08 February 2016

Dihydroisoquinolones as fragments

It’s a common problem: you find a fragment that binds to your target and want to grow it to improve affinity. A search for commercial analogs comes up empty, so you look into modifying the hit, only to discover that you’ve got a six-step synthesis on your hands. Or worse; perhaps there is no precedent at all. The chemical literature is replete with total syntheses of complicated natural products, but seemingly simple fragments are often not well-represented. Last year, researchers from Astex exhorted chemists to develop synthetic routes for attractive fragments, and in a recent paper in Org. Biomol. Chem. David Rees and colleagues take up their own challenge in the case of dihydroisoquinolones.

Dihydroisoquinolone itself is a nifty little fragment. It has just 11 atoms, cLogP = 1.0, and its solubility is > 5 mM in aqueous buffer. Its cis-amide moiety can serve as a hydrogen bond donor and acceptor, and the adjacent phenyl ring provides a bit of grease for interacting with hydrophobic protein residues.

The researchers built on existing methodology using a rhodium catalyst to introduce polar groups (such as hydroxymethyl and dimethylamino) at the R position. Depending on the nature of the R group, regioisomers in which the substituent ends up at the 4-position could sometimes also be isolated.

The methodology is robust and tolerates air, moisture, and various substituents. The alkene starting material is easy to come by, and the aromatic starting material is easy to make. By varying this, the researchers could generate 6- or 7- substituted dihydroisoquinolones, though 5- and 8- substituted versions seem harder to access. The team was also able to use other aromatics as starting materials, including thiophene, thiazole, and pyridine.

Thus, if dihydroisoquinolone comes up as a hit, this paper will allow you to quickly explore most of the vectors. So how often does this fragment show up? It is not clear why some fragments, such as 7-azaindole and 4-bromopyrazole, show up again and again, while others languish so lazily in the library that they might as well not even be there. We’ve highlighted at least one case where a dihydroisoquinolone was a useful hit.

Practical Fragments would love to know your experience. Do you have dihydroisoquinolones in your library? How often do they show up as hits? And what other fragments do you find that are in need of better synthetic routes for further exploration?

01 February 2016

Fragment-Based Drug Discovery: Lessons and Outlook

In 2006, Wolfgang Jahnke and I co-edited the very first book on fragment-based drug discovery. Half a dozen books have followed, most of which have been reviewed at Practical Fragments (see right-hand column). These are now joined by a new book edited by Wolfgang and me in Wiley’s Methods and Principles in Medicinal Chemistry series.

At 500 pages and 19 chapters, this is the most extensive treatment since the Methods in Enzymology volume five years ago. In the interest of space I can’t write more than a sentence or two about each chapter, but I would like to thank all the contributors. Although I’m undoubtedly biased, I believe this work will set the standard for years to come.

The book is divided into three sections, starting with The Concept of FBDD. Rod Hubbard (Vernalis and University of York) opens with a chapter on the role of FBDD in lead-finding, which provides an introduction, historical overview, and summary of current thinking and future challenges. One particularly interesting section compares the contents of the 2006 book with the state of the art today, highlighting the fact that many of the basic techniques were already in place a decade ago, but the number of success stories has increased dramatically.

Chapter 2, by Glyn Williams and colleagues at Astex, discusses how to choose targets for FBDD, including concepts such as ligandability. Key principles are nicely illustrated with several important targets including the IAPs and HCV-NS3.

The last two chapters in this section focus more on numbers. Chapter 3, by Jean-Louis Reymond and colleagues at the University of Berne, covers the computational enumeration of chemical space, with a special emphasis on the contents and uses of their GDB-17 set of the 166 billion possible molecules with up to 17 non-hydrogen atoms. And chapter 4, by György Ferenczy and György Keseű at the Hungarian National Academy of Sciences, provides an overview of various metrics (such as ligand efficiency and LELP) and how these can be useful for fragment optimization.

The next nine chapters comprise the longest sub-section of the book, Methods and approaches for FBDD. To start screening fragments, you need a library, and designing one is the subject of chapter 5, by Martin Drysdale and colleagues at the Beatson Institute. This chapter also touches on concepts such as molecular complexity and “three-dimensional” fragments.

Screening techniques are best used in combination, and in chapter 6 Ben Davis (Vernalis) and Tony Giannetti (Google[x]) describe the synthesis of results from SPR, NMR, X-ray, ITC, functional screens, and other techniques to overcome challenges in several discovery programs. They emphasize that universal agreement among different methods is not always necessary, but carefully analyzing discrepancies can reveal unexpected problems with the screening conditions, target, or hits.

Differential scanning fluorimetry (DSF) – or thermal shift (TS) – is perhaps the most controversial screening method, and in chapter 7 Chris Abell and colleagues at the University of Cambridge cover this approach in depth. The chapter starts with a thermodynamically detailed yet nonetheless lucid discussion of the theory behind DSF, including the interpretation of negative thermal shifts. The chapter also includes plenty of practical advice and case studies, some of which we’ve covered briefly (for example here and here).

Chapter 8, by Sten Ohlson and Minh-Dao Duong-Thi at Nanyang Technological University, covers three emerging fragment screening technologies: WAC, native MS, and MST. And Chapter 9, by Sandor Vajda (Boston University) and collaborators, does an excellent job of summarizing computational approaches.

As others have noted, some of the biggest challenges are not technical but organizational, and in chapter 10 Michelle Arkin and colleagues at UCSF describe how to make FBDD work in academia. The chapter also includes some interesting polling data, concise but cogent summaries of fragment-finding techniques, and case studies on p97 and caspase-6. And in chapter 11, Jim Wells and colleagues – also at UCSF – describe using Tethering to find allosteric sites in proteins.

One area that has grown dramatically since 2006 is the use of FBDD in complex systems (such as membrane proteins), the subject of a chapter by Miles Congreve and John Christopher at Heptares. Chapter 12 also includes successful case studies, some of which we’ve covered. But finding fragments against these targets is still not easy, as illustrated in the final figure: of 18 fragment hits on 15 targets, almost all have ligand efficiency values > 0.3 kcal/mol per atom, and most of them are relatively potent, with affinities in the mid-micromolar range or better. While everyone wants to find strong binders from the start, such numbers suggest many weak-binding hits are overlooked.

Chapter 13, by Jörg Rademann and colleagues at Freie Universität Berlin, covers protein-templated fragment ligation methods, both reversible and irreversible. The chapter is wide-ranging and includes methods such as dynamic libraries and various types of “Click” chemistries.

The last section of the book, which was mostly absent a decade ago, is entitled Successes from FBDD. This starts with a chapter by Daniel Wyss, Andrew Stamford, and colleagues from Merck on BACE inhibitors. As we’ve noted, fragments have had a major role in most of the BACE inhibitors to enter the clinic, with phase III results from Merck’s verubecestat expected next year.

Epigenetics has also been strongly influenced by fragments, and in chapter 15 Aman Iqbal (Proteorex) and Peter Brown (Structural Genomics Consortium) survey the field, with case studies on several proteins that modulate epigenetic marks. These include BRD4, ATAD2, BAZ2B, SIRT2, and others.

One of the original selling points of fragment-based methods is the ability to go after difficult targets such as protein-protein interactions, and this is the subject of chapter 16, by Feng Wang and Stephen Fesik (Vanderbilt University). In addition to general guidelines, the researchers describe a number of case studies, including RPA, MCL-1, and K-Ras.

Some enzymes can be just as difficult as protein-protein interactions, and in chapter 17 Alexander Breeze (University of Leeds) and former AstraZeneca colleagues describe programs to find inhibitors of LDHA (see here and here). They also discuss how some previously reported inhibitors turned out to be artifacts.

More than two dozen kinase inhibitors have been approved by the US FDA, including the first drug derived from FBDD. In chapter 18, Gordon Saxty (Fidelta) surveys a number of kinase programs, including most of the fragment-derived inhibitors in clinical trials.

And finally, in chapter 19 Simon Rüdisser and colleagues from Novartis present an extensive discussion of renin, with special attention to their campaign, which involved a combination of HTS and fragment-based approaches.

While it may not be possible to judge a book by its cover, the cover of this book does illustrate some of the fruits of the field, with structures of three fragment-derived drugs that have entered the clinic. These are just a small fraction of the 30+ drugs working their way through the pipeline, and of the many more that will spring from the research described and informed by the work presented.

25 January 2016

Fragments vs plasmepsins

Plasmodium, which causes malaria, is a nasty bag of tricks. These include the plasmepsins, aspartic proteases that – among other duties – digest the hemoglobin in red blood cells. In a recent paper in J. Med. Chem., an international group of collaborators led by Kristaps Jaudzems and Aigars Jirgensons at the Latvian Institute of Organic Synthesis describe how they discovered inhibitors.

The team started by performing a fragment screen against plasmepsin II (Plm II), one of ten plasmepsins encoded in Plasmodium falciparum. A library of 976 rule-of-three compliant fragments (from ChemBridge) were screened in pools of six using three different NMR methods: STD, WaterLOGSY, and T1ρ. A total of 49 fragments hit at least two assays and were competitive with a known aspartic protease inhibitor, and ten of these showed functional inhibition in an enzymatic assay. Fragment 1 was the most potent.

Crystallography was unsuccessful, but the researchers were able to use ILOE NMR to show that another aromatic fragment could bind near fragment 1. Based on this information, the researchers appended a phenyl moiety to produce compound 3a and obtained a 10-fold boost in potency.

Crystallography still didn’t work, but modeling based on similar compounds on a different aspartic protease suggested that adding another hydrophobic substituent could fill another pocket, leading to compound 3b. At this stage the researchers were finally able to obtain a crystal structure of compound 3b bound to Plm II, which confirmed the predicted binding mode and also revealed another pocket that could be grown into as in in the case of compound 4b. This and several related compounds inhibited the growth of Plasmodium falciparum at low micromolar concentrations and were minimally cytotoxic to mammalian cells.

There is still much to do. Selectivity for the one human aspartic protease tested was generally modest. Also, as the researchers acknowledge, the most active compounds are seriously lipophilic. Still, this is another example of fragment-based lead discovery in academia. More importantly, it provides more ideas on how to tackle a pernicious parasite.

18 January 2016

Microscale thermophoresis revisited

One of the less commonly used fragment-finding methods is microscale thermophoresis (MST). This measures the movement of proteins in a temperature gradient; ligand binding changes the movement. When we first described MST in 2012, we noted that the technique seemed relatively low throughput. In a paper recently published in J. Biomol. Screen., Alexey Rak and colleagues at Sanofi teamed up with Dennis Breitsprecher and researchers at NanoTemper (which makes MST instruments) to try to increase this.

The researchers chose the kinase MEK1 and carefully developed assay conditions; their detailed description is a useful resource for those who decide to give MST a try. Adding nonionic detergent to the assay proved to be essential for reproducibility and to prevent the protein from sticking to the capillary or aggregating. Also, rather than relying on the weak chromophores (such as tryptophan) in native proteins, MEK1 was labeled with a fluorescent dye. The substrate ATP was used as a positive control, and the measured affinity was in good agreement with previous results.

The screen itself was performed on a set of 193 fragments that had been computationally preselected as potential ligands for the kinase MEK1 (work we blogged about here). These were serially diluted using automated liquid handling and tested in 12-point dose-response curves to try to determine dissociation constants (Kd values) for each fragment. All together this run of more than 2000 capillary tubes required only 90 micrograms of protein and took less than 7 hours. Retrospective analysis suggested that a single-point screen at 150 µM of each fragment would have caught most of the best hits and cut analysis time to 70 minutes, so it looks like MST is becoming competitive with other biophysical screening methods in terms of time and reagent consumption.

What about results? The overall hit rate was nearly 38%, which is high, though not outrageously so given that the fragments were computationally pre-selected. Of these, the best 25 fragments showed well-defined dose-response curves with
Kd < 200 µM and competition with ATP. One nice feature of the method is that pathological behavior such as aggregation or denaturation could be observed directly in the form of irregular or bumpy MST traces, thus allowing false positives to be rapidly weeded out. Similarly, a loss in fluorescence signal was interpreted as the protein unfolding and sticking to the wells or pipette tips.

It is always useful to cross-check hits in orthogonal assays. As we noted previously, these fragments had previously been screened against MEK1 using surface plasmon resonance (SPR) and differential scanning fluorimetery (DSF). Most of the best hits from DSF were rediscovered by MST, though MST found many hits DSF had missed. In contrast, most of the SPR hits did not confirm in MST. The rank order of hits was also similar for MST and DSF but not for MST and SPR.

A picture is worth a thousand words, and some of the best hits were subjected to crystallography. In fact, 7 of the top 15 MST hits had previously been characterized by crystallography, and 7 new crystal structures could be determined out of 11 additional MST hits for which crystallography was attempted.

Overall then it appears that MST is coming into its own. If you’ve tried it, please share your experiences.

11 January 2016

Universal fragments for discovering hot spots and aiding crystallography

A couple years ago we highlighted a paper from Eddy Arnold’s group at Rutgers University in which crystallographic fragment screening revealed over a dozen secondary ligand binding sites on HIV-1 reverse transcriptase (RT). Shockingly, the fragment 4-bromopyrazole bound to every single site, which led us to ask “is this a privileged fragment or a promiscuous binder? And as for the sites with no known functional activity, are these useful?” The Arnold group asked themselves these same questions, and provide answers in a new paper in the open-access journal IUCrJ.

The researchers first considered whether 4-bromopyrazole is special. They collected about 20 halogenated aromatic fragments and soaked these into crystals of HIV-1 RT at concentrations ranging from 20 to 500 mM. Of these, 4-iodopyrazole also bound at multiple sites, but most of the others – even closely related molecules such as 3-bromopyrrole or 4-bromothiazole – did not bind to any.

Next, the authors extended these observations to other proteins. When they soaked their molecules into crystals of the endonuclease from the 2009 pandemic influenza strain, they found that 4-bromopyrazole bound to four sites, including two of three identified in a previous crystallographic fragment screen. In one case, a phenylalanine side chain shifted to open up a new hydrophobic binding site. A similar and previously unobserved shift occurred with a tyrosine side chain when 4-bromopyrazole was soaked into the protein proteinase K. Thus, this fragment is able to identify otherwise cryptic binding sites.

Interestingly, the 4-bromopyrazole binding sites could be strikingly dissimilar, ranging from hydrophobic to mildly electropositive to strongly electronegative. The researchers note that the halogen can form either hydrophobic or polar interactions. Also, one pyrazole nitrogen can act as a hydrogen-bond acceptor while the other can independently act as a donor, and these interactions can be with the protein directly or through bridging water molecules.

Last week we highlighted work from Astex suggesting that secondary binding sites in proteins are common, but in most of those cases the proteins had only one additional site, and only a couple had five or six. In contrast, 4-iodopyrazole bound to 21 sites in HIV-1 RT, although only five of these had sufficiently good electron density to allow the entire fragment to be built. (That is, crystallography only clearly revealed the location of the iodine atom in the others.) How many of these sites are bona fide hot spots, and how many could be predicted using computational techniques such as FTMap?

This is all quite interesting, but, as we asked previously, is it useful? The researchers provide two applications.

First, 4-bromopyrazole may be a general probe to assess whether a protein is ligandable. Soaking crystals of the catalytic core domain of HIV-1 integrase in 500 mM of 4-bromopyrazole revealed no binding sites, in sharp contrast to HIV-1 RT, endonuclease, and proteinase K. Integrase also showed a very low hit rate in a general fragment screen, and a plot of binding sites vs fragment-screening hit rate for three proteins showed a linear correlation. Obviously this is a tiny data set, but if it holds up it could be an easy experimental way to assess the difficulty of targets.

Second, the bromine or iodine atoms in the pyrazole fragments could be used in single-wavelength anomalous dispersion phasing, a useful approach for solving crystal structures. The researchers demonstrated this experimentally for HIV-1 RT, endonuclease, and proteinase K, and suggest that 4-bromopyrazole and 4-iodopyrazole could be inexpensive and helpful additions to a “crystallographer’s toolkit.”

Thus, unlike other frequent-hitters such as PrATs, 4-halopyrazoles might be promiscuous yet specific – and useful. I look forward to seeing whether these "universal fragments" catch on.

06 January 2016

Secondary ligand binding sites are common

Anyone who has been exposed to much crystallography will have seen examples where a ligand binds somewhere besides the active site of a protein. This is probably all the more likely in the case of fragments, both because fragments are soaked at high concentrations (and thus weaker ligands can be detected) and also because, being less complex, fragments will be able to bind to more sites. In some cases, such as FPPS and HCV NS3, ligands that bind at these “secondary sites” could be advanced to potent allosteric inhibitors. But how common are such sites? This is the question addressed by Harren Jhoti and Astex colleagues in a paper just published in Proc. Nat. Acad. Sci USA.

The researchers were privileged to have 5590 crystal structures of 24 proteins with at least one bound ligand from crystallographic fragment screens. Careful analysis to exclude buffers and molecules bound at crystallograpic interfaces left them with 53 sites total, with each protein having a fragment bound in at least 1 site; one had 6 (still far from the record 16 sites in HIV-1 RT discussed here). Importantly, 16 of the targets had at least 2 ligand binding sites, with an overall average of 2.2. This number of secondary sites is likely a lower bound, as some sites may have been blocked by crystal packing.

What can be said about these ligand binding sites? The researchers compared the sequence conservation between orthologous proteins from different organisms and found that primary binding sites are more conserved than the overall protein sequences. This is expected because, since the proteins likely have similar functions, there are more evolutionary constraints on the active site residues surrounding the primary sites. Interestingly though, the secondary sites were also significantly conserved, suggesting that they too may have some sort of function.

Protein mobility was also examined computationally, with the thought being that functional binding sites should be more rigid than the overall surface of the protein so as to minimize entropic costs of ligand binding. This turned out to be the case for all primary ligand binding sites, but it was also true for most of the secondary sites. Surprisingly, and in contrast to previous results, there were no differences in normalized B factors (roughly, temperature-related motions) for residues in either primary or secondary binding sites compared with surface residues in general.

Comparing the physical properties of the primary and secondary sites revealed that both were more lipophilic than the rest of the protein surface. Ligands tended to be slightly more buried in primary binding sites than in secondary sites, but there didn’t seem to be any differences among the ligands themselves, though the twelve shown in the paper are mostly “flat.”

These combined results suggest that the majority of proteins have multiple sites capable of binding to small molecule ligands. The researchers note that most of their examples are enzymes, so it may not be fair to extrapolate to other protein classes. That said, many GPCRs also have multiple ligand binding sites.

Secondary binding sites have several things going for them. First, allosteric sites provide a means to target proteins in which the primary binding site is problematic, perhaps because it is too closely related to other proteins. Allosteric sites can also be useful for targeting viral or cancer targets in which resistance is an issue, as in the case of ABL001. Finally, secondary sites provide an opportunity to develop not just inhibitors, but activators.

Of course, just because a fragment binds at a site doesn’t necessarily mean that the site is ligandable. Indeed, HSP70 appears to have 5 sites, yet by all accounts is an extremely difficult target. Four of the proteins (including HSP70) are described in some detail in the paper, with protein-fragment structures deposited in the protein data bank. It would be interesting to see how the secondary sites score as potential hot spots using software such as FTMap.

Still, knowing that secondary binding sites are the norm rather than the exception gives new impetus to look for them. It also suggests new areas of biology to explore. Molecular complexity is one thing, but it pales in comparison to biological complexity.

04 January 2016

Fragment events in 2016

Happy 2016! This looks like a good year for fragment events, so start planning now!

February 21-24Zing conferences is holding its inaugural Structure Based Drug Design Conference in Carlsbad, California. This looks like a cousin of their 2014 Caribbean meeting, so it should be interesting.

April 20-21: CHI’s Eleventh Annual Fragment-Based Drug Discovery, the longest-running fragment event, will be held in San Diego. You can read impressions of last year's 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.

June 7-10: Although not strictly fragment-focused, the third NovAliX Conference on Biophysics in Drug Discovery is likely to 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 here, here, and here.

July 12-15: The second FBDD Down Under will be held at Monash University in Melbourne. The first was lots of fun (see here) and even resulted in a special issue of the Aust. J. Chem., so definitely check this out if you can.

October 9-12: FBLD 2016 will be held in Boston, MA. This marks the sixth in an illustrious series of conferences organized by scientists for scientists, the last of which was in Basel in 2014. Surprisingly, this also seems to be the first dedicated fragment conference in Boston. You can read impressions of FBLD 2012FBLD 2010, and FBLD 2009.

November 7-9: Finally, the OMICS Group is holding their second Drug Discovery & Designing in Istanbul, Turkey, with a track on FBDD.

Know of anything else? Add it to the comments or let us know!

30 December 2015

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!

21 December 2015

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!

14 December 2015

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 Kd 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.

07 December 2015

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.

30 November 2015

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 α2C 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 α2C 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 α2C 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 α2C 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.

23 November 2015

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.

16 November 2015

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.

09 November 2015

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.

02 November 2015

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?

28 October 2015

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)?

26 October 2015

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.