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.

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.

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?

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.

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!