography, which was somewhat unusual at the time; Vicki Nienaber’s seminal SAR by Crystallography paper only came out in 2000.

Astex researchers have made many practical contributions to FBLD, from the (sometimes controversial) rule of three to the LLE_AT metric to the Astex Viewer familiar to anyone who has seen a presentation from the company. More than 100 publications have come from Astex, including one of the earliest comprehensive reviews of the field. And the company has also delivered: of 28 fragment-derived compounds to make it into the clinic, Astex has had a role in nearly a quarter, including AT13387, AT7519, AT9283, JNJ-42756493 (with J&J), LEE011 (with Novartis), AT13148, and AZD5363 (with AstraZeneca and ICR).

In terms of price, $866 million is indeed a tidy sum, more than the up-front Daiichi Sankyo paid for Plexxikon (though a bit under the total deal value of $935 million) and more than an order of magnitude higher than the $64 million Lilly paid for SGX back in the dark days of 2008. Even with close to a billion dollars on the table, some are calling the price too low, with one analyst suggesting Astex is worth $13 per share rather than the $8.50 offered by Otsuka.

Of course, the Astex pipeline is not entirely fragment-based; a merger with SuperGen in 2011 brought in a marketed product (decitabine) as well as other clinical compounds. Still, from what Otsuka has said publicly, it does appear that the FBLD technology was a major driver: it is the first item mentioned under the heading “Objectives of the Acquisition.”

As Derek Lowe pointed out over at In the Pipeline, Japanese firms have a good track record of not breaking or shuttering acquired companies; last I checked Plexxikon was still going strong. Hopefully this will hold true for Astex as well. Practical Fragments offers congratulations and wishes continued success to everyone involved.

What Do Fragments Get you?

Parroting In the Pipeline, what do fragments get you?  886 Million dollars, that's what!  As pointed out by the buying company:

"Astex's unique fragment-based drug discovery technology [Ed: emphasis added] and clinical oncology research and development capabilities, born out of the passion of its researchers, exemplify our corporate mottos and belief in "Sozosei (Creativity) and Jissho (Proof through Execution). I would like Otsuka Pharmaceutical to continue to respect Astex's uniqueness and leverage it to bring further growth for Otsuka Pharmaceutical."

Congratulations to the folks at Astex, the next time you see them at a conference, make sure they pick up the check.

Another NMR Tool...

There are many things which aid in the successful prosecution of fragments.  Most people would agree that structural information is one of those things.  However, in many cases there is no structure, nor any hope of obtaining one.  Many different methods have been developed to try to address this gap.  Oftentimes they are impractical, sometimes they are useful.  In this paper, Gregg Siegal, Marcellus Ubbink, and co-workers from his academic lab present a new NMR-based structural tool.  [Editor's Note: I used to have a business relationship with Gregg's commercial side.]  So, is this a practical or impractical tool?  You can skip down to the bottom for the answer, or keep reading and follow me down the rabbit hole.

Their approach is not to generate high-resolution structures, but low resolution models of how initial fragments bind to the target. To accomplish this, the use pseudocontact shifts  (PCS)induced by paramagnetic ions.  To those of you whose eyes just glazed over, let me explain.  We typically only use diamagnetic atoms in NMR, because paramagnetic atoms cause line broadening, sometimes to extinction.  For ease of explanation, the PCS is similar to any dipolar coupling, it is a way to relax between atoms, like the NOE, but with a longer distance dependence r^-3 (PCS), vs. r^-6 (NOE).  However, with good decisions like the choice of the ion, the placement of the ion, and so on, you can get subtle effects on your ligand, rather than wiping it out. In the end, you need to know a few things: the actual fraction of ligand bound, the structure of the target (or a good homology model), and the PCS tensor (see below).  This work used rigid, paramagnetic ion binding tags attached to the target via engineered disulfide linkages (CLaNP).

 In total, they made three different tagged proteins and used Yb3+ as the paramagnetic ion and Lu3+ as the diagmagnetic ion. 

This data represents a mixture of bound and free ligand, so using the experimentally determined Kd and the known concentrations of ligand and target, the % bound ligand can be determined.  This can then be converted into PCS of only the bound state. 

However, the authors then tried to calculate the tensor, which is necessary to calculate the orientation of the PCS tensor.  When compared to the orientation of the ligand determined by NOE, there was an 4.7 A RMSD.  This approach only gives the relative location of the binding site.  When they formally calculated the PCS tensors they were able to get a better match of the PCS-derived orientation compared to the NOE-derived, but still not perfect agreement.  That is expected for different methods which can be considered orthogonal.  There ends up being a lengthy discussion of the shortcomings of this method and why it could be possibly better than NOE-based methods, in particular it does not need labeled protein.  However, I would argue if you are not producing your protein in E. coli it is likely being made in insect cells or mammalian cells.  In the case of insect cells, why would you wait two months, to get ligand orientation information on an initial hit?  The project has come and gone on the initial screen hits by that time.

While this is a interesting approach academically, it is really impractical.  Why?  As the authors state, this method is best for ligands with high micromolar to low millimolar affinity.  This positions it firmly in the very early stages of FBHG.  You need to have the structure of the target, or a good homology model.   You need to generate multiple mutants (they do state you can get by with only two positions, but three is better).  You need to do some seriously involved computation; something that is not routine at all.  This would be a much better tool if it could be robustly used at late hit expansion/early lead generation, but that doesn't seem likely.  So, you have what is largely an academic tool for generating models of ligand-target binding with fragments, but not something that would be routinely used.  

3D Fragments...An Analysis

[**Programming Note**  Sorry about two posts in one day, but I thought this was too cool to wait.]

 

The 3D-arity of fragments is a common topic of discussion in this field.  ICYMI, Chris recently did an analysis of PPI interactions and the compounds that target them.  However, even more recently, Chris just put up his analysis of the 3DFrag consortium's fragment collection.  The 3DFrag collection does not look any more 3D than commercially available collections.  Justin Bower from the Beatson points out that this is because their fragment collection is largely due to culling from commercial collections.  

Now, no one will argue that nPMI is the best metric for assessing 3D-arity.  But it is the best we have so far.  So, Chris has tried to improve on the visualization of nPMI for very large libraries.  Chris has divided the PMI plot into regions that are disc, rod-like, or spheres (he details how he classifies them at his page).  The upshot of this is that he can then generate very simple plots like this:

 

I think this is a great leap forward.  Obviously, because of the nature of the chemistry performed over the past umpteen years, this would be totally expected.  As Peter Kenny has pointed out previously, rods have volume, but I think that is not what the 3D-eers are aiming at.  What's really nice is that 3DFrag has chemists to make fragments.  In May, they reported that they had added 221 synthesized fragments.  I would like to see how these 221 fragment differ from the commercially available ones.  The proof is always in the pudding after all. 

NMR as an Impractical Tool, Again

When I was in grad school, I was faced with the choice of two NMR labs to join (after starting as a organic chemist and flirting with enzymology).  Both used NMR, but with very different goals.  One lab used NMR and found systems to study using NMR.  The other studied interesting problems.  The PI would say if NMR is most appropriate than using, but don't be a slave to it.  I have taken that attitude my entire career.  In industry, it also has to be the mantra: best tool for the problem.  Academics tend to have the opposite mindset: let's make my tool work for anything.  

The Krimm lab has been cited here, here, here, here, and here on the blog and I hold their approach to academic tool creation for drug discovery in good regard.  In this paper, they present a combination computational/NMR method for determining if a fragment induces conformational changes in the target.  In their own words: 

The approach relies on the comparison of experimental fragment-induced Chemical Shift Perturbation (CSP) of amine protons to CSP simulated for a set of docked fragment poses, considering the ring-current effect from fragment binding.

Sometimes good people do bad things. 

I am not going to get into the details, but rest assured the science is sound.  Their approach is to evaluate H-N (you could also use H-C, why not) chemicals shifts from titration data to simulated CSPs.  When they did compare experimental with calculated CSPs they could not explain some of these shifts, even when they included ring current-induced shifts.  To further investigate this phenomenon, they used Residual Dipolar Couplings (RDCs) to further explore these unexplained CSPs.  It does.  

What are my problems with this paper?  Practicality, primarily.  Is this another anti-compchem rant?  Nope.  The problem here is that everything they propose to do, and they do it well, relies upon a whole sh!tpile of a priori knowledge: 1. the structure of the protein (typically from X-ray) and 2. the assignments of the protein (not trivial).  Additionally, the RDCs require acquiring two sets of data, aligned and unaligned.  RDCs are wholly impractical.  All of this should red flag this paper as a "Impractical" approach.  It also does not present a method for interrogating structural changes induced by ligands that is any better or more robust than the current standard of analysis.

Myriad metrics – but which are useful?

Practical Fragments recently introduced WTF as a light-hearted jab at the continuing proliferation of metrics to evaluate molecules, but there is an underlying problem: which ones are useful? In a provocative paper just published online in Bioorg. Med. Chem. Lett. (and also discussed over at In the Pipeline) Michael Shultz asks:

If one molecular change can theoretically alter 18 parameters, two shapes, the rules of 5, 3/75, 4/400 and ‘two thumbs’ while simultaneously affecting at least nine composite parameters and countless different methods of representing data, how is a practicing medicinal chemist to know if any specific modification was actually beneficial?

Shultz focuses on three parameters in depth: ligand efficiency (LE), ligand-efficiency-dependent lipophilicity (LELP), and lipophilic ligand efficiency (LLE, also referred to as lipophilic efficiency or LipE). He conducts a number of thought experiments to see how these metrics change when, for example, a methyl group is changed to a t-butyl group or a methyl sulfone. He also examines how the metrics perform against historical data from Novartis lead-optimization programs.

One problem with LE is that, although it was introduced to normalize potency and size, it is still highly dependent on number of heavy atoms (heavy atom count, or HAC): addition of one atom to a small fragment will have a more dramatic effect on LE than addition of one atom to a larger molecule. This has led to metrics in which larger molecules are treated more leniently, but because of the way all these metrics are mathematically defined, none achieve completely size-dependent normalization.

More seriously, LE ignores lipophilicity, which seems to be correlated with all sorts of deleterious properties. With a nod to Mike Hann’s “molecular obesity,” Shultz notes that the widely used body mass index (BMI) “cannot distinguish between the truly obese and professional athletes of identical height and weight. Similarly, HAC based composite parameters such as LE cannot distinguish between ‘lean molecular mass’ and groups of real molecular obesity.”

LELP addresses this shortcoming by incorporating clogP, but it has problems of its own. For example, “the effects of lipophilicy are magnified as molecular size increases.” More alarmingly, as clogP approaches zero, LELP becomes increasingly insensitive to both size and potency; a femtomolar binder would have the same LELP as a millimolar binder when clogP = 0.

In contrast to both LE and LELP, LipE (or LLE) is size-independent, so a change in potency or lipophilicity will produce the same change in LipE no matter the size of the initial molecule. Shultz uses data from two lead optimization programs to show that LipE behaves better than LE or LELP. This is in contrast to a previous report that suggested LELP to be superior to LipE, albeit against a different data set.

Shultz further notes that LipE can be thought of as the tendency of a molecule to bind to a specific protein rather than to bulk octanol:

LipE = pK_i - clopP = log [EI]/([E][I]) – log ([I_octanol]/[I_water])

where E stands for protein and I stands for inhibitor

Although this is a simple consequence of the math, it is a nice way of visualizing an otherwise abstract number. Moreover, it suggests that optimizing for LipE could optimize for enthalpic interactions, a topic Shultz explores in depth in a companion paper.

Overall Shultz raises some excellent points, but I still believe there is value in LE (and LLE_AT), particularly in the context of fragments, which usually have low affinity. Ligand efficiency can prioritize molecules that might otherwise be overlooked. For example, it is hard to get too excited over a 1 mM binder, but if the hit has only 8 heavy atoms it could be valuable.

Turning to my own miniature thought experiment, fragments 1 and 2 have very similar LipE values, but the LE of Fragment 1 is better, and arguably makes a more attractive fragment hit.

Of course, in the end, rules should not be followed slavishly; the most lucrative drug of all time, Pfizer’s atorvastatin, violates Lipinski’s rule of five. Papers like this are important to highlight the problems and inconsistencies that underlie some of our metrics. Ultimately I’ll take biological data and the intuition of a good medicinal chemist over any and every rule of thumb.

What do you think? What role should LE, LELP, and LipE play in drug discovery?

Fragment Design Done Right

As many of you probably know, I am not a fan of virtual screening, computational design, in silico much of anything.  I think it tends to be poorly applied, or academic.  Now, don't mark me as a Luddite, I think that computational tools can be quite useful, when appropriately applied.  What is appropriate?  Read on and let Hoffmann-LaRoche-Nutley show you in this beautiful paper.  

This is one of a line of great papers coming out of the closing Nutley site, so that is the one upside.  In this paper, the authors present how they leveraged the expertise of their chemists to design fragments against HCV NS5B, a well known drug target.  The story starts (I hesitate to say "their efforts start...") with a screen of 2700 fragments by SPR.  They identified 163 hits of which 29 were selected (criteria unstated) for co-crystallization.  Only one fragment delivered, 1.   

Fragment 1 had a 78uM KD, 130 uM IC50, but it could not be optimized for affinity, physicochemical or ADME properties at all.  They one important discovery from the co-crystal of 1 was an unexpected, and they believe, first ever interaction of its type: the NH hydrogen interacting with Q446 (Figure 1). 

Figure 1. 

Using this and the published structures internally (2-3) and externally (4-6) the built a model. The following guidelines were proposed for the new fragments: 1. Satisfy carbonyl of Q446 and NH of Y448, optionally displacing or engaging the conserved water molecule, 2. Occupy large hydrophobic pocket, exploring its size, 3. Position aromatic chain to make edge to face interaction with Y448, 4. And at least one hydrophobic interaction with G410 and/or M414.

Figure 2.

In a triumph of democracy and teamwork, the chemist woud discuss his ideas with the compchemist and have them modeled.  The ideas were presented to the team and the best ideas selected for synthesis.  They also chose to avoid acidic functionality.  Since 1 was the only known binder in the region without acidic functionality, they focused on incorporating the unique Q446 interaction.  What they found was that compounds capable of 1,2 and 1,3 interactions were best.  Table 1. shows their SAR. 

Their first two compounds were dead, dead as a this parrot.  Compound 9 satisfied 3 of the 4 criteria they established, yet showed very poor activity and bad ligand efficiency. Using LE was crucial, the authors state, because it allows the to distinguish affinity through bulk, vs. affinity through efficiency.  Finally, adding substituents to the hydantoin to explore the hydrophobic pocket showed significant increases in activity, e.g. 9->12.  Fragment 12 was co-crystallized and confirmed the expected binding mode.  A 2-pyridone fragment (13) gave similar activity to 9.  So, starting with a Pfizer-inspired compound gave 14 which demonstrated an increase in potency, but NOT ligand efficiency.   They next tried 15 and voila! a 100x increase in affinity with four less heavy atoms, the ligand efficiency went way up!  Co-crystallization showed that 15 bound as expected.  Two more heavy atoms added to 15 led to 16 and showed increased potency and ligand efficiency while retaining the desireable phyiscochemical properties of 15.  Compound 16 was further optimized and entered clinical trials with all of the atoms presented in it. 

So, what makes this "Fragment Design Done Right" in my eyes?  In this case, they utilized fragment docking as an aid to chemist's designing ligands.  They used in silico tools, like modeling, to test the potentially validity of the chemist's hypotheses.  In the end, their computation was as good as their experimental follow up, in this case X-ray.  What differentiates humans from the brute beasts (except for all the exceptions out there where tool usage has been shown) is that we use tools.  Using tools correctly, is what differentiates the smart humans from the herd.

Fragments vs CHK2: high-concentration screening comes through

Checkpoint Kinase 2 (CHK2) is an oncology target that has been kicking around for years. Its relevance is still debated, so having more small molecule inhibitors would go a long way toward assessing its therapeutic potential. In a recent paper in PLoS One, Rob van Montfort and colleagues at The Institute of Cancer Research (UK) present their fragment-based efforts on CHK2.

The researchers describe the design of their screening library in some detail, starting with a series of typical computational filters on commercially available molecules. Although most of the molecules had MW < 300, molecular weights up to 320 Da were allowed for fragments containing F, Cl, or SO₂ moieties. Also, all fragments were required to have at least 10 heavy atoms, which is on the high-side for a minimum. A total of 1869 fragments were purchased. All of these were analyzed for solubility and purity (by nephelometry and LC-MS, respectively), though unfortunately the researchers do not provide pass rates.

Having assembled the library, the researchers then screened each fragment at 300 micromolar against CHK2 in a biochemical assay (AlphaScreen). This led to 45 hits, but 25 of these showed some interference with the AlphaScreen assay itself. However, the remaining 20 all showed dose-response curves in a different assay format, giving IC₅₀ values from 2.7 to 944 micromolar.

In parallel, the researchers screened CHK2 using a thermal shift assay, with each fragment present at 2 mM. Perhaps not surprisingly given the higher concentrations used, this led to 63 hits.

Where things got interesting – and encouraging – was when the researchers compared hits identified using the two methods. In contrast to others' experiences, there was reasonable overlap; of the 14 hits from both assays, 12 yielded measurable IC₅₀ values when assessed using a microfluidic functional assay. Most of the AlphaScreen hits that didn’t produce thermal shifts came from the set of 25 that had previously been flagged as interfering with the AlphaScreen assay itself, and several were also insoluble. Of the 49 thermal shift hits that did not show up in the AlphaScreen assay, 13 were insoluble. Regarding the remaining 36, the researchers propose that they may bind to CHK2 outside its active site and thus don’t inhibit enzymatic activity.

Next, the researchers attempted to characterize the binding modes of the fragments crystallographically. Of the nine fragments that produced structures, eight came from the set of fragments confirmed using both AlphaScreen and thermal shift. Significantly, the only fragment to yield a structure that was identified solely from the thermal shift assay also produced the worst IC₅₀ value (228 micromolar) and the lowest ligand efficiency. All nine fragments bind to the so-called hinge region of the kinase.

One interesting observation was that, although the library did contain larger molecules, 6 of the 9 fragments characterized crystallographically had MW < 200, and the other 3 were well under 300 Da. This is exactly what you would expect according to the concept of molecular complexity, and suggests that adding larger fragments to your library may actually lower your hit rate (though admittedly it may be a stretch to conclude too much from this one study).

Another interesting note is that co-crystallization was used in all cases. Folks sometimes believe that you need to grow vats of crystals for fragment soaking experiments, but co-crystallizing worked fine here, and in some cases may allow the protein to adopt different conformations than if grown in the absence of small molecules.

Overall, the paper presents some nice starting points against CHK2. Perhaps more important, this is a thorough, well-written, and open access account of fragment screening that is well worth perusing by anyone embarking on a fragment campaign.

A library of fragment slides

Pete Kenny, of FBDD & Molecular Design fame, has generously uploaded 13 slide presentations on SlideShare. Several of these directly relate to fragment library design and screening, while others are broader overviews of drug discovery or touch on important topics such as lipophilicity and hydrogen bonding. Pete wrote recently about the danger of "correlation inflation," and you'll find a slide show on that too.

The presentations are ornamented with photos collected from Pete's extensive travels and suffused with his trademark sense of humor: where else can you see Carl von Clausewitz expounding on covalency?

There's a wealth of information and it's all free, so check it out!

PAINS made painless

Practical Fragments has several entries on pan-assay interference compounds, or PAINS: see here for an introduction, here for their (mis)incorporation into a fragment library, here for the sad results of such misincorporations, here for a much longer review, and here for something that will hopefully bring a smile to your face after reading the previous tales of woe.

But artifacts are not only (or even primarily!) restricted to fragments, so Michael Walters at The University of Minnesota has established a blog devoted to PAINS (www.htspains.com).

As with any blog, success depends to a large degree on engagement with the broader community, so please check it out – and leave comments!

Click first, ask questions later

Three years ago Beat Ernst and his colleagues at the University of Basel described using NMR to identify two molecules that bind next to one another on the protein MAG. They then used in situ “click chemistry” to link these together to obtain a more potent binder. In a recent issue of J. Am. Chem. Soc. they have taken a similar approach to the protein E-selectin, but without the in situ part.

The selectins are cell adhesion proteins involved in a variety of biological processes, notably inflammation and tumor metastases. They bind to carbohydrates on the surface of leukocytes, but the affinities of any one selectin for a given carbohydrate tends to be low – often only millimolar. In the current paper, the researchers started with a reasonably potent modified carbohydrate, compound 3.

The researchers performed an NMR screen of 80 fragments in which they looked for increased relaxation of protons in the fragments upon binding to protein. This led to five hits. To determine whether these bound near compound 3, the researchers modified compound 3 with a “spin-label,” a moiety that would increase the rate of relaxation of nearby molecules and so make them detectable (see the previous post for more details). Two of the fragments appeared to bind near compound 3, and the researchers chose to pursue compound 4 – the very same fragment they had pursued previously for MAG.

At this point the researchers replaced the spin label with an alkyne (attached via spacers of various lengths) and added azide groups (again, with various spacers) to compound 4 and attempted to perform in situ click chemistry in the presence of the protein, as they had done previously. Nothing happened. Having come this far, they used more conventional conditions (ie, without the protein present) to make a small library of 20 triazoles and tested these for binding, leading to 5 hits with nanomolar activity, such as compound 43.

Given the high affinity of compound 43 for E-selectin, why didn’t it form in situ? The researchers suggest that:

Given its flat binding site, E-selectin does not act as an effective supramolecular catalyst for the alkyne-azide cycloaddition, because even upon simultaneous binding of first- and second-site ligands their azide- and acetylene-substituted linkers are not sufficiently preorganized to accelerate the cycloaddition reaction.

This is an example of a false negative from in situ fragment assembly. We previously wrote about another case in which in situ click chemistry yielded the less potent of two regioisomers due to trace amounts of contaminating copper. There is something conceptually beautiful about having a protein template the formation of its own inhibitor, but how often does it really work?

Ending on a positive note, the NMR approach described here is an example of linking without the need for protein structure. You just might want to click before you assay.

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.