05 December 2016

Molecules special issue:
Developments in Fragment-Based Lead Discovery

Last December the first-ever Pacifichem symposium on FBLD was held in Honolulu. Two of the organizers, Martin Scanlon and Ray Norton, invited participants to submit manuscripts to a special issue of Molecules, which has now published.

The collection starts with a very brief Foreword by me describing the Symposium itself. The first actual paper, from Qingwen Zhang and collaborators at the Shanghai Institute of Pharmaceutical Industry, WuXi AppTec, and China Pharmaceutical University, focuses on kinase inhibitors. The researchers examine fragment-sized substructures of 15 approved drugs that inhibit kinases and use these to design a high-nanomolar inhibitor of the V600E mutant form of BRAF, which modeling suggests should bind to the protein in the “DFG-out” conformation.

Next comes a fragment-finding paper from Thomas Leeper and collaborators at the University of Akron and the University of North Carolina, Chapel Hill. The researchers were interested in finding inhibitors of the glutaredoxin protein (GRX) from the pathogen Brucella melitensis, which causes brucellosis. An STD NMR screen of 463 fragments (each at 0.5 mM in pools of 5-7) resulted in 84 hits, though 75 also hit human GRX. Subsequent experiments including chemical shift perturbation and modeling identified a mM binder with modest selectivity over the human enzyme. Next, the researchers introduced several covalent warheads (including a rather exotic ruthenium analog), one of which led to improved affinity, though the stoichiometry was not determined.

The remaining papers are all reviews, starting with one on native mass spectrometry (MS) by Liliana Pedro and Ronald Quinn at Griffith University. This provides a good historical, theoretical, and practical overview of the technique generally, as well as various applications for fragment-screening. It also covers most of the published examples and discusses both the strengths (such as speed and low protein consumption) as well as the weaknesses (false positives and false negatives) of native MS.

NMR is up next, with a paper by Pacifichem organizer Ke Ruan and colleagues at the University of Science and Technology of China, Hefei. This provides a concise but detailed description of library design, ligand- and protein-detected fragment screening, structural model generation, and hit to lead optimization.

Protein-directed dynamic combinatorial chemistry (DCC) is tackled by Renjie Huang and Ivanhoe Leung, both at the University of Auckland. In addition to summarizing the theory and various literature examples, the authors do an excellent job covering the pros and cons of different types of chemistries and analytical techniques.

Next comes a review by Begoña Heras and collaborators at La Trobe University and Monash University on the subject of bacterial Dsb proteins, which are essential for disulfide bond formation in virulence factors. The review covers the biology as well as several approaches to finding inhibitors, some of which we’ve previously covered (here and here). There is much more to do: as the researchers conclude, “the development of Dsb inhibitors is still in its infancy.”

Finally, Ray Norton and colleagues at Monash University discuss applications of 19F NMR for fragment-based lead discovery. In addition to covering fluorine-containing fragments, the researchers also discuss using fluorine-containing probe molecules and – even more unusual – fluorine-labeled proteins, in this case using 5-fluorotryptophan. The paper includes previously unpublished results on how these latter two approaches can be used to understand protein-ligand interactions.

One nice feature of this journal is that it is open-access, so if you are lucky enough to be back in Hawaii this December you can pull up the papers on your smartphone while lying on the beach.

28 November 2016

How do cryptic pockets form?

Earlier this year we highlighted crystallographic work out of Astex showing that secondary ligand binding sites on proteins are common; in addition to an active site, an enzyme may have several other pockets capable of binding small molecules. Many of these secondary sites are present even in the absence of a ligand. But there are also “cryptic” binding pockets that only appear when a ligand is bound. These are the subject of a new paper in J. Am. Chem. Soc. by Francesco Gervasio and collaborators at University College London and UCB Pharma.

Cryptic pockets are appealing in part because they can salvage an otherwise unligandable target: a featureless flat surface involved in a protein-protein interaction may crack open to reveal a crevasse capable of binding small molecules. Finding these pockets computationally, though, is difficult. In the current paper, the researchers performed molecular dynamics simulations on three different proteins with known cryptic pockets, and the pockets remained mostly closed over hundreds of nanoseconds. Increasing the temperature didn’t help, and even when the simulations were started with structures of the protein-small molecule complexes (with the small molecules removed), the pockets quickly slammed shut. Further calculations suggested that the open forms of the proteins are thermodynamically unstable.

The nice thing about computational approaches is that – unlike Scotty – you can change the laws of physics. In this case, the researchers changed the simulated water molecules to be more attractive to carbon and sulfur atoms in the proteins. (They call this SWISH, for Sampling Water Interfaces through Scaled Hamiltonians). This caused the known cryptic sites to open up during molecular dynamics simulations, even in the absence of ligand.

Next, the researchers added very small fragments (such as benzene), and found that these caused the cryptic pockets to open even further. The researchers speculate that this might reflect how cryptic pockets form in the real world: a ligand could worm its way into a transient pocket, stabilizing it and exposing more room for another ligand (or a different part of the first ligand) to bind.

Of course, just because something shows up in silico doesn’t make it real; how do you avoid false positives? Once the researchers found cryptic pockets using “enhanced” water, they reran simulations using standard parameters to see which pockets remained. The researchers found that subtracting the “density” of fragments bound in a conventional molecular dynamics simulation from the density of fragments in a SWISH simulation causes minor, irrelevant pockets to disappear for their three test proteins, leaving only the known cryptic pockets. Running this subtraction experiment on the protein ubiquitin caused a couple weak superficial pockets to disappear, consistent with the absence of cryptic pockets in this protein.

SWISH is an interesting approach, and I look forward to seeing how it compares with other programs, such as Fragment Hotspots and FTMap. It would also be fun to apply SWISH prospectively to therapeutically important but currently undruggable targets to see whether it is worth taking another look at some of them.

21 November 2016

New tools for NMR

As most of you know, Teddy has retired from active blogging, which is unfortunate not just for the loss of his wit but also for the loss of his expertise – particularly regarding NMR. But you blog with the army you have, not the army you want, so I'll take a stab at two recent papers on the subject.

The first, published in J. Med. Chem. by Chen Peng and colleagues at software maker Mestrelab in collaboration with Andreas Lingel and colleagues at Novartis, describes an automated processing program for just about any type of ligand-observed NMR data. After going into some detail on how “Mnova Screen” works, the program was benchmarked on three experimental data sets (on undisclosed proteins) which had previously been processed manually. The first was 19F data from a collection of 19 mixtures of up to 30 fluorinated compounds each – 551 altogether. Here the program performed quite well, identifying 56 of the 64 hits identified manually and misidentifying only 4 compounds as hits. Most of the false positives and false negatives were close to the predetermined cutoff threshold, which can be set as stringent or lax as desired.

T1ρ and STD NMR experiments on 55 individual protein-compound complexes were also examined, and the results were similarly positive. Of course, single compound experiments are easy to analyze, and the real test was with a set of 1240 compounds in 174 pools. Here the program was not quite as good, missing 16 of the 50 manually identified hits and coming up with 74 hits that had not been identified manually. Although most of these were false positives, closer inspection revealed that 10 of them are probably real. Moreover, some of the “false negatives” should perhaps not have been classified as hits in the first place. Clearly the program isn’t perfect, but it does seem to be a quick way to triage large amounts of data.

Of course, ligand-detected NMR provides at best only limited information on binding modes, which is where the second paper comes in, published in J. Biomol. NMR. by Mehdi Mobli (University of Queensland), Martin Scanlon (Monash University) and collaborators at Bruker and La Trobe University. The researchers were interested in finding inhibitors of the bacterial protein DsbA, and a previous screen had identified a weak fragment that initially proved recalcitrant to crystallography.

One of the best methods to determine the binding mode of a ligand is to look at intermolecular NOEs, NMR signals which only show up when two atoms are in close proximity to one another. In theory you can look at NOEs from ligands to the backbone amide protons in proteins, but this is technically challenging for aromatic ligands, of which there are many. Proteins have plenty of methyl groups – so many in fact that it can be difficult to correctly assign each methyl group to a specific residue, leading some researchers to only focus on isoleucine, leucine, and valine (ILV). However, by carefully studying more than 5000 high-quality protein ligand complexes, the researchers found that looking at all the methyl groups in a protein (ie, including those found in alanine, threonine, and methionine) greatly increases the number of protein-ligand complexes suitable for analysis.

The researchers were able to assign most of the methyl groups in DsbA using several approaches, and this allowed them to identify 11 NOEs between their ligand and ILV methyl groups. Modeling was unable to provide a unique binding mode, but by including 8 more NOEs to threonine and methionine methyl groups a single binding mode for the ligand was determined. Crystallography came through in the end too and confirmed the NMR-derived model.

Teddy would normally end his NMR posts by stating – often forcefully – whether he thought the tools under discussion were practical or not. NMR is one of the most popular methods out there, so new tools are clearly welcome. Since I'm no expert on the subject, I'll ask readers to weigh in – what do you think?

14 November 2016

CYP121 revisited: fragmentation approaches

Three years ago we highlighted work out of Chris Abell’s lab at the University of Cambridge targeting CYP121, an important enzyme for the pathogen Mycobacterium tuberculosis (Mtb). Two new papers from his group discuss progress on this target using conceptually similar approaches.

A previous fragment screen had identified some very weak fragments, and merging had led to low-micromolar compound 2 – the starting point for a (free access) J. Med. Chem. paper by researchers at Cambridge, the University of Manchester, the Francis Crick Institute, and São Paulo State University. The researchers used a “retrofragmentation” or deconstruction approach: systematically dissecting the molecule into component fragments (such as compounds 4 and 5) to see which bits were most important. Group efficiency analyses revealed that the two lower aromatic rings were important, while the upper one was much less so.
Crystallography revealed that compound 2 did not make direct interactions with the active-site heme molecule in CYP121, so the researchers sought to create some by growing out from compound 4. This led to a nice increase in affinity (compound 19a). Incorporating the other ring led to compound 25a, with sub-micromolar affinity as measured by isothermal titration calorimetry (ITC). Of course, heme is common to every CYP – including those found in humans – raising the question of selectivity. Happily, compound 25a turned out to be reasonably selective for CYP121 compared with a panel of Mtb and human enzymes.

There’s lots more in this (30 page!) paper, including extensive SAR supported by crystallography, ITC, native mass spectrometry, and an interesting spectroscopic binding assay. But unfortunately, the compounds are not active in a cellular assay, and the researchers are trying to figure out why.

The second (open access) paper also takes a deconstruction approach, this time starting from the substrate cYW. Fragmentation of this and related cyclic dipeptide substrates into amino acid derivatives and analogs led to the testing of 65 commercial compounds in a thermal shift assay, resulting in seven hits that increased the denaturation temperature by more than 1 °C. Compound 1a was the most stabilizing, and a spectroscopic assay suggested interaction with the heme group.


The spectroscopic assay also revealed a high micromolar affinity for the fragment. Attempts to improve this ultimately led to compound 31, with comparable affinity as cYW but with improved ligand efficiency. The thioester could be replaced with only a modest loss in potency, and interestingly the stereochemistry of these molecules did not seem to make a difference. Compound 31 was also reasonably selective for CYP121 in a panel of other CYPs.

Both papers cover lots of ground. Reading some publications you can be lulled into thinking that FBLD is an easy progression of increasingly potent compounds. These examples are useful reminders that many compounds turn out to be dead ends, and that even potent and selective molecules may not have the desired biological effects. Sometimes doing everything right can still leave you short of the goal – at least for a while.

07 November 2016

Disrupting constitutive protein-protein interfaces

Protein-protein disruptions are notoriously difficult because the interfaces between proteins tend to be large and flat, with few of the deep pockets where small molecules prefer to bind. That's not to say they're impossible: the second approved fragment-derived drug targets a protein-protein interaction. This interaction, as with most others studied (see here, here, and here, for example), is transient: two proteins come together to transmit a biological signal, then dissociate. But many proteins form constitutive dimers or oligomers, and these tend to be even more challenging to disrupt. This is the class of targets discussed in a paper just published in J. Am. Chem. Soc.

Wei-Guang Seetoh and Chris Abell (University of Cambridge) were interested in the protein kinase CK2, a potential anti-cancer target. The enzyme is a tetramer containing two identical catalytic subunits (CK2α) and two identical regulatory units (CK2β). Previous experiments had shown that introducing mutations into CK2β that disrupted dimer formation decreased enzymatic activity and increased protein degradation. Would it be possible to find small molecules that did this?

Chris Abell is a major proponent of the thermal shift assay, in which a protein is heated in the presence of a dye whose fluorescence changes when it binds to denatured protein. The way this assay is normally conducted, small molecules are added, and if they bind to the protein they stabilize it, thus increasing the melting temperature (see here for an interesting counterexample).For oligomeric proteins, one might expect that anything that disrupts the oligomers would destabilize the proteins, thus lowering the thermal stability, and indeed this turned out to be the case in a couple model systems. Thus, the researchers screened dimeric CK2β against 800 fragments, each at the (very high) concentration of 5 mM. No fragments significantly increased the melting temperature, but 60 decreased the stability by at least 1.5 °C.

Best practice for finding fragments includes using multiple orthogonal methods, so all 60 hits were tested (at 2 mM each) in three different ligand-detected NMR assays: STD, waterLOGSY, and CPMG. Impressively, 40 of these showed binding in all three assays. There was no correlation between the binding affinity and the magnitude of thermal denaturation, which is not surprising because the thermal shift incorporates not just the enthalpy change of ligand binding but also the enthalpy change of protein unfolding. Thus, as the researchers note, “the extent of thermal destabilization cannot be used as a measure of its binding affinity.”

Next, all 40 confirmed fragments were tested at 2 mM to see whether they caused CK2β dimer dissociation, as assessed by native state electrospray ionization mass spectrometry (ESI-MS). 18 fragments shifted the equilibrium to monomeric protein, though interestingly no protein-fragment complexes could be observed. These 18 fragments also decreased dimerization in an isothermal titration calorimetry (ITC) assay.

There is still a long way to go: all the fragments are very weak, and preliminary SAR studies were unable to find analogs with significantly improved activity. Indeed, it is unclear where the fragments bind, or whether the binding site(s) are even ligandable. Still, the combined use of biophysical techniques on a particularly gnarly target make this an interesting study on the frontiers of molecular recognition.

31 October 2016

Fragments vs renin: growing this time

Renin is a key player in the regulation of blood pressure, and thus an important therapeutic target for hypertension. Indeed, the approved drug aliskiren is a renin inhibitor. However, this drug has very low oral bioavailability as well as other problems – surely something better could be developed? This was the goal of a team of researchers at Takeda, described in two recent papers in Bioorg. Med. Chem.

One challenge with renin is that it is an aspartic protease with a large active site – similar to the difficult target BACE1. Like BACE1, fragment-based approaches proved to be useful. In the first paper, Michiko Tawada and colleagues conducted an enzymatic screen (at 100 µM) of their fragment library. Although this library contained many positively-charged fragments – which would be expected to interact with the negatively charged catalytic aspartic acid residues – none came up as hits. Neutral compound 1, however, was identified, and crystallography revealed that it binds in the hydrophobic S1, S3, and S3sp pockets. Novartis researchers published a similar experience several years ago.

Compound 1 was poorly soluble, lipophilic, cytotoxic, and offered suboptimal vectors for fragment growing, so the researchers sought an alternative by constructing and testing a library of analogs. Compound 4a had a similar affinity to the initial fragment, and crystallography revealed a similar binding mode. This was used as the core of a second library, leading to compound 6b, which also displayed a similar binding mode to the initial fragment. Although the affinity was similar (and indeed, the ligand efficiency was lower), the new fragment had better physicochemical and biological properties. It was also more synthetically tractable for subsequent optimization, which is the focus of the second paper.

As with the initial fragment, compound 6b did not make interactions with the catalytic aspartic acid residues, though they are nearby. By redesigning the compound and introducing a basic nitrogen in compound 7, Yasuhiro Imaeda and colleagues were able to engage these residues. Also, the crystal structure of compound 4 (top) revealed that a hydrophobic substituent would be tolerated, which led to compound 9, with nanomolar affinity. Further growing into a hydrophobic pocket led to compound 14, with high picomolar activity. This compound was active in human plasma, showed excellent selectivity against other aspartic proteases, and exhibited encouraging bioavailability and pharmacokinetic properties. The paper notes that this molecule has been optimized further.

For me, the most striking lesson from these two papers is how much effort it took to improve the potency of the initial fragment hit: lots of analogs were made without notable improvements, and it would have been easy to give up. But in the end, a combination of managed serendipity and careful structure-based design increased the affinity by 74,000-fold to a promising lead. Something to keep in mind the next time you find yourself lost in a forest dark, where all paths seem to lead to dead compounds.

24 October 2016

Fragments vs secreted phospholipase A2: AZD2716

Many success stories were presented at the recent FBLD 2016 meeting in Boston, some of which are appearing in the literature. A case in point is published in this month’s issue of ACS Med. Chem. Lett.

Fabrizio Giordanetto, Daniel Pettersen, and colleagues at AstraZeneca were interested in finding inhibitors of secreted phospholipase A2 (sPLA2) enzymes, which cleave glycerophospholipids and are implicated in the lipid accumulation and inflammation associated with atherosclerosis. Of the eleven different isoforms, sPLA2-IIa, sPLA2-V, and sPLA2-X are considered particularly good targets, and the researchers sought an inhibitor that would hit all three. Other companies had shown that a primary amide can form multiple hydrogen bonds at the catalytic site, so the AstraZeneca team reanalyzed previous internal screening data to look for fragment-like hits (defined as having 10-18 non-hydrogen atoms) containing a primary amide. They found many, including compound 1.

In addition to being a potent inhibitor of both sPLA2-IIa and sPLA2-X, compound 1 was quite active in human plasma, which is physiologically relevant. A crystal structure of  sPLA2-X revealed that the compound bound as expected, and modeling suggested that adding a carboxylic acid moiety could make additional interactions with the catalytic calcium ion. Several molecules were made, the most potent of which turned out to be compound 4, with a satisfying 2000-fold boost in activity against sPLA2-X. Shortening or lengthening the linker connecting the acid with the rest of the molecule reduced affinity, observations which could be rationalized by modeling.

Compound 4 was characterized in some detail, which revealed bioavailability in rats and dogs, good pharmacokinetics, and a fairly clean off-target profile. Unfortunately, it was reasonably active against OATP1B1, which recognizes carboxylic acids. Among other duties, OATP1B1 transports statins to the liver, and since many people with atherosclerosis are taking statins this activity would obviously be a problem. However, crystallography suggested that introducing substitutions very close to the carboxylic acid moiety would likely be tolerated by sPLA2 but not by OATP1B1. Indeed, simply adding a methyl group maintained or increased activity against the three relevant sPLA2 isoforms while completely abolishing OATP1B1 inhibition. Happily, AZD2716 had excellent pharmacokinetics and bioavailability in mice, rats, dogs, and cynomolgus monkeys.

This is a lovely example of what has been called fragment-assisted drug discovery. The researchers explicitly looked for a small, ligand-efficient starting point and relied heavily on structure-based design during optimization. The paper ends by noting that AZD2716 was selected as a clinical candidate, though it does not appear in the AstraZeneca pipeline or in clinicaltrials.gov; if this changes we’ll make a note on our running list.

At FBLD 2016 Jenny Sandmark presented this story, and she also described another compound derived from a different fragment. This turned out to be selective for sPLA2-X over sPLA2-IIa and sPLA2-V, and was therefore deprioritized. The experience working with this earlier series was, however, useful in guiding the discovery of AZD2716 – a reminder of the importance of having multiple good fragment starting points.

17 October 2016

FBLD 2016

Last week the sixth FBLD meeting was held in Cambridge, MA. Like its predecessors in 2014, 2012, 2010, 2009, and 2008, this meeting was an enormous success, mixing more than 230 scientists with excellent (and liberal) food and drink. With 33 talks, more than 30 posters, and several vendor booths and workshops I won’t be able to do more than capture a few highlights.

The most striking feature for me was the number of success stories. This began with Steve Fesik’s keynote lecture, in which he discussed the MCL-1 inhibitors he and his team at Vanderbilt have discovered. When we highlighted his work last year he had reported low nanomolar inhibitors, but these did not have cell-based activity. His group has now optimized the molecules to low picomolar biochemical potency, low nanomolar cellular activity, and good activity in mouse xenograft models. This has not been easy: more than 2210 compounds were made, guided by 60 X-ray structures and dozens of pharmacokinetic experiments. It seems to be paying off though, and the researchers are developing biomarkers with the goal of advancing a compound into clinical testing.

Two other notable success stories about clinical candidates must be mentioned, though I’ll wait until publications come out before going into detail. Kathy Lee described how she and her colleagues at Pfizer chose a fragment that was less potent and ligand-efficient than other hits due to its interesting binding mode and were able to advance it to PF-06650833, an IRAK4 inhibitor with potential for inflammatory diseases. And Wolfgang Jahnke discussed how he and his colleagues at Novartis were able to discover and advance ABL001, an allosteric inhibitor of BCR-ABL, despite having the project halted twice – a reminder that persistence is essential.

Several other success stories have been covered at least in part on Practical Fragments, including inhibitors against PDE10A (presented by Izzat Raheem of Merck), Dengue RNA-dependent RNA polymerase (presented by Fumiaki Yokokawa of Novartis), lipoprotein-associated phospholipase A2 (presented by Phil Day of Astex), and BACE1 (presented by Doug Whittington of Amgen).

Crystallography was another theme, and several of the success stories relied on crystallographic fragment screening. Frank von Delft of the Structural Genomics Consortium described developments that allow screening 1000 crystals per week at Diamond’s Xchem facility in the UK, which include acoustic dispensing of compounds into crystallization drops – while carefully avoiding hitting the crystals head-on.

Several computational talks reported results that run contrary to conventional wisdom. Vickie Tsui of Genentech discussed their CBP bromodomain program (which we recently discussed here). Several water molecules form a highly ordered network in the protein, and a WaterMap analysis suggested that these were high-energy and that displacing them would lead to an enhancement in activity. Unfortunately this turned out not to be the case, though the researchers were able to get to low nanomolar inhibitors by growing towards a different region of the protein.

Li Xing mined the Pfizer database of 4000 kinase-ligand structures to extract 595 unique hinge binders. Not surprisingly, some of these – such as adenine and 7-azaindole – bound to multiple kinases, but 427 were complexed to just a single kinase. Hinge binders typically form 1 to 3 hydrogen bonds to the protein, and while there didn’t seem to be a correlation between the number of hydrogen bonds and potency, more hydrogen bonds did correlate – perhaps counterintuitively – with lower selectivity. To the extent that hydrogen bonds are thought of as enthalpic interactions, this further muddies the argument that enthalpy and entropy can be useful in drug design.

On a more positive note, Sandor Vajda (Boston University) suggested that, according to analyses done in FTMap, perhaps 60-70% of protein-protein interactions may be druggable – as long as we accept that this may require building larger molecules than commonly accepted. And Chris Radoux (Cambridge Crystallographic Data Centre) discussed the computational tool for characterizing hotspots that we previously covered here; a web server for easy search should be available soon.

Library design was also a key topic. Richard Taylor of UCB described his analysis of all FDA-approved drugs, which revealed >350 ring systems. Interestingly though, 72% of drugs discovered since 1983 rely exclusively on ring systems used prior to that date. Clearly there is plenty of untapped chemical real estate.

But getting there won’t necessarily be easy. David Rees stated that 33 fragments recently added to the Astex library required 13 different reaction types. Importantly, many of the fragment to lead successes at Astex have required growing the fragment from the carbon skeleton rather than from more synthetically tractable heteroatoms. Knowing in advance how to do this with every new member of a fragment library should make life much easier in the long run, though it is a serious challenge for chemists.

There is far more to write about, including a great discussion led by Rod Hubbard on how FBLD is integrated effectively into organizations and how it enables difficult targets, but in the interest of space I’ll stop here. If you were at FBLD 2016 (or even if you weren’t) please share your thoughts!

10 October 2016

Tips for high-throughput crystallography

X-ray crystallography is tied for second place among methods used in fragment-based lead discovery, according to our most recent poll. This makes sense, since structures are usually essential for advancing fragments to leads. Faster fragment-finding methods are usually used to triage fragments down to a manageable number of hits to feed into crystallography, but the high incidence of false negatives means that promising fragments might be inadvertently discarded. If structures are key goals at the end of a fragment screening campaign, why not start directly with crystallography?

In fact, this is exactly what more and more groups seem to be doing. The problem, historically, has been throughput. Increasing automation has been solving some of the mechanical issues (such as mounting crystals and collecting data at a synchrotron), but what about the actual processing? A recent paper in Structure by Andreas Heine and collaborators at Philipps-University Marburg and Helmholtz-Zentrum Berlin für Materialien und Energie provides some useful advice.

The protein in question is endothiapepsin, a model aspartic protease that is easy to crystallize and diffracts to high resolution. Earlier this year, we discussed the researchers’ work soaking 360+ fragments against this protein, and a companion paper gives detailed information on how several dozen fragment hits bind. The Structure paper describes an automated refinement pipeline, and highlights some of its most important features.

Determining a crystal structure involves iterative cycles of modeling the protein backbone and side chains into regions of “electron density.” One risk is “model bias,” illustrated memorably in this brief video. This is especially important for small molecules: since they represent such a tiny fraction of the overall structure, it is especially easy to see what you want to see. To avoid this, people often look for regions of electron density – which in addition to a bound small molecule could represent co-solvents, buffer, or an amino acid side chain that has unexpectedly moved – before doing much refinement.

The problem is that the electron density might be very spotty and easy to overlook. This is especially true for fragments that bind weakly and which are small by definition. Some initial refinement can thus improve the quality of the electron density maps. The researchers find that adding water molecules and including these in the refinement is the single most important step. Adding bound hydrogen atoms to the protein model is also helpful: even though each hydrogen only contributes one electron to the overall density, there are more than enough to make a meaningful difference. Finally, for very high resolution structures (better than 1.5 Å), it can help to treat each atom of the protein individually (anisotropic refinement of B factors, or atomic displacement parameters). However, at lower resolution, doing this can lead to overfitting. Incorporating these steps into the automated process revealed that 25% of fragments would have been missed had conventional methods been used.

The paper includes lots more detail that will be of interest primarily to crystallographers. Moreover, the data for all 364 fragment soaks has been uploaded to the protein data bank. This is a very high-quality data set: all the crystals diffracted to better than 2.0 Å resolution, with the mean being 1.35 Å, and should be a useful resource for those of you establishing your own automated processing system.

03 October 2016

Poll results: affiliation, metrics, and fragment-finding methods

The latest poll has just closed, and the results are quite interesting – I’ll get to these in the next paragraph. First, a quick note on methodology. The poll ran from August 27 through September 30. Due to issues with polling in Blogger, we began running polls in Polldaddy in 2013; its interface gives the total number of votes for a question but not the number of individual respondents. Thus, for the questions on metrics and methods, I assumed that the number of respondents was equal to the number of people who identified themselves as practicing FBLD in the first question, or 123 out of a total of 154. The true percentages for the metrics and methods that people use could be higher or lower if not everyone answered all the questions.

Readership demographics have been remarkably stable since 2010 and 2013, with just over half of respondents from industry, and around 80% of all respondents actively practicing FBLD.


The next question asked about screening methods, and here things get more interesting.

The first thing to notice is that, as we also saw in 2013, nearly all fragment-finding techniques are being used more, with the average user employing 4.1 distinct methods today compared with 3.6 in 2013 and 2.4 in 2011. Ligand-detected NMR has jumped to first place in terms of popularity, with SPR and X-ray crystallography tied for second, followed closely by thermal shift. MST, while still in the minority, has had the largest percentage increase. The use of crystallography has certainly jumped since 2011, which fits with recent publications.

Finally, with regards to metrics, ligand efficiency (LE) continues to dominate, followed by LLE (or LipE), though overall usage of both is down compared with 2014. Only one of the other metrics broke the 10% mark. 
Again, if some practitioners answered the first question of the poll, but not the next two, the use of all methods and metrics could be underestimated. Still, these results seem to fit with what I’ve heard talking with folks – any surprises?

26 September 2016

Fragments vs DOT1L, two ways

This past July Practical Fragments was devoted almost entirely to bromodomains, an important type of epigenetic protein. Protein lysine methyltransferases (PKMTs) are another significant class: 51 human enzymes that transfer a methyl group from the cofactor S-adenosylmethionine (SAM) to the side chain amine of lysines, typically in histones. In two recent papers in ACS Med. Chem. Lett., researchers from Novartis describe how they discovered inhibitors of DOT1L, a target for certain leukemias.

The first paper, by Frédéric Stauffer and colleagues, started with a fragment screen of the DOT1L catalytic domain using surface plasmon resonance (SPR). This led to the discovery of compound 1, which is teetering on the edge of molecular obesity (at least for a fragment) but did show activity in a functional assay as well as binding by NMR. Moreover, a co-crystal structure revealed that it binds in a new pocket near the SAM binding site, primarily through hydrophobic and stacking interactions.


Replacing the potentially unstable pyrrole with a quinoline led to compound 3, and subsequent structure-based design led to compound 5. Interestingly, while the methoxy substituent on compound 5 was installed to form a hydrogen bond with the protein, this instead caused a shift in binding mode – a reminder that fragments don’t always retain their original orientations during optimization. This new binding mode provided a vector to grow through a narrow channel into another pocket, ultimately resulting in compound 8, with low nanomolar activity.

The second paper, by Christoph Gaul and colleagues, started with a high-throughput screen (HTS). One low micromolar hit turned out to be a (felicitous) regioisomeric impurity from a commercial supplier. Crystallography revealed that this binds in the same pocket as the fragment in the previous paper, and subsequent medicinal chemistry led to low nanomolar inhibitors such as compound 3’. Unfortunately these turned out to have low permeability, probably due to the high number of hydrogen bond donors and acceptors. Fragmenting compound 3’ led to compound 4’, with a dramatic loss in potency, but structure-based design ultimately led to potent molecules such as compound 12’. This compound is also selective against other PKMTs, cell active, and orally bioavailable in rats.

These two papers provide a nice window into the complexity of lead discovery. In contrast to other examples, the fragment made largely hydrophobic interactions, while the HTS hit made numerous hydrogen bonds. Both hits bound in a new pocket, a reminder that secondary ligand binding sites are common. And in both cases, extensive medicinal chemistry was necessary and led to molecules that scarcely resemble their starting points. Interestingly, a previously described clinical candidate against this target, EPZ-5676, was identified by yet another approach: structure-based design starting from the cofactor SAM. All of which is to say that there are lots of ways to find inhibitors, and they don’t always fall into neat categories.

19 September 2016

Fragments vs GSK3β via DOS

Diversity-oriented synthesis, or DOS, enables the rapid and systematic synthesis of multiple related compounds from small sets of molecules and reactants. By creatively choosing the chemistry, DOS practitioners can selectively generate all diastereomers and produce more complicated molecules than are usually found in commercial screening collections. While much of the attention has been focused on larger molecules, DOS offers clear applications for addressing the chemistry challenges of FBLD. This is illustrated nicely by a recent paper in ACS Med. Chem. Lett. by Alvin Hung, Damian Young, and collaborators at the Broad Institute, Harvard, the Albert Einstein College of Medicine, A-STAR, and Baylor College of Medicine.

The researchers started with a very small (86 fragment) library, which Damian is in the process of expanding to 3000 compounds. Differential scanning fluorimetry was used to screen the molecules against the kinase GSK3β, which is implicated in cancer and Alzheimer’s disease. Three related fragments slightly increased the melting temperature of the enzyme, of which the simplest was compound 1S.

One nice feature of DOS is that – by design – analog synthesis is straightforward. Thus the researchers made a dozen or so derivatives to flesh out the SAR. This revealed that the enantiomer, compound 1R, stabilized the protein even more than the initial hit. STD and WaterLOGSY NMR confirmed binding, and isothermal titration calorimetry (ITC) revealed modest but measurable affinity. Synthesis of a few additional analogs led to compound 15R, with low micromolar affinity as assessed both by ITC and an enzymatic assay. Ligand efficiency was also good, though the ligand efficiency by atom number (LEAN) values of the molecules do not quite meet Teddy’s Safran Zunft Challenge – a wager due to be settled at FBLD 2016 in a few weeks.

A key selling point of DOS is that, by accelerating chemistry, it enables optimization even without structural information. In this case the researchers suspected that the fragment binds in the hinge region of the kinase, and subsequent crystallography revealed that this was indeed so. Interestingly though, the quality of the crystal structure was insufficient to unambiguously place compound 1R; perhaps it binds in multiple conformations. The crystal structure of compound 15R, on the other hand, was clear.

Of course, there is still a long way to go for this series, and it remains to be seen how broadly applicable DOS will be for FBLD. I look forward to seeing additional examples.

12 September 2016

Improving FBLD at AstraZeneca

FBLD started early at AstraZeneca (AZ). The first conference Practical Fragments covered was held at their erstwhile Alderley Park site; Pete Kenny is an AZ alum, and the company has put at least four fragment-derived drugs into the clinic. Clearly their scientists have learned plenty about what works and what doesn’t, and much of this wisdom is distilled into an excellent recent review in Drug Discovery Today. The authors include Nathan Fuller, Joe Patel, and Lorena Spadola, the first two of whom are organizers for the upcoming FBLD 2016 – for which there is still just barely time to register.

Things weren’t easy in the beginning: of the 63 FBLD targets screened between 2002 and 2008, a mere 10% led to tractable lead series with interpretable SAR. This improved to 37% of the 19 campaigns conducted between 2009 and 2011, and to 64% of the 11 projects between 2012 and 2014.

What accounts for these improvements? Target selection certainly played a role. In earlier years many targets were dropped due to portfolio reasons or lack of validation – nearly half for the period 2009-2011. Often FBLD was tried in desperation when all else failed, and chemists were not always available for fragment-to-lead efforts. Today, fragment screening is considered for all water-soluble targets at AZ, and fully integrated teams are brought into the process earlier. In 2012 the company established a team of medicinal chemists dedicated to FBLD – a strategy that has also been used at other companies.

But many of the improvements are technological rather than organizational. Biophysical screens are displacing high-concentration biochemical screens, which are particularly prone to false positives and false negatives. 1D and 2D NMR remain mainstays, but SPR and X-ray crystallography are increasingly being used in primary screens.

Another major effort was revamping the fragment library, which currently stands at 15,000 members. Each fragment was experimentally confirmed to be soluble to at least 0.5 mM in water and 100 mM in DMSO, and the rule of three was used more as a guideline than a rule. The collection was designed to include a good proportion of “three-dimensional” fragments, as assessed by plane of best fit (PBF) and principal moment of inertia (PMI). About a quarter of the fragments are proprietary, and the company also has another 750,000 molecules within their corporate collection that could be classified as fragments, greatly facilitating follow-up studies.

A 15,000 member library is atypically large, but in practice smaller subsets of the library are deployed: 384 for crystallographic screening, 1152 for NMR screening, and 3072 for SPR screening. Each subset is optimized for the technique. For example, because the crystallographic subset is so small, it is designed to sample chemical space as efficiently as possible. This is done by maximizing the diversity of the fragments and choosing the smallest fragments possible – less than 17 non-hydrogen atoms, as at Astex. In contrast, the NMR and SPR subsets contain fragments having up to 21 non-hydrogen atoms, and the SPR set also contains close analogs of some fragments to improve confidence and provide preliminary SAR. There is some overlap between the sets to facilitate confirmation; for example, a 768-member “ligandability set” is shared between the NMR and SPR screening libraries. Finally, AZ has built a customized set of 800 covalent fragments.

For the most part, fragment hits from each subset tend to have similar properties as the subset in general, suggesting that each sub-library is well-suited for its technique. Importantly, this is true even for three-dimensional fragments, which comprise nearly half of the hits across 19 targets. The researchers also examined how effectively fragments were able to fill the volume of a given binding pocket for five targets with multiple crystal structures. They found that shapely fragments were at least as good as – and sometimes better – at filling the pockets, even with fewer three-dimensional fragments.

Finally, the article summarizes eight projects in which fragment hits were progressed. Dissociation constants for the hits ranged from 50 to 3230 µM; these were advanced to leads with affinities ranging from 1.5 to 180 nM. In half these cases the ligand efficiency improved, and in all cases the three dimensionality increased as defined by PBF. Two of the targets, phosphoglycerate dehydrogenase and mInhA, are discussed in some detail, complete with chemical and crystal structures. Hopefully all will be covered more fully in upcoming publications.

There’s lots more in this paper than I can summarize in a blog post, including multiple figures and tables, so definitely check it out.

27 August 2016

2016 polls!

We're heading into election season here in the United States, which reminds us that we haven't run any polls recently at Practical Fragments. How has the community changed in the past few years? To find out, please answer the three questions in the poll on the right-hand side of the page, under "Editors." Also, please note that you need to hit "vote" for each question separately.

The first question asks whether you are in academia or industry and whether you practice FBLD.

The second question asks what methods you use to find fragments. For purposes of this poll please choose all that apply, whether primary or secondary screens. You can read about these methods in the following links.

Affinity chromatography, capillary electrophoresis, or ultrafiltration
BLI (biolayer interfermotry)
Computational screening
Functional screening (high concentration biochemical, FRET, etc.)
ITC (isothermal titration calorimetry)
MS (mass spectrometry)
MST (microscale thermophoresis)
NMR – ligand detected
NMR – protein detected
SPR (surface plasmon resonance)
Thermal shift assay (or DSF)
X-ray crystallography
Other – please specify in comments

The third question asks what metrics (listed below) you use. Again, you can choose multiple answers.

Antibacterial efficiency
BEI (binding efficiency index)
Enthalpic efficiency 
FQ (fit quality)
Fsp3
GE (group efficiency)
LE (ligand efficiency)
LELP (ligand-efficiency-dependent lipophilicity)
LLE or LipE (ligand lipophilic efficiency)
LLEAT
%LE
PEI (percentage efficiency index)
SEI (surface-binding efficiency index)
SILE (size-independent ligand efficiency)
Other
None

Finally, are there other topics you'd like to see polled? Please let us know in the comments.

22 August 2016

Crystallographic screening of a nuclear receptor

Crystallography as a primary screen seems to be gaining traction. As the old cliché goes, a picture is worth a thousand words. And as Andrey Grishin recently commented on an earlier post, the increasing speed and capacity at synchrotrons lowers the barrier for data collection. A new paper in ChemMedChem by Yafeng Xue and colleagues at AstraZeneca provides yet more support for starting with crystallography.

The researchers were interested in the retinoic-acid related orphan receptor γt (RORγt), a potential target for autoimmune diseases. The protein is a nuclear hormone receptor, and like many members of this family, ligands tend to be lipophilic with poor physical properties. Also, work by other companies around this target had created a thicket of intellectual property claims. To find new and attractive chemical matter, the researchers turned to fragments.

The ligand binding domain of RORγt was crystallized and soaked against a library of 384 fragments chosen on the basis of maximum diversity and previous success in crystallography. Fragments were screened at 75 mM concentration in pools of four, with members chosen to have different shapes. This process did require “extensive optimization”, and even then about 15% of the datasets were not usable. But the effort paid off, resulting in 21 hits from 18 pools. Hits were then tested by SPR, revealing that the best had an affinity of just 0.2 mM (though with an impressive LE of 0.42 kcal mol-1 per heavy atom), while some were > 5 mM.

As expected, many of the fragments bound in the large and lipophilic ligand binding pocket, accessing various binding modes previously seen with other ligands. This is a nice confirmation that fragments are able to sample chemical space very efficiently, as shown five years ago for HSP90. Indeed, for one particularly productive pool, three of the fragments bound simultaneously at different subsites within the ligand binding pocket!

Of course, proteins are often highly dynamic in solution, and one concern with crystallographic screening is that the protein crystals may not allow much movement. In this case the researchers did observe several cases of induced fit, with one side chain residue shifting more than 3 Å to accommodate a fragment. This revealed a type of interaction that was not predicted using a computational approach: a victory – for now – for the power of empiricism.

As discussed earlier this year, secondary ligand binding sites appear to be common, and indeed five fragments bound outside the ligand binding pocket. Three of these bind at what seems to be a protein-protein interface for other receptors, which could lead to highly selective molecules.

It’s a long way from a 0.2 mM fragment to a useful lead series, but having a structure (or 21) dramatically improves the odds – as demonstrated here and here. The paper ends by suggesting that such a series has indeed been identified, and it will be fun to watch as the story unfolds.

15 August 2016

Dynamic combinatorial chemistry and fragment linking

Dynamic combinatorial chemistry (DCC) sounds incredibly cool. The idea is that libraries spontaneously form and reform. Add a protein and Le Châtelier's principle favors the formation of the best binders. In other words, not only does cream rise to the top, more cream is actually created.

The applications of DCC for fragment linking are obvious, and indeed early reports date back nearly twenty years to the dawn of practical FBDD. The latest results are described in a new paper in Angew. Chem. Int. Ed. by Anna Hirsch and collaborators mostly at the University of Groningen.

The researchers were interested in the aspartic protease endothiapepsin, which is a model protein for more disease-relevant targets. This is a dream protein: it is easy to make in large amounts, crystallizes readily, and is stable for weeks at room temperature. Readers will recall that this protein has also been the subject of multiple screening methods. Previous efforts using DCC had generated low micromolar inhibitors such as 1 and 2. These acylhydrazones form reversibly from hydrazides and aldehydes. Crystallography had also previously revealed that compound 1 binds in the so-called S1 and S2 subsites of endothiapesin while compound 2 binds in the S1 and S2’ subsites. In the current paper, the researchers enlisted DCC to try to combine the best of the binding elements.

To do this, the researchers chose isophthalaldehyde, which contains two aldehyde moieties, and nine hydrazides, which could give a total of 78 different bis-acylhydrazones. They incubated 50 µM of isophthalaldehyde with either four or five of the hydrazides (each at 100 µM), with or without 50 µM protein, and in the presence of 10 mM aniline to accelerate the exchange. Reactions were allowed to incubate at room temperature at pH 4.6 for 20 hours, after which the protein was denatured and the samples were analyzed by HPLC to see whether some products were enriched in the presence of protein.

Biologists may want to consider whether their favorite proteins would remain folded and functional under these conditions, and chemists may also balk at molecules containing an acylhydrazone moiety – let alone two. Leaving aside these concerns, though, what were the results?


As one would hope, some molecules were enriched over others when protein was present, though only by a modest two or three-fold. Two of the enriched molecules – both homodimers – were resynthesized and tested. Compound 13 was quite potent, and crystallography revealed that it binds in a similar fashion to compound 1, though electron density is missing for part of the molecule. Compound 16, on the other hand, is only marginally more potent than the starting molecules. Unfortunately the researchers do not discuss the activities of molecules that had not been enriched at all.

The paper ends by stating rather hopefully that DCC “holds great promise for accelerating drug development for this challenging class of proteases, and it could afford useful new lead compounds. This approach could be also extended to a large number of other protein targets.”

I’m not so sure.

This is an interesting study; the work was carefully done and thoroughly documented—but I’m less sanguine about whether DCC will actually ever be practical for lead generation. Indeed, the very fact that the experiments were done well yet are incapable of distinguishing a strong binder from a weaker one argues that the technique is inherently limited. I would love to see DCC work, but it seems to me that, even after two decades of effort, DCC has not been able to move beyond proof of concept studies. Does anyone have a good counterexample?

08 August 2016

Metallophilic fragments revisited

Way back in 2010 we highlighted work out of Seth Cohen’s lab at UC San Diego on “metallophilic fragments”, which are specifically designed to bind to metal ions. As long as one avoids PAINS, the approach could be useful for targeting metal-dependent enzymes. Indeed, multiple drugs derive much of their affinity by binding to metals; these include HDAC inhibitors (for cancer) and integrase inhibitors (for HIV). In a recent paper in J. Med. Chem., Cohen and colleagues describe work against an influenza target.

The researchers were interested in the so-called “PA subunit” of RNA-dependent RNA polymerase, which is both essential and highly conserved among influenza strains. The endonuclease in the PA subunit requires two metal ions, either Mn2+ or Mg2+, and in fact previous publications had demonstrated that metal chelators could inhibit the enzyme. In the current paper, the team screened about 300 fragments at 200 µM in an activity assay; those that inhibited >80% were retested to produce dose-response curves. Compound 1 came in as reasonably potent and impressively ligand-efficient, as is often the case with metal-binding fragments. Docking studies suggested that it could bind to both of the metal ions in the active site.
Initial SAR around compound 1 led to compound 10, with a significant improvement in potency that the researchers attribute to increased basicity and thus stronger interactions with the metals. Taking pieces from previously published molecules led to another increase in potency (compound 63). Separate fragment growing efforts off compound 1 led to sub-micromolar inhibitors such as compound 35. Combining both series led to compound 71, which is the best of the bunch with low nM activity, though it fell short of the hoped-for additivity of binding energies.

Compound 71 was also tested in cellular assays. Happily, it was able to protect cells from a lethal dose of influenza virus with an EC50 in the low micromolar range, about 100-fold below the cytotoxic dose observed in the same cell line. Of course, there is still a long way to go: no pharmacokinetic data are provided, and selectivity against other metalloproteins may be a challenge. Still, it will be interesting to watch future developments, both with this series and with the approach in general.

01 August 2016

Lead Generation: Methods, Strategies, and Case Studies

Lead generation refers to that point in drug discovery when initial screening hits against a target are wrought into compelling chemical matter. This chemical matter is often plagued with deficiencies in terms of potency, pharmacokinetics, or novelty, yet it provides a starting point for further optimization. This is the subject of a massive (800+ pages!) new two-volume work edited by Jörg Holenz (GlaxoSmithKline, formerly AstraZeneca) as part of Wiley’s Methods and Principles in Medicinal Chemistry series. Readers of this blog will not be surprised to find that fragments play a major role; indeed, the molecule on the cover of the book came out of FBLD. I won’t attempt to summarize all 25 chapters here, but will simply highlight those most relevant to FBLD.

Mike Hann (GlaxoSmithKline) sets the stage in chapter 1 by briefly describing the characteristics of successful leads. He emphasizes the importance of physicochemical properties and avoiding molecular obesity, and how judicious use of metrics can help navigate away from perilous chemical space. He also summarizes internal programs that again demonstrate that fragment-derived leads tend to be smaller and less lipophilic than those from other lead discovery techniques.

In chapter 3, Udo Bauer (AstraZeneca) and Alex Breeze (University of Leeds) discuss the concept of ligandability – the ability of a target to bind to a small molecule with high affinity. Fragments are ideally suited for assessing ligandability, and the researchers briefly describe fragment-based experimental and computational approaches to do so. They also include a nice 11-point summary of factors to consider when starting lead generation on a new target, ranging from the presence of small-molecule binding sites to the number of patent applications.

Chapter 6, by Ivan Efremov (Pfizer) and me, is entirely about fragment-based lead generation. I'm undoubtedly biased, but I think it provides a self-contained and fairly detailed guide to FBLD, including topics such as screening methods, hit validation, metrics, hit optimization, fragment growing vs fragment linking, and case studies on vemurafenib, BACE, MMP-2, LDHA, venetoclax, MCL-1, and GPCRs.

Helmut Buschmann and colleagues at RD&C Research, Development, and Consulting, focus in chapter 9 on optimizing side effects of known molecules to develop new drugs, but they also discuss some interesting older work reporting that 418 of 1386 drugs contain other drugs as internal fragments.

Chapter 12, by Dean Brown (AstraZeneca), is devoted to the hit-to-lead stage, and much of his advice is applicable to FBLD. Dean also includes a fantastic metaphor to illustrate the size of chemical space: "if a typical corporate screening collection were to fit on a postcard, the rest of the earth is the amount of available drug-like space." This assumes a million-compound library and a conservative estimate of 1023 drug-sized molecules, so if anything it is an understatement.

Molecular recognition is critical for both FBLD and lead generation in general, and this is the topic Thorsten Nowak (C4X Discovery Holdings) tackles in chapter 13. He covers key areas such as thermodynamics, emphasizing the importance of enthalpy while acknowledging the difficulty of prospectively using thermodynamic data. The role of water and halogen bonds are covered, along with some freakishly high ligand efficiency values. There are a couple errors: one paper is categorized as using dynamic combinatorial chemistry when in fact it actually used static libraries, and Tethering is confused with Chemotype Evolution, but overall there's lots of good stuff here.

Biophysical methods are covered in chapter 14, by Stefan Geschwindner (AstraZeneca). These include NMR, SPR, ITC, thermal shift assays, native mass spectrometry, microscale thermophoresis, and more.

Chapter 16, by Ken Page and colleagues at AstraZeneca, discusses "lead quality." This often entails various metrics, from simple ones such as ligand efficiency and LLE to more complicated attempts to predict clinical dosages. Although it is easy to poke fun at metrics, most thoughtful scientists find them useful for making sense of the reams of data generated in lead optimization campaigns.

Chapter 17, by Steven Wesolowski and Dean Brown (both AstraZeneca), is arguably the most entertaining. Entitled "The strategies and politics of successful design, make, test, and analyze (DMTA) cycles in lead generation," it is replete with pithy quotes and even an original (and highly geeky) cartoon. Along with multiple examples, the chapter formulates plenty of questions to consider during lead optimization, and ends with a particularly relevant quote by Billings Learned Hand: “Life is made up of a series of judgments on insufficient data, and if we waited to run down all our doubts, it would flow past us.”

In chapter 23, Sven Ruf and colleagues at Sanofi-Aventis Deutschland describe a success story generating leads against cathepsin A, a target for cardiovascular disease. HTS yielded three different chemical series with sub-micromolar activities, each with different liabilities. Crystallography revealed their binding modes, and this allowed the team to mix and match fragments across the different series to generate a molecule that ultimately went into the clinic. Although this may not be classic FBLD, it does seem to be a good case of using concepts from the field, or fragment-assisted drug discovery.

A similar, if less directed, approach is the subject of chapter 25, the last in the book. Pravin Iyer and Manoranjan Panda (both AstraZeneca) describe "fragmentation enumeration," in which known drugs or clinical candidates are fragmented into component fragments and recombined. On some level the fragments themselves are likely to be privileged; the researchers cite the famous quote by Sir James Black that "the most fruitful basis of the discovery of a new drug is to start with an old drug." Most of the work is computational, although one molecule derived from the approach has encouraging cellular activity against Mycobacterium tuberculosis.

There's far more to this book than could be listed even in this relatively long post, including multiple case studies, so for those of you who are interested in lead generation definitely check it out!