Biophysics bonanza of fragments for pantothenate synthetase

As Teddy just mentioned, biophysics provides multiple methods to find fragments, and it's best to use several. They may not always agree, but fragments that hit in several assays are more likely to be real. Although this requires using different skills, using diverse methods does not take a village, as illustrated by a recent paper in Proc. Nat. Acad. Sci. USA by Hernani Silvestre, Tom Blundell, Chris Abell and Alessio Ciulli at the University of Cambridge.

The researchers were interested in the enzyme pantothenate synthetase (Pts) from the bacterium that causes tuberculosis (a target we’ve covered previously here and here). They used a thermal shift assay to screen 1250 fragments from Maybridge, each at a whopping 10 mM concentration, resulting in 39 hits (3.1%) that stabilized the enzyme by at least 0.5 ˚C. Another 17% of the fragments slightly stabilized Pts, while 73% of the fragments destabilized the protein (a poorly understood phenomenon that does not seem to reflect specific binding).

Despite being relatively small, the fragment library had some scaffolds that were over-represented, and some of these were enriched among the hits, providing early SAR. Perhaps not surprisingly given the anionic character of the enzyme’s ATP cofactor and pantoate substrate, about half the hits were carboxylic acids.

All 39 hits were analyzed by WaterLOGSY and STD NMR, and only 17 showed evidence of binding, although the NMR experiments were done at a much lower concentration of fragment (0.5 mM). Competition experiments revealed that all except one of the 17 fragments bound at either the substrate or cofactor sites.

Next, isothermal titration calorimetry (ITC) was used to measure the dissociation constants of the 17 validated fragments. Measurements could not be obtained for three of the fragments; values for the rest ranged from 0.5 to 17 mM. There seemed to be a rough correlation between affinity and the extent of stabilization in the thermal shift assay, and binding was enthalpically-driven.

The 17 fragments were then soaked into crystals of Pts, resulting in 8 co-crystal structures. Most of the fragments that did not produce structures also had low affinities as assessed by ITC. Four fragments bound in a pocket normally occupied by the adenine ring of the cofactor ATP, while the other four bound in the substrate pantoate pocket. One of these bound quite deeply in this pocket and caused a conformational change in the protein. The researchers were able to obtain a structure of Pts bound to both this fragment and ATP.

There’s lots of nice data in this paper, and all the new structures have been deposited in the protein data bank. More generally, the “integrated biophysical approach” provides a practical template for applying FBLD. The paper has just four authors, and only two of them actually performed experiments according to the author contributions section. That short list provides more evidence that a very small but dedicated team can successfully find and validate fragments.

Biophysics in Drug Discovery

Folks,

Posting has been light since Dan has been traveling and I have been super busy.  The fun news is that I will be attending the Novalix conference on Biophysics in Drug Discovery this week.  The agenda is shaping up to be very exciting and I think of keen interest to the readership of this blog.  I will (WiFi-willing) be blogging daily about the conference and tweeting random thoughts about the talks. 

For those of you who will be there, I am always happy to meet up and chat.  There are a number of very interesting Fragment-focused talks, so I will be focusing on those. 

To have shape or not?

I consider the debate/discussion/civil discourse on what/where/when/why/how of 3D fragments to be one of the most interesting topics in the fragment field right now.  A paper has come out from the 3Dfrag consortium.  The first take away from this paper is we have a NEW acronym, FBHI (Fragment-based hit Identification).  Their central thesis is that fragments are too flat for certain target classes of targets, thus resulting in too low hit rates. This paper describes their efforts in pre-competitive space to prove/disprove this hypothesis.  It's fascinating.  

Using the nPMI as their metric for "3D-arity" they compare 1000 fragments representation "commercial" space to Zinc InMan Compounds fragmented by RECAP.  It is obvious that InMan has more 3D-arity, but the question is always, is this biased by target type of compound type (like natural products)?  But, to the greater point, will 3D fragments give better hit rates than 2D for certain target classes?  Many people point to the Hann complexity model a (MIP) and say no, because there is more potential "bad" interactions.  However, the authors point out if the NUMBER of sites of interaction is the same, this should not be the case.  Recent data from Evotec seems to support this they state. 

The 3Dfrag consortium's goal is to explore the role of "3D-arity" in fragment screening success.  As shown in this figure, they apply pretty standard selection rules for their collection, including the Pfizer Rule (medchemists inform on the final selection process).  

Interestingly, even after all of this, trying to select for "3D-arity", 70% of their final 200 molecules were largely flat (nPMI1+nPMI2< 1.1).  Making matters worse, 10% of the compounds could not be delivered by suppliers and 5% failed QC (by NMR). The nPMI and maximum similarity is shown below for 170 compounds.  For the maximum similarity, a right-shifted plot shows a high degree of internal similarity, while left-shifted would be more diverse.  So, it appears that their library is diverse (most compounds < 0.8).  Importantly, of the 170 compounds, they appear to fill a much wider region of 3D-space.   

One of the tools generated by the consortium is 3Dfit, a webtool that is free and allows fragments to be evaluated.  They generate up to 9 conformers (blue to red, low to highest energy) that are then plotted on the PMI triangle.  They also calculate a radar plot of molecular properties:

Molecules outside the consortium's GUIDELINES are flagged for consideration (see (b)).  The consortium has now undertaken chemistry to generate additional 3D fragments.

As the authors state:

We believe that this strategy will build a library with broader coverage of biologically relevant chemical space compared with current fragment collections and the identified hits will offer higher-quality start points for medicinal chemistry projects.

They will now experiment and try to validate (or invalidate) this hypothesis.  But, as they point, these sorts of Pre-Competitive Initiatives have difficulty getting buy in from partners willing to devote time, resources, assets to this.  They are looking for more people to participate.  I have no skin in this game, but I would love to see people step up and help this initiative prove/disprove a crucial hypothesis in Fragments.

Fluorinated fragments vs FAAH – functionally

Fluorine NMR is a topic that has come up several times on Practical Fragments (see here, here, and here). As readers will recall, the ¹⁹F nucleus can be readily detected with a properly equipped NMR spectrometer. The isotope has a wide range of chemical shifts, and sensitivity to the local environment makes it easy to detect whether fluorine-containing fragments bind to a protein. But you don’t need a dedicated fluorine-containing library: in a new paper in ChemBioChem, Claudio Dalvit and coworkers at Fondazione Istituto Italioano di Tecnologia describe using fluorinated substrates to screen a membrane enzyme.

The researchers use an approach they call n-fluorine atoms for biochemical screening (n-FABS). A substrate or cofactor is labeled with fluorine, and when this is processed by an enzyme, the resulting change in chemical structure affects the ¹⁹F chemical shift, which is easily detected by NMR. Either substrate or product (or both) can be observed, and a decrease in product can be attributed to inhibition of the enzyme.

The researchers were interested in the protein fatty acid amide hydrolase (FAAH), a membrane-bound enzyme that hydrolyzes lipids such as endocannabinoids. Of course, membrane-proteins are tough to screen using fragment-based approaches, and the fact that this enzyme processes lipophilic substrates makes things even more challenging. The researchers synthesized several fluorine-containing substrates, but most of these turned out to be insoluble or formed aggregates, even at low micromolar concentration and even in the presence of detergent. Ultimately they were able to make one substrate that was soluble at 30 micromolar, sufficient for screening.

Next, the researchers assembled a library of fragments. Although these did not need to contain fluorine for the n-FABS assay, the researchers chose to focus on fluorine-containing fragments anyway, perhaps so they could use other NMR methods to confirm binding. Of 160 commercial fluorine-containing fragments purchased, 113 showed solubility ≥ 0.1 mM, purity ≥ 75%, and no aggregation. These were combined into 23 pools of 5 and screened for inhibition in the n-FABS assay at 200 micromolar of each fragment. Pools that showed >15% inhibition were deconvoluted to find the active fragments; some contained more than one hit. This process led to a remarkably high hit rate of 16.5%. The IC₅₀ values of all 19 of these hits were then determined using n-FABS and they showed quite a range, from quite potent (3 micromolar) to low millimolar.

These are nice results and there are clear opportunities for advancing some of the fragments, but I must admit I was left wanting more. The n-FABS assay is essentially an inhibition assay, and of course there are all kinds of things that can show inhibition without proper binding. However, since all the fragments do contain fluorine, it would be straightforward to actually measure direct binding using NMR; it would be very interesting to see how many fragments show up in both assays. Perhaps we will see this in a follow-up study.

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.