Fragments vs KAT6A: ligand efficiency in action

Epigenetic writers such as histone acetyltransferases (HATs) control gene expression, which often goes awry in cancer. The gene encoding the lysine acetyltransferase KAT6A, for example, is amplified in multiple types of cancer, and its overexpression is associated with poor clinical outcomes for patients with ER⁺ breast cancer. A recent paper in Bioorg. Med. Chem. Lett. from Andrew Buesking and colleagues at Prelude Therapeutics reports a new series of inhibitors.

 

The researchers started by considering previously reported molecules such as PF-9363. Recognizing the importance of the central sulfonamide, they generated a library of 150 fragments containing this core and screened them in a biochemical assay. Taking a similar approach as other researchers reporting on a different epigenetic target we discussed last year, the Prelude team explicitly used ligand efficiency to call hits, setting the cutoff at > 0.3 kcal per mol per heavy atom.

 

A direct deconstruction of PF-9363 led to compound 6, but, seeking something novel, the researchers were drawn to compound 8, with similar ligand efficiency. Modifications were made to both of the terminal aromatic groups, leading to compound 13, which was co-crystallized with KAT6A. This led to further structure-guided modifications, with compound 25 being the most potent.

 

Unfortunately, further improvements in potency were not forthcoming, and the ligand efficiency had dropped steadily from both the initial fragment as well as PF-9363. The researchers conclude by stating that they “decided to pursue alternative approaches.” Still, this paper is a nice, concise example of using metrics both for pursuing a chemical series and, ultimately, discontinuing it.

Efficiency metrics in action for a bromodomain inhibitor

The metrics ligand efficiency (LE) and lipophilic ligand efficiency (LLE or LipE) are frequently used during fragment-to-lead optimization. A recent paper in J. Med. Chem. by Philip Humphreys and colleagues at GSK describes how they were useful in developing an “oral candidate quality” inhibitor of BET-family bromodomains.

 

Practical Fragments has written frequently about bromodomains, which bind to acetyl-lysine residues in histones to epigenetically modulate gene transcription. Some 17 bromodomain inhibitors have entered the clinic, of which at least three (pelabresib, PLX51107, and ABBV-744) came from fragments. GSK was an early pioneer in the field, and researchers there were interested in using fragments to develop a differentiated class of molecules that would inhibit all four members (BRD2, BRD3, BRD4, and BRDT) of the BET family, each of which contains two separate bromodomains designated as either BD1 or BD2.

 

GSK already had BRD4 BD1 binding data for 50,000 compounds, and these were analyzed to find molecules with LE>0.3 kcal/mol per atom that were structurally differentiated from known bromodomain binders. Compound 9 was quite potent and had high LE as well as respectable LipE. (As the researchers note, LE is “the more relevant metric” for fragments, with LipE becoming increasingly important during later optimization.) A crystal structure of this molecule superposed onto another bromodomain inhibitor suggested that adding a methyl group to fill a small pocket could boost affinity, and this was confirmed by compound (R)-10. This molecule showed cell activity and good permeability, although hepatocyte stability was poor, likely due to the two methoxy groups. Removing these led to compound 12, the most ligand-efficient compound that had been seen. (All values in the figure below are for binding to BRD4 BD1.)

 

Compound 12 mimics the N-acetyl lysine residue of the natural ligand, and previous research had revealed two additional regions of the bromodomain that could be targeted for enhanced affinity, the so-called “WPF shelf” and the “ZA channel.” Structure-based design was used to independently explore both areas, leading to compounds such as 24 and 31. In addition to assessing LE and LipE, the researchers paid close attention to other factors such as permeability. Virtually combining the best moieties that bind at the WPF shelf and ZA channel led to 770 potential molecules to make, which were winnowed down to just 40 on the basis of predicted lipophilicity (specifically chromLogD_pH7.4), molecular weight, and TPSA. The best of these were more extensively profiled, including in pharmacokinetic studies. I-BET432 emerged as the winner.

 

I-BET432 binds tightly to both bromodomains of the four BET family proteins and is at least 80-fold selective against two dozen other bromodomains. It shows excellent oral bioavailability in rats and dogs, does not inhibit hERG, is not mutagenic in an Ames test, and does not inhibit CYP3A4. The molecule is also clean in a panel of four dozen off-target proteins. Human oral dose predictions come in at 5-18 mg per day. A crystal structure of the molecule bound to BRD2 BD2 showed the expected binding pose, and that the two alcohol substituents may be forming an intra-molecular hydrogen bond, which could explain the high permeability.

 

This is a nice case study in metric-driven optimization. As the researchers note, I-BET432 “has the highest LipE (6.2) and LE (0.43) of the candidate quality GSK pan-BET inhibitors disclosed to date.” Although the molecule does not seem to have gone forward into development, the story is nonetheless worth reading to see how metrics can yield quality molecules.

Review of 2019 reviews

The year ends, and with it the awkward teenage phase of the twenty-first century. As we have done since 2012, we're using this last post of the year to highlight conferences and reviews over the previous twelve months.

There were some good events, including CHI’s Fourteenth Annual Fragment-based Drug Discovery meeting in San Diego in April, their Discovery on Target meeting in Boston in September, and the third Fragment-based Drug Design Down Under 2019 in Melbourne in November, which also saw the launch of the Centre for Fragment-Based Design. Our updated schedule of 2020 events will publish next week.

Turning to FBLD reviews, Martin Empting (Helmholtz-Institute for Pharmaceutical Research Saarland) and collaborators published a general overview in Molecules. This is a nice up-to-date summary, covering library design, methods to find, confirm, and rank fragments, and optimization approaches. It’s also open access so you can read it anywhere.

Targets

Protein-protein interactions can be particularly challenging drug targets, and these are covered in a Eur. J. Med. Chem. review by Dimitrios Tzalis (Taros Chemicals), Christian Ottmann (Technische Universiteit Eindhoven) and colleagues. The focus is on clinical compounds, and several of these – including venetoclax, ASTX660, mivebresib, onalespib – are discussed in detail. The article is particularly useful in discussing late-stage optimization of pharmacokinetic and pharmacodynamic properties. It also provides a nice summary of physicochemical properties for fragment hits and derived candidates.

Target selectivity is always important, and this is the focus of a review in Exp. Opin. Drug Disc. by Rainer Riedl and collaborators at the Zurich University of Applied Sciences and the Università degli Studi dell’Insubria. Although the broader topic is de novo drug design, fragment-based methods are prominent, and include case studies we’ve discussed on nNOS, pantothenate synthetase, and MMP-13.

In terms of specific targets, Fubao Huang, Kai Wang, and Jianhua Shen at the Shanghai Institute of Materia Medica provide an extensive review of lipoprotein-associated phospholipase A2 (Lp-PLA₂) in Med. Res. Rev. This serine hydrolase has been studied for four decades but – as the researchers note – “divergence seems to be ubiquitous among Lp-PLA₂ studies.” At least this is not for lack of good chemical tools, fragment-derived (see here, here, and here) and otherwise.

Methods

Although NMR has fallen behind crystallography in our latest poll, that is certainly not reflected in terms of reviews. In particular, ¹⁹F NMR is covered in three papers. CongBao Kang (A*STAR) manages to pack a lot (including 261 references!) into a concise review in Curr. Med. Chem. Topics include protein-observed ¹⁹F NMR, in which one or more fluorine atoms are introduced into a protein genetically, enzymatically, or chemically, as well as ligand-observed methods, in which fluorine-containing small molecules are directly observed or used as probes that are displaced by non-fluorine-containing molecules.

Protein-observed ¹⁹F NMR (PrOF NMR) is covered in Acc. Chem. Res. by William Pomerantz and colleagues at the University of Minnesota. Although the first example was published 45 years ago, only in the past few years has the technique been used for studying protein-ligand interactions. The researchers note that introducing fluorines into aromatic residues is ideal because they are relatively rare, simplifying interpretation, and overrepresented at protein-protein interactions, maximizing utility. Several case studies are described, and even proteins as large as 180 kDa are amenable to the technique.

Ligand-based fluorine NMR screening is simpler and more common than techniques that focus on proteins, and this topic is thoroughly reviewed by Claudio Dalvit (Lavis) and Anna Vulpetti (Novartis) in J. Med. Chem. After a section on theory, the researchers discuss library design, including a long section on quality control (which involves assessing solubility, purity, and aggregation of the molecule in a SPAM filter). Direct and competition-based screening approaches are covered in detail; for the latter, a new method for determining binding constants is provided. The paper concludes with more than a dozen case studies. Clearly much has changed in the ten years since I wondered “why fluorine-labeled fragments are not used more widely.” This perspective is a definitive guide to the topic.

Moving to less common methods for characterizing fragments, György Ferenczy and György Keserű (Research Center for Natural Sciences, Budapest) cover thermodynamic profiling in Expert Opin. Drug Disc. After discussing several case studies, they conclude that “thermodynamic quantities are not suitable endpoints for medicinal chemistry optimizations” due to the complexity of contributing factors. This is consistent with another recent paper on the subject (see here), though the information provided is still interesting for understanding molecular interactions.

And although you might have thought the 2017 VAPID publication was the last word on the limitations of ligand efficiency (LE), Pete Kenny has published a splenetic jeremiad on the topic in J. Cheminform. (see also his blog post on the topic, which includes a sea serpent). This is largely a retread of a 2014 article on the same topic (reviewed by Teddy in his inimitable manner here). Pete also describes a more complicated alternative to LE involving residuals, though unfortunately he provides no evidence that it provides more useful information. Pete is of course correct to remind us that metrics have limitations, but assertions that LE “should not even be considered to be a metric” are overwrought.

Chemistry

Two articles discuss virtual chemical libraries. In J. Med. Chem., W. Patrick Walters (Relay Therapeutics) describes efforts to measure, enumerate, and explore chemical space. He notes that false positives could quickly overwhelm a virtual screen of a hundred million molecules, but as we saw earlier this year, progress is being made. Indeed, Torsten Hoffmann (Taros Chemicals) and Marcus Gastreich (BioSolveIT) focus on navigating the vastness of chemical space in Drug Disc. Today. They note that the Enamine REAL Space is up to 3.8 billion commercially accessible compounds, more than double the number of stars in the Milky Way. But this pales in comparison to the 10²⁰ potential compounds in Merck’s MASSIV space. Just storing the chemical structures of these in compressed format would require 200,000 terabytes – and searching them exhaustively is beyond current technology.

Ratmir Derda and Simon Ng (University of Alberta) discuss “genetically encoded fragment-based discovery” in Curr. Opin. Chem. Biol. This involves starting with a known fragment that is then coupled to a library of peptides and screened to find tighter binders. The researchers provide a number of case studies, though adding even a small peptide to a fragment will generally have deleterious effects on ligand efficiency. And – Rybelsus not withstanding – oral delivery of peptides is challenging.

Finally, Vasanthanathan Poongavanam, Xinyong Liu, and Peng Zhang, and collaborators at Shandong University, University of Bonn, University of Southern Denmark, and K.U. Leuven review “recent strategic advances in medicinal chemistry” in J. Med. Chem. Among a wide range of topics from drug repurposing to antibody-recruiting molecules is a nice, up-to-date section on target-guided synthesis. As I opined a couple years ago, I still doubt whether this will ever be generally practical, but from an intellectual standpoint I’m happy to see work continue on the approach.

And with that, Practical Fragments says goodbye to the teens and wishes you all a happy new year. Thanks for reading and commenting. May 2020 bring wisdom, and progress.

Review of 2018 reviews

As 2018 recedes into history, we are using this last post of the year to do what we have done since 2012 – review notable events along with reviews we didn’t previously cover.

This was a busy year for meetings, starting in January with a FragNet event in Barcelona, then moving to San Diego in April for the annual CHI FBDD meeting. Boston saw an embarrassment of riches, from the first US-based NovAliX meeting, to a symposium on FBDD at the Fall ACS meeting, followed closely by a number of relevant talks at CHI’s Discovery on Target. Finally, the tenth anniversary of the renowned FBLD meeting returned to San Diego. Look for a schedule of 2019 events later this month.

If meetings were abundant, the same can be said for reviews.

Lead optimization

Writing in J. Med. Chem., Dean Brown and Jonas Boström (AstraZeneca) asked “where do recent small molecule clinical development candidates come from?” For three quarters of the 66 molecules published in J. Med. Chem. in 2016 and 2017 the answer is from known compounds or HTS, though fragments accounted for four examples. Although average molecular weight increased during lead optimization, lipophilicity did not, suggesting the importance of this parameter.

The importance of keeping lipophilicity in check is also emphasized by Robert Young (GlaxoSmithKline) and Paul Leeson (Paul Leeson Consulting) in a massive J. Med. Chem. treatise on lead optimization. Buttressed with dozens of examples, including several from FBLD, they show that the final molecule is usually among the most efficient (in terms of LE and LLE) in a given series, even when metrics were not explicitly used by the project team. Perhaps with pedants like Dr. Saysno in mind, they also emphasize the complexity of drug discovery, and note that “seeking optimum efficiencies and physicochemical properties are guiding principles and not rules.”

Lipophilic ligand efficiency (LLE) is also the focus of a paper in Bioorg. Med. Chem. by James Scott (AstraZeneca) and Michael Waring (Newcastle University). This is based largely on personal experiences and provides lots of helpful tips. Importantly, the researchers note that calculated lipophilicity values can differ dramatically from measured values, and go so far as to say that “this variation is sufficient to render LLEs derived from calculated values meaningless.”

Turning wholly to fragments, Chris Johnson and collaborators (including yours truly) from Astex, Carmot, Vrije Universiteit Amsterdam, and Novartis have published an analysis in J. Med. Chem. of fragment-to-lead success stories from last year. This review, the third in a series, also summarizes all 85 examples published between 2015 and 2017, confirming and expanding some of the trends we mentioned last year.

Targets

Two reviews focus on specific target classes. Bas Lamoree and Rod Hubbard (University of York) cover antibiotics in SLAS Discovery. After a nice, concise review of fragment-finding methods, the researchers discuss a number of case studies, many of which will be familiar to regular readers of this blog, including an early example of whole-cell screening.

David Bailey and collaborators from IOTA and University of Cambridge discuss cyclic nucleotide phosphodiesterases (PDEs) in J. Med. Chem. The researchers provide a good overview of the field, including mining the open database ChEMBL for fragment-sized inhibitors. As they point out, the first inhibitors discovered for these cell-signaling enzymes were fragment-sized, so it is no surprise that FBLD has been fruitful – see here for an example from earlier this year. Interestingly though, although at least six fragment-sized PDE inhibitor drugs have been approved, none of these were actually discovered using FBLD.

PDEs are an example of “ligandable” targets, for which small molecule modulators are readily discovered. In Drug Discovery Today, Sinisa Vukovic and David Huggins (University of Cambridge) discuss ligandability “in terms of the balance between effort and reward.” They use a published database of protein-ligand affinities to develop a metric, LIG_exp, for experimental ligandability, and also describe their computational metric, Solvaware, which is based on identifying clusters of water molecules binding weakly to a protein. Comparisons with experimental data and with other predictive metrics, such as FTMap, reveal that while the computational methods are useful, there is still room for improvement.

We have previously written about how target-guided synthesis methods such as dynamic combinatorial chemistry have – despite decades of research – yielded few truly novel, drug-like ligands. Is this because the targets chosen were simply not ligandable? In J. Med. Chem., Anna Hirsch and collaborators at the University of Groningen, the Helmholtz Institute for Pharmaceutical Research, and Saarland University review some (though by no means all) published examples and examine their computationally determined ligandability scores. There seems to be no difference between these targets and a set of traditional drug targets.

Finding fragments

Crystallography continues to be a key tool for FBLD: as we noted in the review of the 2017 literature, 21 of the 30 examples made use of a crystal structure of either the starting fragment or an analog, and only 3 projects didn’t use crystallography at all. That said, FBLD is possible without crystallography, as illustrated through multiple examples in a Cell Chem. Biol. review by Wolfgang Jahnke (Novartis), Ben Davis (Vernalis), and me (Carmot).

In the absence of a crystal structure, NMR is best suited for providing structural information, and this is the subject of a review in Molecules by Barak Akabayov and colleagues at Ben-Gurion University of the Negev. The researchers provide a nice summary of NMR screening methods and success stories within a broader history of FBLD. They also include an extensive list of fragment library providers as well as a discussion of virtual screening.

Speaking of virtual screening, three reviews cover this topic. In Methods Mol. Biol., Durai Sundar and colleagues at Indian Institute of Technology Delhi touch on a number of computational approaches for de novo ligand design, though the lack of structures sometimes makes it challenging to read. A broader, more visually appealing review is published in AAPS Journal by Yuemin Bian and Xiang-Qun Xie at University of Pittsburgh. In addition to an overview and case studies, the researchers also provide a nice table summarizing 15 different computational programs. One of these, SEED, is a main focus of a review in Eur. J. Med. Chem. by Jean-Rémy Marchand and Amedeo Caflisch (University of Zürich). The researchers describe how this docking program can be combined with X-ray crystallography (SEED2XR) to rapidly identify fragments; we highlighted an example with a bromodomain. Their ALTA protocol uses SEED to generate larger, more potent molecules, as we described for the kinase EphB4. The researchers note that together these protocols have led to about 200 protein-ligand crystal structures deposited in the PDB over the past five years.

Rounding out methods, Sten Ohlson and Minh-Dao Duong-Thi (Nanyang Technological University) provide a detailed how-to guide in Methods for performing weak affinity chromatography, and how this can be combined with mass spectrometry (WAC-MS), as we noted last year.

Chemistry

One drawback of some computational approaches for fragment optimization is that they do not consider synthetic accessibility. In Mol. Inform., Philippe Roche, Xavier Morelli, and collaborators at Aix-Marseille University and Institut Paoli-Calmettes focus on hit to lead approaches that do, and provide a handy table summarizing nearly a dozen computational methods. We highlighted one from the authors, DOTS, earlier this year.

DOTS is an example of using DOS, or diversity-oriented synthesis. In Front. Chem., David Spring and colleagues at University of Cambridge review recent applications of DOS for generating new fragments, some of which we recently highlighted. Only a couple examples of successfully screening these new fragments are described, but the authors note that this is likely to increase as virtual library screening continues to advance.

Perhaps the most productive fragment of all time is 7-azaindole, the origin of three fragment-derived clinical compounds. (The moiety appears in both approved FBLD-derived drugs, vemurafenib and venetoclax.) Takayuki Irie and Masaaki Sawa of Carna Biosciences devote their attention to this little bicycle in Chem. Pharm. Bull. The researchers count six clinical kinase inhibitors that contain 7-azaindole (not all from FBLD) as well as more than 100,000 disclosed compounds containing the fragment. More than 90 kinases have been targeted by molecules containing 7-azaindole, and the paper provides a list of 70 PDB structures of 37 different kinases bound to molecules containing the moiety.

Finally, in J. Med. Chem., Brian Raymer and Samit Bhattacharya (Pfizer) survey the universe of “lead-like” drugs. Among the most highly prescribed small molecule drugs, 36% have molecular weights below 300 Da. Only 28 of 174 drugs approved between 2011 and 2017 fall into this category, consistent with the increasing size of newer drugs. The researchers discuss 16 recently approved drugs, and find that 13 have very high ligand efficiencies (at least 0.4 kcal mol^-1 per heavy atom). As noted above, optimization often entails adding molecular weight by growing or linking, and the researchers suggest that alternative strategies such as conformational restriction and truncation also be investigated.

And with that, Practical Fragments wishes you a happy new year. Thanks for reading some of our 686 posts over the past decade plus, and please keep the comments coming!

Ligand efficiency invalidated!

Practical Fragments has had quite a few posts on ligand efficiency (see here, here, and here, for starters). Ligand efficiency (LE) is defined simply as the free energy of binding for a ligand divided by the number of heavy atoms in the ligand. One of the criticisms of LE is that the definition of free energy depends on the definition of standard state, which may be different on different planets. With the discovery of silicon-based life on Venus, this is no longer just an academic argument. Indeed, a recent paper in Venusian Analytical, Physical, & Inorganic Discoveries describes an excellent case study.

Professor Perelandra and colleagues at East Eistla University performed a crystallograhic fragment screen on the enzyme silica hydratase, which is essential for the life cycle of the viciously parasitic Crystalline Horde. Fragment 1 binds in the active site, and although it has low affinity, structure-guided medicinal chemistry rapidly led to compound 42, with low nM activity in vitro and good efficacy in a silicon resorption model.

Things get even more interesting when you calculate the ligand efficiency values. The Venusians define standard temperature and pressure very differently from us. More importantly, they don't believe that standard state concentration should be 1 M. Given the extreme conditions on their home world, they choose a standard state concentration of 10 M.

LE = - ΔG/HA

(where HA = number of non-hydrogen atoms)

Thus, LE_Venus = -RTln(K_D/[A]⁰)/HA

(where T = 737 K and [A]⁰ = 10 M)

Using our terracentric definitions, the (impressive) LE of the fragment hit stays roughly the same during optimization, suggesting that the medicinal chemists have done a good job. However, by Venusian standards, the LE decreases!

This rock-solid example shows that Dr. Saysno was right: ligand efficiency is arbitrary and should never be used – on Venus.

An interview with Dr. Saysno

Readers of a certain age may fondly remember the interviews with Dr. Noitall that used to enliven the pages of Science. Sadly, he died a few years back. But his cousin, Dr. Saysno, is still very much alive. Practical Fragments caught up with him at a recent conference in Shutka.

Practical Fragments (PF): Dr. Saysno, you've stated that experts should never be trusted.

Dr. Saysno (DS): Niels Bohr defined an expert as a person who has made all the mistakes that can be made in a very narrow field. If someone has made every possible mistake, how could you possibly trust them?

PF: But don't you think they may have learned from their mistakes?

DS: Balderdash! Hegel was right: the only thing we learn from history is that we learn nothing from history.

PF: What's your opinion of ligand efficiency (LE)?

DS: Ligand efficiency is an abomination! It's mathematically invalid!

Worse, determining the free energy of binding from a dissociation constant is not even wrong: if you change your definition of standard state, you can make ΔG° any positive or negative number you want. Just how relevant do you think your definition of standard state is on the surface of Venus? Or Pluto?

For the same reason, pH is utterly meaningless. You really ought to throw out your pH paper, not to mention your pH meters, since they all assume an arbitrary reference state.

PF: But what about all the researchers who find pH and LE useful?

DS: Usefulness is the last refuge of the scoundrel!

Look, many of the best selling drugs on the market are antibodies, and when you calculate their ligand efficiencies, they are close to zero. How can you have a metric that doesn't work on some of the most important drugs out there?

I only believe in equations that are universal and apply in all situations, unsullied by the physical world. Anything that involves standard states is just mumbo-jumbo.

PF: What do you think of pan-assay interference compounds, or PAINS?

DS: Now that’s a topic that really gets my blood boiling! PAINS were defined on the basis of just six assays. Six assays I tell you!!! [DS vigorously pounds his shoe on the desk.] Just because something hits six assays – or six hundred for that matter – doesn’t mean it will hit the six hundred and first!

PF: But aren't there some chemical substructures that are so generically reactive they should never be used in probes?

DS: Nothing is universal! All molecules are unique, like little snowflakes. If a compound comes up as a hit in your assay, by all means publish it as a chemical probe in the best possible journal, and try to encourage suppliers to start selling it so other people can use and cite your brilliant discovery.

No one has a right to criticize your molecule unless they test it against every single protein in the human body and show that it hits all of them.

When the revolution comes, the imperialist PAINS stooges will be swept into the dustbin of history along with the lackeys of ligand efficiency!

PF: So if you don't trust experts, you don't like metrics, and you can't make generalizations, how can we move forward in science short of deriving every result ourselves from first principles?

DS: That's simple: just ask me!

Fragments vs MKK3: modeling all the way to low nanomolar

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

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

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

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

Group efficiency

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

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

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

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

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

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

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

Fragment-Based Drug Discovery

This is the straight-to-the-point title of a new book published by the Royal Society of Chemistry, edited by Steven Howard (Astex) and Chris Abell (University of Cambridge). It is the second book on the topic published so far this year, and it is a testimony to the fecundity of the field that the two volumes have very little overlap.

After a brief forward by Harren Jhoti (Astex) and a preface by the editors, the book opens with two personal essays. The first, by me, is something of an apologia for Practical Fragments and the growing role of social media in science (and vice versa). If you’ve ever wondered how this blog got started or why it keeps going, this is where to find out. The second essay is by Martin Drysdale (Beatson Institute). Martin is a long-time practitioner of FBDD, dating back to his early days at Vernalis (when it was RiboTargets) and he tells a fun tale of “adventures and experiences.”

Chapter 1, by Chris Abell and Claudio Dagostin, is entitled “Different Flavours of Fragments.” With a broad overview of the field it makes a good introduction to the book. There are sections on fragment identification, including the idea of a screening cascade, as well as several case studies, some of which we’ve covered on Practical Fragments, including pantothenate synthetase, CYPs, RAD51, and riboswitches.

The next two chapters deal with two of the key fragment-finding methods. Chapter 2, by Tony Giannetti and collaborators at Genentech, GlaxoSmithKline, and SensiQ, covers surface plasmon resonance (SPR). This includes an extensive discussion of data processing and analysis, which is critical for improving the efficiency of the technique. Competition studies are also described, as are advances in hardware, notably those from SensiQ. This is a good complement to Tony's 2011 chapter.

Chapter 3, by Isabelle Krimm (Université de Lyon), provides a thorough description of NMR methods, both ligand-based (STD, WaterLOGSY, ILOE, etc) and protein-based (mostly HSQC). The chapter does a nice job of describing techniques in terms a non-specialist can understand while also providing practical tips on matters such as optimal protein size and concentration.

Chapter 4, by Ian Wall and colleagues at GlaxoSmithKline, provides an overview of FBLD from the viewpoint of computational chemists. The chapter includes some interesting tidbits, such as the observation that fragment hits that yield crystal structures tend to be less lipophilic but also contain a smaller fraction of sp³ atoms and more aromatic rings. The researchers note that the current fashion for “3D” fragments is yet to be experimentally validated. They also include accessible sections on modeling, druggability, and integrating fragment information into a broader medicinal chemistry program.

The remaining chapters focus on specific types of targets. Chapter 5, by Miles Congreve and Robert Cooke (both at Heptares) is devoted to G protein-coupled receptors (GPCRs). This includes descriptions of how to screen fragments against these membrane proteins using SPR, TINS, CE, thermal melts, and competition binding. It also includes a detailed case study of their β1 adrenergic receptor work (summarized here). Congreve and Cooke assert that, although many of the GPCR targets screened to date have been highly ligandable, technical challenges only now being addressed have caused this area of research to lag about a decade behind other targets. They predict a bright future.

Rod Hubbard (Vernalis and University of York) turns to protein-protein interactions in Chapter 6. After describing why these tend to be more challenging than most enzymes and covering some of the methods for finding and advancing fragments, he then presents several case studies, including FKBP (one of the first targets screened using SAR by NMR), Bcl-2 family members (including Bcl-x_L and Mcl-1), Ras, and BRCA2/RAD51. He concludes with a nice section on “general lessons,” which boils down to “patience, pragmatism, and integration.” As Teddy recently noted, this can lead to substantial rewards.

Allosteric ligands have potential advantages in terms of selectivity and addressing otherwise challenging targets, and in Chapter 7 Steven Howard (Astex) describes how fragments can play a role here. This includes how to establish functionality of putative allosteric binders, as well as case studies such as HIV-1 RT, FPPS, and HCV NS3. Astex researchers have recently stated that they find on average more than two ligand binding sites per protein, and this chapter includes a table listing these (including 5 binding sites each on bPKA-PKB and PKM2).

The longest chapter, by Christina Spry (Australian National University) and Anthony Coyne (University of Cambridge) describes fragment-based discovery of antibacterial compounds. After discussing some of the challenges, the authors report several in depth case studies including DNA gyrase, DNA ligase, CTX-M, AmpC, CYP121, and pantothenate synthetase, among others. At least one fragment-derived antibacterial agent entered the clinic; hopefully more will follow.

Chapter 9, by Iwan de Esch and colleagues at VU University Amsterdam, focuses on acetylcholine-binding proteins (AChBPs), both as surrogates for membrane-bound acetylcholine receptors and as well-behaved model proteins on which to hone techniques (see for example here, here, and here). Since AChBPs have evolved to bind fragment-sized acetylcholine, these proteins can bind tightly to small ligands; 14-atom epibatidine binds with picomolar affinity, for example, with a ligand efficiency approaching 1 kcal mol^-1 atom^-1.

And Chapter 10, by Chun-wa Chung and Paul Bamborough at GlaxoSmithKline, concisely covers epigenetics. Bromodomains are well-represented, including a table of ten examples (see for example here, here, here, here, here, and here). Happily, although some of these projects started from similar or identical fragments, the final molecules are quite divergent. However, the authors note that much less has been published on histone-modifying enzymes, such as demethylases and deacetylases, perhaps reflecting the challenges of achieving specificity with what are often metalloenzymes.

Finally, this is the 500^th post since Teddy founded Practical Fragments way back in the summer of 2008. Thanks for reading, and special thanks for commenting!

Fragments and HTS vs BCATm

One of the themes throughout this blog is that fragments are useful not just in and of themselves, but as part of a broader tool kit, what Mark Whittaker referred to as fragment-assisted drug discovery, or FADD. A nice example of this has just been published in J. Med. Chem. by Sophie Bertrand and colleagues at GlaxoSmithKline and the University of Strathclyde.

The researchers were interested in mitochondrial branched-chain aminotransferase (BCATm), an enzyme that transforms leucine, isoleucine, and valine into their corresponding α-keto acids. Knockout mouse studies had suggested that this might be an attractive target for obesity and dyslipidemia, but there’s nothing like a chemical probe to really (in)validate a target. To find one, the researchers performed both fragment and high-throughput screens (HTS).

The full results from the fragment screen have not yet been published, but the current paper notes that the researchers screened 1056 fragments using biochemical, STD-NMR, and thermal shift assays. Compound 1 came up as a hit in all three assays, and despite modest potency and ligand efficiency, it did have impressive LLE_AT. The researchers were unable to determine a crystal structure of this fragment bound to the protein, but STD-NMR screens of related fragments yielded very similar hits that could be successfully soaked into crystals of BCATm.

The HTS also produced hits, notably compound 4, which is clearly similar to compound 1. In addition to its increased biochemical potency, it also displayed good cell activity. Moreover, a crystal structure revealed that the bromobenzyl substituent bound in an induced pocket that did not appear in the structure with the fragment, or indeed in any other structures of BCATm.

The researchers merged the fragment hits with the HTS hits to get molecules such as compound 7, with a satisfying boost in potency. Interestingly, the fragment-derived core consistently gave a roughly 10-fold boost in potency compared to the triazolo compounds from HTS. Comparison of crystal structures suggested that this was due to the displacement of a high-energy water molecule by the nitrile.

Extensive SAR studies revealed that the propyl group could be extended slightly but most other changes at that position were deleterious. The bromobenzyl substituent was more tolerant of substitutions, including an aliphatic replacement, though this abolished cell activity. Compound 61 turned out to be among the best molecules in terms of potency and pharmaceutical properties, including an impressive 100% oral bioavailability and a 9.2 hour half-life in mice. Moreover, this compound led to higher levels of leucine, isoleucine, and valine when mice were fed these amino acids.

This is a lovely case study of using information from a variety of sources to enable medicinal chemistry. Like other examples of FADD, one could argue as to whether the final molecule would have been discovered without the fragment information, but it probably at least accelerated the process. More importantly, molecules such as compound 61 will help to answer the question of whether BCATm will be a viable drug target. 

Fragments vs BTK: metrics in action

Sometimes the discussions over metrics, such as ligand efficiency, can devolve into exegesis: people get so worked up over details that they forget the big picture. A recent paper in J. Med. Chem. by Chris Smith and (former) colleagues at Takeda shows how metrics can be used productively in a fragment-to-lead program.

The researchers were interested in developing an inhibitor of Bruton’s Tyrosine Kinase (BTK) as a potential treatment for rheumatoid arthritis. This is the target of the approved anti-cancer drug ibrutinib, but ibrutinib is a covalent inhibitor, and the Takeda researchers were presumably concerned about the potential for toxicities to arise in a chronic, non-lethal indication. Many of the reported non-covalent BTK inhibitors are large and lipophilic, with consequently suboptimal pharmacokinetic properties. Thus, the team set out to design molecules with MW < 380 Da, < 29 non-hydrogen atoms (heavy atoms, or HA), and clogP ≤ 3.

The first step was a functional screen of Takeda's 11,098 fragment library, all with 11-19 HA, comfortably within the bounds of generally accepted fragment space. At 200 µM, 4.6% of the molecules gave at least 40% inhibition. Hits that confirmed by STD NMR were soaked into crystals of BTK, ultimately yielding 20 structures. Fragment 2 was chosen because of its high ligand efficiency, novelty, and the availability of suitable growth vectors.

Close examination of the structure suggested a fragment-growing approach. Throughout the process, the researchers kept a critical eye on molecular weight and lipophilicity. This effort led through a series of analogs to compound 11, with only 24 heavy atoms and clogP = 1.7. This molecule is potent in biochemical and cell-based assays and has excellent ligand efficiency as well as LLE (LipE). Moreover, it has good pharmacokinetic properties in mice, rats, and dogs, with measured oral bioavailability > 70% in all three species. Finally, compound 11 shows efficacy in a rat model of arthritis when dosed orally once per day.

Although compound 11 is selective over the closely related kinase LCK, unfortunately it is a double digit nanomolar inhibitor of oncology-related kinases such as TNK2, Aurora B, and SRC, which would probably be unacceptable in an arthritis drug. Nonetheless, this study is a lovely example of fragment-growing guided by a strict commitment to keeping molecular obesity at bay.

Tenth Annual Fragment-based Drug Discovery Meeting

Last week marked the tenth anniversary of CHI’s three-day Drug Discovery Chemistry conference in San Diego. The conference consists of six tracks, with three happening simultaneously. The FBDD track is the only one which dates all the way back to the beginning in 2006. In fact, this is the oldest recurring fragment conference, predating both the Royal Society Fragments meetings as well as the independent FBLD meetings.

It’s worth reflecting on how far fragments have come since 2006. Back then, as Rod Hubbard (Vernalis and University of York) noted, most of the talks were prospective and methodological. Even as late as 2010 there were talks describing how dedicated fragment groups needed to be shielded from the larger organization. Now fragments are mainstream: a large fraction of the talks in the protein-protein interaction track involved fragments, as did both plenary keynote addresses to the entire conference.

Harren Jhoti’s keynote focused on lessons learned at Astex over the past 15 years. There has been some debate in the literature over ligand efficiency (LE), and one slide that struck me was a summary of 782 dissociation constants (measured by ITC) against 20 projects. The vast majority of these compounds had LE > 0.3 kcal/mol/atom. Given that Astex has put multiple fragment-derived drugs into the clinic and was acquired by Otsuka in one of the largest M&A events of 2013, the metric appears to have some utility.

Still, it’s important not to be dogmatic, particularly for difficult targets. Harren described a program for XIAP/cIAP where they started with an extremely weak fragment with LE < 0.2, but its binding mode was sufficiently interesting that they were willing to work on it. This program also revealed the importance of biophysical measurements, as functional activity was uninterpretable and even misleading until higher affinity compounds were discovered.

One theme throughout the conference was the observation that fragments bind at multiple sites on proteins. Harren noted that Astex researchers have found fragments bound (crystallographically) to 54 sites on 25 targets – an average of 2.2 sites per target. Some targets are even more site-rich: Joe Patel (AstraZeneca) performed a crystallographic screen on a complex of Ras and SOS and found four binding sites, including one previously discussed here. In this effort, 1200 fragments were screened in pools of 4, and in one case two fragments from the same pool each bound only when they were both present at the same time – each fragment alone showed no binding by NMR or crystallography.

Troy Messick (Wistar) described his work against the EBNA1 protein from Epstein-Barr virus. An HTS screen of 600,000 compounds came up with at best marginal hits, but soaking 100 different Maybridge fragments into protein crystals led to 20 structures, with fragments bound to four different sites. Some of these fragments were then merged to give cell-active compounds with good oral bioavailability.

Rather than exploring different ligands binding at different sites, Ravi Kurumbail (Pfizer) described an interesting case of the same ligand binding at different sites. A screen against the kinase ITK identified a (large) fragment that could bind both in the adenine binding pocket as well as a nearby pocket, as determined crystallographically. Determining the affinities of the same fragment for the two sites necessitated some clever SPR and enzymology, but did lead to a highly selective series.

In terms of targets, BCL-family proteins were certainly well-represented, featuring heavily in talks by Chudi Ndubaku (Genentech, selective Bcl-x_L inhibitors), Mike Serrano-Wu (Broad Institute, MCL-1 inhibitors), Zaneta Nikolovska-Coleska (University of Michigan, MCL-1), Roman Manetsch (Northeastern, Bcl-x_L and MCL-1), and Andrew Petros (AbbVie, BCL-2 and MCL-1). Of course, it was AbbVie (neé Abbott) that pioneered BCL inhibitors as well as FBLD in general, and I was happy to hear that there is a renaissance occurring there, with fragment approaches being applied to all targets, even those undergoing HTS.

Finally, there were some interesting practical lessons on library design. Peter Kutchukian described how the Merck fragment library was rebuilt to incorporate more attractive molecules that chemists would be excited to pursue. There is an ongoing debate as to whether a fragment library should be maximally diverse or contain related compounds to provide some SAR directly out of the screen, and in the case of the Merck library the decision was to target roughly five analogs in the primary library, with a secondary set of available fragments for follow-up studies.

The utility of having related fragments in a library was illustrated in a talk by Mark Hixon (Takeda) about their COMT program. A HTS screen had failed, and even a screen of 11,000 fragments came up with only 3 hits (with an additional close analog found by catalog screening). Remarkably, all of these are extremely closely related, but other analogs in the library didn’t show up; had they not had multiple representatives of this chemotype in their library they would have come up empty-handed.

In the interest of space I’ll close here. Teddy will post his thoughts later this week, and please share your own. CHI has announced that next year’s meeting will be held in San Diego the week of April 19. And there are still several great events on the calendar for this year!

Physical properties in drug design

This is the title of a magisterial review by Rob Young of GlaxoSmithKline in Top. Med. Chem. At 68 pages it is not a quick read, but it does provide ample evidence that physical properties are ignored at one’s peril. It also offers a robust defense of metrics such as ligand efficiency.

The monograph begins with a restatement of the problem of molecular obesity: the tendency of drug leads to be too lipophilic. I think everyone – even Pete Kenny – agrees that lipophilicity is a quality best served in moderation. After this introduction Young provides a thorough review of physical properties including lipophilicity/hydrophobicity, pKa, and solubility. This is a great resource for people new to the field or those looking for a refresher.

In particular, Young notes the challenges of actually measuring qualities such as lipophilicity. Most people use log P, the partition coefficient of a molecule between water and 1-octanol. However, it turns out that it is difficult to experimentally measure log P for highly lipophilic and/or insoluble compounds. Also, as Kenny has pointed out, the choice of octanol is somewhat arbitrary. Young argues that chromatographic methods for determining lipophilicity are operationally easier, more accurate, and more relevant. The idea is to measure the retention times of a series of compounds on a C-18 column eluted with buffer/acetonitrile at various pH conditions to generate “Chrom log D” values. Although a stickler could argue this relies on arbitrary choices (why acetonitrile? Why a C-18 column?) it seems like a reasonable approach for rapidly assessing lipophilicity.

Next, Young discusses the influence of aromatic ring count on various properties. Although the strength of the correlation between Fsp³ and solubility has been questioned, what’s not up for debate is the fact that the majority of approved oral drugs have 3 or fewer aromatic rings.

Given that 1) lipophilicity should be minimized and 2) most drugs contain at most just a few aromatic rings, researchers at GlaxoSmithKline came up with what they call the Property Forecast Index, or PFI:

PFI = (Chrom log D_7.4) + (# of aromatic rings)

An examination of internal programs suggested that molecules with PFI > 7 were much more likely to be problematic in terms of solubility, promiscuity, and overall development. PFI looks particularly predictive of solubility, whereas there is no correlation between molecular weight and solubility. In fact, a study of 240 oral drugs (all with bioavailability > 30%) revealed that 89% of them have PFI < 7.

Young summarizes: the simple mantra should be to “escape from flatlands” in addition to minimising lipophilicity.

The next two sections discuss how the pharmacokinetic (PK) profile of a drug is affected by its physical properties. There is a nice summary of how various types of molecules are treated by relevant organs, plus a handy diagram of the human digestive track, complete with volumes, transit times, and pH values. There is also an extensive discussion of the correlation between physical properties and permeability, metabolism, hERG binding, promiscuity, serum albumin binding, and intrinsic clearance. The literature is sometimes contradictory (see for example the recent discussion here), but in general higher lipophilicity and more aromatic rings are deleterious. Overall, PFI seems to be a good predictor.

The work concludes with a discussion of various metrics, arguing that drugs tend to have better ligand efficiency (LE) and LLE values than other inhibitors for a given target. For example, in an analysis of 46 oral drugs against 25 targets, only 2.7% of non-kinase inhibitors have better LE and LLE values than the drugs (the value is 22% for kinases). Similarly, the three approved Factor Xa inhibitors have among the highest LLE_AT values of any compounds reported.

Some of the criticism of metrics has focused on their arbitrary nature; for example, the choice of standard state. However, if metrics correlate with a molecule's potential to become a drug, it doesn’t really matter precisely how they are defined.

The first word in the name of this blog is Practical. The statistician George Box once wrote, “essentially, all models are wrong, but some are useful.” Young provides compelling arguments that accounting for physical properties – even with imperfect models and metrics – is both practical and useful.

Young says essentially this as one sentence in a caveat-filled paragraph:

The complex requirements for the discovery of an efficacious drug molecule mean that it is necessary to maintain activity during the optimisation of pharmacokinetics, pharmacodynamics and toxicology; these are all multi-factorial processes. It is thus perhaps unlikely that a simple correlation between properties might be established; good properties alone are not a guarantee of success and some effective drugs have what might be described as sub-optimal properties. However, it is clear that the chances of success are much greater with better physical properties (solubility, shape and lower lipophilicity). These principles are evident in both the broader analyses with attrition/progression as a marker and also in the particular risk/activity values in various developability screens.

In other words, metrics and rules should not be viewed as laws of nature, but they can be useful guidelines to control physical properties.