Review of 2015 reviews

In the Northern Hemisphere the winter solstice has passed but the days are still short, and 2015 is hurtling into history. As we did in 2014, 2013, and 2012, Practical Fragments will spend this last post of the year highlighting notable events as well as reviews we didn't previously cover.

Two major conferences this year were CHI’s Tenth Annual FBDD meeting in San Diego (discussed here and here) and Pacifichem 2015. If you missed these don’t worry – we’ll have an updated list of 2016 events soon.

After a three year drought, two new books published in 2015: Fragment-based methods in drug discovery and Fragment-based drug discovery. And the trend looks set to continue, with a new book edited by Wolfgang Jahnke and me set to publish in early 2016.

In addition to complete books, several book chapters may be of interest to readers, the first being “Fragment-based drug discovery” by Jean-Paul Renaud and NovAliX colleagues, published in Small molecule medicinal chemistry (Wiley). This is a general review of the topic, focused heavily on biophysical techniques, especially SPR, NMR, and native MS. It also includes a couple case studies – one on the clinical compound AT9283 and one on the bromodomain BRD2.

The next three chapters all come from Springer’s massive Methods in molecular biology series. Continuing the biophysical theme is “Biophysical methods for identifying fragment-based inhibitors of protein-protein interactions,” by Michelle Arkin and colleagues at UCSF. This provides background and step-by-step instructions for SPR, differential scanning fluorimetry (DSF), NMR (including STD, WaterLOGSY, and HSQC/HMQC), and X-ray crystallography. A more detailed guide to STD NMR is provided by Hai-Young Kim and Daniel Wyss (Merck) in “NMR screening in fragment-based drug design: a practical guide,” while Byeonggu Han and Hee-Chul Ahn (Dongguk University-Seoul) discuss STD NMR applied to kinases in “Recombinant Kinase Production and Fragment Screening by NMR Spectroscopy.”

Moving on to journals, two reviews focus on protein-protein interactions. The first, by Thomas Magee (Pfizer) in Bioorg. Med. Chem. Lett., briefly touches on challenges and solutions before focusing on several case studies, including navitoclax, Mcl-1, RPA, KRas, Rad, bromodomains, XIAP, HCV NS3, and more. The second, by Chunquan Sheng (Second Military Medical University, Shanghai), Wei Wang (University of New Mexico and East China University of Science and Technology) and colleagues is published in Chem. Soc. Rev. This is much broader, covering not just fragment-based approaches but others as well, and includes 229 references and 21 figures. There’s a lot of good stuff in this paper, but unfortunately the authors do not discuss the numerous false positives that can occur, such as aggregation and PAINS, and some of their examples are artifacts. Caveat lector.

The next two papers focus on specific therapeutic areas. Xinyong Liu and colleagues at Shandong University discuss the application of fragment approaches to HIV targets in Expert Opin. Drug Discov. In addition to recent examples, this covers some of the older literature, as well as less conventional topics such as dynamic combinatorial chemistry. And in Front. Neurol., Jeffry Madura and Christopher Surratt (Duquesne University) discuss the role fragment-based approaches can play in developing drugs that target the central nervous system (CNS). This review is particularly focused on computational methods.

The next three papers continue the computational theme. Dima Kozakov, Adrian Whitty, Sandor Vajda (Boston University) and co-workers have two reviews discussing work we highlighted earlier this year. The first, in Trends Pharmacol. Sci., is an excellent summary of how computational hot spot analysis can predict whether a protein will be ligandable, and includes a number of case studies. The second, a Perspective in J. Med. Chem., is a much more wide-ranging analysis of the approach. This paper also considers difficult targets, some of which may be tackled with larger molecules such as macrocycles, and others of which may simply not be druggable. And in Chem. Biol. Drug Des., Matthew Bartolowits and V. Jo Davisson (Purdue University), focus on “subpockets,” which are essentially the regions surrounding individual amino acid residues in proteins. This paper also includes an extensive list of software tools for analyzing binding sites.

Finally, Chris Murray and David Rees (Astex) have a brief but lively essay in Angew. Chem. Int. Ed. After providing essentially a target product profile for an ideal fragment, they challenge chemists to devise new routes to superior fragments. Although fragments may seem simple, the “precision synthesis” required to elaborate them “is often rate-limiting.” Diversity-oriented synthesis (DOS) is one potential solution, although there does not seem to have been as much activity here as might have been hoped. Some of the problems are prosaic but significant: as we’ve noted, highly water soluble fragments can be hard to isolate. The authors call for new synthetic methodology compatible with small fragments containing diverse hydrogen-bonding functional groups.

And with that, Practical Fragments says farewell for the year. Thanks for reading (and especially for commenting) and may 2016 bring brilliant breakthroughs!

Pacifichem 2015

Last week saw the first-ever fragment-based symposium at Pacifichem. These are massive meetings held in Honolulu every 5 years to bring together scientists from countries surrounding the Pacific. Competing with views like this can be challenging.

Nonetheless, Practical Fragments is happy to report that the symposium was popular, with some talks at close to standing-room-only capacity. There were over 40 presentations and posters from eight countries, and Derek Cole (Takeda) and Chris Smith (Coi) also chaired a lively round-table discussion. I’ll just try to convey a few broad themes.

The utility of “three-dimensional” fragments (as opposed to “flatter” aromatic fragments) came under fire. Jane Withka of Pfizer reported that a small library of 400 fragments, 80% of which had chiral centers, produced lower hit rates and lower confirmation rates in SPR screens than her company’s original fragment library, consistent with what Astex reported.

Another theme was decreasing the concentration at which fragments are screened. Tom Peat (CSIRO) said that even weak (1-10 mM) hits can be found by screening fragments at 100-200 µM using SPR. This seems to be something of a “sweet spot;” aggregation artifacts become significantly more problematic at higher concentrations. For native mass spectrometry, even 10 µM fragment seems to work well, though Tom has a rather impressive MS instrument. Similarly, commentator sgcox noted that DSF is best conducted below 100 µM fragment concentration.

As we noted six years ago, fluorine NMR is also ultrasensitive. Brad Jordan (Amgen) stated that he routinely detects 4-5 mM binders even when screening fragments at 20 µM. Brad also discussed an update of work we covered previously, in which a fragment-linking approach ultimately led to picomolar inhibitors of BACE1. Continuing the fluorine theme, Ray Norton (Monash Institute of Pharmaceutical Sciences, MIPS) described his group’s work with protein-observed ¹⁹F NMR. Clearly more people are catching the fluorine bug, as attested by its popularity in our recent poll.

NMR in general was well-represented. In addition to standard approaches, Bill Marathias (Beryllium) used NMR to find hits against microRNA 21, Ivanhoe Leung (University of Auckland) used boron NMR as part of a dynamic combinatorial chemistry program, Biswaranjan Mohanty (MIPS) described methyl-specific labeling, and Shigeru Matsuoka (Osaka University) discussed solid-state NMR.

Crystallography remains king when it works, though several speakers noted that they had obtained dozens or even hundreds of structures of their protein without capturing a bound fragment. And even successful protein-ligand structures can mislead; Carsten Detering (BioSolveIT) reported that his computational approach detected problems in about half of 107 published structures. Still, structures can be extraordinarily useful: we recently highlighted an AstraZeneca paper that released dozens of structures, and Greg Warren (OpenEye) used these to address questions about solvation. What’s more, crystallographers are looking to improve things: Janet Newman (CSIRO) highlighted an app called Cinder (“Crystallographic Tinder”) to speed up the identification of protein crystals. It’s available for Android, with an iOS version coming soon.

Of course, although the science is fun, the ultimate goals of fragment-based drug discovery are better drugs, and here too we are making progress. Jane Withka noted that several Pfizer kinase candidates had come from fragments. Tatsuya Niimi provided an overview of fragment projects at Astellas between 2009 and 2014: of 88 programs, 15 have produced compounds with IC₅₀ values < 200 nM. Not counting projects that were dropped for strategic reasons or are still in progress, this is an overall success rate of 43%. As expected, the successful targets were computationally predicted to be more tractable than those that failed, though unexpected conformational changes or covalent approaches proved that at least one “undruggable” target may need to be reclassified.

Gianni Chessari (Astex) provided an update of their cIAP/XIAP program and revealed that ASTX660 has recently entered a phase 1-2 clinical trial for cancer. I learned of another drug that has just entered clinical trials, though as its fragment origins have not yet been disclosed I’ll defer naming it. In any case, I’m looking forward to adding several new molecules the next time I update the list of fragment-derived clinical programs.

At the other end of the clinical spectrum, Chaohong Sun (AbbVie) briefly touched on their late-stage ABT-199, which is expected to be approved in the near future. And Daniel Wyss discussed Merck’s BACE1 inhibitor MK-8931, or verubecestat (see here for a nice summary in C&EN), which is in phase 3 clinical trials for Alzheimer’s disease (AD). The results, expected in early 2017, will be either a new hope for the millions of patients with AD – and the billions of people who hope to live long enough to one day be at risk – or a colossally expensive disappointment. Either way, they will provide the best test yet of the amyloid hypothesis.

I could go on but will instead end here – just as higher fragment concentrations lead to more artifacts, more words likely lead to fewer readers. Thanks to all who presented, organized, and sponsored the symposium. If you attended, please share your thoughts!

Fragments vs MKK3: modeling all the way to low nanomolar

The mitogen-activated protein kinase (MAPK) signaling pathway is a rich source of targets, particularly for inflammation. Within this cascade the p38 kinases have been heavily studied, but many of the inhibitors that entered the clinic derailed for various reasons, including efficacy. Thus, some groups have sought to block the pathway upstream of p38. A paper just published online in Bioorg. Med. Chem. Lett. by Steve Swann and colleagues at Takeda describes some of their efforts to accomplish this.

The researchers focused on MKK3 and to a lesser degree the related MKK6, both of which phosphorylate and activate p38. They began by screening their 11,012 fragments in a biochemical assay at 100 µM each. Hits were prioritized by estimating the IC₅₀ values and thus approximate ligand efficiency (LE) and lipophilic ligand efficiency values (LLE) for each compound that inhibited >30%. Of these, 93 gave LE ≥ 0.35 kcal/mol per heavy atom and LLE ≥ 4. (Incidentally, this seems like a perfectly reasonable use of metrics to triage a large number of compounds, and the speed and simplicity is a good counterargument to more complicated proposals.) Some hits were tested using full dose-response curves to determine actual IC₅₀ values and surface plasmon resonance assays to determine K_d values; compound 1 was particularly compelling.

Readers may recall that Takeda found this very same fragment as an inhibitor of BTK (a kinase in an unrelated pathway), and they used the compound/BTK crystal structure along with the published crystal structure of MKK6 to develop a binding model. In their pursuit of MKK3/6 inhibitors, the Takeda team performed biochemical screens of available related compounds. This led to compound 2, which modeling predicted would bind in a similar fashion. The binding model also suggested the possibility of picking up a hydrogen bond to a lysine residue, leading to the more potent compound 3. Further optimization led to compounds 4 and 6, both with low nanomolar potency against MKK3 and low micromolar or high nanomolar cell-based activity. Profiling these against a dozen other kinases within the p38 signaling pathway revealed good selectivity against all except MKK6.

This is a nice, concise paper that illustrates how modeling, even without direct structural information, can be used to advance a fragment to low nanomolar inhibitors, albeit in a well-studied class of targets. It is also another illustration that the same fragment can be used to develop completely different series. And finally, these molecules look promising as chemical probes and possibly drug leads; it will be fun to watch as more data are disclosed.

Fragments vs PDE10A revisited

Independent teams have reported using fragments to identify structurally distinct inhibitors against a popular psychiatric target.

Last month Practical Fragments highlighted a paper from Merck describing researchers’ success in advancing a fragment to a potent selective inhibitor of PDE10A, a potential target for schizophrenia. The final molecule had picomolar activity but suffered from various shortcomings, and the post ended by stating that “there is still plenty of work to do, and it will be fun to watch this story unfold.” Well, we didn’t have to wait long: a new paper in Bioorg. Med. Chem. Lett by Izzat Raheem and Merck colleagues describes further optimization of this series – again using fragments.

The team started by making various changes to compound 15h (shown in the previous post), ultimately leading to compound 4. Although this had lower affinity, it had significantly improved solubility and pharmacokinetic properties. Unfortunately, although selective against other PDEs, it was less selective against a broader panel of off-targets and inhibited both CYP2C9 and CYP3A4. In fact, 1000 analogs (!) containing the central fragment also hit these two enzymes, suggesting the problem was inherent to this core.

At this point the researchers returned to their original fragment screen and recognized that compound 5 had a similar structure to the original fragment. Appending the two “arms” of compound 4 onto this core led to the compound called Pyp-1, with good potency, solubility, and >5800-fold selectivity against other PDEs. Importantly, this molecule did not show the CYP activity of the previous series, and also displayed good pharmacokinetic properties in rats, dogs, and rhesus monkeys. A rat toxicity study didn’t reveal any red flags, and the molecule showed good pharmacodynamic effects in several animal models. The researchers acknowledge that this is a crowded field, with at least 7 compounds having entered the clinic, but Pyp-1 looks promising; at the very least it is a worthy chemical probe.

Continuing the theme of PDE10A, a second paper in Bioorg. Med. Chem. Lett. by Jeffrey Varnes and Jeffrey Albert reports an earlier-stage program from AstraZeneca. In this case, the researchers used a fragment-assisted drug discovery approach, integrating fragment information with data from high-throughput screening.

A functional screen of 3000 fragments led to a fairly high hit rate, with 414 compounds having ligand efficiencies ≥ 0.3 kcal/mol per atom. Many of these were similar to previously described PDE10A inhibitors and were thus deprioritized. On the other hand, compounds 6 and 7 were rather unusual structurally.

A high-throughput screen was conducted at the same time, and this also generated a high hit rate: ~5%, or 11,000 compounds. Unlike the Merck group, the AstraZeneca researchers were unable to obtain crystal structures of their fragments bound to PDE10A, so instead they looked for HTS hits similar to fragments 6 and 7, resulting in 14 compounds. Most of these were false positives or contained unattractive functionalities, but compound 8 turned out to inhibit significantly better than either fragment. Further medicinal chemistry led to compound 12, which is both potent and structurally distinct from other PDE10A inhibitors.

Together these papers reveal how fragments can be exploited to develop quite different molecules against the same target. Although the Merck series is clearly more advanced, it is impressive that the AstraZeneca work was done in the absence of crystallographic support. And in both cases, medicinal chemistry played an essential role: Valinor may beckon, but it will have to wait.

Fragments vs GPCRs – virtually vs experimentally

G protein-coupled receptors (GPCRs) are common drug targets that present challenges for fragment-based approaches. Biophysical studies of these membrane proteins are often difficult. Moreover, while many fragment-finding methods reveal binders, GPCR ligands can be agonists, inverse agonists, neutral antagonists, and more – and directing a search toward desired functionality can be tough (though see here). In a paper published earlier this year in Bioorg. Med. Chem. György Keserü and colleagues at Gedeon Richter and the Hungarian Academy of Sciences describe how they have tackled this problem.

The researchers were interested in the adrenergic α_2C receptor; agonists could be useful for a variety of indications, though selectivity is challenging. No crystal structure has been reported in the literature, so the researchers investigated a radioligand displacement assay as well as a cell-based functional assay (calcium mobilization) for agonists. A test set of 160 fragments from Maybridge was screened in both assays at 250 µM, giving 3 hits in the functional assay but a whopping 48 hits in the displacement assay. A 30% hit rate in an unbiased screen generally means something’s wrong, so the researchers chose to focus on the functional assay.

For the full screen, 3071 fragments having 9-22 non-heavy atoms were tested at 250 µM in the cell-based functional assay, resulting in 318 hits – a much higher rate than the initial set. However, when these were retested, only 86 reproduced, which the researchers attribute to variability in the cell-based assay. Many of the hits were also active against an unrelated GPCR; ultimately 16 were specific for the α_2C receptor and were also active in the radioligand displacement assay (as was one of the three original Maybridge hits). The chemical structures and activities of these molecules are shown in the paper; they are all quite potent with inhibition constants from 2-220 nM in the displacement assay, with correspondingly high ligand efficiency scores.

Despite the lack of a crystal structure, the researchers also performed a virtual screen of the same set of 3071 fragments using a homology model of the α_2C receptor. Two of the top 30 hits were fragments that had been discovered in the functional assay. Although this is not as impressive as another docking study on a different GPCR, it is certainly better than chance, and not too shabby considering the lack of an actual structure for the protein.

Next, the researchers attempted to find more potent analogs by testing compounds chemically related to their best hits. Some of these did show good potency in the radioligand displacement assay, but interestingly all of these were antagonists as opposed to the desired agonists. This is further evidence that gaining affinity may be easier than maintaining functionality.

As the authors concede (and we’ve noted elsewhere), the α_2C receptor has evolved to bind fragment-sized ligands. Still, the computational discovery of agonists is encouraging. It will be interesting to see whether such approaches will work against more difficult targets, such as peptidergic GPCRs.

Fragments vs DAPK3, computationally and experimentally

Computational approaches for discovering hits often involve sorting through many possibilities and examining a few closely. With luck, some of the predicted molecules will bind to the protein of interest. However, these don’t always bind for the “right” reason: sometimes a fragment predicted to bind one way will turn out to bind in quite a different manner. A recent Angew. Chem. Int. Ed. paper by Gisbert Schneider and colleagues at the ETH in Zürich and SARomics in Lund reports a possible example.

The researchers were interested in death-associated protein kinase 3 (DAPK3), which is implicated in several diseases. Previous work had shown that fasudil inhibits this kinase, though it hits others as well. Fasudil was used as a starting point for de novo fragment discovery using software called DOGS (Design of Genuine Structures). This is a scaffold-hopping approach in which virtual chemistry is used to generate readily accessible alternatives to a starting molecule. In this case, 347 of the 521 suggested inhibitors were fragment-sized. These were prioritized using in-house software, and compound 2 – one of the top hits – was chosen for synthesis and characterization.

Happily, compound 2 turned out to be fairly potent for its size, with impressive ligand efficiency. It is also quite different from fasudil (Tanimoto similarity = 0.16). Indeed, while fasudil is likely to be positively charged at physiological pH, compound 2 is likely to be negatively charged. Moreover, of 27 other kinases tested, compound 2 hit only one other with similar potency.

For those who have worked on kinases, compound 2 does appear unusual. A crystal structure of this molecule bound to DAPK3 revealed that it sits in the ATP-binding pocket but without making any conventional hydrogen bond interactions to the so-called hinge region of the kinase. Although no reported crystal structures show fasudil bound to DAPK3, structures with other kinases reveal the nitrogen of the isoquinoline moiety making a hydrogen bond to a backbone amide in this part of the protein.

The software used to prioritize compound 2 is based not on docking but on machine learning using the ChEMBL database, and the researchers were interested in what else this fragment might inhibit. Not surprisingly given the aryl sulfonamide moiety, several carbonic anhydrases came up, and two were confirmed experimentally.

Interestingly, the diuretic drug azosemide, whose physiological target is unknown, contains compound 2 as a substructure, and the researchers found that this molecule inhibits DAPK3 with low micromolar affinity. It also binds human carbonic anhydrase IX with similar affinity. The researchers suggest that these targets could at least partially explain the mechanism of the drug, as well as some of its side effects. It would be interesting to see cell data against these two targets, as well as the crystal structure of azosemide bound to DAPK3.

The ability to predict biological targets of molecules with the aid of machine learning would clearly be valuable (see also here). And of course new approaches for scaffold hopping are always valuable. In this case DOGS did retrieve an active (albeit odd) molecule when fed a conventional kinase inhibitor; it is as if you threw a ball and your dog fetched a slipper. I will be curious to see this applied to more systems.

Fragments vs PDE10A: growing potency and selectivity

People often wonder how selective fragments need to be. According to molecular complexity theory, the answer is “not very”. After all, it would be hard to get a decent hit rate with a library of just a few thousand fragments if they were too selective. In the case of kinases, experimental studies support this theory. Indeed, a single fragment has given rise to several drugs – one of which is approved. In a new paper in J. Med. Chem., William Shipe and colleagues at Merck demonstrate the utility of a non-selective fragment for another class of enzymes, phosphodiesterases (PDEs).

The human genome contains more than 50 different PDEs, which cleave phosphodiester bonds. PDE10A hydrolyzes cyclic guanosine monophosphate (cGMP) and cyclic adenosine monophosphate (cAMP) and is a potential target for schizophrenia. It has been pursued extensively, both with fragments (see for example here and here) as well as more traditional approaches.

The researchers started with a biochemical assay that screened each fragment at 200 µM; 60 of the 1600 tested gave > 80% inhibition. Nine of these were soaked into PDE10A crystals, producing seven structures, including compound 5, with impressive potency and ligand efficiency. Initial SAR by catalog led to the even more potent compound 6, which revealed that an amino group was tolerated and pointed nicely towards another pocket, offering a way for further elaboration.

Fragment growing from the amino group was accomplished through several rounds of parallel synthesis, with crystallography used to understand and optimize the binding interactions. Compound 9s showed particularly impressive low nanomolar potency, as well as at least 80-fold selectivity against nine other PDEs. In contrast, the initial fragment 5 was at most only 11-fold selective against any of the other PDEs.

Previous work with PDE10A had revealed another “selectivity pocket” nearby,  and the researchers further grew their molecule towards this, leading ultimately to compound 15h, with low picomolar affinity and at least >5900-fold selectivity against nine other PDEs. The compound also showed functional activity in a rat model, though it suffered from suboptimal pharmacokinetic properties.

This is a beautiful illustration of the power of combining fragment screening, structure-based drug design, and parallel synthesis. The researchers were able to gain more than a million-fold improvement in potency and take a marginally selective fragment to a highly selective lead. Of course, there is still plenty of work to do, and it will be fun to watch this story unfold.

Group efficiency

Ligand efficiency (LE) is one of the more controversial topics we cover at Practical Fragments. One critic asserted – incorrectly – that it is mathematically invalid. Another has stated that it is “not even wrong,” because the metric is predicated on standard state conditions and thus "arbitrary". (As he acknowledges, this also applies to the value and even the sign of the Gibbs free energy for a reaction.) A related metric that has received less attention is group efficiency (GE). In a paper just published in ChemMedChem, Chris Abell and colleagues at the University of Cambridge use this to help them optimize pantothenate synthetase (Pts) inhibitors.

Ligand efficiency is defined simply as the free energy of binding divided by the number of non-hydrogen, or “heavy” atoms (often abbreviated as HAC for heavy atom count) in the ligand. (Geek notes: although the binding energy is negative, LE is expressed as a positive number, so LE = - ΔG / HAC. Also, on Practical Fragments, units are assumed to be kcal mol^-1 per heavy atom unless otherwise stated.)

Instead of focusing on a single ligand, group efficiency compares two ligands that differ by the presence or absence of a given group of atoms. To calculate GE, you simply subtract the ΔG values for the two ligands and divide by the number of heavy atoms in the group. For example, if you add a methyl group to your molecule and are lucky enough to get a 100-fold pop in potency, the methyl group has a group efficiency of 2.7 kcal mol^-1 per heavy atom.

The current paper chronicles lead discovery for Pts, a potential target for tuberculosis. Previous screening efforts followed by fragment growing and fragment linking had generated low micromolar and high nanomolar inhibitors. The researchers turned to group efficiency to improve their molecules further.

As expected from ligand deconstruction studies (see for example here, here, and here), different portions of a molecule are likely to have vastly different group efficiencies. Indeed, this turned out to be the case here: the acetate moiety had high group efficiency, whereas the pyridyl moiety had lower group efficiency. Thus, the researchers set out to replace the pyridyl with ten diverse substituents. Happily, one of these improved the dissociation constant to 200 nM as assessed by isothermal titration calorimetry of the fully elaborated molecule. Compound 11 also showed reasonable enzyme inhibition in a functional assay.

One potential problem with group efficiency is that it assumes the molecules being compared bind in a similar fashion, which is not always a safe assumption. In this case, the researchers obtained a crystal structure of compound 11 bound to the enzyme, which not only revealed that it binds similarly to compound 5, but also suggested that inserting a methylene may improve binding. The resulting compound 20 showed better activity in the inhibition assay, as well as activity against M. tuberculosis in a cell assay (though unfortunately the dissociation constant was not reported).

This paper offers a clear illustration of how group efficiency can be useful for prioritizing which portions of a molecule to change. In some cases, such as the example here, it makes sense to try to replace groups with low group efficiency. On the other hand, the core fragment may bind in a hot spot, and so just a slight tweak can dramatically boost potency. As with lead optimization in general, there are many paths – both to enlightenment and to perdition.

NMR poll results

The results of our latest poll are in – thanks to all who participated! Of the 119 people who responded to the first question, 87% said they use NMR for finding or validating fragments. Even if we assume that responses were biased towards NMR aficionados, big magnets are clearly popular.

The second question asked about specific NMR techniques. If everyone who said they used NMR in the first question also answered the second, this means the average user applies more than 3 different techniques; I’ll let Teddy weigh in to see whether this matches his experience.

One surprise for me was that, although many techniques are widely used, none are nearly universal; even the most popular methods seem to be used by just over half of respondents.

Among ligand-detected methods (blue in the figure), STD ranks at the top, with line-broadening, WaterLOGSY, and fluorine-based techniques all tied for second place.

Protein-detected methods (red in the figure) also appear quite healthy, with nearly as many respondents using ¹⁵N-HSQC/HMQC as STD.

Finally, 11 of you said you use "other" techniques. We didn't include TINS, even though it seems quite useful, because it is only available through the services of ZoBio. But what else is out there?

Hidden gem of a finding or not?

Todays paper is from a group in Korea.  It's a typical "we did some in silico screening, limited biochemical testing, made a compound or two, and voila!" paper.  In this case, the target is Tyk2 (the target of Xeljanz).

Figure 1.  Xeljanz (tofacitinib)

2000 diverse fragments were selected from the Otava library and docked against Tyk2.  64 top ranked fragments were selected and 9 were selected that had inhibition over 50% at 100 microM, with the best compound (1) having 60% inhibition at 3 microM.  

Figure 2.  Cpd 1 docked to Tyk2. 

What I don't like here is that they didn't do full dose-response curves.  That seems lazy.  Also, the only structures they show are the docked structures.  Maybe its just me, but show me some line drawings.  They then did some limited SAR (3 cpds) based on 1 as the scaffold.  Cpd 12 was the best compound 

Figure 3.  Cpd 12

(10nM IC50).  In the end, 12 was equipotent (or superior) with tofacitinib in terms of shutting down Tyk2/Stat3 signalling.  However, they could not rule out that this is due to non-specific inhibition of other JAK proteins. So, is this a great result?  If so, why BOMCL (not to be snobby)?

Fragments in the clinic: PLX3397

Practical Fragments covers a wide variety of journals. J. Med. Chem., Bioorg. Med. Chem. Lett., Drug Disc. Today, and ACS Med. Chem. Lett. are all well-represented, but we also range further afield, from biggies such as Nature and Science to more niche titles such as ChemMedChem, Acta. Cryst. D., and Anal. Chim. Acta. The increasingly clinical relevance of fragment-based approaches is highlighted by a recent paper by William Tap and a large group of collaborators appearing in the New England Journal of Medicine. This reports on the results of the Daiichi Sankyo (née Plexxikon) drug PLX3397 in a phase I trial for tenosynovial giant-cell tumor, a rare but aggressive cancer of the tendon sheath.

The story actually starts with a 2013 paper by Chao Zhang and his Plexxikon colleagues in Proc. Nat. Acad. Sci. USA. The researchers were interested in inhibiting the enzymes CSF1R (or FMS) and KIT; both kinases are implicated in cancer as well as inflammatory diseases. The team started with 7-azaindole, the same fragment they used to discover vemurafenib. Structural studies of an early derivative, PLX070, revealed a hydrogen bond between the ligand oxygen and a conserved backbone amide. Further building led to PLX647, with good activity against both CSF1R and KIT. Selectivity profiling against a panel of 400 kinases revealed only two others with IC₅₀ values < 0.3 µM. The molecule was active in cell-based assays, had good pharmacokinetics in mice and rats, and was active in rodent models of inflammatory disease.

The new paper focuses on the results of a clinical trial with PLX3397, a derivative of PLX647. Despite its close structural similarity to PLX647, it binds to CSF1R in a slightly different manner. Both inhibitors bind to the inactive form of the kinase, but PLX3397 also recruits the so-called juxtamembrane domain of the kinase to stabilize this autoinhibited conformation. Pharmacokinetic and pharmacodynamics studies in animals were also positive.

Tenosynovial giant-cell tumor seems to be dependent on CSF1R, so the researchers performed a phase 1 dose-escalation study with an extension in which patients treated with the chosen phase 2 dose were treated longer. Of the 23 patients in this extension, 12 had a partial response and 7 had stable disease. A quick search of clinicaltrials.gov reveals that PLX3397 is currently in multiple trials for several indications, including a phase 3 trial for giant cell tumor of the tendon sheath.

Several lessons can be drawn from these studies. First, as the authors note, one fragment can give rise to multiple different clinical candidates. Indeed, in addition to vemurafenib, 7-azaindole was also the starting point for AZD5363. This is a good counterargument to those who believe that novelty is essential in fragments.

A second, related point is that selectivity is also not necessary for a fragment. The fact that 7-azaindole comes up so frequently as a kinase-binding fragment has not prevented researchers from growing it into remarkably selective inhibitors. An obvious corollary is that even subtle changes to a molecule can have dramatic effects: the added pyridyl nitrogen in PLX3397 is essential for stabilizing a unique conformation of the enzyme.

Finally, careful patient selection is critical to answering biological questions. I confess that I had never heard of tenosynovial giant-cell tumor, nor the role of CSF1R, but I’m glad others had. I look forward to seeing an increasing stream of fragment papers in clinical journals.

LO-MS...Coming of Age.

There are many ways to screen for fragments.  One of the really emerging areas uses mass spec detection: WAC, native mass spec, HDX, and ligand-observed MS.  The group that we highlighted back in March has a new paper where they look at the method in terms of accuracy of Kd determination and compare the results to other biophysical methods.  

Their previous work they looked at relative affinity ranking of bound fragments.  In this study, they compared the accuracy of Kd determination in this method to ITC.  First they used a pool of 3 or 4 known CAI inhibitors and a pool of 50 fragments (from their collection).  The conditions were defined:

The hCAI protein was incubated with each inhibitor mixture in the binding buffer with a total volume of 50 mLat room temperature for 40 min. The protein concentration was maintained at 25 mM and the inhibitor concentration increased from 1 mM to 50 mM. The control was prepared by using the binding buffer substitute for hCAI during incubation. The incubation solution was then filtered through a 10 kDa MW cutoff ultrafiltration membrane by centrifugation at 13,000 g for 10 min at 4 C followed by a quickwash with 10mM ammonium acetate (pH 8.0) to remove the unbound compounds.

 Two different methods were used to calculate Kd: 1. saturation curves and 2. measuring the unbound fraction of ligand .  Method 1 was deemed unsuitable for determining Kds for ligands with largely different Kds.  Method 2 did not depend on saturation curve fitting, instead using a calibration curve and did not observe any fragment competition at higher P:L ratios (6:1 or 8:1).  This approach also improved the sensitivity of the assay allowing better detection of lower affinity ligands.  Kds determined using this method matched those determined by ITC.  

To further test the method, they ran a pool of 50 fragments against HCV RNA polymerase NS5B.  This gave, as expected, a complicated chromatographic baseline.  To exclude promiscuous binders, they ran against BSA in parallel.  Eight fragments in the mixed pool showed selective binding to NS5B using unbound fraction analysis vs. 2 from the bound fraction analysis.  7 of 8 fragments were confirmed by SPR (ITC could not be used).  The 1 BFA fragment to be analyzed by SPR showed that it was a very weak binder.  

This sort of work makes me happy.  Methods always need to be pushed and evaluated.  When evaluating methods, if this sort of cross validation hasn't been done, question as to why not? 

Fragments vs Trypanosoma cruzi spermidine synthase, allosterically

Chagas disease, caused by Trypanosoma cruzi, is spread throughout Central and South America by a nasty blood-sucking insect. A couple drugs are approved to treat it, but they can cause severe nausea and peripheral neuropathy, so there is room for improvement. In a recent paper in Acta Cryst D., Yasushi Amano and colleagues at Astellas Pharma describe their efforts against the T. cruzi spermidine synthase (TcSpdSyn).

TcSpdSyn transfers an aminopropyl moiety from the cofactor decarboxylated S-adenosylmethionine (dcSAM) to the evocatively-named putrescine (1,4-diaminobutane) as one step in the synthesis of an essential antioxidant. Small amines can bind in the putrescine-binding pocket and inhibit the enzyme with low micomolar activity, so the researchers decided to find other fragments that could bind in this pocket. They screened in the presence of dcSAM, using surface-plasmon resonance (SPR), with each fragment present at 0.25 mM, as well as in thermal shift assays, with each fragment present at 2 mM. Although nothing is reported about library size or hit rate, hits from either assay were taken into crystallography, resulting in six structures described in detail and deposited in the Protein Data Bank (pdb).

Two fragments were found that bind in the putrescine-binding pocket, and in both cases the enzyme shows some conformational changes to accommodate the fragments. Although these two fragments have only modest potency (IC₅₀ = 0.18-0.48 mM), they do have satisfying ligand efficiencies, and are good starting points for structure-based design.

Unexpectedly, the other four fragments bound not in the putrescine-binding pocket but at an interface between two proteins of TcSpdSyn, which forms a homodimer. One of these fragments, an isothiazolinone, showed mid-nanomolar activity in a functional assay. Readers may recall a paper we pilloried earlier this year which also reported an isothiazolinone as a screening hit. In that case, the researchers failed to recognize that this PAINS compound has ample precedent for reacting with thiols. Happily, in the current paper the researchers are not only aware of this, they actually see covalent bond formation between the fragment and a cysteine residue in the crystal structure. Interestingly though, the fragment reacts with only a single cysteine residue at the dimer interface, despite the presence of six other cysteine residues in the protein.

The researchers carefully analyzed this structure and found that binding of the fragment disrupts the putrescine-binding pocket; in other words, the fragment is an allosteric inhibitor. Moreover, the other three fragments that bind at the dimer interface also appear to act allosterically, and one of them is a single digit micromolar inhibitor.

This is a nice example of how even PAINS compounds can be useful if they are well-characterized and not hyped. Moreover, the structures suggest new approaches for tackling a target for a neglected tropical disease, either covalently or more conventionally.

Magic Methyl and SBDD

As sites have closed down, we have seen a fair number of paper come out describing work at various sites.  Today's paper is another of these, from Roche, Nutley.  The target is Tankyrase, blogged previously here.  

This group started with a biochemical screen of their in house fragment library (here for analysis of their library) against both TNKS1 and 2.  This screen resulted in two compounds: 1. a pyranopyridone and 2. a benzopyrimidone (that looks like the first published TANK inhibitor).  

Figure 1.  Fragments identified from biochemical screen.

They were able to co-crystallize 1 with the TANKS2 and then were able to model 2's binding based upon the known inhibitor.  This pleasingly revealed both similarities and differences in the binding.  the main recognition elements are largely the same.  A big difference is that the phenyl maintains hydrophobic interactions that the cyano group does not. 

Figure 2.  TANKS2 X-ray structure with 1 (orange carbons) and 2 (yellow carbons). 

This lead to the obvious decision to merge the two fragments leading to fragment 3. This compound was reasonably potent (320 nM), however it should moderate to high clearance in an in vivo PK study. 

Compound 3. 

Then of course, the medchem kicks in aiming to decrease cLogP and increase solubility by focusing on two areas: the fused phenyl ring and the isopropyl side chain. This work was able to achieve their goals, but they also stumbled on a "magic methyl" (9) which improved potency 100 fold.  

9.  Magic methyl on fused phenyl ring.

This magic methyl works by sliding into a little pocket defined by three tyrosine residues. 

Figure 3.  Magic methyl binding mode. 

The t-butyl alcohol derivative of 9 had excellent properties: reasonable solubility, excellent permeability, stopped axin degradation in cells in a dose dependent manner, prevented mRNA production of beta-catenin dependent genes, and in mouse had satisfactory in vitro activity and PK profile.  

This is an excellent example of FBLG.  The magic methyl does exist and X-ray is a highly enabling technology. 

What works for crystallography?

As a recent post emphasized, crystallography is a key technique for fragment-based lead discovery. We’ve occasionally touched on things that can go wrong in crystallography, but in a recent paper in Drug Discovery Today, Helena Käck and colleagues at AstraZeneca (Mölndal) put things in a more positive light by asking what factors lead to success.

The paper starts with a literature review of successful fragment structures published between 2012 and 2014 and summarizes some of the key findings. First is the need to easily generate robust crystals that diffract well and are stable for long periods of time. If the ligand-binding site is known, it is important that this is accessible and not occluded by protein or ligands. Finally, the crystals should be stable when soaked in high concentrations (> 10 mM) of ligand, ideally in the presence of 10% DMSO.

None of these factors will come as a surprise to experienced crystallographers, but the authors do a nice job of concisely summarizing them as well as providing solutions to common problems. For example, the use of surrogate proteins can help in cases where the target itself is hard to crystallize. Proteins can be grown in the presence of known ligands, which can then be soaked out. And various additives can also help.

All of this is nice, but what really makes this paper noteworthy is the second part, in which the authors discuss their own experience with soluble epoxide hydrolase (sEH), a potential cardiovascular and immune target that we’ve previously discussed here, here, and here. This protein seems to have all the hallmarks of technical success. Indeed, both HTS and fragment screens at AstraZeneca produced high hit rates, and 65% of hits taken into crystallography produced structures. In all, 55 structures were determined, with ligands ranging in size from 130 to 540 Da and affinities ranging between 0.003 and 600 µM. Of these, 38 could be considered fragments. As seen before, the protein is relatively rigid, and the ligands bind in a variety of subsites within the large lipophilic active site.

With so much data, the researches asked whether ligand properties could predict crystallographic success. The most robust correlation was seen with affinity: 94% of compounds with affinities below 0.1 µM produced structures, while only 36% of compounds with affinities above 100 µM did.

Ligand efficiency (LE) was also correlated with crystallographic success, though three small fragments (MW < 160 Da) with very high LE values did not produce structures – a phenomenon which has been noted by others.

In contrast to another recent study that compared many fragment-screening approaches, solubility did not predict success. The researchers suggest that this is because crystal conditions are so different from the conditions under which standard solubility measurements are run.

Admirably, the structures of 52 of the ligands are reported in the supplementary material – along with their measured affinities – and the resulting crystal structures have been deposited in the protein data bank. Some of the ligands bind at multiple sites and some have dual conformations; these ambiguities are noted. Moreover, a set of inactive analogs has also been included. Together with smaller sets of previously released structures, this provides a bonanza of structural and affinity data with which to benchmark computational docking programs. Hopefully we’ll see more of this public sharing of data.

Fragment finding smackdown: 2015 edition

Our current poll (right-hand side of page) asks about NMR. But of course, there are lots of other ways to find fragments, and the question often arises as to which ones are best. This is the subject of a recent paper in ChemMedChem by Gerhard Klebe and collaborators at Philipps University Marburg, Proteros, NovAliX, Boehringer Ingelheim, and NanoTemper.

Long-time readers will recall that the Klebe group assembled a library of 361 fragments, some of which violated strict “rule of 3” guidelines. These were screened in a high-concentration functional assay against the model aspartic protease endothiapepsin, resulting in 55 hits, of which 11 provided crystal structures. The authors wondered how other techniques would fare. In the new paper, they retested their entire library against the same protein using a reporter displacement assay (RDA), STD-NMR, a thermal shift assay (TSA), native electrospray mass spectrometry (ESI-MS), and microscale electrophoresis (MST). To the extent possible they tried to use similar conditions (such as pH) for the different assays, though the fragment concentrations ranged from a low of 0.1 mM (for ESI-MS) to a high of 2.5 mM (for TSA), while protein concentrations ranged between 4 nM (for the biochemical assay) to 20 µM (for ESI-MS).

All told, 239 fragments hit in at least one assay – a whopping hit rate of 66%. Actually, the number is even higher since, for various reasons, not all fragments could be tested in all assays. And yet, not a single fragment came up in all of the assays! Overall agreement was in fact quite disappointing, with most methods having overlaps of less than 50%, and often below 30%. This is in contrast to a study from a different group highlighted a couple years ago.

What’s going on? One clue might be the solubilities, which were experimentally measured for all library members. In general, hits tended to be more soluble than the library as a whole, emphasizing the importance of this parameter not just for follow-up studies but for identification of fragments in the first place.

Another possibility is that some fragments bind outside the enzyme active site, and thus would not be picked up in a biochemical assay or the RDA. Some evidence for this is provided by follow-up NMR studies in which hits were competed with ritonavir, which binds in the active site. Ritonavir-competitive binders shared greater overlap with biochemical and RDA hits, while there was more overlap between ritnovair-uncompetitive binders and hits from methods such as ESI-MS, TSA, and MST that rely solely on binding. (This could also explain similar observations made earlier this year.)

If a picture is worth a thousand words, how many of the 11 hits that had previously yielded crystal structures would have been identified had they been tested in other methods? Here the numbers vary significantly, from 27% for ESI-MS and MST to 100% for NMR, though these statistics should be taken with a grain of salt since – for example – only 7 of the 11 crystallographically-confirmed hits could actually be tested in the NMR assay. Also, it is possible that some hits from these methods might have generated new crystal structures for fragments not identified in the initial biochemical screen.

One admirable feature of this paper is that the authors provide all their data, including structures and measured solubility numbers for each component of their library. This should provide an excellent dataset for a modeler to use in benchmarking computational methods.

All in all this is a thorough and important analysis and a sobering reminder that, even if a fragment doesn’t hit in orthogonal assays, that doesn’t necessarily mean it’s not a useful starting point. On the other hand, artifacts are everywhere, and paranoia is often justified. The art is deciding which hits are worth pursuing – and how.

Uninteresting GPCR Fragment Work...meant as a Compliment!!

There are certain movies that when they are on TV, I can't not watch.  I call these Broken Leg Movies (as in if I were laid up with a broken leg what would I watch).  As I have said, Road House is one, Apollo 13 another.  Its about America's blase attitude towards the amazing feat of putting men on the moon.  It takes a potential horrific tragedy (For those off you who haven't seen it, let me say (**Spoiler Alert**) don't worry it has a happy ending.) in order for America to care about men in space.  Which of course is in direct contrast to Pigs in Space (with Swedish Subtitles)! 

One of the field changing technologies is Heptares' STAR technology (for creating stabilized, soluble GPCRs).   We have discussed it often on this blog.  Well, they are back with another paper, this time working the voodoo they do so well on a Class C GPCR.  Negative allosteric modulation of the mGluR has the potential for significant medical impact in a variety of diseases.  In a relatively well trod drug space (there have been several molecules in late stage trials), an issue appears to be the acetylenic moiety in these drugs (which appears to be manageable).  So, non-acetylenic molecules would be desireable.  

To attempt to ligand this molecule, they screened 3600 non-acetylenic fragments using a radio-labeled assay.  This is in contrast to previous work where they used SPR. From this screen, 178 fragments were tested in concentration-response curves leading to "a number of promising" hits, including the compound shown below. 

Cpd 5.  pKi=5.6, LE=0.36

This compound was advanced using the tools you would expect (especially from Heptares): modeling, X-ray crystallography, medchem, and so on.  The final molecule is an advanced lead with excellent mGluR selectivity and in vivo activity, clean tox, and so on. 

This is excellent work, but "yawn".  I think it might be interesting to hear why they went with the radioligand approach, as opposed to SPR.  You could quibble that 5 is too big to be a fragment, but really?  Papers like this are uninteresting, we know its going to work.  The science is excellent, but I want to see the triumph out of tragedy.  Not here.  I want to congratulate Heptares for making an achievement like this paper perfectly uninteresting.  And I mean uninteresting as the very best of compliments. 

Aggregation alert

Practical Fragments has quite a few posts about PAINS, or pan-assay interference compounds. In part this reflects their sad prevalence in the literature, but it’s also fair to say that they are easy targets because many are readily recognizable.

But not all artifacts are so easily spotted, as discussed in a new paper just published in J. Med. Chem. by John Irwin, Brian Shoichet, and colleagues at the University of California San Francisco (see also here for Derek Lowe's excellent summary).

The researchers took on one of the most insidious problems, compound aggregation, in which small molecules form colloids that bind to and partially denature proteins, causing false positives in all sorts of assays. This can happen even at nanomolar concentrations of compound, and is all the more problematic at higher concentrations used in fragment screening and early hit to lead optimization. In many cases aggregates can be disrupted or passivated by including nonionic detergents such as Triton X-100 or Tween-80, but not all assays tolerate detergent, and some aggregates form even in the presence of detergent.

Worse, all sorts of molecules can form aggregates, including many approved drugs. Previous attempts to try to predict which molecules will aggregate have not been very successful. Colloid formation is essentially a phase transition, and like other such transitions (crystallization, for example) it is fiendishly difficult to predict what molecules will do this under what conditions. But if we can’t predict from first principles which molecules will form aggregates, can we at least draw empirical lessons?

The researchers assembled a set of >12,600 known aggregators and put together a very simple model that assesses how similar a molecule of interest is to one of these aggregators (using Tanimoto coefficients, or Tcs). Aggregators have a wide range of physicochemical properties, with ClogP values from -5.3 to 9.8, but 80% have ClogP> 3.0. The team hypothesized that a molecule sufficiently similar to a known aggregator – and also somewhat lipophilic – would have a higher probability of being an aggregator than a molecule chosen at random.

To test this idea, the team took  a batch of 40 molecules and tested them for aggregation. Among those most similar to known aggregators (Tc ≥95%), 5 of 7 molecules were confirmed as aggregators. This fell to 10 of 19 for the next set (Tc 90-94%), 3 of 7 after that (Tcs 85-89%) and only 1 of 7 for the least similar (Tcs 80-84%). Thus,Tc ≥85% was chosen as the cutoff.

Next, the researchers examined molecules that had been reported as active in some sort of biological assay, and found that 7% were ≥85% similar to a known aggregator and had ClogP> 3. Ominously, this rate is an order of magnitude greater than the number of commercially available compounds that also fit these criteria. More damning, most of this enrichment has occurred since 1995, when high-throughput and virtual screening really went mainstream. In other words, the past couple decades have seen a sizable enrichment of potential aggregators in the literature.

All of this is fascinating, but what really makes this paper significant is that the researchers have made all their primary data available, and also built a simple to use website called “Aggregator Advisor”. Just draw your molecule or paste a SMILES string to generate a report. For example, entering gossypol tells you that this molecule has previously been reported as an aggregator. (With two catechol moieties, it’s also a PAINS.) Perhaps not coincidentally, it shows up in more than 1800 publications.

Of course, as the researchers note, “just because a molecule aggregates, under some conditions, in the same concentration range as it is active, does not establish that its activity is artefactual.” Indeed, 3.6% of FDA-approved drugs are known aggregators. Still, particularly if your hit has only modest activity (0.1 µM or worse), similarity to a known aggregator should at least make you cautious.

The researchers are at pains to emphasize that their model is “primitive and subject to false negatives and false positives.” Thus, any hits need to be tested to see if they behave pathologically in any given assay. More importantly, a molecule that comes up as a negative should not be presumed to be innocent.

All these caveats aside, Aggregator Advisor is very easy to use. It’s certainly worth running the next time you find an interesting molecule – whether in your lab or in the literature – particularly if there was no detergent in the assay.