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 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!

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.

Metric poll results: plus ça change…

Of all the topics on Practical Fragments, none have generated as much Sturm und Drang as ligand efficiency (LE) and related metrics. But how are researchers actually using these? The results of our latest poll are in, and it appears that ligand efficiency is still widely used.

Lipophilic ligand efficiency (LLE or LipE) has made significant strides in the past 3 years, but despite the proliferation of other metrics none of them have really caught on. (Note that we didn’t ask about enthalpic efficiency or SILE in 2011.)

So at least for now – and while fully acknowledging that all metrics are simplifications – I think we can conclude that most readers of this blog find LE and LLE both useful and sufficient for their needs.

(Full technical disclosure: the 2011 poll was run in Blogger, but the polling feature is buggy and has a tendency to lose votes. This poll was run using Polldaddy, but unfortunately the free version does not reveal how many individuals voted. Since respondents could choose more than one metric, the number of voters is impossible to determine from the results. I tried to account for this upfront by including a “Please Click this Box” to tally individuals, but only 77 people did so, less than the 91 people who voted for LE. I thus had to guess at the total number of respondents. The most conservative choice was to add 91 (the largest single category) to the 8 respondents who use no metrics, giving 99 independent voters. Obviously this might overstate the apparent percentage of people who use any given metric, but as 86% of respondents used LE in 2011 the effect is probably small.)

What we do in life, echoes in eternity (or the life of the patent)

Next week is the Drug Discovery Chemistry conference where Dan and I will be co-teaching our award-winning FBDD short course (or at least our mom's think it is great). We look forward to seeing any/all of you next week.  Blogging may be light next week, but we promise to give an update of the going-ons at the conference soon after.  

Kinases are fun, and those of us who have worked in them have probably all worked on the same ones.  I always loved the MAP family.  Why would I have a favorite kinase family?  Because of the cascading MAP kinases, like the one in this paper, Mitogen-activated protein kinase kinase kinase kinase 4 (that's a lot of kinase!).  But, unlike a lot of other kinases, there is no good tool compound.  So, using SPR, they decided to generate one. This paper is not particular interesting in terms of what they did, but rather it raises interesting questions. While the approach they describe is not novel, it is nice to see the data supporting them. 

They screened their 2500 fragment library against immobilized protein at 100 uM (single point).  225 hits were found with Kd ranging from 10 to 2000 uM (LE =0.24 to 0.59) for a 9% hit rate.This paper is about progressing this oxazole fragment 1: 

Based upon its structure and the wealth of kinase structure knowledge extant, they surmised it would be ATP-competitive and a hinge binder.  Based upon a binding model, the attempted to prosecute this fragment by "close-in" analogs and looking for groups that would extend farther into the hydrophobic pocket, but with MW less than 350 Da and clogP less than 3.5.  Exploring bi-aryl space resulted in 8:  

This compound had an activity of 143 nM and it was at this point that they decided to switch to the biochemical assay as their primary assay.  In the end, using X-ray focusing on LLE, they ended up deliveringa low molecular weight compound with favorable in vivo PK.  It also demonstrated a pathway functional response. 

This raises an excellent point, something I get asked frequently.  When do you switch from a biophysical assay to a biochemical one?  This maybe arguing semantics, but I think as more and more companies enter this arena these are exactly the things we need to discuss.  I think the switch happens when you feel comfortable, there is no hard and fast rule.  There is a difference in the SPR Kd and biochemical IC50 by more than 10x.  It is very important to note that they relied heavily on LE (-RTlnKd/HA) and LLE (pKd-cLogP), or pIC50 for biochemical assays.  But, it also raises the issue of correlation between SPR Kd and IC50.  I raise these socratically, and as maybe as a topics for discussion next week (or in July and September).   

Myriad metrics – but which are useful?

Practical Fragments recently introduced WTF as a light-hearted jab at the continuing proliferation of metrics to evaluate molecules, but there is an underlying problem: which ones are useful? In a provocative paper just published online in Bioorg. Med. Chem. Lett. (and also discussed over at In the Pipeline) Michael Shultz asks:

If one molecular change can theoretically alter 18 parameters, two shapes, the rules of 5, 3/75, 4/400 and ‘two thumbs’ while simultaneously affecting at least nine composite parameters and countless different methods of representing data, how is a practicing medicinal chemist to know if any specific modification was actually beneficial?

Shultz focuses on three parameters in depth: ligand efficiency (LE), ligand-efficiency-dependent lipophilicity (LELP), and lipophilic ligand efficiency (LLE, also referred to as lipophilic efficiency or LipE). He conducts a number of thought experiments to see how these metrics change when, for example, a methyl group is changed to a t-butyl group or a methyl sulfone. He also examines how the metrics perform against historical data from Novartis lead-optimization programs.

One problem with LE is that, although it was introduced to normalize potency and size, it is still highly dependent on number of heavy atoms (heavy atom count, or HAC): addition of one atom to a small fragment will have a more dramatic effect on LE than addition of one atom to a larger molecule. This has led to metrics in which larger molecules are treated more leniently, but because of the way all these metrics are mathematically defined, none achieve completely size-dependent normalization.

More seriously, LE ignores lipophilicity, which seems to be correlated with all sorts of deleterious properties. With a nod to Mike Hann’s “molecular obesity,” Shultz notes that the widely used body mass index (BMI) “cannot distinguish between the truly obese and professional athletes of identical height and weight. Similarly, HAC based composite parameters such as LE cannot distinguish between ‘lean molecular mass’ and groups of real molecular obesity.”

LELP addresses this shortcoming by incorporating clogP, but it has problems of its own. For example, “the effects of lipophilicy are magnified as molecular size increases.” More alarmingly, as clogP approaches zero, LELP becomes increasingly insensitive to both size and potency; a femtomolar binder would have the same LELP as a millimolar binder when clogP = 0.

In contrast to both LE and LELP, LipE (or LLE) is size-independent, so a change in potency or lipophilicity will produce the same change in LipE no matter the size of the initial molecule. Shultz uses data from two lead optimization programs to show that LipE behaves better than LE or LELP. This is in contrast to a previous report that suggested LELP to be superior to LipE, albeit against a different data set.

Shultz further notes that LipE can be thought of as the tendency of a molecule to bind to a specific protein rather than to bulk octanol:

LipE = pK_i - clopP = log [EI]/([E][I]) – log ([I_octanol]/[I_water])

where E stands for protein and I stands for inhibitor

Although this is a simple consequence of the math, it is a nice way of visualizing an otherwise abstract number. Moreover, it suggests that optimizing for LipE could optimize for enthalpic interactions, a topic Shultz explores in depth in a companion paper.

Overall Shultz raises some excellent points, but I still believe there is value in LE (and LLE_AT), particularly in the context of fragments, which usually have low affinity. Ligand efficiency can prioritize molecules that might otherwise be overlooked. For example, it is hard to get too excited over a 1 mM binder, but if the hit has only 8 heavy atoms it could be valuable.

Turning to my own miniature thought experiment, fragments 1 and 2 have very similar LipE values, but the LE of Fragment 1 is better, and arguably makes a more attractive fragment hit.

Of course, in the end, rules should not be followed slavishly; the most lucrative drug of all time, Pfizer’s atorvastatin, violates Lipinski’s rule of five. Papers like this are important to highlight the problems and inconsistencies that underlie some of our metrics. Ultimately I’ll take biological data and the intuition of a good medicinal chemist over any and every rule of thumb.

What do you think? What role should LE, LELP, and LipE play in drug discovery?

Leave Them Asking for More

ret·ro·spec·tive  (rtr-spktv) adj.

1. Looking back on, contemplating, or directed to the past.

2. Looking or directed backward.

3. Applying to or influencing the past; retroactive.

I would add: 4. Looking back on the past, to influence the future.

In this vein, a recent paper by Ferenczy and Keserű in J. Med Chem looks back on hit-lead optimizations derived from fragment starting points.  In this very interesting paper, they look at 145 fragment programs and evaluate the properties of the original hit and then again as it progresses into the lead.  Of the 145 programs, these were aimed at 83 proteins of which 76 are enzymes, 6 are receptors, and 1 is an ion channel.  These programs evolved into leads, tools, and clinical candidates.  The authors set out to answer three questions: 1. do fragments eliminate the risk of property inflation, 2. how do ligand efficiency metrics support fragment optimizations, and 3. what is the impact of detection method, optimization strategy, and company size on the optimization.

Table 2 shows the median the calculated properties for the hits and optimized compounds.  The pIC50 improved by roughly three orders of magnitude, but ligand efficiency (LE) stayed roughly the same.  Log P increased but SILE did not.  SILE was a metric I was not familiar with and is calculated by pIC50/(HAC)^0.3.  SILE is a size-independent metric of ligand efficiency.  I won't attempt to reproduce all the graphs they generated; get the paper.  Some interesting data points: the median fragment hit had 15 heavy atoms (see related poll here), the median size of leads is 28 heavy atoms, and good fraction of hit-lead pairs changed less than 5 heavy atoms (which of course is well known from here). So, what is the answer for their first question?  If you are looking at something like LE, then these hit-lead pairs maintain the efficiency.  If you are looking at logP, then the answer is no, the hit-lead pairs get greasier.  I would really like to see more granularity here (see What's Missing below).  Their major comment here is that SILE and LELP (work by the author's previously reviewed by Dan) are the two best metrics to monitor as hit-lead optimization is underway.  Increases in both metrics correlate with increases in FBDD programs. 

 

They then looked at the  screening method.  The breakout of primary screening (in their definition the first one listed when multiple methods were used) was 38% biochemical, 25% NMR, 18% X-ray, and 11% virtual.  This is an interesting contrast to these results; SPR is not the dominant screening technique (8% tied with MS).  So, does this mean, >40% of practitioners are using SPR, but as a secondary screen?

Table 4 then does a pairwise comparison of the metrics based upon primary screen origin of the fragment (see What's Missing below).  Biochemical screens yield the most potent hits (4.75) while NMR (3.53) has the least potent.  X-ray has the smallest hits( 13 heavy atoms) while virtual screening the largest (17 HA).  I don't think any of this is surprising; the authors point out that hit properties exhibit a significant dependence on the method used. It is noted that optimization tends to diminish differences in hit properties.  Again, I think this is not surprising; thermodynamics and medchem are all the same no matter how big or small the molecule.  They do point out that biochemical based hits preserve their advantage after optimization, primarily relative to NMR.  They posit that the difference is that more potent compounds need less "stuff" to become sufficiently potent, and thus have a better mix of medchem for potency and other property optimization.  Weaker starting points need more bulk, more atoms, to become equipotent with biochemical starting points, they suggest.  Lastly, they show that structural-based optimization efforts are better than those without structural information.  The structural information comes from X-ray primarily (52%) and NMR (10%).  Interestingly, they reference this poll from the blog on whether you need structural information to prosecute fragments.  [As an aside, since I wrote that blog post and it was reference in a paper, do I get to add it to my resume?]

Finally, they break the originating labs into three categories: academic (18%), small/medium enterprises (SME)( 37%), and Big Pharma (45%).  The SME results are superior to those achieved by the academics and Big Pharma.  Their explanation is that SME's tend to be more platform focused and predominantly ensure "structural" enablement of targets.  75% (40/53) optimizations at SMEs used protein structural information, while "only" 62% of those at Big Pharma did.  They do not rule out the differences in target selection at those two different groups of companies.  I would like to propose an alternative hypothesis (tongue-not-entirely-in-cheek): Big Pharma has "old crusty" chemists who don't understand fragments and thus just glom on hydrophobic stuff to increase potency because "that's how we always do it" while SME have innovative chemists.  And of course academia is just making tool compounds and crap.

One thing that I would like to emphasize is that Ro3, metrics, and so on should not be used as hard cutoffs.  As shown in Figure 11, even compounds that are outside the "preferred" space can reach the clinic.  The best way to view them is akin to the Pirate Code; they are more guidelines than rules. 

 

What is Missing?  The supplemental information (which the authors are willing to kindly share) does not break out the targets into specific classes.  However, they do list each target, so it should be easy to add this a data column.  More importantly, they do not break out hit-lead pairs into those that were optimized for use as tools, clinical candidates, and leads (and which of the leads died).  Tools are never supposed to look like leads (but you are lucky if they do), so their inclusion here can be biasing the results.  Although, it is likely that the of the 145 not very many of the examples are strictly tools; it would be nice to know though.   

I am struck by the information denseness of Table 4 and wish that instead of pairwise comparisons, they had instead simply list the hit-lead metrics for each methodology.  I think there is gold to be mined in Table 4 and just like real gold is hard to find.

 

I have not addressed every single point made by the authors.  I, for one, am hoping that they will continue their analyses (especially with an eye to some of What is Missing).  I hope that there will be a significant amount of discussion around these points.  I will make sure we hit on this at the breakfast roundtable at the upcoming CHI FBDD event in SD (I even have the same pithy title as last year!)

ACS Fall Meeting 2012

I recently returned from Philadelphia, where the American Chemical Society held its 244^th national fall meeting. As always this was a massive affair, but fragments were well-represented, particularly in a nice session organized by Percy Carter, Debbie Loughney, and Romyr Dominique.

I opened the session by giving an introduction to fragment-screening, as well as an overview of some of the work we’re doing at Carmot. Andrew Good had perhaps the best title (“Fragment fat wobbles too”), and discussed some of the work done at Genzyme on Pim-1 kinase. Eric Manas next described some of the computational tools being used at GlaxoSmithKline, in particular strategies to deal with water. He also discussed the utility of looking for fragment analogs early in a project. In the last talk before the intermission, Chris Abell from the University of Cambridge described a number of projects from his group, starting with antimicrobial targets (such as this one); we’ll cover another in a separate post. Chris is unabashedly going after difficult targets, not just protein-protein interactions, but oligonucleotides – specifically riboswitches. There is only limited precedent for targeting RNA with fragments, so it will be fun to see how this progresses.

Francisco Talamas next described a nice example from Roche using FBLD to discover hepatitis C NS5B Palm I allosteric inhibitors. An HTS campaign of around 900,000 molecules yielded just 3 hits, none of which were advanced. A fragment screen of about 2700 fragments gave a better hit rate (5.9%), but of the 29 co-crystal structures attempted only a single structure was obtained. However, by combining the information from this crystal structure with information from other crystal structures, both proprietary and public, the researchers put together a set of rules to design a de novo fragment library tailored to this protein. This effort ultimately yielded compounds that were optimized to a clinical candidate.

Next, Nick Wurtz from Bristol-Myers Squibb described his company’s approach to discover neutral Factor VIIa inhibitors. The researchers used a combination of computational, functional, and biophysical approaches to find uncharged fragments that would bind in the P1 pocket, leading to a couple dozen crystal structures. Despite the low affinities of these fragments (typically mM), many of them could successfully be merged onto an existing series, replacing a positively charged moiety to yield potent molecules with better permeability. This is the first time I’ve seen a fragment story out of BMS, so I'm glad to see that they’re active in this area. This is also a prime example of what has been described as fragment-assisted drug discovery.

Finally, Prabha Ibrahim of Plexxikon gave a lovely overview of the discovery and development of vemurafenib, including a more detailed description of the SAR than has been presented in their earlier papers.

In addition to this dedicated session, there was a scattering of other talks and posters, including a notable poster from Timothy Rooney at the University of Oxford using fragment-based approaches to discover bromodomain inhibitors, a target class we’ve previously discussed.

A session entitled “A medicinal chemist’s toolbox” ranged over several topics of interest. Ernesto Freire of Johns Hopkins gave a great overview of thermodynamics in drug discovery, a topic we’ve previously covered. Most readers are probably familiar with the concept of enthalpy-entropy compensation, in which (for example) an added hydrogen bond fails to achieve the desired boost in potency due to unfavorable entropy. Recognizing this, he suggested that one should target groups in proteins that are already well-structured, so you don’t have to pay the cost of structuring a disordered part of the protein. He also suggested that if you introduce one hydrogen bond, you might be better off introducing a second one too, as the incremental entropic cost is likely to be low.

György Keserű of Gedeon Richter discussed the importance of avoiding lipophilicity by using tools such as LELP, which we’ve covered here and here. Continuing this theme, Kevin Freeman-Cook of Pfizer described two examples of using LLE in lead discovery programs, in particular calculating LLE values before making compounds. Although this may seem obvious, what was quite striking was the dramatic effect subtle changes in structure could make to ClogP values.

Of course, these are just a few of thousands of presentations. Please feel free to point out any that caught your eye, or expand on some of those mentioned above. And just a reminder, it’s only 4 weeks to FBLD 2012 in San Francisco – the biggest fragment event of the year!

LLE vs LELP

Besides ligand efficiency (LE), a slew of other metrics has been proposed to help evaluate what compounds to take forward in drug discovery. As seen in our poll and a recent round-table discussion, lipophilic ligand efficiency (LLE) is quite popular:

LLE = pIC₅₀ (or pK_i) – ClogP (or logD)

However, because this metric is not size-adjusted, it is not particularly useful for evaluating fragments, which often have low potency. In contrast, the metric LELP accounts for size:

LELP = logP / LE (where LE = ligand efficiency)

In a recent issue of J. Med. Chem., György Keserű and colleagues evaluate how these two metrics compare in a variety of settings.

The authors examine eight different compound sets: fragment hits and derived leads, HTS hits and derived leads, leads that subsequently became drugs (ie, “successful leads”), development candidates, compounds that entered phase II trials, and drugs on the market. Not surprisingly, drugs and phase II compounds had better LLE and LELP scores than other molecules. Also not surprisingly, fragments scored misleadingly poorly on the basis of LLE but well on the basis of LELP. What is perhaps unexpected, though, is that LELP was better at identifying successful leads than was LLE. Moreover, when compounds were evaluated for pharmacokinetic and safety parameters, LELP was more effective at predicting problems than was LLE. The authors state:

In summary, evaluation of pharmacokinetic and safety parameters revealed that LELP has benefits over LLE, as compounds with acceptable in vitro ADMET profiles are discriminated from compounds with significant liabilities.

Despite these potential advantages, LELP doesn’t seem to be widely used, perhaps because it is less intuitive than some of the other metrics. Indeed, it would be interesting to see these studies repeated using LLE_AT, which also takes lipophilicity into account but has the same scale as LE.

Molecular Medicine Tri-Con 2012

Molecular Medicine Tri-Con has just ended in San Francisco, and although it is one of those massive conferences with a very wide scope, from bioinformatics to stem cells, there were some talks relevant to FBLD.

Teddy recently summarized how fragment-based approaches have been used to develop bromodomain inhibitors at GlaxoSmithKline, and they’re certainly not alone: Mark Bunnage of Pfizer described how they used fragment-based methods to discover nanomolar inhibitors of BRD4 that are active in cells. This work was done in collaboration with the Structural Genomics Consortium (SGC), and one well-characterized compound, PFI-1, is being released as a chemical probe to the worldwide research community with no intellectual property entanglements.

There was a heavy emphasis on metrics, and as we saw in the poll last year ligand efficiency and LLE (sometimes also called LipE) seem to be dominant. However, the latter metric is used more for advanced leads than for fragments. Siegfried Reich from the Lilly Biotechnology Center (neé SGX) said of LLE that it is less important where you start than where you end up, though starting from a very polar fragment gives you the luxury of adding lipophilicity during optimization.

Slight changes to fragments can often cause them to bind in different orientations, and the same fragment may bind differently in closely related proteins, but Siegfried argued these multiple orientations could be advantageous by providing multiple opportunities for optimization. Siegfried also mentioned that deconstruction of HTS hits to fragments has been successful at identifying fragments with high ligand-efficiency that could subsequently be optimized to new series.

Richard Law of Evotec gave as clear account as possible of fragment molecular orbital (FMO) calculations, a high-level quantum mechanical method for understanding protein-ligand interactions. Although quantum mechanical calculations are notorious for taking hours, days, or even weeks to run, the calculations can be done much more efficiently by breaking larger molecules into fragments.

Richard also kept thorough notes at a break-out discussion I moderated, and was kind enough to share them; I’ve also made a few additions. There were nine people from both large and small organizations, all but one from industry.

Screening technologies

• As we’ve seen here, SPR seems to be the most common fragment-finding technique; in his presentation, Walter Huber said that it is the primary screening method at Roche.
• "Reverse SPR" - the Graffinity/Novalix technology we’ve discussed previously, has been applied to over 100 targets. It is reversed because the small molecules are immobilised on the chip, in multiple orientations to present different moieties to the target protein. This is also a larger library (~25,000).
• NMR was not being used as much as SPR, and no one at the table was using 19F NMR, though its use does appear to be growing, and compound suppliers are coming out with 19F fragment libraries.
• FCS++ is being used at Evotec; it has the advantage of being high-throughout but still highly sensitive and therefore accommodates a larger than average fragment library (~20,000).

3-D fragments

• There is increasing desire for sp3 (3-dimensional) fragments and a move away from planar fragments, though one participant had seen chemists shy away from too many chiral centers.
• Advantage of more vectors for SBDD, and additional solubility versus otherwise equivalent flat compounds.
• Despite these advantages, often planar fragments yield more hits - likely because planar compounds are more likely to form dispersive/non-specific interactions, whereas sp3-fragments must form very specific H-bond interactions in order to bind. Does sp3 therefore also enrich for enthalpic binders?

Use of indices
LE was used by everyone at the table, whereas LipE/LLE were not used until lead compound stage. BEI/SEI and other metrics were not really used.

FBLD vs HTS
Fragment screening is being used on most programs at many companies, in parallel with HTS and virtual screens. A subset of targets is addressed only with fragment screening either because of target-specific information or specific requirements to lower costs of screening.

Fragment screening of GPCRs
There was very limited experience using FBLD on G-protein coupled receptors, though one company is trying to use nanodiscs to stabilize GPCRs for fragment screening. As more crystal structures are solved people may become more comfortable tackling this target class.

Finally, there was widespread agreement that fragments are ideal for designing in the desired physico-chemical properties of molecules as fragments are developed. The parent fragment is not biasing, and could often be med-chemed away. Richard Law offered the analogy of a rock-climber:

An HTS gets you halfway up the cliff, but the route to the top may not be from where you are, or may just be too difficult to find from that position. Whereas a fragment hit is at the base of cliff but enables you to see and select the exact route to the top that you need.

If you attended the break-out discussion or the meeting please comment on your impressions, and if you missed this conference don’t worry – there are many more great events coming up throughout the year.