Merging Fragments for Matriptase

We often talk about methods here: how to screen, how to prosecute those actives, and everything in between.  This is one of those what you do with the actives posts.  In this paper, a group from Aurigene and Orion present their results on Matriptase.  There have been multiple reported matriptase inhibitors, small molecule and peptide based.  Previous work from this group showed compounds that were active in cell-based migration and invasion assays and in mice with tri-substituted pyridyls and benzene compounds.  For this work, they take a SBDD approach:  "structure divulges a trypsin-like S1 cavity, a small hydrophobic S2 subpocket, and a solvent exposed spacious S4 region."

In screening benzamidine fragments (MW less than 300) they found 2 actives, ~80uM (Figure 1).

Figure 1.  Benzamidine screening actives

These were modeled in to the active site and obviously the amidine moiety went into S1.  Cpd 1's benzene moiety went nicely into S4 while 2's piperidyl went into S1'.  S4 easily accepted the more hydrophobic napthyl instead of phenyl and then they decided to see if the napthyl compound and 2 could be "linked" and the beta carbon. [So, my first quibble here is that this is not really a linking approach; this is fragment merging. Linking involves modeling, SBDD, and discovery of different linkers.  Its very difficult to do without specialized methods.  What they did here was see huge spatial overlap of compounds and voila, "we can add something right here".]  Well, not surprisingly, this worked.  They describe their SAR around each pocket to pick the compounds to merge, go read it if that interests you. The did crystallize the merged compound and it confirmed the modeling.  The final compound showed activity in cell-based assays and in mice.  That's good.  

This work can be summarized as follows: if you have significant spatial overlap you have a very good chance of merging disparate moieties.  So, two things bother me here.  First, the actives 1 and 2 are mighty big for fragments (more than 22 HAC).  That's fine, tomato...to-mah-to.  The final compound ends up pretty honking big too (37 HAC).  What is really bothersome, at least to me, is the LE.  Both actives start well below 0.2 (for a protease!) and they never improve on it. Now, Pete may disagree, but metrics have a place in FBDD.  Does the LE metric in this case tell us anything? 

What we do in life, echoes in eternity (or the life of the patent)

Next week is the Drug Discovery Chemistry conference where Dan and I will be co-teaching our award-winning FBDD short course (or at least our mom's think it is great). We look forward to seeing any/all of you next week.  Blogging may be light next week, but we promise to give an update of the going-ons at the conference soon after.  

Kinases are fun, and those of us who have worked in them have probably all worked on the same ones.  I always loved the MAP family.  Why would I have a favorite kinase family?  Because of the cascading MAP kinases, like the one in this paper, Mitogen-activated protein kinase kinase kinase kinase 4 (that's a lot of kinase!).  But, unlike a lot of other kinases, there is no good tool compound.  So, using SPR, they decided to generate one. This paper is not particular interesting in terms of what they did, but rather it raises interesting questions. While the approach they describe is not novel, it is nice to see the data supporting them. 

They screened their 2500 fragment library against immobilized protein at 100 uM (single point).  225 hits were found with Kd ranging from 10 to 2000 uM (LE =0.24 to 0.59) for a 9% hit rate.This paper is about progressing this oxazole fragment 1: 

Based upon its structure and the wealth of kinase structure knowledge extant, they surmised it would be ATP-competitive and a hinge binder.  Based upon a binding model, the attempted to prosecute this fragment by "close-in" analogs and looking for groups that would extend farther into the hydrophobic pocket, but with MW less than 350 Da and clogP less than 3.5.  Exploring bi-aryl space resulted in 8:  

This compound had an activity of 143 nM and it was at this point that they decided to switch to the biochemical assay as their primary assay.  In the end, using X-ray focusing on LLE, they ended up deliveringa low molecular weight compound with favorable in vivo PK.  It also demonstrated a pathway functional response. 

This raises an excellent point, something I get asked frequently.  When do you switch from a biophysical assay to a biochemical one?  This maybe arguing semantics, but I think as more and more companies enter this arena these are exactly the things we need to discuss.  I think the switch happens when you feel comfortable, there is no hard and fast rule.  There is a difference in the SPR Kd and biochemical IC50 by more than 10x.  It is very important to note that they relied heavily on LE (-RTlnKd/HA) and LLE (pKd-cLogP), or pIC50 for biochemical assays.  But, it also raises the issue of correlation between SPR Kd and IC50.  I raise these socratically, and as maybe as a topics for discussion next week (or in July and September).   

Fragments vs NAMPT, maximum ligand efficiencies, and off-target activities

The enzyme nicotinamide phosphoribosyltransferase (NAMPT) is essential for the synthesis of the important cofactor NAD and thus an intriguing target for blocking cancer cell metabolism. In two recent papers, researchers from Genentech, Forma Therapeutics, Pharmaron Beijing, and Crown Biosciences describe how they used fragment-based approaches to discover new inhibitors of this enzyme.

In the first (J. Med. Chem.) paper, Peter Dragovich (Genentech) and collaborators start with a screen of 5000 fragments using surface plasmon resonance (SPR) at the relatively low concentration of 100 µM. This yielded 283 hits which were retested at 150 µM and also competed with a known high-affinity inhibitor; noncompetitive fragments, which presumably bind outside the active site, were discarded. This winnowed interesting hits to 118 fragments, each of which was characterized in full dose-response curves. Only 6 were extremely weak (K_D> 2 mM) or nonspecific, while 35 were quite potent (K_D< 100 µM).

As an interesting aside, the substrate for NAMPT is nicotinamide, and this was characterized by SPR as having a remarkably high ligand efficiency (LE) approaching the “soft limit” Teddy recently discussed. The researchers suggest:

The LE exhibited by nicotinamide for NAMPT is the highest we have observed for a fragment lead and, given that NAMPT is highly optimized to efficiently bind this substrate, may approach an upper limit of this parameter for such molecules.

Keep in mind that Genentech has done lots of screens, so this is a significant statement. Indeed, I can think of only a few fragments (here and here) with comparable LE values.

But back to NAMPT. More than 30 co-crystal structures of fragments bound to the enzyme were solved, and several of these fragments were advanced. In doing so a variety of information was used, including data from molecules previously discovered in-house and elsewhere. Lots of nice SAR are presented, and if you’re into structure-based drug design I’d strongly encourage a close reading of the paper. Just to give you a flavor, compounds 12 and 13 (blue), despite their structural similarity, bound in very different orientations. A bit of engineering led to compound 15, and crystallography revealed that only a single enantiomer of a racemic mixture binds to the enzyme. Borrowing information from other NAMPT inhibitors led to the potent single enantiomer compound 17; the other enantiomer is 250-fold less active. Further modification yielded an orally active molecule with activity in a mouse xenograft model.

In the Bioorg. Med. Chem. Lett. paper, members of the same team describe two other series of molecules derived from fragments – and provide some important warnings about interpreting data.

One series (not shown here) was optimized to nanomolar potency in biochemical assays and antiproliferative cell assays. However, the team did a series of careful follow-up studies to show that these molecules are probably acting through off-target mechanisms. For example, the molecules do not reduce NAD levels as they should, and addition of the product of NAMPT did not rescue the cells, as it would were NAMPT the primary target.

For the other series, compound 7 (red above) was characterized crystallographically bound to NAMPT. Initial attempts to improve affinity were unsuccessful, but the co-crystal structure of another fragment suggested that replacing the pyrazole moiety with a simple phenyl group would be tolerated, leading to compound 25. Subsequent fragment growing ultimately led to Compound 51, with low nanomolar potency in both biochemical and cell-based assays. Importantly, this molecule did reduce NAD levels in cells, and the antiproliferative effects could be rescued by adding the product of NAMPT. Taken together, these data show that compound 51 is a nanomolar inhibitor of NAMPT both biochemically and in cells.

The importance of such rigorous characterization is driven home by a footnote, in which the researchers reveal that compound 51 was previously alleged to be an inhibitor of glucose transporter 1 (GLUT1). This was published in a high-profile journal, and several chemical suppliers now sell this compound (called STF-31). Although the current paper does not explicitly say so, it is possible the results in the earlier paper could be attributed to NAMPT inhibition rather than GLUT1 inhibition.

In the hope that views on STF-31 will evolve, I’ll close this Darwin Day post with a quote from The Descent of Man:

False facts are highly injurious to the progress of science, for they often long endure; but false views, if supported by some evidence, do little harm, as every one takes a salutary pleasure in proving their falseness; and when this is done, one path towards error is closed and the road to truth is often at the same time opened.

How weak is too weak for PPIs?

Ben Perry brought up an interesting question in a comment to a recent post about fragments that bind at a protein-protein interface: “At what level of binding potency does one accept that there may not be any functional consequence?” I suspect the answer will vary in part based on the difficulty and importance of the target, and many protein-protein interactions (PPIs) rank high on both counts. In a recent (and open-access!) paper in ACS Med. Chem. Lett., Alessio Ciulli and collaborators at the University of Dundee, the University of Cambridge, and the University of Coimbra (Portugal) ask how far NMR can be pushed to find weak fragments.

The researchers started with a low micromolar inhibitor of the interaction between the von Hippel-Lindau protein and the alpha subunit of hypoxia-inducible factor 1 (pVHL:HIF-1α), an interaction important in cellular oxygen sensing. The team had previously deconstructed this molecule into component fragments, but they were unable to detect binding of the smallest fragments.

In the new study, the researchers again deconstructed the inhibitor into differently sized fragments and used three ligand-detected NMR techniques (STD, CPMG, and WaterLOGSY) to try to identify binders. As before, under standard conditions of 1 mM ligand and 10 µM protein, none of the smallest fragments were detected. However, by maintaining ligand concentration and increasing the protein concentration to 40 µM (to increase the fraction of bound ligand) or increasing concentrations of both protein (to 30 µM) and ligand (to 3 mM), the researchers were able to detect binding of fragments that adhere to the rule of three.

Of course, at these high concentrations, the potential for artifacts also increases, but the researchers were able to verify binding by isothermal titration calorimetry (ITC) and competition with a high-affinity peptide. They were also able to use STD data to show which regions of fragments bind to the protein, suggesting that the fragments bind similarly on their own as they do in the parent molecule. (Note that this is in contrast to a deconstruction study on a different PPI.) Even more impressively for a large (42 kDa) protein, the researchers were able to use 2-dimensional NMR (¹H-¹⁵N HSQC) to confirm the binding sites.

Last year we highlighted a study that deconstructed an inhibitor of the p53/MDM2 interaction. In that case, the researchers were only able to find super-sized fragments, and they argued that for PPIs the rule of three should be relaxed. The current paper is a nice illustration that very small, weak fragments can in fact be detected for PPIs, though you may need to push your biophysical techniques to the limit.

But back to the original question of how weak is too weak. With K_d values from 2.7-4.9 mM, these are truly feeble fragments. Nonetheless, they could in theory have been viable starting points had they been found prospectively. That assumes, though, that these fragments would have been recognized as useful and properly prioritized. The ligand efficiencies (LE) of all the fragments, while not great, are not beyond the pale for PPIs. Previous research had suggested that much of the overall binding affinity in compound 1 comes from the hydroxyproline fragment (compound 6, which was originally derived from the natural substrate). Not discussed in the paper, but perhaps more significantly, the LLE (LipE) and LLE_AT values are best for compound 6, which despite having the lowest affinity is the only compound that could be crystallographically characterized bound to the protein. In the Great Debate over metrics, this suggests that LLE and LLE_AT may be more useful than simple LE for comparing very weak fragments.

Leave Them Asking for More

ret·ro·spec·tive  (rtr-spktv) adj.

1. Looking back on, contemplating, or directed to the past.

2. Looking or directed backward.

3. Applying to or influencing the past; retroactive.

I would add: 4. Looking back on the past, to influence the future.

In this vein, a recent paper by Ferenczy and Keserű in J. Med Chem looks back on hit-lead optimizations derived from fragment starting points.  In this very interesting paper, they look at 145 fragment programs and evaluate the properties of the original hit and then again as it progresses into the lead.  Of the 145 programs, these were aimed at 83 proteins of which 76 are enzymes, 6 are receptors, and 1 is an ion channel.  These programs evolved into leads, tools, and clinical candidates.  The authors set out to answer three questions: 1. do fragments eliminate the risk of property inflation, 2. how do ligand efficiency metrics support fragment optimizations, and 3. what is the impact of detection method, optimization strategy, and company size on the optimization.

Table 2 shows the median the calculated properties for the hits and optimized compounds.  The pIC50 improved by roughly three orders of magnitude, but ligand efficiency (LE) stayed roughly the same.  Log P increased but SILE did not.  SILE was a metric I was not familiar with and is calculated by pIC50/(HAC)^0.3.  SILE is a size-independent metric of ligand efficiency.  I won't attempt to reproduce all the graphs they generated; get the paper.  Some interesting data points: the median fragment hit had 15 heavy atoms (see related poll here), the median size of leads is 28 heavy atoms, and good fraction of hit-lead pairs changed less than 5 heavy atoms (which of course is well known from here). So, what is the answer for their first question?  If you are looking at something like LE, then these hit-lead pairs maintain the efficiency.  If you are looking at logP, then the answer is no, the hit-lead pairs get greasier.  I would really like to see more granularity here (see What's Missing below).  Their major comment here is that SILE and LELP (work by the author's previously reviewed by Dan) are the two best metrics to monitor as hit-lead optimization is underway.  Increases in both metrics correlate with increases in FBDD programs. 

 

They then looked at the  screening method.  The breakout of primary screening (in their definition the first one listed when multiple methods were used) was 38% biochemical, 25% NMR, 18% X-ray, and 11% virtual.  This is an interesting contrast to these results; SPR is not the dominant screening technique (8% tied with MS).  So, does this mean, >40% of practitioners are using SPR, but as a secondary screen?

Table 4 then does a pairwise comparison of the metrics based upon primary screen origin of the fragment (see What's Missing below).  Biochemical screens yield the most potent hits (4.75) while NMR (3.53) has the least potent.  X-ray has the smallest hits( 13 heavy atoms) while virtual screening the largest (17 HA).  I don't think any of this is surprising; the authors point out that hit properties exhibit a significant dependence on the method used. It is noted that optimization tends to diminish differences in hit properties.  Again, I think this is not surprising; thermodynamics and medchem are all the same no matter how big or small the molecule.  They do point out that biochemical based hits preserve their advantage after optimization, primarily relative to NMR.  They posit that the difference is that more potent compounds need less "stuff" to become sufficiently potent, and thus have a better mix of medchem for potency and other property optimization.  Weaker starting points need more bulk, more atoms, to become equipotent with biochemical starting points, they suggest.  Lastly, they show that structural-based optimization efforts are better than those without structural information.  The structural information comes from X-ray primarily (52%) and NMR (10%).  Interestingly, they reference this poll from the blog on whether you need structural information to prosecute fragments.  [As an aside, since I wrote that blog post and it was reference in a paper, do I get to add it to my resume?]

Finally, they break the originating labs into three categories: academic (18%), small/medium enterprises (SME)( 37%), and Big Pharma (45%).  The SME results are superior to those achieved by the academics and Big Pharma.  Their explanation is that SME's tend to be more platform focused and predominantly ensure "structural" enablement of targets.  75% (40/53) optimizations at SMEs used protein structural information, while "only" 62% of those at Big Pharma did.  They do not rule out the differences in target selection at those two different groups of companies.  I would like to propose an alternative hypothesis (tongue-not-entirely-in-cheek): Big Pharma has "old crusty" chemists who don't understand fragments and thus just glom on hydrophobic stuff to increase potency because "that's how we always do it" while SME have innovative chemists.  And of course academia is just making tool compounds and crap.

One thing that I would like to emphasize is that Ro3, metrics, and so on should not be used as hard cutoffs.  As shown in Figure 11, even compounds that are outside the "preferred" space can reach the clinic.  The best way to view them is akin to the Pirate Code; they are more guidelines than rules. 

 

What is Missing?  The supplemental information (which the authors are willing to kindly share) does not break out the targets into specific classes.  However, they do list each target, so it should be easy to add this a data column.  More importantly, they do not break out hit-lead pairs into those that were optimized for use as tools, clinical candidates, and leads (and which of the leads died).  Tools are never supposed to look like leads (but you are lucky if they do), so their inclusion here can be biasing the results.  Although, it is likely that the of the 145 not very many of the examples are strictly tools; it would be nice to know though.   

I am struck by the information denseness of Table 4 and wish that instead of pairwise comparisons, they had instead simply list the hit-lead metrics for each methodology.  I think there is gold to be mined in Table 4 and just like real gold is hard to find.

 

I have not addressed every single point made by the authors.  I, for one, am hoping that they will continue their analyses (especially with an eye to some of What is Missing).  I hope that there will be a significant amount of discussion around these points.  I will make sure we hit on this at the breakfast roundtable at the upcoming CHI FBDD event in SD (I even have the same pithy title as last year!)

With the proper tool, I could move the world

As noted before, bromodomains are a "hot" area of drug discovery.  Dan mentioned last year that PFI-1 was being released as a tool compound by the SGC.  In this paper, Fish et al. describe its discovery (Supplemental Information here).  They started their discovery with potential fragment-sized acetyl-lysine mimics (DMSO need not apply!), like others have described.  In particular, 3,4-dihydro-3-methyl-2(1H)-quinazolinones like Cpd 7 and its bromoequivalent.  These two compounds had sub-30uM potency and thus LE>0.45.  The efforts of Conway et al. and Chung et al. were highly instructive to the Pfizer group.  Crystallography was a key driver of confirming the binding modes seen by Conway are possible and that the quinazolinones are a viable acetyl-lysine mimic. 

The crystallography pointed out that the bromine is pointing towards solvent and thus the appropriate place to start doing chemistry.  Based upon the structures, a "bent" substituent at the 6 position appeared to be promising; sulfonamides were chosen for this role.    Compounds 9 and 11 were also noted as attractive, novel compounds in their own right.  These were used for very limited library construction.  The compounds derived from 9 were profiled first.  While better than the parent bromide, subsequent structural analysis showed that they were not making good interactions with the sites intended (WPF shelf).  The sulfonamides derived from 11 on the other hand showed significantly improved activity.  The SAR was relatively insensitive to the substitution of the aryl group, due to the optimized placement on the shelf and the reversed sulfonamide.  

PFI-1 has 0.22uM activity against BRD4 and it was nominated as the probe molecule.  They further investigated its binding via X-ray.  They also looked at it in a much broader array of assays: broader pharmacological selectivity, a cell-based inflammatory end-point assay, and rodent pharmacokinetics.  It had < 50% inhibition against 15 targets at 10uM (GPCR, ion channels, enzymes) and < 20% inhibition against 50 kinases. It fits the criteria for a good probe.

As the authors state, it was designed in a little over 250 molecules from an efficient fragment starting point covering only two design cycles.  I think this is an excellent example of probe design/discovery.