17 April 2017

Fragments vs PRC2 revisited: a chemical probe

Earlier this year we highlighted a paper from Novartis in which ligand deconstruction was used to deconstruct an HTS hit against the epigenetic target methyltransferase polychrome repressive complex 2 (PRC2). In a new J. Med. Chem. paper, Ying Huang and Novartis colleagues report a similar approach on a different HTS hit, ultimately yielding a promising chemical probe.

Compound 7 was identified as one of about 1400 hits from a high-throughput biochemical screen of 1.4 million molecules (described here at PLOS ONE). Crystallographic studies revealed that, like the previous molecule, this one also binds in the site on the EED subunit of PRC2 that normally recognizes trimethylated lysine 27 on histone H3.

Crystallography also suggested that the left half of the molecule didn’t seem to be making productive interactions with the protein. Lopping this off actually increased the activity and dramatically improved the ligand efficiency. Fragment-sized compound 8 was then subjected to extensive medicinal chemistry, ultimately resulting in EED226. Crystallography revealed that the binding modes of the initial hit and the final molecule are quite similar.

In addition to good biochemical activity, EED226 also shows good cell potency and impressive selectivity against other histone methyltransferases, kinases, and unrelated safety targets. It also shows excellent oral bioavailability in mice and acceptable pharmacokinetic properties. The compound caused complete tumor regression in a mouse xenograft model.

A separate paper in Nat. Chem. Biol. further characterizes EED226. A chemoproteomics study of a labeled version of EED226 revealed that it is remarkably selective for the PRC2 complex in human cell lysates. Also, EED226 is active against mutant cell lines that are resistant to other PRC2 inhibitors currently in the clinic, which are competitive with the cofactor rather than the trimethylated lysine residue. In fact, EED226 can bind to the PRC2 complex simultaneously with these other inhibitors, so dosing both together could give improved efficacy and slow the emergence of resistance.

As with the previous post on this target, the discovery of EED226 is a nice example of fragment-assisted drug discovery (FADD). Unlike that case, in which fragmentation led to an initial loss in potency, here trimming back the molecule paid immediate dividends.

Artists often talk about finding a sculpture within a stone by cutting away excess material. It is rewarding to see that chemists can use the same strategy.

10 April 2017

Unexplored but promising fragments

Sir James Black famously said that the best way to find a new drug is to start with an existing one. A drug not only has to bind to a target with reasonable affinity, it also has to survive an onslaught of metabolic insults – and avoid doing too much collateral damage. Compound libraries are often populated with derivatives of known drugs, but as Richard Taylor and collaborators at UCB and Bohicket Pharma Consulting show in a recent J. Med. Chem. paper, there is plenty of untapped chemical real estate out there.

The researchers started by deconstructing all FDA-approved drugs into component rings. As they’ve previously shown (and presented), this gives a surprisingly small set: just 95 monocyclic rings (such as benzene and succinimide), 124 bicycles (purine and quinazoline), and 58 tricycles.

Next, they computationally combined these rings with one another in various ways, focusing on monocycles and bicycles to maintain low molecular weights. For example, one set contained all combinations of drug-derived monocycles connected either to another monocycle or to a bicycle by linkers containing up to four bonds. That provides about 14.4 million possibilities. Among commercially available molecules, about 1.6 million are monocycles connected to another monocycle or a bicycle by up to four bonds, but many of these monocycles and bicycles have never appeared in a drug. Remarkably, the overlap among the computed and commercial sets is less than 58,000 compounds: only about 3% of relevant commercial compounds contain two rings which have both appeared in a drug.

Of course, chemical space is large; how do things fare among fragments? The researchers examined a subset of theoretical molecules having two monocycles or a monocycle and bicycle connected by just two bonds and with molecular weights less than 280 Da. They also allowed “decoration” with a fluorine atom or a methyl, amino, or hydroxyl group. This provided 421,929 molecules – a sizable number but, as the researchers note, a small enough set to be tractable with computational docking approaches.

Even with this fragment set the commercial availability is less than 1%. In fact, less than half of the decorated monocycles and less than 40% of the decorated bicycles are for sale. This seems like a ripe business opportunity for enterprising vendors of fragments. Unfortunately the researchers do not provide a comprehensive list of structures, but the analysis would be relatively straightforward to repeat.

This paper draws similar conclusions to one we highlighted a couple years ago focused on kinase inhibitors. Some chemists enjoy the challenge of making entirely novel molecules, but it may be worth taking another look at more conventional pharmacophores, particularly when they are connected in new ways.

01 April 2017

Ligand efficiency invalidated!

Practical Fragments has had quite a few posts on ligand efficiency (see here, here, and here, for starters). Ligand efficiency (LE) is defined simply as the free energy of binding for a ligand divided by the number of heavy atoms in the ligand. One of the criticisms of LE is that the definition of free energy depends on the definition of standard state, which may be different on different planets. With the discovery of silicon-based life on Venus, this is no longer just an academic argument. Indeed, a recent paper in Venusian Analytical, Physical, & Inorganic Discoveries describes an excellent case study.

Professor Perelandra and colleagues at East Eistla University performed a crystallograhic fragment screen on the enzyme silica hydratase, which is essential for the life cycle of the viciously parasitic Crystalline Horde. Fragment 1 binds in the active site, and although it has low affinity, structure-guided medicinal chemistry rapidly led to compound 42, with low nM activity in vitro and good efficacy in a silicon resorption model.

Things get even more interesting when you calculate the ligand efficiency values. The Venusians define standard temperature and pressure very differently from us. More importantly, they don't believe that standard state concentration should be 1 M. Given the extreme conditions on their home world, they choose a standard state concentration of 10 M.

LE = - ΔG/HA
(where HA = number of non-hydrogen atoms)

Thus, LEVenus = -RTln(KD/[A]0)/HA
(where T = 737 K and [A]0 = 10 M)

Using our terracentric definitions, the (impressive) LE of the fragment hit stays roughly the same during optimization, suggesting that the medicinal chemists have done a good job. However, by Venusian standards, the LE decreases!

This rock-solid example shows that Dr. Saysno was right: ligand efficiency is arbitrary and should never be used – on Venus.

27 March 2017

Dynamic undocking for better predictions

Computational screening continues to improve, due in part to a better understanding of the energetics of protein-ligand interactions. But for low affinity fragments, differentiating binders from nonbinders is still challenging. In a recent paper in Nature Chemistry, Xavier Barril and collaborators at the Universitat de Barcelona, Discngine, Vernalis, and the University of York describe a new approach that sidesteps thermodynamics.

The researchers started with the notion that, in many cases, a single hydrogen bond is critical for the stability of a protein-ligand complex. Rather than trying to calculate the binding energy of the complex, they instead ran “dynamic undocking” (DUck) experiments. This involved “steered molecular dynamics” simulations in which the researchers calculated how much work (WQB) is required to move the ligand from the bound state to a quasi-bound state in which the key hydrogen bond is broken. The calculations do not consider what happens under equilibrium conditions (ie, unbinding and rebinding), so WQB should not necessarily correlate with binding affinity. Still, one might expect ligands that require a particularly high energy to dissociate (for example, WQB > 6 kcal/mol) to have higher affinities. This turned out to be the case for ligands targeting several different proteins: the kinase CDK2, the GPCR adenosine A2A receptor, and the protease trypsin. Indeed, receiver operating characteristic curves (an analysis comparing known binders and decoys) showed a significant enrichment of true binders.

Next, the researchers compared DUck with several commonly used computational docking approaches. Again, and not surprisingly, there was essentially no correlation. However, the researchers argue that this is a feature, not a bug, since the very orthogonality of the approaches should provide better predictions: a molecule that docks favorably and has a high WQB is more likely to be a real hit.

This is a nice idea, but does it work in practice? To find out, the researchers turned to the old work-horse protein HSP90 and performed docking experiments on 280,000 fragments. Of the top 450 hits, 139 diverse molecules were chosen for DUck. Several dozen of these were then tested for binding using three different ligand-observed NMR experiments.

Of 21 molecules with WQB > 6 kcal/mol, 8 confirmed as binders by all three NMR methods – an impressive hit rate of 38%. SPR confirmed binding for four of these (with dissociation constants between 0.077 and 0.73 mM), while three yielded crystal structures. In contrast, only one out of 15 molecules with WQB between 3 and 6 kcal/mol confirmed, while none of 11 molecules with WQB < 3 kcal/mol were clear hits. In other words, not only is DUck able to improve identification of true binders, it appears to have a fairly low false negative rate.

In a sense, this approach addresses the question of kinetics. Molecules that dissociate slowly from their target are becoming increasingly fashionable; perhaps DUck can be used to identify them. Although the researchers do not make this claim, several of the authors described an experimental “off-rate screening” approach a few years ago. It will be fun to see further developments, particularly as the method is extended to incorporate information beyond a single hydrogen bond.

20 March 2017

DOT1L revisited: fragment linking to picomolar binders

Last September we highlighted two papers out of Novartis in which various fragment-based methods delivered two separate series of low nanomolar inhibitors against the histone methyltransferase DOT1L, an epigenetic anticancer target. The next installment of this story, by Christoph Gaul and Novartis colleagues, has just published in ACS Med. Chem. Lett.

DOT1L uses the cofactor S-adenosylmethionine (SAM), but the previously identified fragments bind in a site adjacent to the cofactor’s binding site. Seeking to gain affinity the researchers decided to look for fragments that bind in the same pocket as SAM. SAM and ATP both contain an adenine ring, and plenty of fragment libraries have been built around the fact that ATP is a kinase cofactor. About 200 kinase-targeted fragments were tested in a high-concentration biochemical screen. Compound 2 was active, and crystallography revealed that it forms multiple hydrogen bonds with DOT1L. More importantly, crystallography also revealed that compound 2 could bind simultaneously with inhibitors occupying the site adjacent to the cofactor binding site.

Modeling suggested that it would be possible to link compound 2 with second-site binder compound 3. This proved successful, with compound 4 having low nanomolar affinity. For aficionados of linking, the free energy of binding for compound 4 is comparable to the sum of the free energies of binding for compounds 2 and 3. This level of additivity is similar to results on different targets at Ariad and Evotec. Interestingly, the two fragments (2 and 3) conform to a previous suggestion from Evotec that – to maximize the likelihood of success in fragment linking – one fragment should be polar and make multiple hydrogen bonds with the protein, while the second fragment should make largely lipophilic interactions.

But the researchers didn’t stop there. Adding a methyl group to the secondary amine on compound 4 improved affinity by more than an order of magnitude, and further tweaking the right-hand side of the molecule led to compound 7, with low picomolar affinity. This compound also showed low nanomolar activity in several different cell-based screens, and was completely inactive against a panel of 21 other methyltransferases. Selectivity against kinases is not shown, and given the origin of the left-hand fragment this could be an issue, potentially complicating the interpretation of biological assays. That said, polypharmacology can be useful, particularly in oncology.

Fragment linking has a reputation for being difficult, but this is another example that it can work. The high affinity of compound 7 is particularly remarkable considering the number of rotatable bonds. Of course, plenty of challenges likely remain: no data are provided for in vitro stability, let alone pharmacokinetics. Although the structure of compound 7 may raise eyebrows, it is worth remembering that even larger, more lipophilic molecules have become successful drugs.

13 March 2017

Fragment-linking on proteins: amide formation

Among fragment-linking approaches, protein-templated reactions have a special appeal. When two fragments that bind next to one another on a protein react, the protein essentially creates its own ligand. The reaction can be reversible, as in dynamic combinatorial chemistry, though the choice of chemistries tends not to be very drug-like, necessitating replacement of the linker for lead development to proceed. Irreversible chemistries, such as cycloadditions (“click chemistry”), give more stable linkers, but these still tend to be unusual. In a recent open-access paper in Angew Chem., Jörg Rademann and collaborators at the Freie Universität Berlin and Philipps-Univesität Marburg have extended the approach to amides – one of the most vanilla chemistries out there.

The researchers used factor Xa as a model protein. Using information in the literature they designed compound 15, found that it was a low nanomolar inhibitor, and obtained a crystal structure of the molecule in complex with the enzyme. Cleaving one of the amide bonds provides two fragments, compounds 16 and 17, each with very weak activity. Put another way, linking these two fragments leads to superadditivity of binding.

Chemists often make amides by “activating” a carboxylic acid and reacting it with an amine. We usually want this to happen quickly, but here the researchers wanted the reaction to only occur when the two fragments bind next to one another on the surface of the protein.

The team made a dozen activated esters of compound 1 and incubated 5 mM of each of these with 0.285 mM of amine 14, in the presence or absence of 14.5 nM factor Xa. (Each combination of reactants would produce compound 15.) After two hours protein was added to the no-protein controls and fluorogenic substrate was added to all the samples, which were tested for enzyme activity. Not surprisingly, inhibition was complete for particularly active esters (such as 4-nitrophenyl ester), whether or not protein had been present during the incubation. Similarly, the presence of protein also didn’t matter for particularly inactive esters (such as methyl ester): inhibition was minimal because very little product 15 formed. But for two Goldilocks esters (phenyl ester and 2,2,2-trifluoroethyl ester), inhibition was greater when protein was present during incubation.

The researchers used mass spectrometry to quantify the amount of compound 15 formed. For the 2,2,2-trifluoromethyl ester, after 1.7 hours the concentration of compound 15 was 10 nM in the presence of factor Xa, but only 0.15 nM in its absence. (The phenyl ester gave less selective results.)

Of course, as these numbers suggest, product formation could be limited by the concentration of the enzyme, possibly complicating detection. More significantly, even relatively unreactive esters could react with the dozens of nucleophiles on proteins, potentially inactivating or denaturing them. Still, this is an intriguing approach, and it will be fun to see whether it works in more challenging systems.

06 March 2017

Fragments vs BRPFs: A chemical probe

Bromodomains, a type of functional module within proteins, recognize acetylated lysine residues in other proteins to act as epigenetic “readers.” Humans have 61 of them spread across 46 different proteins, and figuring out what recognizes what and in which context has been a major undertaking for the past several years. Fragment-based approaches have proven very successful: Practical Fragments devoted the entire month of last July to bromodomains. In a new paper in J. Med. Chem., a large group of academic and industry investigators led by Paul Fish describe their successful efforts to find a chemical probe for the four members of the BRPF family.

The story actually starts with a fragment screen against a different bromodomain, PCAF, which we discussed last year. Compound 5b was a hit against that target, but bromodomain fanciers will recognize this as a privileged pharmacophore against the target class, and it turned out to be even more potent against BRPF1.

Crystallography confirmed that compound 5b bound in the acetyl-lysine pocket as expected, and also revealed a potential vector for growing, as exemplified by compound 6. Introducing a methyl group led to a 10-fold boost in affinity for compound 7, which as the researchers point out is near the limit of what you can expect for hydrophobic interactions. Further growing ultimately led to NI-42, with low nanomolar potency.

Of course, potency is one thing, but when you’re dealing with dozens of related proteins you really need specificity to understand the biology. Happily, NI-42 was quite selective for members of the BRPF family. Importantly, it has only 4.5 µM activity against BRD4, which can dominate cell phenoytpes when inhibited. The selectivity is actually remarkable given the close structural similarity of NI-42 to PFI-1, which hits BRD4 and which Teddy wrote about here. The researchers suggest that the difference between the N-H in PFI-1 and the N-methyl in NI-42 drives the selectivity – so one could argue that the probe actually makes use of two magic methyls.

NI-42 also showed good cell permeability and target engagement in cells, adequate solubility, decent pharmacokinetics, and respectable oral bioavailability in mice. Although a screen of 211 cancer cell lines did not reveal stunning activity, cell models for other diseases are being evaluated. Also, the researchers have generated an inactive but closely related control simply by replacing both methyl groups with ethyl groups.

This is a lovely fragment optimization story. It is also a useful reminder that, as with phosphodiesterases and kinases, a nonselective fragment can ultimately yield a selective chemical probe.

27 February 2017

When do ligands change their binding modes?

Elaborating a fragment to improve its affinity relies on the assumption that the fragment will maintain its position and orientation during optimization. Although this is usually the case, exceptions are common, and when flips go unrecognized the resulting SAR can be confusing. Is it possible to predict which ligands are most likely to change their binding mode? This question is addressed in a recent J. Med. Chem. paper by Shipra Malhotra and John Karanicolas of the Fox Chase Cancer Center and the University of Kansas.

The researchers scoured the Protein Data Bank (PDB) to find pairs of molecules bound to the same protein where one ligand was a substructure of the other. (In most cases these were not actually from fragment-based efforts, and the two structures were often solved by different research groups.) This generated 297 pairs of crystal structures. Computational and manual analyses revealed 41 instances (14%) in which the larger ligand had a significantly different binding mode than the smaller ligand. Careful inspection revealed that these observations were probably not due to crystallographic artifacts or differences in experimental conditions. The researchers then examined well over a dozen parameters to look for correlations with changes in binding mode.

Size matters: for the 73 rule-of-three compliant smaller ligands, the binding modes were not conserved in the larger ligands 23% of the time. Binding modes changed 30% of the time when the smaller ligand was ~100 Da, but only 5% of the time when the smaller ligand was ~400 Da.

Potency also matters: as might be expected, weaker ligands were statistically less likely to preserve their binding mode. (Of course, as the researchers observe, potency often correlates with size.) More polar ligands, as assessed by clogP, were also less likely to maintain their binding modes.

Looking beyond molecular properties to those of the initial complex, ligands binding to a small pocket were less likely to maintain their binding modes. Also, ligands for which a large amount of solvent-accessible surface area was buried upon binding to the protein were more likely to maintain their binding modes.

Many other properties showed no statistically significant correlation with binding modes. These included ligand efficiency, fraction of the ligand buried, and various descriptions of the protein binding site, such as hydrophobicity and the fraction of polar or aromatic amino acid residues.

The open-access hot-spot finding software FTMap has previously been used to assess when ligands change their conformation, and it performed well on this set of molecules, although as it requires structures of both the larger and smaller ligands it has limited predictive value. The researchers also introduced another computational tool, RMAC (RMSD after Minimization of the Aligned Complex) which did even better.

This paper is a fun read, and there’s lots more than can be summarized here, including detailed analyses of specific examples. The researchers have done a great job collecting and synthesizing a huge amount of data. Admirably, all of the calculated properties are available in the supporting information. Of course – in view of the title of this blog – we have to ask, how practical is it? For any given fragment to lead program, it is still impossible to predict whether the binding mode will shift. But if you’ve got a particularly small, weak fragment binding to a little dimple on a protein surface, you should probably expect surprises.

20 February 2017

Many measures of molecular complexity

Molecular complexity is a fundamental concept underlying fragment-based lead discovery: fragments, being simple, can bind to more sites on proteins and thus give higher hit rates than larger, more complex molecules. The ultimate example of this is water, which – at 55 M concentration – binds to lots of sites on proteins. But although the concept is easy to describe, it is much harder to quantify: everyone can agree that palytoxin is more complex than methane, but by how much? And if complexity could be measured, could it help in optimizing libraries? This is the subject of a review by Oscar Méndez-Lucio and José Medina-Franco at the Universidad Nacional Autónoma de México published recently in Drug Discovery Today.

There are many ways to measure molecular complexity. Two of the simplest to calculate are the fraction of chiral centers (FCC) and the fraction of sp3 carbons (Fsp3). These range from 0 to 1, and larger numbers imply a higher number of unique molecules with the same formula.

More complicated methods to measure complexity abound, but many of these require specialized software. Two that are publicly available are PubChem complexity and DataWarrior complexity. In PubChem, complexity incorporates the number of elements as well as structural features such as symmetry, though stereochemistry is not explicitly considered, and aromaticity is scaled such that both benzene and cyclohexane have the same complexity – a sharp contrast to FCC and Fsp3. DataWarrior uses its own metric, though I couldn’t find the definition. (Ironically, though the software itself is open source, the paper describing it is not.)

So, do more complex molecules have lower hit rates? The researchers looked at several public databases of screening data for dozens of assays against thousands of molecules. Using each of the four metrics, they classified molecules as “simple,” “intermediate,” or “complex”. For FCC and Fsp3, simple compounds did appear to be more promiscuous, in line with theory and with previous findings. However, for PubChem and DataWarrior, the trends were not clear – and even reversed in some cases. The researchers note that the median complexity of molecules in each dataset may vary, and as Pete has also observed simple binning strategies can be misleading.

Do these different definitions of complexity even measure the same thing? The researchers plotted each pair-wise measurement of complexity for >400,000 molecules – for example, Fsp3 vs DataWarrior. Not only are there no universal correlations, those that do exist are conflicting. "For example," the authors write, “compounds with high FCC values are associated with low PubChem complexity values, whereas the same molecules have high DataWarrior complexity." 

Teddy has previously invoked Justice Potter Stewart and his famous “I know it when I see it” expression, and I think that just about sums up where things stand in terms of molecular complexity. From a practical standpoint this probably doesn’t matter; a complex molecule is not even necessarily more difficult to make, as evidenced by the ease of oligonucleotide and peptide synthesis. Still, it would be nice if someone could come up with a reliable measurement for such a fundamental property – or even demonstrate whether or not such measures are possible.

13 February 2017

Fragments in Cell(s)

Last year we highlighted work out of Ben Cravatt’s group at Scripps on screening covalent fragments in cells. Now, in a new Cell paper, his group and collaborators at the École Polytechnique Fédérale de Lausanne, Bristol-Myers Squibb, and the Salk Institute have gone further by screening for non-covalent fragments in cells.

The researchers started by synthesizing a collection of just 14 “fully functionalized” fragments (FFF). In addition to the variable fragment (averaging 176 Da), each FFF probe contains a diazirine group, which, when exposed to UV light, generates a highly reactive species that can covalently react with proteins (or anything else) in close proximity. (In contrast, covalent fragments typically work via chemistries in which bonds form without requiring UV exposure.) The FFF probes in the current study also contain a "clickable" tag: an alkyne moiety that can react with azide-containing molecules using copper-catalyzed azide alkyne cycloaddition.

Cells were incubated with 20 µM of each fragment for 30 minutes, then exposed to UV light for 10 minutes on ice to capture non-covalent interactions. The cells were then lysed, treated with an azide-containing flurorescent dye in the presence of copper, and analyzed by gel electrophoresis to visualize those proteins with bound fragments. The results were striking: lots of proteins were labeled, and each fragment labeled a different set of proteins. This is what you would expect for low-complexity molecules, but it is nice to see reality match predictions.

Not content to look at gels, the researchers switched to mass spectrometry for a more global analysis using “stable isotope labeling with amino acids in cell culture” (SILAC). In this approach, one population of cells grown under normal conditions was treated with one of the FFF probes, while a second population of cells containing isotopically labeled proteins was treated with a control probe containing just a methyl group instead of the variable fragment. The resulting cell lysates could then be proteolyzed and analyzed by mass spectrometry; most peptides would show two peaks of similar intensities, one from each isotopically distinct population of cells. However, if an FFF probe bound preferentially to a protein compared with the control probe, the resulting peptide would be enriched.

Examining 11 FFF probes at 200 µM concentration allowed the researchers to identify an impressive 2000 different protein targets. Both soluble and membrane proteins were found, with expression levels ranging over 100,000-fold (i.e. the technique seems to work for both rare and abundant proteins). Remarkably, only about 17% had known ligands. There was also little overlap with the proteins targeted by the researchers’ previously described covalent fragments.

Where did the FFF probes bind? An analysis of 186 proteins whose crystal structures had previously been reported showed that about 80% of the modified peptides were close to a computationally predicted pocket, as might be expected.

Next, the researchers made or purchased analogs of some of their FFF probes. When added to screens, these decreased labelling of hundreds of targets; this competition assay both suggests the FFF probes make specific interactions while also providing more potent analogs. Two proteins – the enzyme PTGR2 and the transporter SLC25A20 – were studied in some detail. Probe 8 modified two peptides near the active site of PTGR2 and could be competed by compound 20. Compound 20 was also a modest inhibitor of the enzyme. Further modification led to compound 22, with nanomolar activity against the isolated enzyme and in cells. Since this protein previously lacked any good chemical probes, this could be useful.

This approach also lends itself well to phenotypic screening, so the researchers expanded their FFF probes to 465 members, increasing the size of the variable fragment portion to an average of 267 Da. They also made competitor molecules for most of these, which contained the fragment but not the alkyne or photoreactive group.

The researchers screened about 300 of their new FFF probes (at 50 µM each) to look for molecules that would increase the differentiation of mouse preadipocytes to adipocytes. This led to nine hits, one of which was active at 10 µM. SILAC experiments revealed the target of this to be a somewhat obscure membrane protein called PGRMC2. Subsequent experiments suggested that PGRMC2 is a positive regulator of adipogenesis, and that the identified compound is an activator.

This is a remarkable paper, and it is impressive that the researchers have described not just the approach but several success stories – each of which could well form a stand-alone publication. The covalent platform described last year has already led to a company - Vividion - which recently raised $50 million, and I’m sure the new approach will find use in both academia and industry.

06 February 2017

Beware self-reacting fragments

Long-time readers will know that I have a peculiar fascination for artifacts of all kinds, particularly when they provide learning opportunities. A lovely example by Gerhard Klebe (Philipps-Universität Marburg) and collaborators has just appeared in Angew. Chem. Int. Ed.

The researchers have long been using the aspartic protease endothiapepsin (EP) as a model protein: we previously discussed how they compared half a dozen fragment-finding methods against EP, more recently arguing that crystallography is the best of the bunch. The new paper focuses on Compound 1. This molecule was a hit in five out of six fragment screens, each employing a different method, and was among the top ten hits in four of the screens. It produced the highest thermal shift (+3.4 °C), strongly inhibited the enzyme in two different biochemical assays, and even showed a dissociation constant of 115 µM by isothermal titration calorimetry (ITC).

Crystallography, though, told a different story. The researchers obtained high resolution structures (initially 1.25 Å, and ultimately 1.03 Å!) These revealed that the bound ligand was actually compound 2, which is composed of three molecules of compound 1. A variety of experiments, including anomalous scattering, high resolution mass spectrometry (HR-MS), and MS/MS fragmentation supports this assignment.

So what’s going on? The team notes that, although compound 1 does not aggregate and is not a PAINS compound, high-level quantum mechanical modeling suggested that the chlorine is susceptible to nucleophilic displacement, which probably wouldn’t surprise many medicinal chemists. Rearrangement of the resulting dimeric molecule produces compound 4, which could then react with yet another molecule of compound 1 through a radical mechanism to produce compound 2.

Allowing compound 1 to sit in buffer or methanol provided support for this mechanism and allowed the isolation of compound 4 and other degradation products, though compound 2 itself could not be detected. The researchers suggest it is particularly reactive and only stable when surrounded by the protein.

I applaud the investigators for pursuing this fascinating bit of science. This is academic research in the best sense of the phrase.

This is also the kind of investigation that would fall outside the scope of most industrial researchers, where the mandate is to discover promising drug leads as quickly as possible. More somberly, this story could have ended in embarrassment or worse had the researchers been less rigorous. The difficulty (and unlikelihood) of such lengthy investigations is why triaging shortcuts such as PAINS filters have been introduced, and why scientists using these tools must still be cautious: even molecules that aren’t PAINS can act through pathological mechanisms.

This is also why I believe that arguments that PAINS filters are inadequately defined and should thus be discarded are misguided. Sure, some PAINS molecules are drugs, and any rubric can be improved. But the nice thing about fragment screens is that they often produce a plethora of hits to pursue. A flawed triaging scheme will jettison some pearls among the pebbles, but without triage, far more resources will be lost chasing will-o'-the-wisps.

30 January 2017

Fragments vs PRC2: ligand deconstruction

Ligand deconstruction is a strategy for early-stage drug discovery in which a known hit is dissected into component fragments and one or more of them is optimized. When successful, it can lead to new and improved chemical series. One such example was just published in J. Med. Chem. by Andreas Lingel and colleagues at Novartis.

The researchers were interested in finding inhibitors of the protein methyltransferase polychrome repressive complex 2 (PRC2). Although some drugs have entered the clinic against this anticancer target, all of these are competitive with the cofactor S-adenosylmethionine (SAM), and resistant mutants are already being detected. Thus, the team sought a molecule that would act through a different mechanism.

PRC2 is actually a complex of four different proteins. The SET domain of the protein EZH2 contains the catalytic machinery, but a protein called EED stabilizes the protein complex and is necessary for activity. EED also recognizes trimethylated lysine residues on histone substrates, allosterically activating methyltransferase activity.

A high-throughput biochemical screen identified compound 1, which has low micromolar activity and is noncompetitive with the SAM cofactor and substrate peptide. Subsequent NMR and crystallography experiments revealed that compound 1 binds to EED, with the tertiary amine binding in the same pocket that normally recognizes trimethyllysine. However, compound 1 is quite complex, with three stereocenters. Thus, the researchers sought to deconstruct it to something simpler. They began by chopping off two of the rings – an unconventional disconnection but one supported by crystallography, which revealed that the terminal rings were not closely associated with the protein.

The resulting fragment 2 was down more than an order of magnitude in potency but had improved ligand efficiency. Crystallography confirmed that it binds in a very similar fashion to compound 1. Initial SAR was conducted around the methoxybenzyl moiety, which is buried within the enzyme. Most changes were not tolerated, but compound 9 did show somewhat improved activity.

Next, the researchers sought to optimize the positively charged portion of the molecule. Replacing the amine with a guanidine improved the affinity but at a cost to cell permeability. This led to a search for less conventional replacements, ultimately yielding the 2-aminoimidazole moiety in compound 16. Not only did this regain the activity of the initial molecule, it also shows good permeability and cell activity. Crystallography revealed that it too binds in a similar fashion to the original hit.

This is a nice example of fragment-assisted drug discovery (FADD), in which concepts from FBDD were used to simplify and optimize a hit from HTS. There is of course much more to do with this series, not the least of which is figuring out exactly how the molecules actually inhibit PRC2. Trimethyllysine-containing peptides that bind to EED normally activate the enzyme, yet the small molecules that bind to the same site somehow allosterically inhibit activity. Despite multiple crystal structures, the researchers frankly acknowledge that they were “not able to decipher the molecular basis for this phenomenon.” A number of conformational changes occur when EED binds to ligands, and perhaps these propagate through the protein complex. A picture may be worth 1000 words, but we may have to wait for the movie to learn the full story. 

23 January 2017

New tricks for old methods: STD NMR

According to our last poll, ligand-detected NMR is the most popular method for finding fragments. And among the several ligand-detected NMR techniques, the most popular appears to be saturation transfer difference (STD) NMR. The basic concept behind this approach is to selectively irradiate a protein, which then transfers its magnetization to any bound ligand, thus “saturating” (reducing the signals) for the ligand. Subtracting this spectrum from a reference spectrum reveals which ligand (if a mixture) or individual protons within a ligand are in close proximity to the protein.

Although STD NMR is fast and easy to run, it does have drawbacks. One is the fact that it requires pure protein: if there are other proteins in solution, it will be impossible to tell whether the small molecule binds to the protein of interest or to something else. This shortcoming has been overcome in a paper published recently in J. Biomol. NMR by Tamas Martinek and collaborators at the University of Szeged, the Hungarian Academy of Sciences, and the University of Debrecen.

In a normal STD experiment, the protein protons that are irradiated are far upfield (often around -0.5 ppm) – a region not relevant to most small molecules. These protons then transfer the magnetization throughout the protein and ultimately to any bound small molecules. In order to choose a specific protein, the researchers add an 15N-labeled antibody selective for the protein. They can then selectively irradiate the 15N-labeled antibody, which transfers its magnetization to the bound protein and from there to any bound ligand. They call this approach monoclonal antibody-relayed 15N-group-selective STD, or mAb-relayed 15N-GS STD.

To demonstrate the approach, the researchers observed the binding of 2 mM lactose to galectin-1 (Gal-1) using an 15N-labeled antibody against Gal-1. Lactose binds to Gal-1 with a dissociation constant of 0.155 mM, which is a relevant affinity for fragment screening. Gal-1 was present at 20 µM and the antibody was present at 10 µM, both of which are reasonably low. Control experiments established that both Gal-1 and the antibody were necessary, and the experiment was successful even in a cell extract.

So, as Teddy would ask, is this approach practical? You need an 15N-labeled antibody against your target, and it is important that the antibody is specific and does not compete with your ligand (ie, that it is non-neutralizing). Also, the amount of time required to acquire the spectra appears to be more than an hour. If this could be reduced, would 15N-GS STD assume a useful niche in the NMR toolbox?

16 January 2017

Enthalpy revisited – and retired

The relative importance of enthalpy versus entropy for protein-ligand interactions has been a subject of considerable attention. In a 2009 post we suggested that it might be worthwhile to focus on fragments that bind predominantly enthalpically, and in 2011 we highlighted a paper suggesting that enthalpic binders may be more selective than entropic binders. But the universe has a way of confounding pet models – as we acknowledged in 2012 (twice). The best way forward is often with lots of data, which is exactly what we have in a new paper in Drug Disc. Today by György Keserű and collaborators at the Hungarian Academy of Sciences, Astex, and AstraZeneca.

The data in this case are sets of 284 protein-ligand interactions with thermodynamic binding data from the literature, 782 from Astex, and about 230 from AstraZeneca. Commendably, these data are provided in 103 pages of supporting information.

In order to analyze the data, the researchers developed a new metric, the Enthalpy-Entropy Index:


If IE-E = 0, it means that enthalpy and entropy both contribute equally to the free energy of binding; if IE-E > 0 it means that enthalpy dominates, and if IE-E > 1 it means that enthalpy needs to overcome an unfavorable entropy. Similarly, negative values mean that entropy dominates – completely so when IE-E < -1. (Note that, unlike enthalpy efficiency, this is a dimensionless ratio, which should please our friends over at Molecular Design.)

As it turns out, the vast majority of fragments bind to their targets with favorable enthalpy, and almost all of those that don’t are charged compounds in which desolvation of the charged bit could entail an enthalpic cost. The researchers also examined a set of 94 neutral fragment-sized and 44 larger molecules binding to 17 targets and found that, statistically speaking, enthalpy plays a more important role in the free energy of binding for fragments than for larger molecules. But things can change quickly: in one case, adding just two non-hydrogen atoms to a molecule improves the affinity by more than 4000-fold and changes the IE-E from -1.5 to +0.5.

The paper does an excellent job describing the challenges of collecting high-quality isothermal titration calorimetry (ITC) data. In a typical experiment, the heat measured with each injection is the same as “would fall on an A4 sheet of paper in 1 second when illuminated by a 40 Watt bulb placed nearly 5 kilometers away.” Errors can be caused by inaccurate concentrations, heat of dilution, and changes in buffer concentration or protonation state. An analysis of replicate measurements at Astex found that, while most of the replications were within 1 kcal/mol of each other, some were off by nearly 3 kcal/mol. However, these larger values were all associated with different forms of the protein, and so may not be considered true replicates, though they do indicate how changes in the protein far from the active site can have an effect on what is often considered (erroneously) a local interaction.

This also emphasizes the fact that, as the researchers note, “the measured binding enthalpy is a net value and the dissection of the individual contributions might be ambiguous.” Or, as Pete has previously stated, “the contribution of an individual protein-ligand contact is not strictly an experimental observable”.

From a molecular recognition standpoint I find all this quite interesting and even intuitive in a hand-wavy sort of way. As the researchers suggest, fragments, being small, have minimal surface area with which to make (often but not always entropically-driven) hydrophobic interactions. Instead, much of the binding energy comes from hydrogen-bond interactions, which are (again often but not always) predominantly enthalpic. Moreover, since the entropic cost of locking down any ligand onto a protein is on the order of 3-5 kcal/mol, fragments are already fighting against entropy, and this is exacerbated by low affinity.

But from a practical perspective, my earlier suggestion to focus on enthalpic fragments may have been simplistic: if you’ve found a fragment, its enthalpy of binding is almost certainly favorable, and even if it’s not, this could change with the slightest tweak. So unless we see something truly new, don’t expect many new posts on this topic.

11 January 2017

Cussed curcumin

Teddy’s retirement from the blog has cut down on the number of PAINS-shaming posts, and truth be told there are so many candidate papers that they could easily swamp fragments, which I suspect would drive away most of the readership. That said, I did want to highlight an exhaustive Perspective about a particularly diabolical natural product just published today in J. Med. Chem. by Mike Walters and collaborators at the University of Minnesota, Brigham and Women’s Hospital, and the University of Illinois (and also covered in a news story in Nature.)

We’ve previously discussed some of the types of artifacts that can plague small molecule screens: aggregation, covalent adducts, redox cycling, fluorescence, photoreactivity, and more. Curcumin is a jack of all trades in that it is capable of all of the above. It’s also unstable even at neutral pH, and can decompose into other reactive species. It is the quintessential chemical con artist: if you have an assay, curcumin will probably be active in it.

The new paper is a thorough investigation (18 pages, with 164 references) of the chemistry and biology of curcumin, covering in gruesome detail all the many ways it can deceive. After discussing the history and physicochemical properties (and liabilities), several literature case studies where curcumin is proposed as having biological activity are explored and thoroughly demolished; one of these has been retracted but continues to be cited uncritically years later.

One might expect that something which hits so many assays would be toxic. This turns out not be the case: curcumin is present at 1-6% in tasty turmeric and only seems to show any adverse events at very high doses – several grams per day. The reason, the researchers show, is that curcumin’s pharmacokinetics are lousy, with oral bioavailability of less than 1%. This is a very literal example of the cliché “garbage in, garbage out.”

Sadly, these properties have not dampened interest in testing curcumin in people. The researchers identify 135 registered clinical trials, only eight of which have reported study results, with 49 either recruiting or not yet recruiting. The few examples where results have been reported are not particularly encouraging.

Typing curcumin into PubMed pulls up close to 10,000 papers, with more than 150 published in J. Med. Chem. alone. Will this devastating exposé help? For honest and diligent researchers, it should serve as a flashing warning to be extremely careful with any data gathered using curcumin. Unfortunately, some in the scientific community may not care as long as they are able to pump out papers. Indeed, according to Wikipedia, at least one prominent curcumin researcher had to retract several papers because of questionable “data integrity”. And there may be still darker motives: type curcumin into Google and the top results are ads touting the stuff. There’s money to be made, and even more if you slap on some scientific lipstick.

And despite specific J. Med. Chem. author guidelines to be cautious about “interference compounds” and “provide firm experimental evidence in at least two different assays that reported compounds with potential PAINS liability are specifically active and their apparent activity is not an artifact”, the journal recently published a paper fully devoted to the synthesis and SIR of rhodamine derivatives, with no consideration of mechanism nor mention that they can be problematic. (Indeed, the researchers do not even bother to include detergent in their enzymatic assay!)

All of which is to say that it’s easy to publish crap. But hopefully now, more people will recognize it as such.

09 January 2017

Fragments in the clinic: verubecestat

Of all the fragment-derived drugs in the clinic, perhaps none is so closely watched as verubecestat (MK-8931), Merck’s BACE1 inhibitor in phase 3 clinical trials for Alzheimer’s disease (AD). With tens of millions of cases worldwide, few other diseases in the developed world are as simultaneously widespread, expensive, and terrifying. And despite billions of dollars thrown at the problem, failure rates are nearly 100%. A recent open-access paper by Jack Scott, Andrew Stamford, and collaborators at Merck and AMRI in J. Med. Chem. provides an excellent overview of this latest contender.

We first wrote about Merck’s BACE1 program almost exactly seven years ago, describing how an NMR screen had provided a weak hit that was optimized to nanomolar inhibitors of the enzyme. However, the molecules could fairly be called molecularly obese. This led the researchers to trim back portions of the molecule, losing affinity but gaining cell-based activity and permeability, ultimately resulting in compound 5 (below) – which is itself a fragment. The current paper describes the optimization of this molecule.

Growing compound 5 and expanding the heterocyclic ring led to compound 7, with low nanomolar biochemical and cell-based activity. The iminopyrimidinone core was becoming increasingly crowded from an intellectual-property standpoint, so the researchers replaced this with the iminothiadiazinane dioxide in compound 9, which modeling suggested should have a similar conformation – a result confirmed by crystallography. However, the alkyne moiety appeared to be metabolically unstable. More importantly, compound 9 was only 47-fold selective against the enzyme cathepsin D (CatD). An earlier Lilly BACE1 inhibitor with a similarly modest selectivity had failed due to toxicity possibly associated with CatD, and the researchers were keen to avoid a similar fate. This led them through additional rounds of optimization, ultimately resulting in verubecestat.

In addition to having low nanomolar biochemical and cell-based activity against BACE1, verubecestat is >45,000-fold selective against CatD, has good pharmacokinetics, is orally bioavailable, and is highly soluble (1.6 mM!) It does not inhibit CYP enzymes and has good brain penetration. Rule-checkers might be surprised at this later point given the high calculated polar surface area (115 Å2), a fact the researchers attribute to an intramolecular hydrogen bond between the amide and the pyridine nitrogen, effectively masking these moieties from the point of view of membranes.

A couple potential liabilities stood out. First, one metabolite is an aniline, and anilines can be mutagenic. Reassuringly, an Ames test on this particular aniline showed no mutagenicity. Also, verubecestat is a 2.2 µM hERG inhibitor, and inhibitors of this channel can cause cardiac arrhythmias. However, this concentration is significantly higher than the highest expected in humans, and studies in primates revealed no safety issues. All of which is a useful reminder that, in our business, rules are at most guidelines, and data is king.

The paper also includes some human data demonstrating that the compound is safe at doses up to 550 mg (!) and causes a dose-dependent reduction in β-amyloid levels. With the results of the first Phase 3 trial expected later this year, we could know soon whether this is a billion dollar molecule or yet another massively expensive failure. If the former, verubecestat could be one of those transformational advances in drug discovery that comes along once in a generation. But even if fails, this is the clearest test yet of the amyloid hypothesis. And fragments made it possible.

02 January 2017

Fragment events in 2017

What better way to start the year than with a list of upcoming conferences? Here's what we know about so far.

March 5-7: The UK may have voted to leave the EU, but that's not stopping the Royal Society of Chemistry from holding Fragments 2017 in Vienna, Austria. This is the sixth in an illustrious conference series that alternates years with the FBLD meetings. You can read impressions of Fragments 2013 and Fragments 2009. Registration is already open!

April 25-26: CHI’s Twelth Annual Fragment-Based Drug Discovery, the longest-running fragment event, will be held in San Diego at a brand new venue. You can read impressions of last year's meeting here; the 2015 meeting herehere, and here; the 2014 meeting here and here; the 2013 meeting here and here; the 2012 meeting here; the 2011 meeting here; and 2010 here. Also as part of this event, Ben Davis and I will be teaching a short course on FBDD over dinner on April 25. Early registration is open until January 27.

June 6-9: Although not exclusively fragment-focused, the fourth NovAliX Conference on Biophysics in Drug Discovery will have lots of relevant talks, and is a good excuse to get to Strasbourg, France. You can read Teddy's impressions of the 2013 event herehere, and here. Registration is open now.

July 23-28: Finally, Australia is coming into its own as a destination for fragment experts, many of whom will be participating in a symposium (on July 27) that is part of the Royal Australian Chemical Institute's Centenary Congress in Melbourne. The entire event should be huge - think ACS with wombats - so if you've been looking for yet another reason to travel Down Under, this is it. Early registration is open through April 23.

It looks like the year is largely front-loaded thus far - know of anything else? Add it to the comments or let us know!

26 December 2016

Review of 2016 reviews

This year is finally coming to an end, and as we've done for the past four years, Practical Fragments will highlight some of the reviews that we didn't cover previously.

In terms of what we did cover, there were several excellent events, including the eleventh annual CHI FBDD Conference in San Diego, an inaugural meeting in Houston, and of course the first-ever major fragment event in Boston, FBLD 2016.

The twentieth anniversary of SAR by NMR was also commemorated by the eighth book devoted to FBLD, as well as a massive two volume work on lead generation. We also covered a special issue of Molecules and reviews on clinical candidates and library design.

Another review on library design was published recently in Drug Disc. Today by Ian Gilbert, Paul Wyatt, and colleagues at the University of Dundee. The researchers have built a set of 356 diverse compounds consisting of “capped” scaffolds, such that any hits could be rapidly expanded. Undergraduates did much of the actual library assembly, learning skills such as parallel chemistry and how to work with polar compounds. There is lots of nice detail in this paper, including on library storage conditions.

Practical Fragments often highlights successful fragment to lead programs, and these were the focus of a Perspective in J. Med. Chem. by Christopher Johnson (Astex) and collaborators: all 27 cases published in 2015 in which the affinity of a fragment was improved at least 100-fold to a 2 µM or better lead. Many of these were covered in Practical Fragments, including BTK, DDR1/2, ERK2, MELK, Mtb TMK, PKCθ, RET, FactorXIa, MMP-13, BCATm, PDE10A, soluble epoxide hydrolase, tankyrase, ATAD2, MCL-1, RAD51, XIAP/cIAP, and mGluR5. The paper also draws general conclusions about target types, molecular weights, cLogP values, and LE.

Targeting tuberculosis (TB) is the subject of two reviews from University of Cambridge researchers, one in Drug Discov. Today by Vitor Mendes and Tom Blundell and one in Parasitology by Anthony Coyne, Chris Abell, and colleagues. Fragment-based approaches have been more or less successful against several TB proteins, including pantothenate synthetase, CYP121, BioA, EthR, and thymidylate kinase, while other targets – such as shikimate kinase and CYP144 – have proven more difficult.

July was bromodomain month at Practical Fragments, and this target class is the subject of a review in Drug Discov. Today: Technol. by Dimitrios Spiliotopoulos and Amedeo Caflisch at the University of Zurich. The focus is on computational fragment screening methods, with examples for BRD4 and CREBBP. And while we’re on the topic of computational methods, Olgun Guvench of SilcsBio has a brief review in Drug Discov. Today on computational functional group mapping.

Rounding out target-focused reviews, Paramjit Arora and colleagues at New York University focus on protein-protein interactions (PPIs) in a Trends Pharm. Sci. paper. This covers multiple approaches to finding PPI inhibitors, including fragment-based, and also touches on hotspots and structure-based design.

It is impossible to imagine FBLD without biophysics, and this is the topic of an authoritative review in Nat. Rev. Drug Disc. by Jean-Paul Renaud (NovAliX), Chun-wa Chung (GlaxoSmithKline), U. Helena Danielson (Uppsala University), Ursula Egner (Bayer), Michael Hennig (leadXpro), Rod Hubbard (University of York) and Herbert Nar (Boehringer Ingelheim). In addition to covering all the major techniques, the paper does a great job of delving into some of the more obscure and emerging methods, providing an excellent discussion of the throughput and requirements for each technique as well as the kinds of information obtained. Although the review is broader than FBLD, the application of biophysical techniques to fragments is a major theme. The researchers also remind us that, “contrary to the belief that all drug discovery challenges are best solved through the introduction of new technologies, substantial advances can also be driven by innovative application.”

Individual biophysical techniques also received plenty of attention over the year, including three on NMR. The first, by Alvar Gossert and Wolfgang Jahnke (Novartis) in Prog. Nucl. Magn. Reson. Spectrosc., is a 44-page practical guide to identifying and validating protein ligands. This contains a wealth of information on most of the NMR methods you will ever likely encounter; it includes a handy chart summarizing the molecular weight and concentration limits for each technique, suggested workflows, and thorough discussions of potential pitfalls. The review may appear daunting to the novitiate – it is replete with equations and pulse sequences – but the writing is clear. In the end, much comes down to the concept of the “validation cross”, a rubric for assessing the integrity of both ligand and protein, and evaluating binding effects on both ligand and protein.

Two additional reviews, both from William Pomerantz and colleauges at the University of Minnesota, focus specifically on protein-observed 19F NMR. The first, a Perspective in J. Med. Chem., is a good general introduction. Despite being the 13th most abundant element on our planet, only five natural products are confirmed to contain fluorine. Introducing this element into proteins – as has been done in more than 70 cases – can be a useful approach for discovering new ligands. And if you want to start doing this yourself, a paper in Nature Protocols provides practical details.

Turning to other biophysical techniques, surface plasmon resonance (SPR) continues to be very popular, and is reviewed by Alain Chavanieu and Partine Pugnière in Expert Opin. Drug Discov. The paper provides a good general overview on using SPR for FBLD, covering the theory, history, various screening strategies, comparison to other methods, recent applications to a variety of different targets, and a suggested workflow.

Calorimetry is less commonly used for fragment screening, even though it can provide thermodynamic data. Michael Recht and collaborators at the Palo Alto Research Center and Zenobia discuss both enthalpy arrays as well as more conventional isothermal titration calorimetry (ITC) in a Methods Enzymol. chapter.

But while biophysics is important, FBLD would be nowhere without chemistry. In MedChemComm, Stefan Kathman and Alexander Statsyuk (then Northwestern, now University of Houston) review one chemical approach, covalent tethering. This touches on the original reversible (thermodynamically-controlled) disulfide tethering approach developed back at Sunesis but is primarily focused on irreversible (kinetically-controlled) methods. The paper does an excellent job summarizing challenges, potential pitfalls, design rules, and recent successes. As of early this year the Statsyuk lab had sent their 100-member covalent fragment library to nine different research groups, three of which had already identified hits. The review ends with some provocative questions, and it will be fun for practitioners to answer them as covalent approaches garner increasing attention.

Another chemical technique we’ve touched on is substrate activity screening (SAS), and this is reviewed in ChemMedChem by Pieter Van der Veken and collaborators at the University of Antwerp. All published examples are summarized, including the modified approach developed by the Van der Veken lab; some unpublished data are also discussed. The paper also includes a good general section on the subtleties and complexities of transforming substrates into inhibitors.

Finally, if all this is a bit too much, a good general review on FBLD was published in Pharmacol. Ther. by Martin Scanlon and colleagues at Monash University. This concise but thorough paper covers theory, history, library design, hit finding and characterization, and select clinical success stories. The longest section is devoted to chemical strategies for elaborating fragments, and includes some of the less commonly used methods such as target-guided synthesis, Tethering, and off-rate screening.

And that’s it for this year. Thanks for reading, and especially for commenting. Take care, do important work, and may 2017 be better than we can reasonably hope.