Fragments vs Pharma

As the year winds down I’ve been catching up on some reading, and finally got to the study that Paul Leeson and Stephen St-Gallay published a couple months ago in Nature Reviews Drug Discovery. They analyzed compounds disclosed in patent applications from 18 large companies (mostly pharmaceutical companies, but also Amgen and Vertex) between 2000 and 2010. Even controlling for different targets, the companies differed considerably in the drug-likeness of their compounds, with some companies producing compounds that are considerably larger and more lipophilic than other companies. In the Pipeline has an excellent summary of the paper overall.

But what caught my eye as being of special interest to readers here is a small part of the main paper. In addition to analyzing large companies, Leeson and St-Gallay dug into the patent applications of a fragment-focused company, Astex Therapeutics (now Astex Pharmaceuticals). A dozen of the kinase targets pursued by Astex were also pursued by one or more of the large companies, and by analyzing the inhibitors from each organization, the authors could compare leads derived from fragments with leads derived using conventional approaches. The results were striking:

With the exception of chirality and sp³ measures, molecular properties are more drug-like in the compounds patented by Astex Therapeutics. This specific application of fragment-based drug design is perhaps the most compelling realization to date of the principle of lead-like chemical starting points that was first proposed more than a decade ago.

This does not mean that FBDD is a panacea: as noted previously, it is all too easy to take a perfectly good fragment and turn it into an obese grease-ball. But an attractive fragment, combined with adept medicinal chemistry and intolerance for unnecessary lipophilicity, can be a powerful combination.

And with that, Practical Fragments says goodbye to 2011. Thanks to all of you for reading, and special thanks for posting comments. May you all have a happy and successful 2012!

Fragments vs matrix metalloproteinase-13: avoiding the metal

Matrix metalloproteinases (MMPs), as their name suggests, are metal-dependent proteases that cleave the extracellular matrix. They have been implicated in a wide variety of diseases including cancer and inflammation. MMPs have also been used as model systems to study the effects of fragment linking. (In fact, the first successful example of SAR by NMR was conducted against MMP-3.) Most inhibitors, including those starting from fragments, interact with the catalytic zinc in order to achieve potency. However, with roughly two dozen human MMPs, all dependent on a zinc ion, selectivity has been tricky. A recent paper in J. Med. Chem. mostly from researchers at Boehringer Ingelheim sidesteps this problem nicely.

MMP-13 is one of the more interesting members of the family due to its apparent role in rheumatoid arthritis. The enzyme is crystallographically well-behaved, and has a large substrate-binding pocket (the S1’ pocket) near the catalytic zinc that is dissimilar from other S1’ pockets. Even better, there is an adjacent side pocket (S1’*) that can open when the S1’ pocket is occupied, providing further selectivity.

The researchers started by performing a virtual screen of their entire corporate library to look for fragments that might bind in the S1’ pocket. These were added to an in-house fragment collection, and the combined set of roughly 1000 compounds was screened at 0.5 mM concentration in a biochemical screen. Compounds were also assessed using NMR (saturation transfer difference) and size exclusion chromatography mass-spectrometry. One of the best hits was Compound 1, which came from the virtual screen and was originally made as a synthetic intermediate in a completely different program.

Crystallography revealed that Compound 1 does in fact bind in the S1’ pocket, making several hydrogen bonds, with the amide moiety pointed towards the S1’* portion of the protein. This compound also displayed some selectivity towards two other MMPs. Fragment growing towards the S1’* pocket led to compound 11, with increased potency and ligand efficiency, and ultimately to compound 15, with low nanomolar potency and > 1000-fold selectivity against 9 other MMPs. Crystallography revealed that, as expected, the compound binds with the benzoic acid moiety in the S1’* pocket. And despite the presence of the ethyl ester, compound 15 is orally bioavailable in rats.

The paper gives no indication of where the program is today, but it is another nice example of fragment growing, as well as taking an unconventional approach to achieve selectivity.

Are enthalpic binders more selective than entropic binders?

Thermodynamics is one of those abstract subjects that can have surprising real-world implications. The two components of free energy, enthalpy and entropy, are simplistically associated in drug discovery with polar interactions for the former and hydrophobic interactions for the later. Some researchers have suggested that enthalpically-driven binders are better starting points for optimization, and that best-in-class drugs rely more on enthalpy than entropy. In a recent paper in Drug Discovery Today, Yuko Kawasaki and Ernesto Freire at Johns Hopkins University suggest that enthalpic binders may also be more selective.

Medicinal chemists apply two general strategies to improve selectivity: increase the affinity of a compound for its target more than for off-targets, or decrease the affinity of a compound for off-targets. Kawasaki and Freire argue that the first is more likely to result from entropic interactions, while the second is more likely to result from enthalpic interactions. This is because nonpolar (entropic) interactions are often tolerant of mismatches; a hydrophobic substituent might improve the affinity of your ligand for its target, but, unless it causes a severe steric clash, it may also improve activity for off-targets – though hopefully less. Indeed, recent findings suggest that more lipophilic molecules tend to be more promiscuous than similarly-sized but less lipophlic molecules. On the other hand, due to the highly directional nature of polar interactions, a mismatched polar (enthalpic) interaction in an off-target is likely to be highly detrimental to binding.

The researchers consider two case studies involving HIV-1 protease inhibitors. In one example, adding two (non-polar) methyl groups improves the affinity of the inhibitor for its target as well as for two off-targets, though it improves the potency towards HIV-1 protease more, thus improving selectivity.

In the second case, a non-polar thioether is replaced with a polar sulfone. This slightly decreases the overall binding affinity for HIV-1 protease, but has a much larger negative effect on two off-targets, resulting in greater selectivity. In this case, the enthalpy of binding for HIV-1 protease is considerably improved, though the effect is compensated for by unfavorable changes in entropy. As the authors note, “even if a strong hydrogen bond does not contribute to affinity, it might contribute significantly to selectivity.”

Ideally you would want to use both strategies (improving affinity for your target and decreasing affinity for off-targets). However, since you probably don’t know all your off-targets, focusing on enthalpic binders may be the way to go, as mismatched polar interactions are likely to exclude lots of unknown off-targets.

Of course, two examples may not make a trend, but they do make a testable hypothesis. For example, there is a veritable plethora of kinase inhibitors with known specificity profiles: it would be interesting to correlate these with their thermodynamic profiles. But at any rate, this is yet another reason to hold down the hydrophobicity of your compounds.

Kinetic efficiency and slow-binding fragments

We’ve previously discussed the proliferation of metrics used to evaluate fragments. Ligand efficiency is by far the most popular, and what it and most other measurements have in common is that they represent binding affinity (or inhibition, or some other surrogate). Binding affinity is associated with thermodynamics – how well a molecule binds to a target – but this measure says nothing about how rapidly a molecule associates and dissociates from the target (kinetics). In the November issue of Drug Discovery Today Geoffrey Holdgate and Adrian Gill at AstraZeneca propose a new metric, kinetic efficiency (KE), to address this issue:

KE = τ / (# of heavy atoms) = t1/2 / (0.693 * (# of heavy atoms))
where τ is the residence time or relaxation constant and is, in the simplest case, 1/koff
koff is the dissociation rate constant
and t1/2 is the half-life for dissociation

Why are the kinetics of dissociation important? Holdgate and Gill list a series of drugs for hypertension and note that compounds that remain bound to the receptor longer avoid rapid clearance and thus have superior clinical activity. On the other hand, drugs for schizophrenia that bind the D2 dopamine receptor can cause side effects if they remain bound too long. Thus, optimal kinetic efficiency is case-dependent .

Though kinetics of ligand binding can be assessed with techniques like SPR, this parameter is often ignored. However, as Holdgate and Gill point out, slow-binders are likely to be lead-sized or drug-sized molecules. Indeed, none of the roughly two-dozen examples they present would satisfy the rule of 3.

This raises an interesting question: how often do fragments dissociate slowly? Slowly-dissociating fragments are often flagged as pathological in SPR studies. Intuitively it seems that smaller molecules would have faster kinetics; a small fragment is likely to be able to dart in and out of a protein-binding site more rapidly than a larger molecule that requires some movement on the part of the protein to accommodate its binding. Still, there must be some cases of fragments with slow dissociation rate constants. If you know of any please mention them in the comments section.

And once more into the breach...

When the market is more than 20 Billion dollars, you will find everyone working there. And so, with this recent publication, we have another entrant into the BACE inhibitor from Fragments competition, discussed previously here. This is the fifth by my count, the first being from Astra Zeneca.

In this paper, Eli Lilly describes their efforts using fragments to generate "the first orally available non-peptidic BACE1 inhibitor that produces profound Abeta-lowering effects in animals." They screened ~8000 compounds at 4.76mM that generated a number of low-affinity, but highly "LEAN" fragments (discussed below). Of most interest were the amino-benzothiazine (1) and amino-thiadiazine (2) compounds.

The authors note that co-crystallilzation was a key advance for their understanding of this system. The co-crystal showed two copies of (1) with high active site occupancy and in the "open-flap" conformation. One copy engaged the catalytic dyad and the other spanned the S1-S3 cavity. This data let them recognize that the planarity of the molecules were not optimal for fragment growth, so they "de-planarized" them, leading to (3). Only one enantiomer of (3) was recognized by BACE. The co-crystal of this compound showed binding identical to the original fragment, one copy engaging the catalytic dyad and one in the S1-S3 region. Addition of the S3 moiety pyrimidine led to (4). Fluorination of the central ring reduced in vivo clearance and and realized a significant increase in potency, while maintaining atom efficiency (5).

The crystal structure of (5) shows that this molecule retains an optimal H-bonding network, efficiently traverses S1, and projects the pyrimidine into S3.

Compound (5) was tested in animal models and pre-clinically in healthy human volunteers given orally. It showed significant reduction in Abeta levels in brain and CSF. Retinal pathology became a concern in longer term animal studies and the compound was not taken any further.

This paper shows the power of Fragments in discovering novel scaffolds for important targets. It is also important to note that the modified fragment hit retained the same binding as the original fragment hit.

The other contribution that the Lilly group brings out in this paper is the concept of LEAN (Ligand Efficiency by Atom Number): -log (IC50)/Number of heavy atoms. This is one of many ways people have developed to gauge the efficiency of their ligand hits, I think this is the simplest to use. As can be seen from the Lilly data, a LEAN of >=0.30 is an efficient molecule. For those of us who don't do logs in our head well, this lends it itself to a simple cheat sheet:

I can send a copy of this spreadsheet to anyone who wants.

What do fragment hits look like?

Our last post highlighted a study showing that most of the best fragment hits loosely followed the Rule of 3, even though the library from which they were selected was not strictly Rule of 3 compliant. As it happens, Chris Swain at Cambridge MedChem Consulting has been tabulating fragment hits reported in the literature and has assembled a database of more than 280. Previously he has assessed the physicochemical properties of commercially available libraries; now, he’s analyzed the fragments that have actually been reported as hits and has published the results here.

For the most part the fragments conform to the Rule of 3. Size-wise most of them are truly fragments, with the majority having molecular weights less than 250 Da. Not surprisingly, they also tend to be fairly aromatic. Interestingly, roughly one third of the fragments are charged at physiological pH, with a pretty even split between acids and bases.

Of course, despite the overall Rule of 3 compliance, there are outliers in all the parameters, especially hydrogen-bond acceptors. So perhaps, to paraphrase Darren Begley channeling Bill Murray, the Rule of 3 should be renamed the Guideline of 3.

Pushing the Rule of 3

The Rule of 3 (Ro3) is commonly used to design fragment libraries. First published as a brief 450-word (shorter than this post!) “update” in the discussion forum of Drug Discovery Today in 2003 by researchers at Astex, it has become the fragment equivalent of Chris Lipinski’s famous Rule of 5. Like that rule, it has its critics, notably our friends at FBDD and Molecular Design. A key point of contention is whether the Ro3 is too restrictive. A new paper in J. Med. Chem. from Gerhard Klebe’s group at Philipps University Marburg addresses this question.

The definition of the Rule of 3 provided by Astex is as follows:

The study indicated that such hits seem to obey, on average, a ‘Rule of Three’, in which molecular weight is <300, the number of hydrogen bond donors is ≤3, the number of hydrogen bond acceptors is ≤3 and ClogP is ≤3. In addition, the results suggested NROT (≤3) and PSA (≤60) might also be useful criteria for fragment selection.

One of the criticisms leveled at the Ro3 is that it is vague in terms of what constitutes a hydrogen bond acceptor. For example, does the nitrogen in an amide count? What about the nitrogen in an indolizine? Presumably for simplicity Lipinski assumed that any nitrogen or oxygen atom would count as a hydrogen bond acceptor. At the risk of engaging in exegesis, I propose that only oxygen or nitrogen atoms most medicinal chemists would consider as acceptors should be counted as acceptors, and that the limits on the number of rotatable bonds (NROT) and polar surface area (PSA) are optional.

In the recent paper, which is also discussed at FBDD and Molecular Design, Klebe and colleagues assembled a library of 364 fragments in which the average properties of the fragments were within Ro3 guidelines (with the exception of “Lipinski acceptors,” which would include the nitrogen of a tertiary amide), but there were some outliers. They then performed a fluorescence-based competition screen against the model protein endothiapepsin, resulting in 55 fragments that inhibited at least 40% at 0.5 or 1 mM concentration. These fragments were taken into crystallography trials, resulting in 11 structures. The paper presents lots of nice analysis of how these fragments bind to the protein. It also notes that:

Only 4 of the 11 fragments are consistent with the rule of 3. Restriction to this rule would have limited the fragment hits to a strongly reduced variety of chemotypes.

This may be an overstatement. Looking at the fragment hits more closely, all of them have molecular weights less than 300, and only one has ClogP > 3. Personally, given the problems of molecular obesity and the dangers of lipophilicity, I’d say that these aspects of the Ro3 are the most important, and find it notable that the hits were so compliant given that the library did contain larger, more lipophlic members.

All 11 of the crystallographically characterized fragments also have 3 or fewer hydrogen bond donors and TPSA < 60 Ų. Only two of the fragments have more than 3 rotatable bonds, but where the majority of the fragments fail to pass Ro3 is in the number of “Lipinski acceptors,” where 6 of the 11 have > 3. However, if you count hydrogen bond acceptors more judiciously (ie, compound 291 would have 3 acceptors rather than 4, since the aniline nitrogen would not be counted), only 1 of the 11 fragments has more than 3 acceptors.

Like most rules, the Rule of 3 should never be treated as a strait-jacket. That said, given the number of possible small fragment-sized molecules, and the necessarily limited size of any fragment collection, there seems to be plenty of room within the Rule of 3 for attractive chemical diversity.

Privilege or selectivity?

Fragment selectivity is something we’ve covered before (see here and here). Sarah Barelier and Isabelle Krimm at the Université de Lyon have published on this topic (see here), and in a recent issue of Current Opinion in Chemical Biology they review the subject and its implications.

The authors document what many investigators have independently observed: some fragments, as expected theoretically, are less selective than larger molecules, but other fragments are quite selective.

They also note that fragments that bind to any protein tend to be slightly more lipophilic than fragments that don’t bind to any target proteins, suggesting that hydrophobic interactions are important:

Hydrophobic interactions play a major role in protein–ligand interactions and are known to be non-directional, thus allowing binding to a multitude of pockets in different conformations. By contrast, hydrogen bonds were shown to confer specificity but do not always add much binding free energy. This is due to the cost of desolvating both the donor and acceptor of the hydrogen bond, which can nearly equal the benefit of the hydrogen bond formation. Therefore, if the hydrogen bond acceptors or donors are not satisfied in the complex, it is likely that more hydrophobic fragments will be preferred.

This observation—lipophilicity for binding energy, hydrogen bonds for specificity—is consistent with the recent publication from Mike Hann and Andrew Leach, which finds that promiscuity increases with increasing lipophilicity.

One figure in the Barelier and Krimm paper shows 30 fragment-like “privileged scaffolds” that should bind to multiple proteins. What struck me is these molecules’ overwhelmingly planar character: more than half are completely aromatic (such as quinoline and indole), and only one is completely aliphatic. Barelier and Krimm note that:

The low specificity of these molecules is probably owing to their rigid and aromatic structures, well-adapted to protein hydrophobic pockets where π-stacking with phenylalanine and tyrosine are commonly observed.

This reminds me of Tony Giannetti’s talk at the FBLD San Diego meeting earlier this month, where he also noted that fragment hits tend to be relatively flat. Of course, given the negative correlation between aromaticity and good pharmaceutical properties, just because aromatics are frequent hits doesn’t mean they are necessarily the best hits – they may be tricks rather than treats. All of which comes back to a key question for library design: do you focus on the flat “privileged” scaffolds that will likely have high hit rates in your assay but may have baggage, or on the more three-dimensional compounds that may have lower hit rates but may ultimately be more developable?

Fragment Based Lead Discovery San Diego 2011

Zenobia Therapeutics hosted a one-day FBLD meeting in San Diego last Friday. It was a very full day of seven talks and plenty of informal discussion, with both lunch and happy-hour on a rooftop terrace overlooking the ocean. I’ll try to give some flavor of the event below – those of you who were there, please chime in with your own impressions.

In the first talk of the day, Michael Recht of Xerox’s PARC discussed the construction and use of array-based nanocalorimetry devices, which analyze samples as small as a couple hundred nanoliters. Although conceptually related to isothermal titration calorimetry, the scale of nanocalorimetry makes direct measurement of binding enthalpy difficult. However, the devices are well-suited to measuring the heat output from enzymatic reactions, thus allowing detailed study of enzyme kinetics and inhibitor characterization (see here for a full description and here for a review, both open-access).

Maurizio Pellecchia of the Sanford-Burnham Medical Research Institute gave a wide-ranging talk with the common theme of NMR as an enabling method. In particular he noted that, due to its low protein requirements, ILOE (which we’ve written about previously) could be considered the “homeopathy of NMR;” a recent paper in J. Med. Chem. describes its application to generate hits against Bcl-family proteins. He used a different NMR technique to discover a potent and selective JNK inhibitor that binds in both the ATP and substrate binding sites. On a completely different topic, Maurizio noted that roughly one-third of drug targets contain metals, prompting the construction of a fragment-library designed with metal-chelation in mind, a strategy we’ve written about previously (see also here). Many of these techniques and libraries have now been licensed into a new company, AnCoreX Therapeutics, founded by Maurizio.

Schrödinger was a major sponsor of the meeting, and company scientist Kathryn Loving gave a good overview of the potential of their suite of software tools for fragment-based lead discovery. After lunch I presented a PDK1 story (see here) as well as applications of Carmot’s Chemotype Evolution, and Michelle Arkin from UCSF discussed some of the capabilities of the university’s Small Molecule Discovery Center, with special focus on protein activators and protein-protein disruptors.

Tony Giannetti of Genentech gave a nice presentation on caspase-6 inhibitors and also discussed the power of SPR, a topic on which he’s written extensively. He noted that as long as one is careful about conducting the experiment, the number of promiscuous or non-specific binders found by SPR tends to be less than the number of true hits – he described the former as “a managed headache.”

Tony is in the enviable position of having screened numerous targets using the same platform, allowing him to do some very interesting data mining. One phenomenon that we’ve previously commented on is the fact that, across multiple companies, only about a third of fragments hit in one or more screens, and Roche is no exception (36% across 13 screens). With the merger of Genentech and Roche now complete, Tony has been able to compare the fragment libraries assembled separately at each institution. Interestingly, although they contain < 5% common fragments and < 10% similar fragments, the libraries are, by broad measurements such as size or lipophilicity, statistically indistinguishable. However, the hits from the Roche collection seem to have higher ligand efficiencies. Also, the most potent binders tend to have 13-17 atoms, with the most ligand-efficient molecules generally having a molecular weight < 200. Finally, there has been a lot of recent discussion on making fragments less flat (aromatic) and more three-dimensional (often aliphatic). However, as I predicted 2 years ago, one needs to be cautious: it turns out that hits were actually enriched for flatter fragments, although Tony did mention that most of the screens were against kinases, which may be particularly “flatophilic”.

Vicki Nienaber, founder and president of Zenobia, closed the conference by discussing the potential for fragment-based approaches to tackle diseases of the central nervous system (CNS). The rationale is that, since CNS drugs need to be small to cross the blood-brain-barrier, it pays to start with fragments. Indeed, Zenobia’s fragment library has a molecular weight averaging just 150-175, with an upper limit of 225, which should allow them to avoid molecular obesity. Vicki described the discovery of nanomolar leads against LRRK2, a Parkinson’s disease target, and against PDE10, a Huntington’s disease target.

As far as I know this was the last face-to-face fragment event this year, but there are still several webinars this month and next, and it’s not too soon to start planning for 2012; please see here for upcoming events.

Fragments vs PI3 kinase

The phosphatidylinositol-3 kinases (PI3Ks) comprise a family of lipid kinases that are important in a variety of biological pathways and have thus become popular targets for drug-discovery; earlier this year we highlighted a fragment screen out of AstraZeneca against four members of the family. In the most recent issue of Bioorg. Med. Chem. Lett., researchers from Pfizer have published their approach to one of these targets.

Samantha Hughes and colleagues first tested 5960 fragments in a biochemical assay (at 0.5-1.5 mM) to find molecules that inhibited PI3gamma. Hundreds of hits resulted, of which 150 were confirmed in full dose-response curves. These were further triaged using orthogonal methods, ultimately resulting in five X-ray structures of co-complexes, including compound 1, which binds to the hinge region of the kinase. Virtual screening of the larger Pfizer library led to the discovery of additional compounds such as compound 2. Growing compound 1 by adding an acetyl group generated compound 9, improving both potency and ligand efficiency, but synthetic challenges stymied further work. However, a closer examination of the crystal structure of compound 1 suggested a merging strategy with the previously reported compound 10 to generate compound 12, with high potency and ligand efficiency.

Astute (or paranoid) readers will notice that compound 12 contains a Michael acceptor that looks suspiciously reactive. In fact, it is closely related to the notorious rhodanines, many of which form covalent bonds with proteins and, because of the resulting promiscuity, have been accused of “polluting the literature.” Nonetheless, crystallography revealed that the compound binds (noncovalently) to PI3gamma exactly as designed. Moreover, it is fairly selective for other kinases, inhibiting only 3 out of 43 tested at 1 micromolar compound. Compound 12 is also metabolically stable, permeable, and cell active. This is a good example of why it is important not to be overly dogmatic in compound triaging, particularly at an early stage in a project.

Finally, it is worth noting that this paper comes from the storied Sandwich Laboratories, which are being closed down. If there is a silver lining to this tragedy it is that the closure has resulted in a flurry of nice publications from the site. Still, such papers hardly offset the opportunity cost of the drugs that would otherwise have been discovered there – nor the disruption caused to hundreds of scientists. Practical Fragments wishes the best of luck to all of them.

Poll results: fragment screening methods

The fragment-screening methods poll is now closed, and the results are pretty interesting:

As expected, people are using multiple techniques – an average of 2.4 according to this poll.
No technique is dominant, though SPR and ligand-detected NMR are each used by more than 40% of respondents. Thermal shift assays are also popular, with about one-third of people using them.

X-ray crystallography was used by just under a quarter of respondents, less than computational screening and not much more than reported using protein-detected NMR, which is surprising given the challenges of advancing fragments in the absence of structures.

Some of the other techniques such as ITC are still pretty niche at less than 10%, though it will be interesting to revisit this survey in a couple years and see how things change.

A decade of molecular complexity

Molecular complexity is one of the key reasons why fragment-based lead discovery should work. As described in 2001 by Mike Hann and colleagues at GlaxoSmithKline, the idea is that very small, simple molecules are likely to be able to bind to many different sites on many different proteins; think of the water molecule as being an extreme example of this. As molecules become larger and more complex, they are less likely to bind to any given site on a protein, though if they are complementary to a site the potency will be greater. Similarly, more complex molecules are more likely to have a single binding mode than smaller, less-decorated molecules, which could assume multiple orientations at a single site. These intuitive ideas were supported by a simple computational model that suggested that there is an optimum complexity where molecules would be simple enough that they would bind to several different targets (and thus be useful in a screening collection) while still being complex enough to bind in single, defined orientations with sufficient potency to permit detection. Mike Hann and Andrew Leach now have a new paper in Current Opinion in Chemical Biology that analyzes how this idea has weathered the past decade.

A central tenet of the molecular complexity model is that more complex molecules should be less promiscuous (bind to fewer protein targets) than less complex molecules. Although defining complexity is itself complex, the authors summarize a number of studies that examine promiscuity as a function of various molecular properties that could be used as proxies for complexity. Interestingly, many of these studies find that as molecular weight or – especially – lipophilicity increases, promiscuity actually increases, an apparent contradiction of the complexity model. Indeed, Hann and Leach present internal data showing that, for a given molecular weight, promiscuity increases with increasing lipophilicity.

The authors consider several explanations for this, such as the notion that larger, lipophilic molecules may not need to be perfectly complementary to a protein: one portion could bind, while the rest of the molecule remains unbound. One explanation that the authors don’t address but that could account for much of the discrepancy is the validity of the measurements from the studies surveyed. Practical Fragments has previously discussed the issue of aggregation artifacts, which can occur even at nanomolar concentration – well below the 10 micromolar cutoff used in many of the cited studies. Indeed, Brian Shoichet has commented that the majority of hits from HTS screens could be artifacts, and an alarming proportion of "active molecules" in published work are also bogus. Thus, the apparent promiscuity of more lipophilic compounds may reflect merely assay artifacts, not true binding.

In other words, I propose at least two kinds of promiscuity. “Legitimately promiscuous” compounds actually bind to multiple proteins in a one-to-one defined fashion. Perhaps these are rare, in line with the complexity model. “Apparently promiscuous” compounds simply interfere with the assay, whether through aggregation, fluorescence artifacts, or other PAINful mechanisms. Given how many discovery programs get side-tracked by these phenomena, these compounds are likely to vastly outnumber legitimately promiscuous molecules, thus distorting the results of data-mining exercises.

There is plenty more in the paper than can be summarized here, and if this piques your interest Mike Hann will be discussing both molecular complexity as well as molecular obesity at the SLAS webinar series starting next month.

Updated: fragment events in 2011 and 2012

As 2011 winds to a close there is still one more addition to the calendar, and 2012 is shaping up nicely.

August 16 - November 15: Is your travel budget limited? Emerald Biosciences is putting together a series of free webinars related to FBLD on August 16, September 20, October 18, and November 15. If you've missed any they are all archived online.

October 21: Zenobia Therapeutics is putting together a FBLD conference in San Diego. Although just one day, there is a nice lineup of speakers, so try to make it if you can.

October 25 - November 15:  The Society for Laboratory Automation and Screening (SLAS) is holding a 4-part webinar titled "Interrogating Chemical Space — Rules, Filters, Fragment-Based Screening and Beyond," with presentations by Chris Lipinski (October 25), Mike Hann (November 1), me (November 8), and Dan Wyss (November 15).

2012

March 19-23: Keystone Symposium: Addressing the Challenges of Drug Discovery – Novel Targets, New Chemical Space and Emerging Approaches will be held in Tahoe City, CA. Although not exclusively devoted to fragments, there are many speakers I look forward to hearing.

April 17-19: Cambridge Healthtech Institute’s Seventh Annual Fragment-Based Drug Discovery will be held in San Diego. You can read impressions of this past year’s meeting here and 2010’s here.

August 19-23: The 244th National ACS meeting will be held in Philadelphia, and there will be at least one symposium on FBLD.

September 23-26: FBLD 2012, the fourth in an illustrious series of conferences, will be held in my fair city of San Francisco. This should be a biggy – the first such event in the Bay Area (and the weather in September is usually decent too). You can read impressions of FBLD 2010 and FBLD 2009.

Know of anything else? Organizing a fragment event? Let us know and we’ll get the word out.

Ultrafiltration to filter fragments

If you’ve taken our poll on finding fragments you’ll have noticed ultrafiltration as one of the possibilities (and if you haven’t voted yet, please do so on the right-hand side of page). Ultrafiltration is grouped with affinity chromatography and capillary electrophoresis because all of these methods involve affinity-based separation of bound from unbound fragments. The technique is described in a recent paper in Anal. Bioanal. Chem.

The basic idea is simple: mix fragments with your protein of interest and then centrifuge through a membrane which retains large molecules such as proteins (and any bound fragments) but allows small molecules (unbound fragments) to pass through. If the composition of the filtrate differs from the composition of the initial mixture, one can assume that any depleted molecules are bound to the protein.

The researchers, all from the University of Washington, Seattle, have been using the technique on internal targets, and their recent paper gives a thorough account of how to do it and describes the results against two protein targets. In both cases, fragments were grouped into pools of 5 to 10 compounds, with each fragment at 0.098 mM and protein at 0.201 mM concentration; these conditions were chosen such that a 1 mM binder would give a 15% reduction in signal, which was roughly 3-fold over their standard deviation for control experiments. The ultrafiltration was done at 4 ˚C in 96-well plates, and the filtrates were analyzed by HPLC using a UV detector (all the fragments contained a chromophore, though the authors suggest that the experiment could also be done using mass spectrometry as a detector).

For the first protein, riboflavin kinase, the researchers found 4 hits out of 134 fragments tested, of which 3 confirmed as single compounds (ie, not in cocktails). Interestingly, these fragments were competed by the enzymatic product flavin mononucleotide but bound more tightly in the presence of the other product and cofactor, ADP and Mg(II). For the other protein, methionine aminopeptidase 1 from the parasite that causes malaria, 10 hits were found out of 243 fragments tested, of which 9 confirmed. The top 6 of these could be competed by methionine, the enzymatic product.

Overall this seems like an interesting method, though I do have one quibble: the authors do not report the activity of these fragments using other techniques. In theory it should be possible to extract dissociation constants from the % reduction in UV signal, and it would be very interesting to see how these values compare to dissociation constants measured using orthogonal methods. Has anyone out there done this?

Poll: fragment screening methods

Finding fragments is now routine, but how are people doing it? NMR of course has a venerable history, but X-ray crystallography provides higher resolution data, SPR is faster than either, and there are all sorts of other methods. To learn what’s state of the art (and to help me gather some data for an upcoming talk) please vote for what method(s) you’re using. Note that the vote is on the right side of the page, and you can vote for more than one method.

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

Fragments vs BACE1: Amgen’s story

Some targets that have proven recalcitrant to standard screening approaches seem to be particularly amenable to fragment-based approaches. Beta-site amyloid precursor protein cleaving enzyme 1 (BACE1) is one such example: Practical Fragments has previously discussed programs from both Evotec and Schering/Merck, the latter of which has resulted in more than one clinical candidate. In a recent issue of J. Med. Chem., researchers at Amgen describe their adventures with this Alzheimer’s disease target.

The researchers started by using SPR to screen a library of about 4000 fragments (which had MW < 300, polar surface area < 30 Ų, and ≤ 2 hydrogen bond donors). This led to 106 hits with 10 mM or better potency, of which 8 confirmed in an orthogonal assay with potency better than 1 mM. Among these was fragment 1, which was also discovered as a BACE1 binder by researchers at Astex using crystallographic screening.

Astex’s crystallographic structure showed compound 1 packed pretty tightly into BACE1, but surprisingly, the Amgen team found that walking a bromine atom around the phenyl ring produced gains in potency at all four positions. In fact, adding an aromatic group off the 6-position, as in compound 34, led to a dramatic increase in potency, and crystallography revealed that the protein undergoes a conformational change to accommodate the extra bulk and form an edge-face interaction between a phenylalanine side chain and the added aromatic group.

Putting substituents off the 3-position, as in compound 44, led to molecules that could access either the P1 pocket or the P2’ pocket of the enzyme, but adding the ortho-tolyl group from compound 34 to give compound 43 locked the binding mode down to the P2’ pocket and gave a satisfying boost in potency such that standard enzymatic assays could be used instead of SPR. Further medicinal chemistry led to picomolar binders such as compound 57 as well as compounds less active in the biochemical assay but with better permeability and lower efflux, such as compound 59. This compound also showed in vivo activity in a rat model, though it is rapidly metabolized.

Although crystallography was clearly enabling throughout the process, this paper is a warning not to be too slavish in adherence to structure, as the initial break (compound 1 to compound 34) would not have been predicted to be active based on the co-crystal structure of compound 1 with BACE1.

This is also another nice example of starting with a rather generic fragment (heck, one published by another group!) and advancing it to a potent, proprietary series.

Fragment docking: it’s all about ligand efficiency

A widespread belief holds that it is more difficult to computationally dock fragment-sized molecules than lead-sized or drug-sized molecules. But is this really true? And if so, why? These questions are tackled by Marcel Verdonk and colleagues at Astex in a recent paper in J. Med. Chem.

The researchers examined 11 targets for which they had multiple crystal structures of each with bound fragments (which contained up to 15 non-hydrogen atoms) and larger molecules (which contained at least 20 non-hydrogen atoms); these crystal structures were the “correct” structures against which computational models could be judged. A total of 106 fragments and 100 larger molecules were then docked against their target proteins using a variety of different methods.

Surprisingly, the overall results were not overly impressive (<70% correct depending on methodology – often much less). But even more surprisingly, there was no difference between the success rates of the fragments and that of the larger molecules. However, the reasons for the poor performance were different. In the case of fragments, the problem was often that the scoring function didn’t recognize the correct solution; the energetics were just too subtle. In the case of the larger molecules, though, the problem was more often one of sampling: the docking program failed to produce the conformation of protein or ligand that corresponded to the correct solution, so it had no opportunity to score it. Potency made no difference: high-affinity compounds fared just as poorly as lower affinity compounds. What did make a difference, though, was ligand efficiency: compounds with high ligand-efficiency (> 0.4 kcal/mol/atom) were docked with considerably greater success than those with lower ligand efficiencies. As the authors point out, this makes sense intuitively:

High LE compounds form high-quality interactions with the target, which should make it easier for a docking program (both from a scoring and search perspective) to dock these compounds correctly.

So the next time you see a computational model of a protein-ligand complex, you might want to take a closer look at ligand efficiency to get a sense of how trustworthy the structure might be.

Journal of Computer-Aided Molecular Design 2011 Special FBDD Issue

The most recent issue of J. Comput. Aided Mol. Des. is entirely devoted to fragment-based drug discovery. This is the second special issue they’ve dedicated to this topic, the first one being in 2009.

Associate Editor Wendy Warr starts by interviewing Sandy Farmer of Boehringer Ingelheim. There are many insights and tips here, and I strongly recommend it for a view of how fragment-based approaches are practiced at one large company. A few quotes give a sense of the flavor.

On corporate environment:

In most cases, the difference between success and failure has little to do with the process and supporting technologies (they work!), but rather much more to do with the organizational structure to support FBDD and the organizational mindset to accept the different risk profile and resource model behind FBDD.

On success rates:

We have found that FBDD has truly failed in only 2-3 targets out of over a dozen or so.

On cost:

FBDD must be viewed as an investment opportunity, not a manufacturing process. And the business decisions surrounding FBDD should factor that in. FBDD is more about the opportunity cost (of not doing it) than the “run” cost (of doing it).

On expertise:

Successful FBDD still requires a strong gut feeling.

On small companies:

In the end, FBDD will always have a lower barrier to entry than HTS for a small company wanting to get into the drug-discovery space.

The key to success for such companies is to identify or construct some technology platform.

There’s a lot of other really great content in the issue, much of which has been covered in previous posts on fragment library design, biolayer interferometry, LLEAT, and companies doing FBLD. The other articles are described briefly below.

Jean-Louis Reymond and colleagues have two articles for mining chemical structures, one analyzing their enumerated set of all compounds having up to 13 heavy atoms (GDB-13), the other focused on visualizing chemical space covered by molecules in PubChem. They have also put up a free web-based search tool (available here) for mining these databases.

Roland Bürli and colleagues at BioFocus describe their fragment library and its application to discover fragment hits against the kinase p38alpha. A range of techniques are used, with reasonably good correlation between them.

Finally, M. Catherine Johnson and colleagues present work they did at Pfizer on the anticancer target PDK1 (see here and here for other fragment-based approaches to this kinase). NMR screening provided a number of different fragment hits that were used to mine the corporate compound collection for more potent analogs, and crystallography-guided parallel chemistry ultimately led to low micromolar inhibitors.

Designing fragment libraries

The topic of fragment library design is similar to foundation construction: most people don’t give it much thought, but any organization that doesn’t take it seriously could quickly find itself on shaky ground. Three recent papers cover different aspects of this topic.

The first paper, published in J. Comput. Aided Mol. Des. by researchers at Pfizer, describes the design and construction of their Global Fragment Initiative (GFI), a 2,885 fragment library meant to be broadly applicable to any target using any screening method (NMR, X-ray, SPR, MS, and biochemical assays). Most of these fragments came from commercial or in-house collections, but 293 were synthesized specifically for the library. All compounds were filtered to remove reactive or otherwise undesirable moieties. Interestingly, a large number of cationic and anionic molecules were included, based on the observation that many approved drugs are charged. Also, roughly a quarter of the compounds contained at least one chiral center.

Potential library members were put through a rather more rigorous selection than the standard Rule of 3 (for example, cLogP < 2.0). Molecular complexity was explicitly considered, and overly complex fragments were excluded. Fragments were also analyzed by 2D and 3D similarity and chosen to maximize diversity, though with the criterion that close analogs were available either in-house or commercially. Compounds were also chosen to allow rapid chemical elaboration. Finally, compounds were evaluated by NMR for purity and solubility at 1 mM in aqueous buffer and 50-100 mM in DMSO.

The bulk of the library (excluding custom-synthesized fragments) has been screened against at least 13 targets in 8 different protein families, mostly by NMR and biochemical assays, resulting in hit rates between 2.8 – 13%. Only one fragment hit all 13 targets, while 766 hit only one; in total 33% of the fragments hit one or more of the targets, a fraction eerily similar to that seen at Genentech and Vernalis. Overall this is a thorough, information-dense paper, and well worth reading if you are considering building or expanding a fragment library.

One of the most productive first steps you can take after identifying a fragment hit is to test close analogs or larger molecules that contain the fragment. Of course, it is easier to buy compounds than to make them, so a fragment library that effectively samples commercial compounds is likely to be useful. This “SAR by catalog” approach is the topic of the second paper, also in J. Comput. Aided Mol. Des., from Rod Hubbard and colleagues at Vernalis and the University of York.

The researchers analyzed catalogs of available compounds from each of three vendors (Asinex, Maybridge, and Specs). Filtering out undesirable functionalities and binning the molecules by size left 5600-7700 fragment-sized molecules and 28,600-252,000 larger molecules per vendor. Compound properties of the fragment sets (MW, polar surface area, number of hydrogen bond donors and acceptors, etc.) are summarized for each of the vendors, similarly to Chris Swain’s analysis. Six different algorithms were then tested to find sets of 200 fragments that would best represent the entire collection. In accordance with Murphy’s Law, the most complicated algorithm proved to be the most effective; it involves an iterative selection procedure with precisely defined similarity criteria. Still, this algorithm is not too difficult to implement, and it should prove a useful tool for selecting fragments from larger sets of commercial or in-house compounds.

Finally, a chapter by James Na and Qiyue Hu at Pfizer in a recent volume of Methods in Molecular Biology gives a broad overview of fragment library design. In addition to general considerations, the paper succinctly summarizes the design of the Pfizer Global Fragment Initiative as well as an earlier fragment library designed specifically for NMR screening. A more lengthy but instructive description of several Vernalis fragment libraries is also provided, as are some of the screening results. Finally, a nice table summarizes fragment libraries from more than a dozen companies.

First fragment-based drug approved

Today marks history with the first FDA approval of a drug to come out of fragment-based screening. The drug is branded as Zelboraf (vemurafenib), but readers of this blog are probably more familiar with its previous name of PLX4032. Although widely expected to be approved, the FDA acted more than two months ahead of schedule. The drug targets a mutant form of BRAF and has received widespread media coverage because of dramatic clinical results showing that it extends life for patients with a particularly deadly form of skin cancer. FiercePharma has an article with links to several others.

The drug was discovered at Plexxikon and developed in partnership with Roche; Plexxikon was acquired earlier this year by Daiichi Sankyo. The PLX4032 story is a case study in how rapidly fragments can enable a program: initiated in Februrary 2005, it took just six years to reach approval. It’s also an example of starting with a profoundly unselective fragment and winding up with a very selective drug (see here for early discovery and here for characterization of PLX4032).

Although I claim no prescience, I did state back in 2008 that it would be nice if a fragment-based drug would be approved by 2011. But more importantly, it is worth pausing to remember that this is a victory not just for the field of fragment-based drug discovery, but for those patients afflicted with metastatic melanoma. In the end, that’s what this is all about.

Fragment selectivity

A constant debate in fragment-based lead discovery is whether to focus on fragments that are selective for the target of interest. Because fragments have lower complexity than larger molecules they are likely to be less specific – that is, after all, one of the main arguments for why a small set of fragments can explore more chemical space than a much larger set of lead-like molecules. But does it make sense to prioritize those fragments that are more selective? In a recent issue of J. Med. Chem. Paul Bamborough and colleagues at GlaxoSmithKline address this question experimentally.

The broad family of kinases was chosen for the investigation. Protein kinases in particular have been a rich field for drug development, including fragment-based methods. The researchers assembled a library of 1065 commercially available fragments, most of which were designed to bind to the so-called “hinge” region of protein kinases where the substrate ATP binds. Of these fragments, 936 passed quality-control and maintained stability over the course of the year-plus study.

The researchers screened these fragments at 0.4 or 0.667 mM against a panel of 30 kinases using several different assay formats: FP (fluorescence polarization), IMAP (immobilization metal affinity phosphorylation), LEADseeker (a scintillation proximity assay), and TR-FRET (time-resolved fluorescence resonance energy transfer). Various experiments suggested that FP was most susceptible to assay artifacts, though the results were still usable.

17 of the fragments screened were chosen based on common fragment motifs in the literature. One example is adenine, a fragment of ATP, which of course is used by all kinases. Despite this universality, adenine actually showed surprising specificity, inhibiting some kinases strongly and not inhibiting others at all. The same goes for other hinge-binding fragments that we’ve seen before (such as indazole). On the other hand, biaryl urea fragments designed to bind to the less-conserved adaptive pocket of kinases were quite selective, hitting just 2 kinases strongly.

Of course, especially for kinases, the trick is not getting fragment hits but in figuring out which ones to pursue. Ligand efficiency is often used to prioritize fragments, but is this necessarily a good idea? The researchers compared published high-affinity inhibitors of several kinases with fragments contained within these inhibitors and found that the fragments often would not have stood out above the pack when compared solely on the basis of ligand efficiency. Even spookier, many of the most ligand-efficient fragments appear to be assay artifacts.

What about selectivity? Are non-selective fragments bound to become non-selective leads? The authors present one example of a rather non-selective fragment that could be optimized to a highly selective molecule; PLX4032, which started life as a promiscuous azaindole, is another example.

These are just anecdotes though, so to get a broader handle on this question the authors examined a set of 577 lead-like compounds that had been screened against 203 kinases. This led to a list of 592 matched pairs of lead-like compounds and fragment substructures (most of which are likely hinge-binders) which could be analyzed for selectivity. The results recapitulate a smaller, earlier study performed with a very different data set. As Bamborough et al. put it:

It is not uncommon to find selective lead-sized compounds based upon unselective fragments. Equally, unselective leadlike compounds are frequently based upon selective fragments. It seems that the property of selectivity need not be maintained between fragments and their related lead-sized molecules.

On one level, Bamborough’s study is a bit discouraging: fragment selectivity should be used cautiously if at all in prioritizing fragments. Even ligand efficiency should not be gating; last year we discussed how a fragment with relatively modest ligand efficiency was transformed into the clinical-stage (and more ligand efficient) Hsp90 inhibitor AT13387. Other factors, such as structural novelty or how amenable a fragment will be to further elaboration, are just as if not more important for choosing fragments. All of which serves to reemphasize the fact that drug discovery is less a series of hard and fast rules than a loose system of guidelines and hunches. This lack of predictability is part of what makes the process so frustrating – and fun.

Fragment-based events in 2011 and 2012

If you missed the fragment events earlier this year there is still one late addition to the calendar as well as some webinars. And it’s not too soon to be thinking about 2012!

August 16: Is your travel budget limited? Emerald Biosciences is putting together a series of free webinars related to FBLD on August 16, September 20, October 18, and November 15.

October 21: Zenobia Therapeutics is putting together a FBLD conference in San Diego. Although just one day, there is a nice lineup of speakers, so try to make it if you can.

2012

March 19-23: Keystone Symposium: Addressing the Challenges of Drug Discovery – Novel Targets, New Chemical Space and Emerging Approaches will be held in Tahoe City, CA. Although not exclusively devoted to fragments, there are many speakers I look forward to hearing.

April 17-19: Cambridge Healthtech Institute’s Seventh Annual Fragment-Based Drug Discovery will be held in San Diego. You can read impressions of this past year’s meeting here and 2010’s here.

September 23-26: FBLD 2012, the fourth in an illustrious series of conferences, will be held in my fair city of San Francisco. This should be a biggy – the first such event in the Bay Area (and the weather in September is usually decent too). You can read impressions of FBLD 2010 and FBLD 2009.

Know of anything else? Organizing a fragment event? Let us know and we’ll get the word out.

Ligand efficiency metrics poll results

Poll results are in, and not surprisingly, ligand efficiency (LE) comes out on top, with 86% of respondents using the metric. What was a surprise to me is how many folks use ligand lipophilic efficiency (LLE) (46%). Coming in a distant third at 15% is LLEAT, but given that this metric was just reported it has a pretty strong showing, and I wouldn't be surprised to see this increase. Binding efficiency index (BEI) comes in fourth with 12% of the vote, and Fsp3 is tied with "other" with 8% of the vote. The other metrics only received one or two votes each.
Since people could vote on multiple metrics, there were more responses than respondents. Subtracting those who voted for "none" leaves 124 data points, suggesting that the average researcher is using 1.9 of these metrics (though unfortunately we don't have information on the median user).

Finally, for the 5 of you who selected "other", what else is out there that we've left out?

Fragments vs RNA revisited: the power of two

RNA can assume complex three-dimensional structures just like proteins, and given the many roles it plays it is perhaps surprising that there are so few drugs that target this class of biomolecules. One problem is that ribonucleic acids are less diverse than amino acids, so there is less scope for developing small molecules that bind to specific regions of RNA. Nonetheless, a few brave souls have tried, some using fragment-based approaches. The latest such effort appears in J. Mol. Biol.

The researchers, led by Gabriele Varani of the University of Washington, Seattle, took a two-step approach to find two fragments that could simultaneously bind to the TAR element of HIV-1, a short stem-loop element essential for viral replication. The protein that normally binds to TAR contains a critical arginine residue, so the researchers started by purchasing a set of 16 arginine mimetics and using NMR to determine if any of them bound to TAR RNA. Several did, and one guanidine-containing molecule (MV2003) gave a strong NMR signal and also contained a hydrophobic element. The researchers decided to use this to hunt for a second fragment.

To find the second binder, the team screened 250 generic (ie, not targeted to RNA) fragments from Maybridge in pools of 5-8 in the presence of the first fragment. Remarkably, saturation transfer difference (STD) experiments, which detect changes in ligand NMR signals upon binding to macromolecules, suggested that more than 100 of these generic fragments appeared to bind to TAR RNA. However, more careful study of 20 representative fragments from 13 different scaffolds rapidly winnowed the set: 5 didn’t repeat when tested outside the pool, 6 gave signals in the absence of RNA, and 3 were not dependent on the presence of MV2003, suggesting that they bind nonspecifically. However, the remaining 6 only produced signals in the presence of both TAR RNA and MV2003, indicating a specific ternary complex. Although two of these fragments contain a (positively charged) primary amine, the rest are likely either neutral or only partially protonated at physiological pH. Interestingly, one of these is closely related to an RNA-binding fragment identified in previous work by a different group.

Next, the researchers constructed a model of how MV2003 bound to RNA. They used NMR data (nuclear Overhauser effects, or NOEs) to determine which atoms of MV2003 were close to which atoms of TAR RNA. Unfortunately no intermolecular NOEs were observed between any of the six fragments and the RNA, but it was possible to observe interligand NOEs (ILOEs) between the fragments and MV2003, and this enabled additional modeling suggesting that the fragments bind in a small pocket that only forms when MV2003 binds to RNA.

The paper ends with a cliff-hanger:

The formation of a new binding pocket allows binding of other fragments and suggests that more powerful ligands can be generated by linking the fragments together.

Although fragment linking is easier said than done, the hydrophobic moiety in MV2003 may improve the odds here, as described in the previous post. Practical Fragments hopes they will give it a shot!

Fragment linking: oil and water do mix

Fragment linking is one of the most seductive forms of fragment-based lead discovery: take two low-affinity binders, link them together, and get a huge boost in potency. But what’s appealing in theory is difficult in practice: the linked molecule rarely binds more tightly than the product of the fragment affinities, and sometimes there is not even an improvement over the starting fragments. In a recent paper in Molecular Informatics, Mark Whittaker and colleagues at Evotec suggest a strategy to maximize the chance of success.

The researchers start by briefly reviewing nine published examples of fragment linking where affinities for both fragments as well the linked molecule are provided (some of these have been discussed previously here, here, and here). Of these, only three examples showed clear superadditivity (in which the linked molecule has a significantly higher affinity than the product of the affinities of the individual fragments), and two of these examples are rigged systems in which a molecule already known for its potency (such as biotin) is dissected into fragments. The challenges of linking are succinctly summarized:

The keys to achieving superadditivity upon linking are to maintain the binding modes of the parent fragments, not introduce both entropy and solvation penalties while designing the linker, and also make any interactions with the intervening protein surface that need to be made.

Also, of course, the resulting molecule needs to be synthetically accessible. Having a certain amount of flexibility in the linker can be useful, as this will allow the fragments some room to shift around, but too much flexibility introduces an entropic cost that defeats the purpose of linking in the first place. Software tools such as those by BioSolveIT can help design the linker, but what if some fragments themselves are inherently better suited for linking?

All three of the examples that show superadditivity start with one fragment that is highly polar and makes hydrogen bonds or metal-mediated bonds with the protein. The researchers suggest that such fragments are likely to pay a heavy thermodynamic penalty when they are desolvated, and that this cost can be reduced by linking them to a hydrophobic fragment. Thus, to maximize your chances of successful linking, the authors suggest you should choose

a fragment pair that consists of one fragment that binds by strong H-bonds (or non-classical equivalents) and a second fragment that is more tolerant of changes in binding mode (hydrophobic or vdW binders).

This is an interesting proposal, though because there are so few examples it is hard to assess. Indeed, the only other case of clear superadditivity I found involves dimerizing a fragment that is reasonably hydrophobic (ClogP = 2.4), albeit negatively charged. Hopefully we’ll see more examples in the coming years, but in the meantime, linking a water-loving fragment to an oily one is worth a shot.

Who's doing FBLD in 2011?

It’s been almost two years since our last attempt at cataloging companies doing fragment-based lead discovery. That list contained 19 entries, and 5 others were mentioned in the comments section. A paper just published online by Phil Hajduk and colleagues at Abbott in J. Comput. Aided Mol. Des. provides another list of 19 companies, which has inspired Practical Fragments to combine the two to provide what we hope is the most comprehensive list of companies working in FBLD. (The paper itself is worth reading too for insights into how fragment screening has evolved. For example, Abbott pioneered the use of NMR for finding and characterizing fragments, but now functional screening, modeling, and X-ray crystallography are dominant.)

Companies doing FBLD:

Abbott Laboratories
Ansaris (previously Locus)
AstraZeneca
Astex
Beactica
BioFocus (Galapagos)
BioLeap
Biosensor Tools
BioSolveIT
Boehringer Ingelheim
Carmot Therapeutics
Crelux
Crown Biosciences
Crystax Pharmaceuticals (web site seems down - are they still around?)
Eli Lilly
Emerald BioStructures (from deCODE)
Evotec
Genentech (Roche)
Genzyme (Sanfi-Aventis)
GlaxoSmithKline
Graffinity Pharmaceuticals (NovAliX)
iNovacia
IOTA Pharmaceuticals
Johnson & Johnson
Kinetic Discovery
MEDIT
Merck
Nerviano Medical Sciences
NovAliX
Novartis
Pfizer
Pharma Diagnostics
Plexxikon
Polyphor
Proteros
Pyxis Discovery
Roche 
Schrodinger
Selcia
Sprint Bioscience
Structure Based Design
Vernalis
Zenobia Therapeutics
ZoBio

Unlike the previous list, this one also includes large pharmaceutical companies known to be active in FBLD. Companies that have been acquired or merged are listed separately if they maintain separate web sites. The list excludes companies solely focused on selling fragment libraries, as these are covered separately.

The current list includes some 44 companies, which illustrates how widespread FBLD has become. The continuity is also encouraging: despite the challenging economic environment of the last few years, aside from a few acquisitions, a name change, and a spin-off, all the companies from the 2009 list are still around (with the possible exception of Crystax).

I’m sure the list is still incomplete, so if you know of someone else please add them to the comment section.

Biolayer interferometry (BLI)

Surface plasmon resonance (SPR) has become a primary tool for finding fragments. One of its attractions is that, in addition to requiring only small amounts of protein, it can provide dissociation constants (Kd values) and, for tighter binders, on-rates and off-rates. However, SPR is not the only biosensor-based technology out there. Biolayer interferometry is a related technique, and, as judged by the discussion following the FBLD 2010 meeting, is clearly of interest to many people. A paper published online by Charles Wartchow and colleagues in J. Comput. Aided Mol. Des. provides a description of the technology and comparison with other methods.

Like SPR, BLI requires immobilization of the protein target to a surface; the current paper uses biotin-labeled proteins and streptavidin coated biosensors from ForteBio. Unlike SPR, the technology does not rely on samples flowing through tiny capillaries, and up to 16 protein-labeled sensors can be simultaneously dipped directly into different solutions of small molecules arrayed in a 384-well plate. BLI relies on changes in the interference pattern of light between the sensor and the solution caused when a small molecule binds to a protein on the surface of the sensor.

In the current study, the authors studied three proteins: Bcl-2, JNK1, and eIF4E. Initially a library of 140 fragments was screened in triplicate at 200 micromolar concentration against each of the three targets. Both JNK1 and Bcl-2 gave very high hit rates (24 and 21%, respectively), but eIF4E gave a much more “fragment typical” hit rate of 3.5%. This protein was subsequently screened against 6500 compounds, a task which required 1 mg of protein, 10 days, and 700 sensors (which needed to be periodically replaced throughout the campaign).

After curating the eIF4E hits to remove compounds that gave anomalously high signals or slow off-rates, the remaining molecules were then retested in a second screen, which confirmed 50% of the remaining hits, for an overall hit rate of 1.3%. However, many of these still looked suspicious when they were tested in 8-point titration curves; it seems that, like SPR, BLI is also prone to false-positive problems.

The researchers also ran biochemical and SPR screens on some of the targets. For eIF4E, the overlap between hits coming from BLI and those from biochemical screens was 52%, though many of these are derivatives of a single scaffold. Another subset of the common hits gave non-ideal behavior, calling into question their mechanism of action. It remains unclear whether the BLI hits that were not active in biochemical assays are real, and if so, relevant.

In the end, the authors conclude that:

These fragment screening studies demonstrate that BLI is suitable for small molecule characterization and fragment screening.

But they continue:

Hit assessment… with BLI and SPR is non-trivial, however, and although numerous hits from the BLI, SPR, and biochemical assays were characterized, most of the BLI and SPR data obtained from the examination of a concentration series in the micromolar range showed linear relationships with respect to concentration, unreasonably high signals, or slow off-rates.

Clearly, like all techniques, one should not rely on BLI alone. What remains to be seen is whether BLI has advantages over related techniques such as SPR, whether in terms of speed, sensitivity, resistance to artifacts, or cost. Several of the authors of the paper are from Roche, but the paper does not make clear whether BLI is becoming integrated into the workflow there. Is anyone else out there using BLI? If so, what has been your experience?