Fragment-Based Drug Discovery & Molecular Design

JCAMD 25th Anniversary Issue

2012-02-04T10:31:00.024Z

The editors of the Journal of Computer-Aided Molecular Design commissioned a number of Perspectives on the state and future of the field to commemorate the journal's 25^th anniversary. They have made this content open access for a limited period (I believe 3 months) so go check it out while the access is still open.

Fragment lead identification by SPR

2012-02-03T03:09:00.021Z

FBDD is a maturing field and one sign this maturation is the publication of a volume of Methods in Enzymology devoted to the subject. The article in this collection that most interested me was the review by Anthony Giannetti on the use of Surface Plasmon Resonance (SPR) in Fragment Lead Generation. The review is described as a ‘comprehensive walk-through’ and in-depth treatment of topics such as target immobilization and buffer/compound preparation justifies this description. I’m still working my way through some of the data analysis sections...

The target is tethered to a surface in SPR and this is usually referred to as ‘immobilization’, which is an unfortunate term, albeit the one that is most commonly used in the literature. Vendors of competing assay technologies (who would naturally prefer you to use their technology instead) often present this as a weakness of SPR. One concern is that tethering will compromise the ability of the target to bind ligands and the review does cite a couple of articles which compare affinities measured with SPR to those measured using methods such as isothermal titration calorimetry.

The system in an SPR assay is heterogenous, which is another way of saying that the concentration of protein is not uniform, particularly in the direction perpendicular to the surface to which it is tethered and this creates some interesting possibilities. Tight binding occurs when the value of the ligand K_d is lower than the concentration of the protein to which it binds. We typically configure assays for measuring affinity and potency so that ligand concentration is significantly greater than protein concentration. This means that ligand binding does not affect the concentration of unbound ligand and the math is a whole lot easier if you can make this assumption. If, however, the protein concentration in your assay is 1nM and you want to measure the potency for a compound with an IC50 of 0.01nM you’re going have a problem because you’ll need the compound at a concentration of 0.5nM in order to occupy half the binding sites. In enzyme inhibition assays, the concentration of the enzyme limits sets an upper limit on the potency that you can measure and this may be an issue for attempts to estimate the maximum potency of ligands.

In a heterogeneous system, things are not quite as simple because concentration is less clearly defined and you need to think in terms of quantities (in molar terms of course) of protein and ligand. Localising a small amount of protein on the chip surface rather than having a larger amount of protein distributed evenly throughout the sample volume means less depletion of the reservoir of unbound ligand when 50% of binding sites become occupied. Also in the SPR assay, the solution of ligand flows over the chip, making depletion of unbound ligand even less of a problem.

Tight binding is not usually a problem when screening fragments and the main reason for bringing up the subject was to get you thinking a bit about assays. There are a number of technologies for detecting the binding of fragments and quantifying the affinity with which they bind. This raises a couple of questions. Firstly, to what extent do we need new screening technologies for FBDD? Secondly, which weaknesses in the current methodology should be addressed with the highest priority?

Literature cited

Giannetti, From experimental design to validated hits: A comprehensive walk-through of fragment lead identification using surface plasmon resonance. Methods Enzymol. 2012, 493, 169-218. DOI

Anyone for tennis?

2012-01-28T12:18:00.051Z

So I'm back in Melbourne, one of my favorite cities, and am currently amusing myself by looking for transition states which some of you will know are merely first order saddle points on potential energy surfaces. I actually find reactivity a lot more interesting than binding so it's great that my hosts are interested in these problems. Of course there's a lot more to do in Melbourne than searching for imaginary forces and negative frequencies and last week I went to watch the Australian Open.

Before I get to the tennis, I'll mention the Research Works Act which can be seen as protectionism (of academic publishers) and readers of this blog will know that I have some issues with journal editors. Well it turns out that protectionism is everywhere even in what you would think would be the fully competitive environment of the Australian Open. Whe you attend the Open, any bag you take in is (quite rightly) inspected and they'll be looking carefully for particularly dangerous items. Like lenses with a focal lengths greater than 200mm. Why are these considered so dangerous, you might ask? Well the danger posed by such contraband is that it might allow its owner to take a really good picture. While the organisers of the Open want you to enjoy the tennis and buy lots of food and drink, the organisers of the Open do not want you to take good pictures. Anyway here's a picture of the official photographers whom the organisers of the Open are 'protecting' from me and my Pentax K200.

The photos are from the match between Maria Sharapova and Angelique Kerber and I'd hoped that it would turn into an epic struggle of Kurskian proportions. As it transpired, Kerber's Panzerfausts were no match for Sharapova's Katyusha batteries. I couldn't help wondering if Maria had modelled her famous 'vocals' on the unique music of Stalin's Organ.

The match was not without its lighter moments and Maria aborted one serve to entertain us with a dance move from Swan Lake that would not have disgraced the Bolshoi. However, it was soon clear that poor Angelique was not having a good day in the office and, after she bashed yet another ball into the net, I was not going to contradict her. On the bright side, at least she didn't try to blame the Romanians or start raving about Steiner's divisions.

As had always seemed probable, the match concluded in two sets and at the post-match interview, Maria demonstrated her familiarity with the principles and practise of High-Throughput Screening.

Rule of 3 takes some flak

2011-11-03T12:27:00.045Z

You might remember my rantlet about the Rule of Three (RO3) at the beginning of the year. Well it seems that others consider Ro3 to be over-restrictive and this JMC article will be as welcome in some parts of Cambridge as a staffel of Ju87s.

The authors of this work describe screening of a library of 364 fragments against the aspartyl protease endothiapepsin and crystal structures of 11 hits bound to the target protein. The library was designed “without strictly applying the rule of 3” and, as it turns out, “only 4 of the 11 fragments are consistent with the rule of 3”. Not exactly a ringing endorsement for RO3 or a compelling incentive to buy a RO3-compliant fragment library.

Hopefully one point that you’ll have taken away from my earlier post is that those who gave us RO3 don’t say a whole lot about how they define hydrogen bond donors and acceptors so it can be difficult to say whether some fragments are RO3-complaint or not. I’m guessing that RO3’s proposers may not be be using the same definitions that are used to apply the Rule of 5 (RO5). However, I really don’t know and don’t really care.

Now you can see the problem. Do any of the 7 fragment hits (I refuse to call them frits since that term is more usually associated with ceramics than Drug Discovery and an association with ceramics is something that you’ll want to avoid for fragments) from the endothiapepsin screen that are reported to be inconsistent with RO3 actually fail to comply with the rule? Let’s take a look at the structures of two fragments for which binding to target was observed crystallographically. You’ll notice that I’ve retained the structure numbering from the article.

There are 2 nitrogens and 3 oxygens in 041 so with RO5 hydrogen bonding definitions this fragment would not be RO3-compliant. Personally, I wouldn’t count the amide nitrogen as an acceptor but that’s my choice. The cyclic ether oxygens in 041 will be very weak hydrogen bond acceptors and even three or four of these together will pack less of a punch than the typical amide carbonyl oxygen. I’d actually be much more worried about the reactivity of the aniline portion of this molecule but this is not the place for that discussion. Fragment 255 has 3 nitrogens and one oxygen which translates into 4 hydrogen bond acceptors if you use RO5 definitions. However, I would not count either the amide nitrogen or the bridging nitrogen of the fused heteroaromatic ring as acceptors so with my definitions the fragment would be RO3-compliant.

The assay and crystallisation were carried out at a pH of 4.6. This means that the heteroaromatic nitrogens of 255 and 291 are likely to be protonated to a significant extent under experimental conditions. It’s interesting that the solubility measurements were run at a pH of 7.4 because basic fragments such as 041, 255 and 291 should be even more soluble in assay and crystallisation buffers. It would be a different story for acids but I didn’t see any of those so I guess no harm done.

It’s good to see the output of a fragment screen being published in this manner and the crystal structures for a number of fragments bound to this target represent a welcome addition to the protein knowledge base. Given that I’ve never been a fan of RO3, I do like to see others questioning the rule although reading this paper gives the impression that RO3 has never before been questioned. I also believe that they could have addressed the issue of hydrogen bonding definitions rather than simply jumping to the conclusion that RO5 definitions (all nitrogens and oxygens are acceptors) were being used by those who gave us RO3.

On a final note, you might wonder why I keep banging on about RO3 when it’s something that I’ve never used to select fragments. It’s a good question and this is a good place to answer it. My own view is that the way many researchers have blindly adopted the rule is merely a symptom of a much bigger malaise in Drug Discovery research. Pharma appears to be dans la merde but the response of its leaders is typically to increase the frequency with which the Titanic’s complement of deck chairs is shuffled. Is this really a good time for those who represent the best chance of a future for the industry to be switching off their critical thinking skills?

Literature cited

Köster et al, A Small Nonrule of 3 Compatible Fragment Library Provides High Hit Rate of Endothiapepsin Crystal Structures with Various Fragment Chemotypes. J. Med. Chem. 2011, in press. DOI

Dans la merde

2011-09-26T15:06:00.015+01:00

I’ve been meaning to write something on the state of Pharma ever since my good friend Anthony Nicholls posted What Is Really Killing Pharma back in April. Ant sees an industry that is rapidly abandoning its science base and he is less than complimentary about Pharma management:

‘One consequence of this shift from science to business in the pharma industry has been less and less appreciation for the realities—as opposed to the hype and hope—of drug discovery. This is reflected both in the quixotic choices made by pharma as to what to pursue and in the stunningly bad management of the core talent in drug discovery.’

Now I don’t happen to fully agree with Ant on the diagnosis and it is possible that the underwhelming management of Pharma is more a symptom of the underlying disease than a disease in its own right. However, I will make some comments on Pharma management before moving onto some of what I see to be the industry's real woes. One brutal assessment of the situation is that, if society has decided that discovery of new medicines is to be a commercial activity, we (as members of society) should not complain when pharmaceutical businesses behave like businesses. I don’t believe that it is actually necessary for a Pharma CEO to understand the science although it’s a bonus if they do. Given that the time to bring a drug from hypothesis to market is longer than the tenure of many CEOs, it’s much more important that they be prepared to take a long term view and understand how the different parts of the business fit (and function) together. The shareholders of the company need to find mechanisms to persuade their CEO take a long term view. The CEO needs to seek advice from people who are prepared to tell the truth and not sideline them when they do. Those managing the drug hunt must avoid becoming panacea-centric in their thinking and remember that, while technology is a good servant, it is a poor master.

We need to take a closer look at the industry to better understand Pharma’s woes. As many people know, bringing a drug to market takes a long time and is also very expensive. The industry is highly regulated and the cost to the regulators of accepting something that they shouldn’t have greatly exceeds that of rejecting something that they should have accepted. Since Pharma companies don’t usually see themselves as in the Generics business, they need a steady stream of new products in order to remain viable. Unfortunately, this stream has slowed to a trickle and it is perfectly reasonable to question whether Drug Discovery is still a commercially viable activity.

It is easy to blame management for the current state of the industry and I’ll be the first to admit that many CEOs appear to be poor value for their employers (the shareholders). However, the current state of pipelines also reflects unmet scientific challenges and one can argue that the frequently bizarre behavior of Pharma leaders reflects increasing desperation in their search for other solutions.

Generally, a Drug Discovery project starts with a hypothesis. Typically, this will take the form that interfering (drugs are usually inhibitory) with the action of a target or system of targets (e.g. a pathway) will result in a therapeutically beneficial effect. Testing these hypotheses is termed Target Validation (TV) and usually one will try to develop an animal model of the human disease before taking a drug into development. Let’s think about why a drug may fail to show efficacy in a Phase 2 clinical trial. One explanation is that there is no link between target and disease (another way of saying that the TV hypothesis is incorrect). However, it could also be that the target is still valid but the animal model is simply not predictive of the human disease. Needless to say, TV is challenging and even reproducing claims made in the scientific literature can be difficult (readers who are LinkedIn may also wish to check out the discussion in the Society of Laboratory Automation and Screening group entitled "Reliability of 'new drug target' claims called into question").

However, there is yet another reason that a drug can fail to show efficacy in Phase 2 and that’s poor pharmacokinetics (PK). Now many of you are probably thinking that I’m talking from the wrong end of my alimentary canal when I say this because everybody knows that Phase 1 is where PK failures happen. You run a Phase 1 clinical trial to check that levels of the drug will be sufficiently high to engage the target and this is most relevant when the target ‘sees’ the blood stream. However, when the target is intracellular or on the far side of the blood brain barrier, we know a lot less about the free (unbound) concentration of drug in the vicinity of the target. Now you’ll see the problem and I’ll leave it to you to decide whether we’re dealing with known unknowns or unknown unknowns. The blood levels look great but we have little idea about what’s happening where it really matters. For intracellular and CNS targets it can be argued that the Phase 1 trial is less complete than for targets such as cell surface receptors that are exposed to the drug circulating in plasma. How much less complete is anybody’s guess because measuring free concentrations of an arbitrary drug in cells is just not something that we can currently do, even in laboratory animals.

This is probably a good time to bring up the subject of toxicity and it’s worth mentioning that the point made about free concentration is also relevant to toxicity (and ‘polypharmacology’). Pretty much the worst thing that can happen to a drug is that idiosyncratic toxicity reveals itself when the drug is already on the market. Rare toxicity is fiendishly difficult to predict and its rareness means that you have to dose a large number of patients (who may also be taking other medication) in order to even observe it. The rareness of the toxicity means that the enrichment studies that are so popular with the virtual screening and QSAR communities are unlikely to shed much light on the toxicity. Choking in Phase 3 is certainly bad but you can always console yourself with the knowledge that it could have been even worse.

So what’s really killing Pharma? There’s no shortage of gutless and witless managers in Pharma and there would be huge benefits in ensuring that undiluted Darwinian principles applied freely to the Leadership Function (surely a strong candidate for oxymoron of the month) of the industry. Would this be enough to save Pharma from a dearth of well-validated targets? Or one bust too many in the Phase 3 Casino? What do you think?

Literature cited

Prinz, Schlange & Asadullah, Believe it or not: how much can we rely on published data on potential drug targets? Nat. Rev. Drug Discov. 2011, 10, 712-713. DOI

A SMARTS way to do things?

2011-09-15T11:48:00.005+01:00

A couple of months ago I returned from a visit to OpenEye in Santa Fe, New Mexico. I’d been helping out with tautomers and ionisation and it really was great to be back in one of my favourite States of the Union catching up with some old friends while making some new ones. However, it’s neither tautomers nor ionisation that I’ll be discussing in this post because I really want to talk about SMARTS. This is a line notation for defining substructural queries and a SMARTS parser with full capability is one of the most powerful weapons in the molecular design arsenal. One of the things that I did in Santa Fe was to learn a bit about using the OpenEye SMARTS parser. I like to think of SMARTS as empowering in that a SMARTS parser allows me to impose my will on a database of chemical structures. This really brings out my latent megalomaniac and makes me want to gaze at large wall-mounted maps of the world…

SMARTS notation is actually very simple but at the same time is highly expressive. It’s best illustrated using some examples. Let’s start with a simple definition for a neutral carboxylic acid and I’ve kept things simple by not requiring a connection between the carbon and another carbon atom.

[OH]C=O

When dealing with commercially available collections of compounds, the carboxylic acids may be registered both in neutral and anionic (salt) forms. Although people in Pharma may whinge about, this one has to remember that a compound vendor needs to distinguish benzoic acid from sodium benzoate and I have no time for lily-livered whingers. As Marie Antoinette might have said, “Let them eat SMARTS”. Here are a couple of SMARTS queries that will match either neutral or anionic forms of carboxylic acids. [O;H,-] specifies an oxygen atom that either has a single hydrogen or a negative charge while [OD1] specifies an oxygen atom with a single non-hydrogen connection.

[O;H,-]C=O

[OD1]C=O

A SMARTS parser with full capability will not only match the substructural pattern but will also map individual atoms. This is really useful for atom typing and remember that you can get a lot of information (e.g. ionisation, interaction potential) about an atom from its connectivity. In a pharmacophore search I would want to treat both oxygen atoms of the carboxylic acid as anionic and might do this using recursive SMARTS as follows.

[$([OH]C=O),$(O=C[OH])

One of my favourite features of SMARTS is the vector binding which associates a SMARTS pattern with a label and allows you to create patterns that are much more human-readable. This is really important when creating a view of chemistry that is to be imposed on chemical databases. I’ll show how you can build a simple definition of aliphatic amines (remember that these usually protonate under normal physiological conditions) using vector bindings. First let’s define a carbon with four connections.

Csp3 [CX4]

Now we’ll use this to define primary, secondary and tertiary amines which we’ll then combine into a single all aliphatic amine definition. Notice how I ‘over-specify’ the nitrogen connectivity in order to prevent matching against amine oxides, protonated amines and quaternary ammonium.

PriAmin [N;H2;X3][$Csp3]
SecAmin [N;H;X3]([$Csp3])[$Csp3]
TerAmin [NX3]([$Csp3])([$Csp3])[$Csp3]
AllAmin [$PriAmin,$SecAmin,$TerAmin]

So that finishes our quick introduction to SMARTS notation. In my own work, I’ve used SMARTS not only to locate structural features in molecules but also to modify the molecules, for example to set ionisation states in a database of structures to be docked into the binding site of a protein. Being able to modify structures automatically and in a controlled manner also makes it possible to do cool stuff like identify matched molecular pairs ( mmp1 | mmp2 ). I should mention that there is a SMARTS-like notation called SMIRKS for modifying structures although I’m not going to say anything about it right now.

There’s plenty of information about SMARTS out there, including a Wikipedia page and the Daylight SMARTS Theory Manual, Tutorial and Examples. The Daylight and OpenEye SMARTS parsers are provided as tool kits (so you can build your own software) and both support recursive SMARTS and vector bindings (not all SMARTS parsers do this so check with your software vendor). I started with the Daylight product back in 1995 and taught myself some C in order to use it. However, the OpenEye SMARTS parser can also be used with 3D structures and I’m looking forward to doing lots more with it.

I’ll finish with some comments on terminology. A substructural definition written in SMARTS notation can be called a SMARTS pattern, a SMARTS string or even a SMARTS. Whatever you do, don’t call it a SMART (you wouldn’t talk about a specie in relation to living organisms) because that will make you look half-witted (and make me cringe). Also to talk about a SMILE or a SMIRK would be equally crass so don’t say I didn’t warn you.

Literature cited

Kenny & Sadowski Structure Modification in Chemical Databases, Methods and Principles of Medicinal Chemistry 2005, 23, 271-285 | DOI

Leach et al Matched Molecular Pairs as a Guide in the Optimization of Pharmaceutical Properties; a Study of Aqueous Solubility, Plasma Protein Binding and Oral Exposure J. Med. Chem. 2006, 49, 6672–6682 | DOI

Birch et al, Matched molecular pair analysis of activity and properties of glycogen phosphorylase inhibitors. Bioorg Med Chem Lett 2009, 19, 850-853 | DOI

Lipophilicity teaser

2011-06-29T19:17:00.054+01:00

This post got prompted one by Dan at Practical Fragments and I'm going to ask you to first take a look at that and at the comments. Now I'd like you to look at some measured octanol/water logP values that I pulled from the Sangster Research Laboratories logPow database. The question I'd like to put to you is whether you think that these measured logP values truly reflect the energetic costs of moving the different isomeric methylimidazoles from water to a truly non-polar environment like a hydrophobic binding pocket in a protein.

Let's take a look these figures. The least lipophilic compound of the set is N-methylimidazole in which the hydrogen bond donor of imidazole has been capped, although the partition coefficients for the three compounds are all very similar. It seems that the octanol/water partitioning system just doesn't seem to 'see' the hydrogen bond donors of the 2-methyl and 4/5-methyl isomers.

Octanol has a hydroxyl group and, in the context of a shake-flask logP determination, gets pretty wet (~2M), making it a unconvincing model for the hydrophobic core of a lipid bilayer or a hydrophobic binding pocket. In contrast, alkanes lack hydrogen bonding capability which also means that they also dissolve less water. The catch is that alkane/water partition coefficients are more difficult to measure than their octanol/water equivalents since polar solutes are poorly soluble in alkane solvents.

The difference between octanol/water and alkane/water logP values for a compound (often termed ΔlogP) is one measure of the polarity of the compound. The octanol/water logP of phenol is 1.5 and it would be reasonable to describe it as lipophilic. However in the alkane/water system the situation is reversed and the logP of -0.6 would lead to phenol being described as hydrophilic.

I'll leave things here for now because this post is really just a teaser and I will be returning to the theme in more depth in the future. If you're interested in finding out more take a look at my harangue from the March 2011 PhysChem Forum at Syngenta and the article that goes with it. I'd also recommend reading this review by Wolfenden if you're interested in the relevance of alkane/water logP values to protein structure and function.

Literature cited

Toulmin, Kenny & Wood, Toward prediction of alkane/water partition coefficients. J. Med. Chem. 2008, 51, 3720-3730. DOI

Wolfenden, Experimental Measures of Amino Acid Hydrophobicity and the Topology of Transmembrane and Globular Proteins. J. Gen. Physiol. 2007, 129, 357-362. DOI

FBDD & Molecular Design

2011-06-25T05:06:00.008+01:00

The FBDD Literature blog is getting a bit of a makeover. One of the reasons for doing this is that since I escaped from Big Pharma my access to literature has been erratic, making it difficult to maintain the required awareness of the current literature. However, a bigger reason for the changes was to broaden the focus of the blog to include Molecular Design, which is my primary scientific interest.

There is of course a lot of molecular design in FBDD, which I like to think of as little more than a smart way to do structure-based design. Molecular design may be defined as control of properties of compounds and materials through manipulation of molecular properties. Although computational chemistry tools are very useful in molecular design, the essence of design is thinking about molecules and I don’t want people without a CompChem background to be put off by the blog having Molecular Design in its title.

There will still be plenty of fragment-based material in the blog since I will be continuing the series on screening library design which came to a halt on Easter Island a year and half ago. However, I’m also planning some posts on physicochemical properties such as logP and logD which are important in FBDD but have a much broader relevance in Drug Discovery.

A short rant about journal editors

2011-04-10T20:43:00.011+01:00

In the previous post I had an indirect dig at journal editors. In this post the dig will be a lot more direct. Recently I accepted an ‘invitation’ to review a manuscript for a journal that, out of tact, I will not name. It always amuses me that these requests for what is effectively free consultancy are presented as ‘invitations’ as if the journal is doing me a huge favour. Nevertheless I do go through with the charade on occasion (although never to the extent of unctuously thanking the editor for his or her magnanimity) since I do regard reviewing manuscripts as the duty of anyone who publishes in journals. The review was duly completed and, given that I was recommending that the manuscript be put out of its misery as quickly and humanely as possible, I’d been thorough, devoting four or five hours to the assignment.

I’d typed the review as word document, planning to paste it into the relevant form in the editorial system. When I logged in the assignment was no longer there so I emailed the Editor and Support assuming that there was a problem with the system. I got a reply from Support explaining what had happened. The Editor had already made the decision and therefore didn’t need my input any more so the assignment had been deleted. Support noted that this was unfortunate and hoped that they could utilize my services again as a reviewer and I’m still waiting for the Editor’s apology. Not wanting to deprive them of feedback, I suggested that they were being overly optimistic if they thought that I would even consider reviewing another manuscript for them. And that’s where things stand. Humph!

So that was the teaser. What I really wanted to talk about was an editorial entitled ‘Science Blogs and Caveat Emptor’ that appeared in another journal late last year. ChemBark was onto it in a flash and soon it had been Pipelined as well. More recently the editorial was reviewed by Michelle Francl (blogs: 1 | 2 ) in her Nature Chemistry column and to be honest there’s not a lot that I can add to what these commentators have already said. Reading the editorial I couldn’t help thinking that it looked like it could have been pulled right out of a blog and Michelle is right on target when she says, “... I had to admire Murray for his ability to raise so many key questions about science writing in a concise and provocative 619 words. He has real potential as a ‘blogger’”. Except that most blogs allow you to post comments.

Provided that their journals score highly enough, Impact Factor becomes a Maginot Line behind which editors can hide and I was not surprised to see it paraded in the first paragraph of the editorial. One statement that I couldn’t quite get my head round was, “By extension, editors and reviewers reinforce the meaningfulness of Impact Factors by explicit attention to the reliability of submitted articles; if the Scientific Method has not been adequately followed, then there should be a downwardly adjusted evaluation of impact”. I’d always thought that impact factor was determined by numbers of citations and a citation made the same contribution regardless whether an author was heaping praises his previous study or drawing the attention of readers to an odour of something other than roses.

One of the more bizarre assertions made in the editorial is, “Bloggers are entrepreneurs who sell “news” (more properly, opinion) to mass media: internet, radio, TV, and to some extent print news”. Having never received payment for any of my bloggings, I do find this statement a little rich coming as it does from somebody whose journal invites me to purchase content ($35 for 48 hours of access) when I try to look at it. Furthermore, some journals are actually devoted to publishing Opinions and these journals certainly don’t let you see their content for free.

I think what the author of the editorial really doesn’t like about scientific bloggers is their ability to do post-publication review of the journal’s articles in a very public manner. The illusion of the infallibility of Peer Review is often the first casualty when bloggers (and their commentators) discuss specific scientific articles. But can you blame the Editors when all they can see is that the Heretics have taken over the Auto-da-Fé.

Literature cited

R Murray, Science Blogs and Caveat Emptor. Anal. Chem., 2010, 82, 8755 DOI

M Francl, Blogging on the sidelines. Nat. Chem. 2011, 3, 183-184 DOI

FBLD versus DOS

2011-02-14T21:10:00.036Z

The relative merits of Fragment Based Ligand Design (FBLD) and Diversity-Oriented Synthesis (DOS) were recently debated in a Nature Forum. This debate has already been reviewed both in Practical Fragments and In The Pipeline.

I believe by setting up the debate like this, the editorial staff of the journal show a poor understanding of Lead Discovery (LD). In essence a comparison is being made between apples and oranges. FBLD (also known as FBLG with the LG for lead generation) is an integrated LD framework and a comparison with conventional high throughput screening (HTS) and associated Hit-to-Lead (H2L) chemistry would have made more sense. DOS is essentially an approach to extending the chemical space covered by screening collections and filling ‘holes’ in the existing chemical space. A DOS approach could be easily used to enhance existing fragment libraries (especially if using molecular shape to quantify similarity) while the output of a fragment screen could be used as input to design of DOS libraries. The ‘Core and Layer’ approach that I’ve used in design of generic fragment libraries (and even one library for cell screening) can accurately be described as diversity-oriented.

The case for FBLD is made by Philip Hajduk who makes the important points that a relatively small number of fragments can be used to cover a relatively large chemical space and that synthetic resource is always directed towards the target of interest. I like to say that leads from FBLD are assembled from proven molecular recognition elements and would add that fragments allow you to search chemical space with a better-controlled resolution than do more elaborated molecules. I don’t happen to agree with his assertion that “there is ample evidence that larger molecules are more likely than smaller ones to succeed as drugs in clinical trials” but this does not weaken the first two points that he makes. It's worth remembering that you usually need protein crystal structures in FBLD both for the target (at the outset of screening) and for complexes with weakly-bound fragments. If you don't obtain these quickly you're going to be working on Project Passchendaele.

DOS is championed by Warren Galloway and David Spring. They note that there are situations in which FBLD is not currently applicable, for example in phenotypic screens (see Derek Lowe's comments In The Pipeline) or for probing certain protein-protein interactions. I agree with this point and believe that we’ll always need a variety of assays for successful LD, especially as Drug Discovery is expected to get even more challenging in the future. If you’re trying to enhance the ability of screening libraries to hit targets then it makes sense to use molecular diversity criteria to extend coverage in a more systematic manner. I don’t see why the term DOS should only apply when molecular size exceeds an arbitrary cut off and believe the real issue is more about how than whether DOS should be used to enhance screening libraries.

The advocates of DOS need to take a close look at how they define diversity. If the conserved core of a DOS library cannot be accommodated in a binding site then, barring nuclear fusion, none of the compounds in the library will fit either. From the point of view of this target the library has no diversity regardless of the number of compounds in it.

I was disappointed that molecular complexity (check this link for an alternative view) was not raised by either party in this debate since it’s a unifying concept that brings together different strategies for compound library design. Very complex molecules leave the H2L chemists with little or no room to manoeuvre. This is less of a problem if the screening hit nails the target with nanomolar potency and has jaw-dropping bioavailability. However, reality is more likely to be micromolar with one or more ADMET issues needing to be addressed. Advocates of DOS really do need to start thinking a bit more about molecular complexity in the context of screening compounds for biological activity. I always encourage folk designing a DOS library to make a relatively large sample of the library prototype so that it can be included in the fragment screening collection.

So what’s the verdict? I believe that FBLG is here to stay although it is not yet clear how widely applicable the approach is. I also believe that some form of DOS can be used to enhance any screening collection provided that:

(1) Diversity is seen in the context of the existing collection
(2) The importance of hit exploitability is recognised

I’d be interested to hear what other people think about this topic so feel free to comment. I’ll also set this up as a discussion for the FBDD LinkedIn group since commenting there is a bit easier. Also don’t forget that the journal allows you to comment on the article directly.

Literature cited

Hajduk, Galloway & Spring, A question of library design (Forum Drug Discovery). Nature 2011, 470, 42-43 | DOI

Nicholls et al, Molecular Shape and Medicinal Chemistry: A Perspective. J. Med Chem. 2010, 53, 3862-3886 | DOI

Hann, Leach & Harper, Molecular Complexity and Its Impact on the Probability of Finding Leads for Drug Discovery. J. Chem. Inf. Comput. Sci., 2001, 41, 856–864 | DOI

Rule of Three considered harmful?

2011-01-11T02:48:00.078Z

I should start this post by saying that I’ve never actually used the Rule of Three for fragment selection. Part of the reason for this is simply a matter of timing since I’d been designing fragment libraries before the Rule of Three came along. However, I believe that there are reasons that you need to take a very close look at the Rule of Three if you’re planning to build a fragment library strategy around it. The rule was introduced in late 2003:

“We carried out an analysis of a diverse set of fragment hits that were identified against a range of targets. The study indicated that such hits seem to obey, on average, a ‘Rule of Three’, in which molecular weight is < 300, the number of hydrogen bond donors is ≤3, the number of hydrogen bond acceptors is ≤3 and ClogP is ≤3. In addition, the results suggested NROT (≤3) and PSA (≤60) might also be useful criteria for fragment selection. These data imply that a ‘Rule of Three’ could be useful when constructing fragment libraries for efficient lead discovery.”

My first criticism of the Rule of Three is that the authors do not say how they define hydrogen bond acceptors. I’ll illustrate this point with reference to the phenylhydantoin below which along with the accompanying properties was retrieved from eMolecules. As far as I’m concerned, this compound would have been perfectly acceptable for inclusion in a fragment library before the Rule of Three was published and the publication of the rule would not make change my mind. If, however, you asked me whether the compound complied with the Rule of Three, I’d have to admit that I simply don’t know. The number of hydrogen bond donors is not an issue because there is only one of these in the molecule. The number of acceptors is more problematic. I would only count the oxygen atoms in this molecule as acceptors and, since there are two of these, the molecule would be compliant with the Rule of Three. However the well-known Rule of Five treats all nitrogen and oxygen atoms as acceptors so if you use those criteria you’ll count a total of four acceptors and conclude that the compound is not compliant with the Rule of Three. This is not a problem for me because I don't use the Rule of Three but spare a thought for the person assembling a commercial fragment library.

My second criticism of the Rule of Three concerns how it was actually derived. The authors describe performing “an analysis of a diverse set of fragment hits” without actually saying anything about what this analysis entailed. If they were analysing hits from their own fragment screens then the characteristics of the hits will reflect the criteria by which compounds were selected for fragment screening. If they were sampling from a more extensive database of screening hits, I’d still want to know how the fragment hits were distinguished from the other hits.

My third criticism is as much about how cut offs get used as it is of the Rule of Three. There’s a diagram of a funnel that you often see in virtual screening reviews. We also use funnels (or filters as we prefer to call them) in screening library design and in fact this activity is not a whole lot different from working up a virtual screen. Typically we apply filters and sample (e.g. using molecular diversity criteria) from what makes it through. Note that I say ‘filters’ rather than ‘a filter’. The Core and Layer (CaL) approach to library design has been described both in this blog and in a journal article. In CaL the filters used prioritise compounds get less restrictive as more compounds are added to the library. The reason for doing this is that it gives better control of chemical space coverage since it forces the selection of the smallest and least complex molecules first. A molecular diversity maximiser such as BigPicker, will tend to pick larger, more complex molecules because these tend to be more dissimilar to each other.

I am also prepared to accept compounds that have measured/calculated logP values in excess of 3 provided that the appropriate precautions (select ionisable compounds and/or use measured solubility values) have been taken to minimise the risk of poor solubility. You don’t want a whole library of compounds with logP values in excess of 4 but having some will increase the range of targets that you can nail. I am more concerned about the distribution of logP and molecular size in a library than I am with their maximum values and believe using multiple cut offs allows better control of these distributions.

You'll find plenty of material on the internet that deals with the Rule of Three although inconsistencies can be observed. It is not clear whether or not the Rule of Three includes the restrictions on NROT and PSA. As I read it in the original article, I don't think it does but I'm not sure and think it could have been made clearer. This webpage (accessed 11-Jan-2011) appears to suggest that Maybridge FBDD team think that the NROT and PSA criteria are included in the Rule of Three. However, another webpage (accessed 11-Jan-2011) seems to suggest that the FBDD team at Chembridge think otherwise. Cambridge Medchem Consulting (accessed 11-Jan-2011; I expect that this page will get updated once the error is discovered) appear to share the Chembridge view that the NROT and PSA criteria are not included in the Rule of Three although they use < instead of ≤ when stating the Rule which makes a big difference when the number in question is 3. Yet another variation on the Rule of Three can be found in the BioScreening.net glossary (accessed 12-Jan-2011) in which the hydrogen bond criteria are stated as "number of H-bond donors and acceptors less than, or equal to 3", which could be taken to imply that the sum of donors and acceptors cannot exceed 3.

I should of course let you know where the title of this post comes from since I borrowed most of it from a computer science paper that is over forty years old. I can’t even claim originality for adapting the title of the earlier paper because my friends at OpenEye have beaten me to that as well. Furthermore my first two criticisms of the Rule of Three have already been made by other bloggers (accessed 11-Jan-2011).

I hope that this post will at least make people ask a few questions when presented with rules like these in the future. I'll also set up a discussion in the LinkedIn Medicinal Chemistry group which will facilitate posting of comments.

Literature cited

Congreve, Carr, Murray & Jhoti, A ‘Rule of Three’ for fragment-based lead discovery? Drug Discov. Today 2003, 8, 876-877 | DOI

Lipinski, Lombardo, Dominy &Feeney, Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Deliv. Rev. 1997, 23, 3-25 | DOI

Blomberg, Cosgrove, Kenny & Kolmodin, Design of compound libraries for fragment screening. JCAMD, 2009, 23, 513-525 | DOI

Dijkstra, go to statement considered harmful. Communications of the ACM, 1968, 11, 147-148 | DOI

BrazMedChem2010

2010-11-16T23:16:00.185Z

It really was great to get back to Brazil. Early in November, I attended the 2010 Brazilian Symposium on Medicinal Chemistry that in Ouro Preto. The conference commemorated the work of Carlos Chagas who identified and fully characterized the disease that now bears his name. His work has been described in Wikipedia as “unique in the history of medicine because he was the only researcher so far to describe completely a new infectious disease: its pathogen, vector (Triatominae), host, clinical manifestations and epidemiology”. Chagas worked at and subsequently became the director of the medical research institute founded by Oswaldo Cruz. Although Chagas spent most of his working life in Rio de Janeiro, he was born a Mineiro and only left his home state for his university studies. Ouro Preto is one the ‘must see’ attractions of Minas Gerais (and Brazil) and here are a couple of pictures to give those who were not there an idea of what they were missing.

Although the work of Carlos Chagas is worthy of celebration the current state of treatment of Chagas Disease is most definitely not. Like the better known African Sleeping Sickness, it is a trypanosomal disease and of minimal interest to Big Pharma. The drugs used for treatment have unpleasant side effects and once the disease enters the chronic phase they become a lot less effective. A number of lectures focus on the nature of the disease and I find these particularly interesting, although occasionally gruesome (I always appreciate these reminders of why I never considered pathology as a profession).

Álvaro Romanha delivers an interesting keynote lecture in which he looks back over a long career in parasitology. He describes characterisation of the effects of a number of compounds (including one developed by my former employer) on the Chagas parasite. Lucio Freitas-Junior and Andrei Leitão both talk about cell-based assays which can be used in target identification as well as lead discovery. I hope the folk doing natural product research are taking notes...

Barry Sharpless delivers an entertaining lecture on Click Chemistry and I also enjoy talks by Mike Gelb and Tom von Geldern since these both have a strong medicinal chemistry focus. The work described by Mike used Tipifarnib (1), a farnesyl transferase inhibitor as a starting point. This compound kills T. cruzi and its good pharmacokinetic properties reflect the fact that it had been in clinical development. It turns out that the compound actually inhibits the trypanosomal lanosterol 14-demethylase and this is the reason that it is able to kill the parasite. The optimized compound (2) shows efficacy against acute Chagas in mice and is sufficiently selective not to inhibit human lanosterol 14-demethylase or farnesyl transferase. The focus of Tom’s lecture is African Sleeping Sickness although this is still highly relevant to Chagas Disease. He describes an interesting series of cyclic boronate esters (3), the mode of action of which is still uncertain. If you’re going after Sleeping Sickness you’re going to have to get your drug through the blood brain barrier. Tom describes some of the approaches that the team adopted to optimize pharmacokinetics and achieving good Central Nervous System (CNS) penetration. In the chronic phase of Chagas Disease the parasites take refuge in the cells of their host so the drug has an additional barrier to cross. I wonder how the presence of an intracellular parasite might stimulate expression of efflux transporters in a host cell? That would indeed be sneaky...

Cristiano Guimarães presents analysis of relationships between permeability and physicochemical properties such as polarity and molecular size. However, I am more interested in what he has to say about re-scoring of docking poses. In particular, he notes that conformational entropy lost on binding tends to get over-estimated and has published some of this work. Enthalpy is the focus of the lecture by John Ladbury who describes how calorimetry can be used as a tool for understanding biomolecular interactions and an aid to drug design. The idea is that enthalpy changes associated with binding reflect the extent to which polar interactions form between the molecules in the complex. If you can exploit polar interactions to increase affinity then hopefully you’ll end up in a better place because polarity tends to be associated with better aqueous solubility, selectivity and metabolic stability. However, interpretation of the enthalpy and entropy changes associated with binding remains a challenging problem. I'd suggest taking a look at John's recent publication and recent discussion in the LinkedIn Medicinal Chemisry and Drug Discovery Group if you want to find out more.

That just about wraps up the technical part of this post and there is a page for links to talks. However, I did manage to get a few pictures including one of Carlos and another of his boss at the opening reception.

The following photos were taken at afternoon coffee on the last day of the conference. The first of these shows three of my friends from Rio and it was Daniel who did an excellent job introducing and chairing the molecular design session in which I spoke.

These pictures were taken towards the end of the conference. The paparazzo certainly knows how make the ladies from Porto smile and Carlos does look happy to be passing the baton to Vera who will be organising BrazMedChem2012.

Then it was time for dinner. Tom, Roberto and Ivan were staying in the annex and had to be be summoned. Tom looks a bit hungrier than the other two.

These folk are from FIOCRUZ in Belo Horizonte apart from Claudia (Federal University of Ouro Preto) and Malu who can't resist the photo opportunity. Andrei is looking quite intense in the next photo, Patricia less so.

These two photos are a couple of my favorites. I wonder if Barry is suggesting to John that the adiabatic stereoelectrostatic compressibility of the polarisability tensor may well be the the elusive Universal Efficiency Metric that he is searching for. Of course they could just be swapping fishing stories. One of the highlights of the dinner was Mike playing classical guitar and I was pleased that caipirinha consumption did not interfere with ability to operate a rather bulky digital SLR.

Literature cited

Guimarães & Cardozo, MM-GB/SA Rescoring of Docking Poses in Structure-Based Lead Optimization. J. Chem. Inf. Model. 2008, 48, 958-970 DOI

Ladbury, Klebe & Freire Adding calorimetric data to decision making in lead discovery: a hot tip. Nat. Rev. Drug Discov. 2010, 9, 23-27 DOI

EuroQSAR 2010

2010-10-29T20:18:00.195+01:00

I thought this would be a good photo to start the post on EuroQSAR 2010. It really was great fun to be in Rhodes and to catch up with a lot of folk whom I've not seen for a while. The photo was taken at Delphi the day after the conference ended. This the Temple of Apollo where the priestess in residence would inhale hot gases and make predictions... of course nothing like QSAR!

You may well ask what a conference on QSAR has to do with FBDD so I'll try to make the connection clearer. The central problem in QSAR is prediction of affinity so it's a good idea to maintain awareness of the field if you're planning to exploit protein or ligand structures in selection of fragments for screening. Also if you're planning to analyse and design compound libraries or assess druggability then it's useful to know a bit about the molecular descriptors and data analytic methods that form the basis of modern QSAR.

The ultimate goal in QSAR is to start with a molecular structure and predict the physiological effects of the compound. In order to to this you need to be able to predict the extent to which the drug binds to its primary target and a number of anti-targets. You can calculate the extent of binding from the affinity of the drug and its unbound (e.g. to plasma protein) concentration in the vicinity of the target. With typical dosing the unbound drug concentration is a function of both time and location (e.g. intracellular versus extracellular) within the body. If that sounds unbearably complex then I should warn you that it can get a whole lot worse because binding might be slow and you might also need to worry about things like reactive metabolites, isoforms and how toasted the patient got in the pub the night before.

I believe that we're a very long way off seeing this goal achieved. Even when the structure of a protein is known, prediction of affinity for an arbitrary ligand is just not accurate enough. Prediction of unbound concentration at arbitrary location in the body is equally difficult although for some targets it may be sufficient to know the unbound plasma concentration. Nevertheless it is possible to build useful models for some of the pieces in this jigsaw (e.g. binding to plasma protein; IC50 for series of chemically similar receptor antagonists) especially when the process in question is strongly influenced by lipophilicity. QSAR models can be local (i.e. only applicable within a restricted regions of chemical space such as a series of analogs) or global (applicable to any arbitrary molecule). My view is that QSAR models presented as global are frequently ensembles of local models and I have expressed this opinion in print.

I guess that something should be said about the talks after such a long-winded introduction. Some of the speakers have made their lecture slides available. I should first point out that I managed to miss the only talk specifically on FBDD because I was retrieving my camera from my hotel room so that I could photograph a friend doing her poster presentation. The inaugural lecture is given by Hugo Kubinyi and although I saw 'The long road from QSAR to virtual screening' two and a half years ago at an OpenEye meeting in Strasbourg, it is still amusing to see polar surface area and connectivity descriptors cop some flak.

Anthony Nicholls delivers a stimulating lecture entitled 'Information Theory & QSAR'. Nearest neighbor models crop up in more than once in this talk and I liked the suggestion that in validation we should be 'making tests NN resistant'. Nearest neighbours also make an appearance in Han van de Waterbeemd's talk (Assessing Drug Safety and Efficacy through ADME predictions) in the context of 'correction libraries'. The idea behind a correction library is to see if there are systematic errors in the predicted values of a property for near neighbours of the compound for which you're making a prediction. If so the differences between the values measured for the neighbor and predicted for them by the model can be used to correct predictions for new compounds. Of course Orwell would have said that all QSAR models are global...

I enjoyed (not least because he did not appear over-awed by the illustrious inaugural lecturer) the lecture by Alex Tropsha entitled 'Novel Approaches to Chemical Toxicity Prediction Relying on the Entire Structure- in vitro in vivo Data Continuum'. Attempting to sum up the talk in one sentence, I'd say that this was a view of how QSAR modelling might be used to integrate diverse data types that vary in complexity and noise level. One comment that I captured from the 'Notes on chemical descriptors' slide (#14; it's marked 'de-Ku' at bottom left) was that 'descriptors are designed to reflect uniqueness of a molecule in comparison with other molecules'. What does this say about nearest neighbours, I wonder...

Jordi Mestres makes the point in his talk, entitled 'Ligand-based Approaches to In-Silico Pharmacology', that we should STOP using the word 'polypharmacology'. I couldn't agree more although I think the term 'pharmacodynamics' is a couple of orders of magnitude more meaningless. This lecture is about predicting affinity across a range of GPCRs and how these affinity profiles might be used, for example, to anticipate side effects of drugs. Intracellular targets will add complexity because less will be known about unbound concentration of drug in the vicinity of the target.

There's also a session on design of agrochemicals kicked off by Klaus-Juergen Schleifer. All molecular design is subject to constraints and it is always educational to see how people designing molecules for different purposes deal with the constraints that apply to them. Designers of agrochemicals need to deal with different species of plants, fungi and insects in a chemical-unfriendly (e.g. bright sunlight, rain) environment. Pharmaceutical QSAR modellers may find that they learn more in an agrochemical session than a pharmaceutical one.

I must confess to being less than alert on the Friday morning and this may have something to do with over-indulging at the conference dinner and ending up on the beach at 2AM (it seemed a good idea at the time but then it always does). Eric Martin describes how QSAR methodology can be used to integrate affinity and protein structural data for protein kinase inhibitors. Tudor Oprea's lecture (Computer-Aided Drug Re-purposing) is notable because of the use of experimental pharmacokinetic data (this is typically available for marketed drugs) to get a handle on unbound plasma concentration. Aspiring systems biologists, take note.

I've taken a look at some of the talks and it's a good time to summarise before getting on to the fun bit where I share some pictures. Firstly I do not see prediction of affinity for arbitrary molecules (e.g. when there is no measured data for analogs) something we can currently do in a useful and general manner. Secondly, I don't see model validation as a solved problem and a session on the subject is something that the Scientific Committee may wish to think about for EuroQSAR2012 in Vienna. Thirdly remember that, "A theory has only the alternative of being right or wrong. A model has a third possibility: it may be right, but irrelevant" (Manfred Eigen).

The excursion to Lindos provides an excellent opportunity get some pictures. First to be papped is fellow AZ-escapee Han.

I sneak up on Anna (who also doesn't work at AZ any more) as we wait for the bus.

Dick is the man behind CoMFA and Topomers and I've lost count of the number of conferences which we have both attended.

Unfortunately I can't get Yvonne to look at the camera. To her right is Cynthia who co-chaired the session on descriptors in which Yvonne and I both did talks. Andreas and Ylva are smiling because I've told them that the chef has got rotted herring on the menu for the conference dinner.

I finally catch Yvonne as we're waiting for the bus back. I'd not seen Eric since the mid-90s so it's good to get the two of them together in this photo.

David (make sure you're in a country with an almost worthless unit of currency should you presume in his presence otherwise prepare to pay out big time) and Frank have been in this business a long time.

Portraits from the conference dinner.

David is animated but Ant looks in need of a dose of the Poisson-Boltzmanns.

Dimitris appears strangely unconcerned to be in the company of two Transylvanians who are discussing anticoagulant QSVRs (Quantitative Structure Viscosity Relationships) as they admire the tone of his carotid arteries.

Graduate students. Birte (4th from left) did a talk and both Juliana (2nd from left) and Andrea (3rd from left) had their posters selected for oral presentation.

Party animals.

Molecular Interactions

2010-10-01T19:10:00.048+01:00

Molecular interactions are an important part of the theoretical framework of modern drug discovery and studying them is a great way to increase your understanding of physicochemical principles of molecular design. One view of molecular design is as a process of tuning the interactions of molecules with the different environments in which they exist. Needless to say, knowledge of molecular interactions is particularly valuable in FBDD. Three Roche scientists have recently published “A Molecular Chemist’s Guide to Molecular Interactions” which should be of interest to anyone working in molecular design and other bloggers ( Derek | Joerg ) have already highlighted the article.

The authors cover plenty of ground and everybody should find their favourite molecular interactions discussed. Given the recent LinkedIn discussion on the value of measuring enthalpy and entropy changes associated with binding, I was pleased to see that the authors noted that interpretation of these quantities is typically difficult. The discussion of cooperativity was useful because we often assume that contributions of interactions to binding are additive.

One of my favorite interactions is halogen bonding and I am pleased to see it discussed in some detail. The halogens all confer a degree of hydrophobicity on a molecule and the heavier halogens (i.e. not fluorine) also exhibit an ability to interact with hydrogen bond acceptors that increases with atomic number. Although this class of interaction is sometimes thought to have been discovered in recent times, it’s actually been around a long time. I can remember learning, as a schoolboy in Port of Spain in the mid-1970s, about why iodine is more soluble in aqueous potassium iodide than in water. I developed this theme a bit more in a light-hearted survey of halogens at EuroCUP in 2008 which starred both Bismarck and a medical writer by the name of Bouchardat who was active in Paris when Pincess Victoria became Queen Victoria. I'm not sure if they ever caught the notorious Parisian dog-poisoner.

Something that I found disappointing was that there was not a lot of information about how much additional affinity you’re likely to get by making the different interactions. This should not be seen as a criticism of the authors who have carried out an impressive trawl of the literature. It’s just disappointing that the information is not available in the current literature base.

I’ve some comments to make on the discussion of hydrogen bonds. There is a widely accepted view that the maximum contribution to affinity that a hydrogen bond between a neutral donor and acceptor can make is just over a log unit and here’s what the Roche authors had to say on the subject:

“Hydrogen bonds always convey specificity to a recognition process but do not always add much binding free energy. Desolvation of the donor and the acceptor must occur for the hydrogen bond to form, such that the effects of hydration and hydrogen bond formation nearly cancel out”

I certainly agree that in some cases the contribution of hydrogen bonds to affinity will be minimal. However, the dataset from which that figure of just over a log unit was derived is actually quite small and not especially diverse in terms of donor-acceptor pairings. I believe that if you can form the hydrogen bond deep in a binding pocket then it can make more than the widely accepted maximum contribution to affinity. We recently published inhibition data which included the example of aza-substitution of a pyridine ring resulting in increases of potency of about two log units against Cathepsins S and L2. Some caution is required in interpreting these results because we didn’t have the relevant crystal structures and the inhibitors are racemic. However, I do believe that these results should make us question the prevailing view of the maximum contribution that a hydrogen bond between a neutral donor and acceptor can make to affinity.

I’ll say some things about the discussion of the hydrogen bonding of sulfonyl groups because it should get you thinking a bit. The authors state that:

“Only 30% of the sulfones and sulfonamides form hydrogen bonds. This raises the question of which type of interaction this functional group prefers.”

I’m not sure that I agree with the second sentence and would be interested to know how many of these sulfones and sulphonamides actually had the opportunity to accept a hydrogen bond. If no hydrogen bond donors are present in a molecule then you can’t really blame the sulfonyl oxygens for making contact with aliphatic carbon in the solid state since that's going to be a better option than getting in the way of the sulfonyl oxygens of a lattice neighbour. Even when a donor is present in the molecule, the favoured interaction may well be with a stronger acceptor than the sulfonyl oxygens.

The authors also took a look at the environments of sulfonyl groups in the PDB and here’s what they had to say.

“Notably, of the sulfonyl groups situated in a hydrophobic environment in the PDB, only 36% are found to interact simultaneously as a hydrogen bond acceptor but 79% of the hydrogen-bonded sulfonyl groups are found to interact simultaneously with a hydrophobic group. These findings clearly indicate a dual character of the weakly polar sulfonyl groups as a hydrogen bond acceptor and as a hydrophobic group.”

I simply don’t buy this idea of sulfonyl oxygens having a dual acceptor-hydrophobic character. Hydrophobicity is a statement of aquous solvation characteristics. It will be easier to place a weak acceptor in a hydrophobic environment than it will to place a strong acceptor there. However, if it’s an acceptor, it’ll still prefer the aqueous environment. Think about the consequences of one of these oxygens accepting a hydrogen bond. When an oxygen atom is hydrogen bonded its ability to accept a second hydrogen bond is likely to be reduced because the donor polarises the acceptor oxygen. Also the second donor will also experience repulsive secondary electrostatic interactions with the existing donor. Provided that the oxygen can still accept a hydrogen bond, it will not be too ‘distressed’ (apologies for anthropomorphising) to be in contact with hydrophobic surface. If you’re interested in this sort of thing then take a look at our article on alkane/water partition coefficients to see how accepting a hydrogen bond (from methanol which we used to model octanol) is likely to affect the ability of carbonyl oxygen to accept a second hydrogen bond.

To be fair, the authors do recognise that accepting a hydrogen bond might affect the probability of a sulfonyl oxygen atom making contact with hydrophobic surface. However, this hydrogen bond donor doesn’t need to come from the protein or a water molecule that the crystallographers can see. How many of the sulfonyl oxygen atoms which lack ‘visible’ hydrogen bonds are sufficiently solvent-exposed to accept hydrogen bonds from ‘invisible’ solvent water molecules? I’ll leave it to you the reader to think about whether analysis of the CSD (as opposed the PDB) has any relevance to hydrophobic interactions.

I’m now going to wrap up with what will be seen by some to be nitpicking although that is not my intention. This is what the authors have to say about QM calculations and hydrogen bonding:

“Where experimental data are not available, acceptor strengths can be obtained from quantum chemical calculations.”

My first criticism of this statement (which is getting very close to nitpicking although as The Blogger I’m allowed to do that) is that quantum chemical calculations can be used to predict donor strengths as well. Like they might say in Buenos Aires, it takes two to tango. My second criticism is that rather than talking about generic “quantum chemical calculations” the authors could also have mentioned that electrostatic potential is a useful predictor of both acceptor and donor strength. I have to declare an interest here as author of reference 98d but I do believe that the effectiveness of electrostatic potential as a predictor of donor and acceptor strength is more important than whether it was calculated quantum mechanically or classically. It tells us something about the nature of the hydrogen bond.

That brings us to the end of my review. The article is definitely a good read and a valuable contribution to the field. To put it bluntly, you need to know this stuff if you want to succeed in FBDD. I’ve flagged up the issue of sulfonyl oxygen hydrogen bonding to hopefully make you think a bit and maybe even generate some discussion. Feel free to make comments of your own.

Literature cited

Bissantz, Kuhn & Stahl, A Medicinal Chemist’s Guide to Molecular Interactions. J. Med. Chem. 2010, 53, 5061-5084 DOI

Davis & Teague, Hydrogen Bonding, Hydrophobic Interactions, and Failure of the Rigid Receptor Hypothesis Angew. Chem. 1999, 38 736-749 DOI

Bethel et al, Design of selective Cathepsin inhibitors. Bioorg. Med. Chem. Lett. 2009, 19, 4622-4625 DOI

Toulmin, Wood & Kenny, Toward Prediction of Alkane/Water Partition Coefficients. J. Med. Chem. 2008 51, 3720-3730 DOI

Kenny, Hydrogen bonding, electrostatic potential and molecular design. J. Chem. Inf. Model. 2009, 49, 1234-1244 DOI

LinkedIn Discussion: Enthalpy/entropy & kinetics of binding

2010-08-24T20:36:00.012+01:00

Readers of this blog may be interested in a discussion on enthalpy/entropy and kinetics of binding that members of the Medicinal Chemistry and Drug Discovery LinkedIn group are currently having.

A short update

2010-08-13T20:44:00.005+01:00

I’m now back in the UK and have been catching up with a few folk. It certainly was a great trip and I’ve set up a travel blog called The Great Escape (better late than never) to share some photos. So far the first month, which included a couple of weeks in Paraguay, has been written up.

You may remember the post on SPR from a while back. It got turned into a letter to the Journal of Molecular Recognition which (to my great amusement) is treated as a publication by Google Scholar. It turned out the review that inspired the post had a few folk spitting feathers and I got the chance to join the fun. I’ve never figured out why people try to eat feathers.

FBDD in Academia 2

2010-08-10T23:38:00.005+01:00

Previously I noted that FBDD provides a means for academic groups (and start ups) to negate the advantage that Big Pharma’s massive screening collections give them. Fragment screening and structural characterisation of fragment binding broadens the scope of a structural biology group’s activities. I do believe that a package of fragment binding modes and affinities is something in which a pharmaceutical company would be interested. But what if an academic group wants to move the fragment hits (which I refuse to call ‘frits’ because I don’t work there any more) further along the optimisation trajectory? I’ll outline some of the issues that need to be addressed and this post is intended to stimulate discussion rather than being a last word on academic FBDD. Please feel free to comment if you’d like to challenge anything or flag up anything that’s been overlooked or oversimplified.

The post-screening phase of lead generation will generally require chemical synthesis. Academic synthetic chemists typically focus on synthesising complex natural products or developing new synthetic methodology so it can be difficult to interest them in the more mundane business of lead generation. To be fair the synthetic chemistry required for lead generation is unlikely to be of sufficient novelty or complexity to earn a graduate student the PhD in synthesis which will be his or her primary objective. There are medicinal chemists in academia but in some cases these happen to be synthetic chemists who think that medicinal chemistry is simply a branch of synthetic chemistry. Also synthetic chemists in academia tend not to be interested in molecular design. The net result is that it can be difficult for a protein structure and fragment screening group to find academic collaborators to take the project into the post-screening phase even when there may be synthetic chemists in the same institution.

As you move from screening to post-screening phases of lead generation the work becomes more multi-disciplinary. Unfortunately ‘multi-disciplinary’ isn’t something that usually gets done well in academic institutions although the problems are often less to do with skills than with organisation (and occasionally egos). In addition to the molecular design and synthesis, it will become necessary to run assays to demonstrate that affinity translates into inhibition of the target enzyme (it’s likely to be an enzyme if you’re doing FBDD). For some targets (e.g. antibacterial or kinases) it’ll be necessary to demonstrate some cellular activity. As you approach micromolar potency you may want to check that compounds in your lead series have sufficient aqueous solubility and don’t have particular affinity for anti-targets such as hERG and CYPs. This is much less of an issue if the main objective is publication but is something to be considered if you’re hoping to flog the results of the work to a pharmaceutical company.

That last comment gets me onto a tough issue for an academic lead generation group. How might you persuade a pharmaceutical company to buy your lead series? The main problem is what you’re trying to sell is information and you’ll need to show that you’ve got something good without giving it away. Life is easier if you own the relevant intellectual property but the synthetic chemistry that needs to get done to secure patent cover is not always going to get people PhDs in synthesis. The other point to remember is that Pharma people may not be willing to look at what you’ve got under a confidentiality agreement because of potential for compromising their own IP position. This can become a serious issue if you’re trying to stake a claim for future activity against related targets (e.g. all tyrosine kinases) or to negotiate exclusivity by preventing a company from using leads from competing programs.

So far this post has focussed on the difficulties (which in Pharma-speak would re-branded as personal development opportunities by the happy-smiley folk who inhabit the HR ether) of doing post-screening fragment-based work in an academic environment. If the primary objective is publication then you can write up at any point that is convenient which means that even a small amount of synthesis can have impact. In contrast, commercial lead discovery organisations need to create a secure IP position before they can publish. When publishing affinities and structures of protein-ligand complexes it’s always worth looking out for results that have relevance that goes beyond the specific project. Examples of synthetic elaboration of a fragment leading to a change in its binding mode are particular relevant and the prototypical (low molecular complexity) nature of fragments means that differences in affinity are more easily interpreted.

Getting pharmaceutical companies interested in the output of an academic fragment project is not trivial. A lot depends on the value of the target and the quality of the leads that have been generated. However, getting to leads requires organisation and realising the value of them requires commercial awareness. Organisation is about persuading people that they’re better off working together and can take time to put in place. Commercial awareness is more difficult to acquire and it’s probably best to try to keep things as simple as possible when starting out. Although this may all seem a bit daunting it’s worth remembering that synthesised compounds will typically be novel and that synthesis can be directed away from known ligands. Also one should not forget the ‘supporting data’ of crystal structures and measured affinities.

This is a good point to wrap up. I believe that FBDD provides an excellent framework in which to both train researchers and do high quality science. FBDD also extends the range of options available to academic researchers for collaborating with industrial partners. That's where I'm going to leave it so feel free to comment if anything that I've said (or not said) has annoyed you.

FBDD in Academia 1

2010-05-12T09:15:00.008+01:00

I’ve now gone back to being a tourist and will be in Australia until the end of the month before heading north to Singapore and Malaysia for most of June. Feel free to get in touch if you’re based in either of those countries and would like to discuss fragment stuff or Drug Discovery in general.

Some time ago, I promised to post on FBDD in academia and really can’t keep putting this off. I’ve realised that it’s not going to be possible to squeeze everything into a single post so there should be at least one more post after this one. You should be warned that my academic career ended some years before people started to talk about FBDD so if I appear to be out of touch, it may well be because I am out of touch. Hopefully some of what I’m going to say may be of interest to some of you and please remember that this blog does allow its readers to comment.

I’ll start by making two points, both of which will be obvious to many of you. First fragment based approaches provide a means for drug discovery researchers (both in academia and start ups) to counter the advantages that Big Pharma derives from having massive screening libraries and automated compound handling. Secondly measurement of weak binding and determination of binding mode of weakly bound complexes remains a frontier area in physical biochemistry and biophysics. Remember that the power of a binding assay is defined by the weakness of the binding that can be measured reliably.

An academic group with strengths in protein structure determination and biophysical measurement of binding is well placed to contribute. I see the output of protein structural studies moving away from only determining the structure of a protein to providing a more integrated view of the protein’s ‘interaction potential’. One point worth making in this context is that measured thermodynamic parameters for fragment binding are particularly useful for developing and validating theoretical models because there are fewer protein-ligand contacts and it is easier to quantify conformational strain. Fragment based approaches also provide a means to validate and explore bioisosteric relationships without the need for a lot of synthesis and I’ve created a graphic showing how this might work.

Assembling and maintaining a usable screening library is likely to be a challenge or at very least an issue for most academic groups. However, a group that has established expertise in fragment screening does have some advantages in negotiating with suppliers of compounds who may value experimental characterisation of how well their compounds have behave under assay conditions. Vendors of specialist fragment libraries really should value this type of feedback and if they don’t they shouldn’t be in the business of marketing fragment libraries. I sometimes wonder if synthesis of fragments might form the basis for final year undergraduate synthesis projects which could be quite self-contained and include a molecular design component. In passing I’ll pose the question to readers from academia as to whether they think they’ve got molecular design adequately covered in courses at their universities although I’ll have to leave this topic for another post.

As we all know there is more to FBDD than fragment screening. Once you’ve found fragments that bind, tested analogues of these and determined crystal structures, you’ll need to do some synthesis. For a group whose main expertise is characterising binding and protein structure determination this may a good point to bail out and prepare the results for publication. A group with some access to synthesis may wish to take the project a bit further and publish once they’ve observed some SAR. One of the attractions of FBDD for academic researchers is that there are a number of points at which they can choose to write up the project for publication. It is also worth pointing out that FBDD provides an excellent framework to gain understanding of molecular properties and interactions between molecules. This understanding is essential if you’re planning to do molecular design the basis of which is manipulation of these properties with predictable results.

What if academic researchers want to take things further and generate lead series that will be of interest to Pharma? Synthesis will be necessary and life will get more complicated. I’ll pick this up in the next post (Kakadu salties permitting) since there’s quite a bit to say and I’m actually still thinking about this.

Melbourne, FBDD & Facebook

2010-04-05T11:14:00.031+01:00

I now have less than a month left in Melbourne and since we’ve just switched over to winter time perhaps the hint should be taken. It has certainly been fun and the project is nicely under control although past experience suggests that it’s usually not a good idea to say that. I’ve not lived in a city since the mid-80s when I was a post-doc in Minneapolis and have really enjoyed the ease of getting around and ready access some of the wide range of music that Melbourne offers. I enjoyed an excellent performance by the ACO Soloists at Hamer Hall and am hoping to return there for a strong dose of Bach in a week’s time. University College, where I’m currently staying, is running a concert series and the second of these promises to be as enjoyable as the first. One of the music tutors at UC plays flute in the VYSO and I got to see them in action last weekend with their truly awesome guest soloist Kana Ohashi. This was also an excellent opportunity to watch the violinists since I was in the third row. The soloist was truly kinetic (difficult to be otherwise with the Tchaikovsky) and the first violin nearest to me appeared to have been given a special 'first violin bob' by her hairdresser. Maybe they will patent it.

On my first trip to the Paris Cat Jazz Club, I found it closed due to flooding (it was the day of The Hailstorm but at least there were back-to-back episodes of Hogan’s Heroes on TV). On returning the following week I was lucky enough to catch Monique diMattina who is an extremely warm, engaging and talented performer. By the way she also writes and composes and, as luck would have it, will be returning there the week AFTER I leave Melbourne. While wandering round town one Saturday afternoon, I caught The Wishing Well on Bourke Street and their next local gig is also the week after I leave. Good reasons to come back, I guess.

Previously, I pointed you towards some LinkedIn groups that are particularly relevant to FBDD. There are also groups on facebook that you might want to take a look at. It’s a bit more difficult to keep discussions going using the facebook groups because you don’t get alerted by email in the same way that you do with LinkedIn. However there are a lot of folk on facebook (especially in universities) and I believe it can play a useful part in extending the FBDD web. Here is a selection of facebook groups that you may find useful:

Fragment Based Drug Discovery (This is the group that is linked to this blog. I do check it frequently and usually respond to queries.)

Crystallography Rocks (Once you’ve got fragments to bind, you’ll want to see how they bind.)

NMR (There are a number of elegant NMR techniques for detection of ligand binding and you’ll find plenty of expertise in this group.)

Chemoinformatics (Particularly relevant to screening library design)

Dan gave my round the world trip a very flattering mention at Practical Fragments which did remind me that I really need to do a post on FBDD in academia since Teddy (who used facebook to tell me where Rapamycin comes from) has also discussed this. As I’m a real sucker for peer pressure, I do promise to make sure that my next blog post focuses on this topic. The FBDD facebook group led to me giving a lecture (I normally call these harangues) in Santiago and through it I’ve also made a couple of contacts in Singapore where I’ll probably do a couple of talks. Being in a facebook group also got me a chance to look round the Australian Synchrotron during maintenance week, when you can get a better look at all the cool stuff. I’ll finish with some pics from that visit.

FBDD and Networking

2010-03-28T03:03:00.014+01:00

Reading an account of the session at the ACS on application of computational methods to FBDD, reminded me that it would be a good time to raise awareness of networking groups in this area. Both this blog and Practical Fragments allow readers to comment on posts although this tends not to happen with the frequency that it does at In the Pipeline, probably reflecting the huge readership, frequent updating and diverse content of what I consider to be the best drug discovery blog by a long way.

People interested in FBDD may already belong to a number of relevant LinkedIn groups. The groups offer some advantages over blogs for getting discussions going in that anyone can start a discussion and group members get alerted by email whenever somebody makes a new comment. I’ll list some of these below in case there are some that you’ve not yet heard about.

Fragment Based Drug Discovery (This group is linked by both FBDD blogs)

Label Free Assay Technology Group (It is the assay that makes FBDD possible. The weaker the binding that you can measure reliably, the more powerful your assay)

Structural Biology (X-ray Crystallography, NMR Spectroscopy, Electron Microscopy) (Generally you’re going to need crystal structures to take fragment hits forward)

Job opportunities in Computational Chemistry and Biology, Xray Crystallography, Fragment Based DD

Recently, I submitted the same item for discussion at a number of LinkedIn groups. I invited group members to share their views on the most appropriate technologies for detecting fragment binding. I learned about some new ways to configure SPR experiments and the use of Tm-shift assays. Most of the discussion was in the Structural Biology group (see discussion) although there was helpful input from the relatively new Label Free Assay Technology Group (see discussion) so thank you to all the participants. It was also great to see a couple of familiar faces from my days in Big Pharma, including a co-author from an article that a number of us wrote back in 2007

Interference, PAIN and cysteine pathologies

2010-03-13T02:38:00.019Z

Dan provided some useful comments on the last post and I think it’s better to respond with a post since this makes everything more visible the readers of both our blogs. I agree with Dan’s point that there are pitfalls, such as compound aggregation, in addition to interference that Adam and colleagues describe in their article. In an ideal situation one would always have the ability to measure weak affinity directly. Protein-detect NMR is one of my personal favorites but you do need labelled protein and, if you want to get full value for your money (labelled protein is not cheap), you’ll also need resonance assignments. The SPR technology is widely applicable and like the protein-detect NMR will provide a direct measurement of affinity (and a whole bunch of other stuff). Isothermal titration calorimetry (ITC) represents another option although I believe that the technique is relatively sample-hungry and more limited than the other two techniques in the weakness of binding that can be measured. Also you do need heat so to speak even though the experiment is isothermal.

Nevertheless you can get to the point of having crystal structures with bound fragments using only a biochemical assay to measure potency. Given that you may well be screening at concentrations one or two orders of magnitude above what is ‘normal’ in HTS, it does make sense to use the approach that Adam and colleagues describe even if you’re going to follow up with SPR or NMR. I do sometimes wonder if the promiscuous behaviour of some inhibitors is due to this sort of interference rather than aggregation. One intriguing question is whether aggregates can ‘inhibit’ by changing spectroscopic and fluorimetric properties of assay mixtures rather than by interacting with proteins. At least there’s usually the option of running assays with added detergent to check for aggregation.

I won’t say much right now about the structural nasties that Jonathan Baell and Georgina Holloway have identified as PAINS since I’ll be visiting Jonathan at WEHI next Friday. I became acquainted with some of these unsavory structural types during my time in Big Pharma and do not believe that their PAINfulness is specific to the AlphaScreen technology that the WEHI researchers are using. Back in those days we had the Decrapper and a program called Flush...

Dan mentioned the Practical Fragments post on a Cruzain Screen so I thought I’d finish with a couple of papers that show how things can get unstuck when you’ve got a catalytic cysteine with a malicious streak. In the dock is none other than PTP1B, a target that is much-loved by disease area strategists and much-hated by screening groups. I’m not going to review the articles or even comment on them right now. Just read them in the correct order and perhaps we can pick up this theme later.

PTP1B: Read this first

PTP1B: Read this second

Literature cited

Baell & Holloway, New Substructure Filters for Removal of Pan Assay Interference Compounds (PAINS) from Screening Libraries and for Their Exclusion in Bioassays. J. Med. Chem. 2010, ASAP | DOI

Liljebris et al, Synthesis and biological activity of a novel class of pyridazine analogues as non-competitive reversible inhibitors of protein tyrosine phosphatase 1B (PTP1B). Bioorg. Med. Chem. 2002, 10, 3197-3122 | DOI

Tjernberg et al, Mechanism of action of pyridazine analogues on protein tyrosine phosphatase 1B (PTP1B). Bioorg. Med. Chem. Lett. 2004, 14, 891-897 | DOI

Interference correction in biochemical assays

2010-03-07T03:42:00.012Z

Surface Plasmon Resonance (SPR) was in focus recently both here and across at Practical Fragments. However, now I’d like to take a look at using biochemical assays to identifying fragments that bind to targets of interest. Biochemical screens can typically be run in high throughput and are compatible with automation for high throughput screening, which makes it easy to do follow up screening with analogs. Furthermore the hits identified by biochemical assay are actually inhibiting rather than just binding. A criticism of biochemical screens is that they measure binding indirectly and are prone to interference. Sometimes they are used as a pre-screen to reduce the number of compounds that need to be evaluated in a lower throughput biophysical assay. However there are things that you can do to make your biochemical assay more reliable and meaningful. And maybe even more fun.

The article that I’ve chosen to take a look at in this post is by Adam Shapiro and some other colleagues from my days in Big Pharma. Before I met these folk, most of my fragment work had been around libraries for NMR screening and I learned from them how it is possible to correct for some of the interference from test samples in biochemical assays.

Inhibition is typically detected in a biochemical assay by quantifying changes in light absorption, fluorescence or luminescence. In high throughput applications ‘assay components are added serially to wells without any filtration or washing steps’ which means ‘that the test sample remains in the well during the optical measurement and can interfere with it’. This means that compounds that absorb in the UV or visible range and that fluoresce or quench fluorescence can all lead to changes in the readout parameter without actually binding to the target protein. Other less obvious causes of interference include insolubility of test compound (turbidity can lead to detection of highly polarised scattered light) and meniscus deepening which decreases path length. Compounds are typically assayed at relatively high concentrations in fragment screening, making it especially important to recognise and account for assay interference in these applications.

In addition to providing a useful discussion on the causes of interference, the article describes a practical approach to correcting for it by running ‘artefact assays’. These involve running additional plates in which wells contain the same test samples but no target protein. The wells in the artefact assay plate also need to contain whatever is responsible for generating the signal (e.g. reaction product) and a baseline can defined by preparing wells without test samples. The authors describe in some detail how they apply the corrections and since this is only a summary of the article, I’ll leave it to you to go and check their article out. However, I would like to conclude by noting that the authors also suggest criteria for deciding to reject data because interference is too great for meaningful correction.

Literature Cited

Shapiro, Walkup and Keating Correction for Interference by Test Samples in High-Throughput Assays. J. Biomol. Screen. 2009, 14, 1008-1016 | DOI

Surface Plasmon Resonance

2010-03-07T03:34:00.005Z

General Reviews

Rich & Myszka, Grading the commercial optical biosensor literature – Class of 2008: ‘The Mighty Binders’ J. Mol. Recognit. 2010, 23, 1-64 Link | Review

Application to Fragment Screening

Perspicace et al, Fragment-Based Screening Using Surface Plasmon Resonance Technology, J. Biomol. Screen. 2009, 14, 337-349 DOI | Review

Binding Pathologies

Giannetti et al, Surface Plasmon Resonance Based Assay for the Detection and Characterization of Promiscuous Inhibitors, J. Med. Chem. 2008, 51, 574-580 DOI | Review

Ligand protein interactions by SPR

2010-02-11T10:58:00.027Z

I have now been in Melbourne for about a month and have found the city very much to my taste. I’m visiting some friends to help out with some fragment stuff and have already been wreck diving (on the HMAS Canberra) and watched the Australian Open and a rather one-sided ODI between Australia and the West Indies. On the science side of things, I was able to gatecrash Surface Plasmon Resonance (SPR) course, hosted by the Biomolecular Interaction Facility at CSIRO, Parkville, and taught by Rebecca Rich and David Myszka of the University of Utah. Not the whole course, I might add, because the participants spent the second day of the course in the lab and I’m sure there was a clause in my visa agreement that stipulated that I was not to enter a laboratory except as an observer accompanied by a responsible adult.

SPR has always represented a bit of a gap in my knowledge base so this was always going to be a great opportunity. As well as being experts in this field, Rebecca and David present their material with great clarity, enthusiasm, charm and humour. I particularly liked David’s take on the Maxwellian Demon (these molecules don’t have eyes).

When using SPR to screen ligands, the protein is typically immobilised on the surface of the sensor chip. The term ‘immobilised’ is actually a bit of a misnomer and ‘tethered’ would actually be a more appropriate term. The SPR technology can be used to look at diverse types of interaction over a wide range of affinities and kinetic parameters (e.g. on and off rates) can also be measured.

There is of course a slight catch. The experiments need to be performed carefully and this was a recurring theme in the lectures (and presumably in the practical sessions as well). Now it turns out that much of the SPR literature is perhaps based on experiments that have been performed less than perfectly and, as a public service, Rebecca and David have reviewed and graded the SPR literature of 2008. GRADED? Yes, GRADED, and there were some Fs! Of course David is just the person to do the grading since he sports whiskers of which a Victorian (historical context rather than geographical) head master would be justifiably proud and it is easy to imagine him summoning the hapless transgressors to his study.

A grading exercise like this is unlikely to win you many friends and the authors are realistic to accept that it is likely to reduce the likelihood of either being elected to the National Academy of Sciences although hopefully they will never have to employ the services of professional food tasters when they attend SPR conferences. Putting on my computational chemistry hat, I couldn’t help thinking that the QSAR and Virtual Screening fields might benefit from a similar treatment...

There are a number of articles describing the use of SPR to screen fragments against target proteins and the one I’ve chosen to take a look at is from some folk at Roche. One of the authors of this work is David Banner, whose talk at RSC BMCS 2009, I greatly enjoyed, not least because he made no reference to ligand efficiency except, if I recall correctly, to say that he would not be referring to it.

The Roche group screened a library of 2226 compounds against chymase at 200 micromolar and found 80 hits so clearly SPR technology can provide the throughput required to run a fragment screen. The compounds were screened against an inactive (zymogen) form of the protein as a check for non-specific binding. The authors also described cross-competition experiments which could be used to determine whether two fragments were binding at the same or different sites and it is worth remembering that you need to be able to measure binding very directly to get this sort of information. It would have been really interesting if the results of the cross-competition assays had been integrated with crystallography since 12 co-crystallised complexes showed fragments binding in the active site.

Both stoichiometry and kinetics of binding can be determined by SPR making it an appropriate technique with which to observe interactions between badly behaved ligands and proteins. In an excellent (A-graded by Rebecca and David) article, another Roche group exploit SPR to classify some of these binding pathologies. It is particularly good reading for anyone who has worked up results from high throughput screens but that is not a place I particularly want to go to right now since it’s getting rather late at night and I really don’t want to have nightmares about pathological fragments.

Literature cited

Rich & Myszka, Grading the commercial optical biosensor literature – Class of 2008: ‘The Mighty Binders’ J. Mol. Recognit. 2010, 23, 1-64 Link

Perspicace et al, Fragment-Based Screening Using Surface Plasmon Resonance Technology, J. Biomol. Screen. 2009, 14, 337-349 DOI

Giannetti et al, Surface Plasmon Resonance Based Assay for the Detection and Characterization of Promiscuous Inhibitors, J. Med. Chem. 2008, 51, 574-580 DOI

Screening libraries: Sampling Chemical Space

2009-11-03T02:26:00.012Z

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I am currently in Rapa Nui (aka Easter Island) and it seemed fitting to continue the series on compound library design from here since the first two posts have been from less commonly visited places like Asuncion and Tierra del Fuego. In the previous post I discussed 2D molecular similarity and showed how this can be used to define diversity and coverage, two important compound library characteristics. In general, compounds in a library need to be mutually diverse in order to provide good coverage although high diversity does not guarantee optimal coverage.

In this post, I’ll take you through an approach to library design called ‘Core and Layer’ (CaL). Although we used this to select compounds for generic fragment libraries and more specialised NMR screening libraries, the method is quite general and I have used it to design a compound library for black box cell screening and to select compounds to complement high throughput screens. The software tools (Flush and BigPicker) used to apply CaL were created at Zeneca by Dave Cosgrove and are described in our article in some detail. Although you might think that the tools were developed in order to apply the CaL method, things actually happened the other way round and it was the availability of the software that led to CaL being adopted as an approach to library design.

Figure 1 shows a schematic view of CaL. The core consists of the compounds currently in the library at any point of the design process and a layer is a set of compounds that have been selected to be diverse with respect to the core. Once a layer has been selected, it is added to the core and the combined set of compounds becomes the new core. The process of selecting layers goes on until you’re either happy with the library or you run out of patience.

You’re probably thinking that this is a very tedious and time-consuming way to build a compound library and might ask whether it would be better to select a maximally diverse set of compounds in a single step. However, there are advantages in building up a library in this manner. In library design, all compounds are not equal and CaL allows you to bias compound selection in a highly-controlled manner. I’ll discuss fragment selection criteria in some detail in future posts in this series so please just assume for now that there are some fragments that you would prefer to have in your library than others. The initial core consists of a sampling of your favourite fragments and as you add layers the compounds in them become progressively less attractive. Another feature of CaL is that it provides a solution to the problem of selected compounds proving to be unavailable as can be the case when trying to source relatively large samples from commercial suppliers.

I think this is a good place to stop as it’s dinner time in Rapa Nui. CaL is an approach to biased sampling of chemical space but it doesn’t tell us about which regions of chemical space should be sampled preferentially. In the next posts of this series I’ll take a look at what makes one fragment better than other. On the travel front, I fly into Auckand in a couple of week’s time for a month and a half in New Zealand and expect to be around Melbourne for the first four months of the New Year. Feel free to get in touch if you’ve got fragment stuff that you’d like to discuss.

Literature cited

Blomberg et al, Design of compound libraries for fragment screening. JCAMD, 2009, 23, 513-525 DOI

Screening Libraries: Diversity & Coverage

2009-10-03T22:27:00.020+01:00

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I’m guessing that this may be the first blog post on screening library design to be written in Tierra del Fuego. The weather is currently rather unpleasant although less so than an hour ago when the snow was horizontal.

I introduced screening library design in the previous post with a generalised view of the work flow for fragment based lead generation. When selecting compounds for screening it can be helpful to think in terms of a chemical space in which all possible compounds (real or virtual) can be found. Now you’ve just got to sample the regions of chemical space that you like and you’ve got your library.

Life of course is not so easy. The main problem is that, despite the occasional claim to the contrary, nobody has found a convincing set of coordinates with which to describe chemical space usefully. You can sort of describe organic molecules by size and polarity without having worry about minor irritations like conformational flexibility, ionisation and tautomers. However, molecular recognition also depends on the shapes of molecules which, even for rigid species, are not so easy to turn into coordinates. Especially when you want these coordinates to be predictive of biological activity.

All is not lost since structurally similar molecules often have similar biological properties. One way that the similarity of a pair of compounds can be quantified is by comparing their molecular connection tables (the structures that you would write down on a piece of paper) and for this reason we sometimes talk about 2D similarity. There is no need for 3D molecular structures when you calculate molecular similarity in this way which means that there is no need to deal with conformations. Molecular fingerprints are used frequently to calculate similarity and the idea behind this is that the fingerprints encode the presence or absence of structural features in molecules. Many shared features suggest that two molecules are likely to be very similar. I’ll not go into the details of fingerprints in this post although you’ll be able to find some detailed discussion in our screening library design article.

The downside of 2D molecular similarity measures is that they are unlikely to reveal any but the most trivial shape match or pharmacophoric (e.g. oxadiazole replaces ester) similarity between molecules. This is not too much of a problem in library design because you’ll often to want select both molecules if they are based on different scaffolds, even if they can both orient their hydrogen bonding groups in a similar way. Once you’ve found some active compounds it becomes a very different game because now you’ll be looking for less obvious similarities between these actives, either to extract structure activity relationships or to define search queries.

So even though we don’t have a set of coordinates that defines chemical space in a way that is predictive of biological properties of molecules, we can still use molecular similarity to sample from a collection of compounds. Figure 1 illustrates how this sampling works and will give you an idea of what we mean by the terms diversity and coverage. The key thing to remember when looking at Figure 1 is that similar compounds are close to each other so there is an inverse relationship between distance and similarity. The stars are selected to cover the chemical space occupied by all the molecules and a star can’t cover its neighbourhood effectively the compounds in it are too far away

Although I needed put the molecules in particular positions (i.e. give them coordinates) to generate the graphic, you only need the distances between molecules to select representative subsets. In our paper we described in house software which can be used to do this and the two programs (Flush and BigPicker) are actually quite complementary to each other. Left to its own devices, BigPicker tends to select compounds with no near neighbours and we typically use Flush to ensure that the compounds that BigPicker is selecting from all have sufficient number of neighbours.

This is probably a good point at which to leave things. In the next post, I’ll describe the Core and Layer approach to selecting compounds for screening. This method is not specific to fragment libraries and in fact I’ve used it in work up of high throughput screening output and selection of compounds for cell-based assays.

Literature cited

Blomberg et al, Design of compound libraries for fragment screening. JCAMD, 2009, 23, 513-525 DOI

Grant & Pickup, A Gaussian Description of Molecular Shape. J. Phys. Chem. 1995, 99, 3503–3510 DOI

Screening Libraries: Introduction

2009-07-22T03:56:00.014+01:00

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Tonight seemed like a good time to start the series of posts on screening library design. For those of you who don’t know, I’m taking a year out to travel and am currently in Asuncion, Paraguay and it is currently raining heavily with excellent electrical activity. It’s not all holiday and I’ll be dropping in on friends in Brasil and Australia to see if I can make myself useful in their research groups. It’s also a good time to send best wishes to my friends who are currently attending the Gordon Research Conference on Computer Aided Drug Design.

The special issue of the Journal of Computer-Aided Molecular Design devoted to FBDD has just come out and this includes an article on screening library design that three of my friends and I put together. I’ll certainly be drawing on the article in the series of posts on screening library design but these should complement the article rather than reproduce it verbatim.

A good place to start is a graphic of the generalised work-flow for fragment based lead generation which illustrates the process from the perspective of the library designer. Both protein structures and known ligands can be used to select compounds for screening but selections can also be made generically. Note the colour-coding of the arrows which illustrate flows of compounds (red) and information (blue). You keep cycling around until you find something cool or your management runs out of patience.

The graphic represents a generalised work-flow and in some cases selections based on target and/or known ligands may not be made. There are a number of reasons why this may be the case. For example, there may not be any known potent ligands for the target. Also specialised screening technologies may require specialised library formatting which favours the use of generic screening libraries. Even with protein structures available, there is still a role for generic libraries because the current state of the art for fast prediction of binding affinity still falls short of what is required for selection of compounds for screening against an arbitrary target. That is not to say that docking, scoring and affinity prediction methods are completely useless but just that at this stage they represent a basket into which you would not want to put all your eggs. Deciding how many eggs to put in the ‘generic’ and ‘targeted’ baskets depends on how much you know (or think you know) about your target. My own view is that we’re still a long way off being able to usefully predict affinity and much of this is due to the difficulties in modelling the displacement of water molecules from contact with protein molecular surfaces. Time will tell and I’ll be delighted if somebody proves me wrong!

Literature cited

Blomberg et al, Design of compound libraries for fragment screening. JCAMD 2009, 23, 513-525 DOI

Scaling potency by lipophilicity and molecular size

2009-06-07T23:09:00.027+01:00

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I’m looking forward to posting on design of compound libraries for fragment screening. However, before launching that series I wanted to comment on an article entitled ‘The influence of lead discovery strategies on the properties of drug candidates’ that has already been reviewed by Dan.

My first comment is that the analysis uses a database of 335 hit-lead pairs from HTS. I think it’s quite difficult to define meaningful hit-lead pairs for HTS. I first got into analysis of HTS output a decade and a half ago and from the start we looked for groups of structurally similar compounds in the actives. The larger the group of similar compounds, the greater the interest since observation of these active clusters increases confidence that activity is real. I’m really not sure that hit-lead pairs can be defined meaningfully for HTS-derived leads, especially when journal articles are the primary information source.

That said, the main reason for this post is to take a closer look at ligand efficiency defined in terms of lipophilicity. The most obvious way to define a ligand efficiency metric in terms of lipophilicity is simple to subtract logP from pIC50. There is the issue of whether one should use logP or logD as the measure if lipophilicity but, particularly when using calculated partition coefficients, I think it’s best to use logP. Some of this was discussed in the AstraZeneca fragment based lead generation review from a couple of years ago although I’m sure that the idea of subtracting logP from pIC50 was not exactly new then. The difference (pIC50 – ClogP) has since become known as ligand lipophilicity efficiency (LLE).

Unlike molecular size measures of ligand efficiency, (pKd – logP) has a firm, although somewhat obscure, thermodynamic basis (at least for neutral molecules). The product (Kd x P) is an equilibrium constant in its own right. Just as Kd is a measure of the relative stabilities of bound and aqueous ligand (Kd x P) is a measure of the relative stabilities of bound ligand (in an aqueous medium) and ligand at its standard state in octanol. The product (Kd x P) and its negative logarithm (pKd – logP) both quantify the extent to which the ligand would ‘prefer’ to be bound to protein or solvated in octanol. It’s worth noting at this point that octanol is quite polar and alkane/water partition coefficients would probably represent a better measure of lipophilicity if they were more accessible. I’ll probably discuss this in more detail at some point in the future but for now you might want to take a look at a recent article on prediction of alkane/water partition coefficients because it reviews a lot of the earlier work.

The authors of the featured article assert that LLE does not include ligand efficiency. I don’t completely agree with that statement because one could say that LLE is a measure of how efficiently a ligand exploits its lipophilicity to bind to the target protein. However it is clear that no explicit measure of molecular size is used in the definition of LLE. The authors propose dividing logP by ligand efficiency (LE) and call this function LELP and suggest that this should be between -10 and 10 (no units specified) for acceptable leads.

I must confess that I just don’t get it. Using this metric, a compound with logP = 1 and LE = 0.1 (in whatever units they’re using) is equivalent to one of logP = 3 and LE = 0.3. Also a compound with logP = 0 becomes an acceptable lead even with a millimolar Kd. I’m not convinced that LELP gets the balance right between penalising size and lipophilicity.

I’m not a great fan of ligand efficiency metrics although they have a place for comparing hits from the same assay and I’ve certainly used them for that purpose. My favored approach to combining size and lipophilicity into a single efficiency measure would be to use one of the following two functions depending on which measure of affinity/potency is being used:

where HA is the number of non-hydrogen (heavy) atoms and measured or calculated values of logP can be used depending on which are available. It’s worth pointing out that the lipophilicity that gets measured is actually logD (typically at pH = 7.4) rather than logP. For neutral compounds the two are the same (unless you get self-association in one of the phases as might happen with a lactam in hydrocarbon) but for compounds that are ionised you’ll either need to know the pKa or determine logD as a function of pH in order to get logP.

Something this article flagged up for me is the difficulty in finding concise but meaningful names for ligand efficiency metrics. I think of efficiency as something associated with a process or action rather than an object and therefore prefer to talk about ‘binding efficiency’. The other benefit of doing this is that it reminds us that the efficiency is defined for the combination of ligand and assay system (protein, co-factors, substrates, buffer components etc) and not the ligand in isolation.

To get us thinking about how we might improve matters, I’ll make some suggestions. I think we should be using pIC50 or pKd to quantify binding since units of energy never seem to get quoted when binding free energies are used to define what I’ll now refer to as binding efficiency. Also we tend to think more in terms of pIC50 than energy when we talk about potency and binding. Here are three equations that we can use to define binding efficiency with the subscripts indicating the property or properties used to scale potency. I suggest calling each quantity binding efficiency by the appropriate property. For example equation 3 defines ‘binding efficiency by size and lipophilicity’.

Common measures of size include number of heavy atoms, molecular weight (molar mass), surface area and volume and equations 1 and 3 can be used with either provided that the appropriate units (Heavy atoms, g/mol, Da, square Angstrom, cubic Angstrom) are quoted. If the unit of size is included, it is possible to tell which measure of size has been used to scale potency. As an aside, note how potency, which has units of concentration, is converted into a dimensionless number by dividing by M (mol/litre). I’m a bit less happy with using subscript-L to denote lipophilicity because it doesn’t allow us to distinguish binding efficiencies derived using logP and logD. Nevertheless, this represents a start although it’s likely that other folk will have ideas that we can use to refine the definitions.

Literature cited

Kesuru & Makara, The influence of lead discovery strategies on the properties of drug candidates. Nature Rev. Drug Discov. 2009, 8, 203-212 DOI

Albert et al, An Integrated Approach to Fragment Based Lead Generation: Philosophy, Strategy and Case Studies from AstraZeneca's Drug Discovery Programs. Curr. Top. Med. Chem. 2007, 7, 1600-1629 Link

Leeson & Springthorpe, The influence of druglike concepts on decision-making in medicinal chemistry. Nature Rev. Drug Discov. 2007, 8, 203-212 DOI

Toulmin et al, Toward prediction of alkane/water partition coefficients J. Med. Chem. 2008, 51, 3720-3730 DOI

The upper limits of binding: Part 2

2009-04-14T20:25:00.013+01:00

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Mel reviewed an important article on maximal affinity of ligands to kick off our sequence of posts on ligand efficiency. There are a number of reasons that this upper limit for potency might be observed and it's worth having a bit of think about them.

One interpretation of the upper limit is that it represents a validation of the molecular complexity concept. If a ligand makes many interactions with the protein they are less likely to be of ideal geometry. Hydrogen bonds between the binding partners and water are more likely to be of near-ideal geometry. Another factor that can impose limits on affinity is the finite size of a binding site. Once the site has been filled, increasing the size of the ligand does not lead to further increases in affinity because all the binding potential of the protein has already been exploited.

However, there is another reason that an upper limit for affinity might be observed and it has nothing to do with molecular complexity or fully exploited binding sites. Measuring very strong binding is not as easy as you might think it would be. In a conventional enzyme assay, you normally assume that the concentration of the ligand is much greater than that of the enzyme. This works well if you’ve got 10nM enzyme in the asaay and a micromolar ligand. However, things will get trickier if you’re trying to characterise a 10pM inhibitor since you’ll observe 50% inhibition of the enzyme for a 5nM concentration of the inhibitor. And you’ll see something very similar for a 1pM inhibitor…

This behaviour is well known and is called tight-binding inhibition. If you want to characterise very potent inhibitors you need reduce the concentration of the enzyme and be a bit more careful with the math. However, not everybody does this and I suspect that this may be one reason there appears to be an upper limit for affinity.

Literature cited

Kuntz et al, The maximal affinity of ligands. PNAS 1999, 96, 9997-10002. Link to free article.

Williams & Morrison, The kinetics of reversible tight-binding inhibition. Methods Enyzmol. 1979, 63, 437-467 DOI

The upper limits of binding: Part 1

2009-04-06T22:07:00.017+01:00

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I originally intended to discuss some of the factors that impose the upper limits on binding that are observed. Unfortunately the introduction got a bit out of hand so this is going to have to be a two-parter.

It’s been a while since I said anything about ligand efficiency although its hard core enthusiasts appear to have worked the concept into something that approaches discipline status. My view is that molecules interact with their environments by presenting their molecular surfaces to those environments. Dividing the standard free energy change for an interaction by the area of the molecular surface is effectively a statement of how effectively the molecule makes use of its surface in making the interaction. I believe that this is the most fundamental measure of ligand efficiency.

Assays are not normally set up to measure standard free energy changes for binding. Ligand efficiency is frequently calculated from an IC50 rather than dissociation constant for the ligand protein complex. The IC50 that you’ll measure for competitive inhibitors depends on the concentration of whatever you’re trying to compete with. This can make comparing IC50 values for different assays risky. For example, you might run inhibition assays for two different kinases at their respective Km values with respect to ATP despite both kinases being exposed to the same intracellular concentration of ATP. If you’re using ligand efficiency to compare hits from the same assay, the distinction between IC50 and dissociation constant is not too much of an issue as long as you remember the two quantities are not the same.

Molecular surface area is not the easiest quantity to deal with if you’re looking for a quick metric with which to compare hits from a screen. You’ll need a 3D model of the molecule in order to calculate this quantity properly and that means that you’ll need to deal with multiple conformations. If you’re going to deal with multiple conformations, you need to be thinking about energy cutoffs and how many conformations you want to use to sample the conformational space of your molecule. You also need to be thinking about how to deal with surface area that is inaccessible even though it is on the molecular surface. All very messy!

A while ago, some folk at Novartis showed that it is possible to calculate molecular surface area directly from the molecular connection table which is more commonly called the 2D structure because that’s what you get when you write it on a piece of paper. It turns out that surface area is roughly proportional to the number of non-hydrogen (often termed heavy) atoms in the molecule. Counting heavy atoms involves nice, predictable integer math and is much better suited for defining ligand efficiency than all the horrid floating point math demanded by 3D structures.

My preferred measure of ligand efficiency is to divide minus the log of whatever potency measure the assay generates by the number of non-hydrogen atoms in the molecule. Because you can’t take a log of a concentration the potency measure should be divided by the appropriate units of concentration. This means that if you use different units of concentration, you’ll get different ligand efficiencies. This isn’t a problem if you’re aware of it and using ligand efficiency to compare hits from a single assay. However, it’s probably pushing it a bit to use a different concentration unit and claim that you’ve found a new ligand efficiency metric. Put another way, you can make the standard free energy of binding for a 10nM compound positive simply by using 1nM as your standard state. If you think this is a crazy idea, imagine what a molar solution of your favourite protein might look like!

Another reason that I prefer to define ligand efficiency in terms of pIC50 or pKd is that these measures of potency/affinity are unitless so that the ligand efficiency has units of reciprocal number of heavy atoms. Once you convert your potency into a free energy you need to state your energy units when you use ligand efficiency. People often don’t bother although it is unlikely that the authors of anything that I have reviewed for a journal will be presenting ligand efficiencies without having defined the appropriate units. The other reason I don’t like converting IC50 of Kd values to energies is that I believe this conveys an impression of thermodymamic rigour which is normally unjustified.

This is is a natural break point. Some what is discussed above is also presented in the AstraZeneca fragment based lead generation paper from a couple of years ago. In the next post I’ll be taking a look at some of the factors which may place upper limits on ligand efficiency.

Literature cited

Albert et al, An Integrated Approach to Fragment Based Lead Generation: Philosophy, Strategy and Case Studies from AstraZeneca's Drug Discovery Programs Curr. Top. Med. Chem. 2007, 7, 1600-1629 link

Ertl et al, Fast calculation of molecular polar surface area as a sum of fragment-based contributions and its application to the prediction of drug transport properties. J. Med. Chem. 2000, 43, 3714-3717 DOI

Physicochemical properties

2009-04-06T21:19:00.014+01:00

Fragments typically have to be screened at high concentration because they normally only bind weakly to their targets and the physicochemical property most relevant to FBDD is aqueous solubility. Both charge state and lipophilicity influence solubility in aqueous media.

General

Avdeef, Physicochemical profiling (solubility, permeability, and charge state). Curr. Top. Med. Chem. 2001, 1, 277-351 Link

Hydrogen bonding

Kenny, Hydrogen bonding, electrostatic potential, and molecular design. J. Chem. Inf. Model. 2009, 49, 1234-1244 DOI

Laurence & Berthelot, Observations on the strength of hydrogen bonding. Perspect. Drug Discov. Des. 2000, 18, 39-60 DOI

Kenny, Prediction of hydrogen bond basicity from computed molecular electrostatic properties: implications for comparative molecular field analysis. J. Chem. Soc., Perkin Trans. 2, 1994, 199-202 DOI

Abraham et al, Hydrogen bonding. Part 9. Solute proton donor and proton acceptor scales for use in drug design. J. Chem. Soc., Perkin Trans. 2, 1989, 1355-1375 DOI

Partition coefficients

Toulmin et al, Toward Prediction of Alkane/Water Partition Coefficients. J. Med. Chem. 2008, 51, 3720-3730 DOI

Leahy et al, Model solvent systems for QSAR. Part 2. Fragment values (f-values) for the critical quartet. J. Chem. Soc., Perkin Trans. 2, 1992, 723-731 DOI

Solubility

Colclough et al, High throughput solubility determination with application to selection of compounds for fragment screening. Bioorg. Med. Chem. 2008, 16, 6611-6616 DOI

Fragment Library Design

2009-03-20T09:06:00.008Z

The first law of computing (garbage in, garbage out) applies equally well to screening fragments. Selection of compounds for fragment screening is a theme that I will explore in some depth in future posts. For now, I'll just let folk know that we have already compiled some literature on the topic and that our article on this topic is now available 'online first' at the Journal of Computer Aided Molecular Design. Three of my friends and I put this together as a contribution for the special issue of this journal on FBDD so you can expect to see more articles on the subject appearing soon.

There's going to be some fragment action at the ACS. Mel will be there so look out for her.

Literature cited
Blomberg et al, Design of compound libraries for fragment screening, J. Comput.-Aid. Mol. Des., in press (online first) DOI

RSC BMCS Fragments 2009

2009-03-08T19:49:00.026Z

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It is during the opening remarks for the RSC BMCS Fragments 2009 Conference that I think to myself that Stalin anticipated the emergence of high throughput screening with his comment, ‘Quantity has a quality all of its own’. Fragment based methods can be seen as representing an attempt to re-introduce manoeuvre to a battlefield that is increasingly dominated by grim attrition and the intellectual property tar pit.

More than one speaker notes the relative maturity of FBDD although I am struck by the high mission statement to results ratio for more than one presentation. I am also struck by the relatively narrow range of targets that appear to be getting tackled. I can’t help thinking that a realistic survey of the target class scope of FBDD would not have been out of place in this program.

There are three talks that I particularly like. Rod Hubbard (University of York | Vernalis) starts his presentation with a look at some of the ‘pre-history’ of FBDD (e.g. GRID | MCSS | MSCS) and notes the increased use of surface plasmon resonance (SPR) to detect fragment binding. I am really pleased to see this ‘pre-history’ mentioned because we’ve done something similar in the introduction to our article on fragment library design that is currently in press in JCAMD. In some ways this would have been a good talk with which to start the conference rather than finish.

Mark Whittaker (Evotec) shows how biochemical assays (see lit) can be used screen fragments and stressed the need for orthogonal detection methods (e.g. NMR) to ensure that activity is real. The Evotec group use a range of fluorescence experiments to quantify and characterise binding and appear to also have significant expertise in design and maintenance of fragment libraries.

One personal highlight of the conference is that I finally get to meet Dan Erlanson (Carmot Pharmaceuticals). Dan runs the Practical Fragments blog with Teddy Zartler and he does an excellent talk on fragment-based chemotype evolution. The idea is to build a reactive group into a fragment that binds (the ‘bait’) and allow this to react with library compounds while it is bound to the protein. The target protein effectively selects the combinations of bait and library compounds that bind most strongly to target and sulfur chemistry (disulfide formation; displacement of halogen) is particularly useful. This probably reflects ease of reaction in aqueous media and similarity of reactant and product hydrogen bonding and charge characteristics when sulphur chemistry is used.

I am surprised that there are no talks on selection of compounds for fragment screening. The rule of 3 gets frequent mention and but at least my ‘efficiency metric fatigue’ is not aggravated by a presentation devoted entirely to the topic. I am refreshed by the fact that David Banner’s (Roche) talk makes no reference to ligand efficiency. David makes reference to ‘needle screening’ which represents an alternative way to think about molecular complexity, although the Roche folk never used the term when they introduced the idea in 2000. As a physical-organic chemist, I am also refreshed by Nino Campobasso’s (GSK) comment that FBDD gets folk thinking more about the molecules and, in some ways, represents a return to traditional medicinal chemistry.

The conference has its amusing moments and we particularly enjoy Molecular Simpleton’s attempts to extract information from Vicki Nienaber. No doubt they run an ‘Interrogation Styles’ course where Molecular Simpleton works and it will be no surprise if he finds himself booked on it.

The conference concludes shortly after Rod’s presentation and I leave, wondering about how broadly applicable FBDD really is. Targets that bind ATP are relatively easy to hit with fragments especially if you’re not worried about physiological ATP concentrations. A number of serine proteases can be nailed with something as small as benzamidine at concentrations that are well within the reach of standard biochemical assays. However, there are plenty of interesting targets that do not recognise adenine or arginine. I guess that time will tell and hopefully a clearer picture will emerge at Fragments 2011.

Substituents and complexity

2009-02-18T17:15:00.005Z

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In the previous post, I noted that two Astex kinase inhibitors were derived from fragments that lacked acyclic substituents. Dan points out that this is actually uncommon and wonders if this reflects a reluctance of medicinal chemists to work on fragments that were seen to be too simple.

The presence of certain molecular recognition elements, for example hydroxyl or carboxylate, implies that at least one acyclic substituent be present. I think this it probably the main reason that fragments are normally encountered with acyclic substituents. However, I do agree with Dan that some fragments can be seen as too simple and re-iterate my point that in the Brave New World of FBDD we really need to start seeing phenyl rings as synthetic handles.

A lack of acyclic substituents typically implies the presence of one or more polar atoms in a ring or spacer. When assembling screening libraries, I do try to select compounds that present heterocyclic molecular recognition elements without acyclic substituents (e.g. 4-phenypyrazole, 2-anilinopyrimidine). Interestingly compounds like these are not as easy to source as you might think.

Molecular complexity and extent of substitution

2009-02-14T02:49:00.020Z

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Having introduced extent of substitution as a measure of molecular complexity in an earlier post, I was particularly interested by Dan's posts on AT7519 and AT9283. In each case, the screening hit used as a starting point for further elaboration lacked acyclic substituents.

You might wonder how you could impose this substructural requirement when selecting compounds for screening. This is actually very easy using SMARTS notation (Daylight SMARTS tutorial | OpenEye SMARTS pattern matching | SMARTS in wikipedia). The requirement that terminal non-hydrogen atoms be absent can be specified as:

[A;D1] 0

D1 indicates a non-hydrogen atom (A) that is connected to only one other non-hydrogen atom and 0 requires that these cannot be present in acceptable molecules. A requirement like this can be combined with a requirement for 10 to 20 non-hydrogen atoms:

* 10-20

I will discuss the use of SMARTS for compound selection in more detail in connection with design of screening libraries so think of this as a taster. I've also tried to keep things simple by assuming that hydrogen atoms are implicit which means that they are treated as a property of the atoms to which they are bonded rather than as atoms in their own right.

Ligand efficiency and molecular size

2009-02-03T16:22:00.010Z

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Molecular complexity models of ligand binding typically predict that ligand efficiency (LE) will decrease during the process of elaborating a fragment hit. This is the basis of the fit quality (FQ) metric that Mel reviewed in her last post. It’s interesting to take a look at a 2006 article which actually pre-dates the FQ articles. This study attempted to track LE through the lead optimisation process for 18 drug leads from 15 different projects. The main conclusion of the study was that ‘a nearly linear relationship exists between molecular weight and binding affinity over the entire range of sizes and potencies represented in the dataset’. In other words, LE changed little during the optimisation process for these leads.

Comments anyone?

Literature cited:
Hajduk, Fragment-Based Drug Design: How Big Is Too Big? J. Med. Chem. 2006, 49, 6972-6976 DOI

Ligand Efficiency (or why size doesn't always matter)

2009-01-28T19:58:00.017Z

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Ligand efficiency (LE), a term that has received a lot of attention recently in the drug discovery world, is defined generally as the binding energy of a ligand normalized by its size. Being the avid bargain shopper that I am, the concept of LE excites me, similar to the thrill I get shopping at the sales after Christmas. And as a staunch advocate of FBDD, the idea of getting the most affinity bang for your chemical buck in general appeals to me. However, the definition of LE raises several questions, the obvious being what appropriate measures of binding energy and size are (more on this later). But perhaps a larger nagging question, though, is why this metric is useful at all or, put more bluntly, aren't bigger molecules always better? The first large scale study to address this question was published in 1999 by Kuntz et al, where they analyzed binding data for a set of metal ions, inhibitors, and ligands containing up to 68 heavy (i.e. non-hydrogen) atoms (HAs) [1]. The bulk of the results of this study are contained in Figure 1 from the paper, with free energy of binding, derived from both Ki and IC50 data, plotted against number of HAs. From this plot a linear increase in binding free energy of -1.5 kcal/mol/HA is observed between molecules consisting of up to 15HAs, whereupon, strikingly, the gain in binding free energy with increased size becomes negligible. Using a larger data set, this topic was revisited in a 2007 study published by Reynolds et al. In their study [2] binding data for 8000 ligands and 28 protein targets were utilized to probe the relationship between molecular complexity and ligand efficiency. Using both pKi and pIC50 data, a linear relationship between affinity and size could not be established (Figures 1 and 2). However, a trend was observed between the maximal affinity ligands and their size (Figure 3). Starting with ligands containing roughly 10 HAs, an exponential increase in affinity was observed for ligands up to 25 HA in size but, similarly to the Kuntz study, affinity values plateaued after 25 HA. The authors then plotted LE values, calculated as either pKi/HA or pIC50/HA, against HA to show that LE values decline drastically between 10 and 25 HA (Figures 4 and 5). Since LE values are demonstrably higher on average for smaller molecules, the authors warn against using LE values to compare compounds of disparate sizes. For such purposes they propose a 'fit quality' (FQ) metric, where LE values are normalized by a scaled value that takes size into account.

The logical question that arises from these studies is why do we see a precipitous decline in affinity gains after a certain molecular size? Since ligand binding affinity is attributed largely to van der Waal interactions, larger molecules should exhibit higher affinities. In the Kuntz study they conjecture that their findings may be attributable to non-thermodynamic effects. In particular, the use of tight-binding high molecular weight compounds may be selected against in the pharmaceutical community for pharmacokinetic and/or pharmacodynamic considerations, resulting in a lack of these molecules in their sample set. Entropic penalties and molecular complexity arguments also come into play here. The authors of the 2007 study note in their discussion that the surface area of a ligand available for interaction and its heavy atom count are not correlated, suggesting that the definition of size itself may be overly simplistic.

So, what are the implications of LE in fragment-based drug design? Expounding on the 2007 study discussed above, where fragment-sized molecules exhibited significantly increased LE values as compared to larger molecules, a new study published by the same authors [3] looked closer at the purported advantages of using fragments as starting points for lead generation. In this study LE and fit quality values of starting fragments and optimized leads for a variety of targets were analyzed (Table 1). Interestingly, while LE values fall off as expected with an increase in size, fit quality values remain steady or improve, suggesting that optimization from fragment leads may be more efficient. That said, the data presented in this study is limited and should be compared to leads generated via HTS campaigns or other strategies for more validity. Let's hope the new year brings us such studies.

Literature cited:

1.Kuntz ID, Chen K, Sharp KA, Kollman PA. The maximal affinity of ligands. PNAS 1999 96:9997-10002. Link to free article.

2.Reynolds CH, Bembenek SD, Touge BA. The role of molecular size in ligand efficiency. Bioorg Med Chem Lett. 2007 17(15):4258-61. DOI

3. Bembenek SD, Touge BA, Reynolds CH. Ligand efficiency and fragment-based drug discovery. Drug Discov Today. In press. DOI

Molecular Complexity (follow up)

2009-01-20T00:09:00.009Z

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Dan Erlanson, who needs no introduction in this forum, commented on the previous post. I have to agree with him that the Hann complexity model is not easy to apply in practice. It predicts that there will be an optimum level of complexity for a given assay system (detection technology + target) but doesn’t really tell us where a specific combination of molecule and assay system sits relative to the optimum.

Screening library design, as Dan correctly points out, involves striking a balance. One needs to think a bit about screening technology and the likely number of compounds that you’ll be screening. Another consideration is whether the screening library is generic or directed at specific targets or target families. I’m very interested in screening library design and expect to post on this topic in the future.

Dan notes that low complexity molecules often don’t find favour with medicinal chemists and I‘ve experienced this as well. Having structural information available gives us confidence to do something other than what a former MedChem colleague called ‘pretty vanilla chemistry’. Put another way, to make the most of the output from fragment screening, the medicinal chemist needs to be seeing a phenyl group as a synthetic handle

Molecular Complexity

2009-01-13T22:20:00.028Z

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Molecular complexity is perhaps the single most important theoretical concept in fragment-based drug discovery (FBDD). The concept was first articulated in a 2001 article by Mike Hann at GSK and has implications that extend beyond FBDD. You might ask why I think that molecular complexity is so much more important than ligand efficiency and variations on that theme. My response is that the concept of molecular complexity helps us understand why fragment screening might be a good idea. Ligand efficiency is just a means with which to compare ligands with different potencies.

A complex molecule can potentially bind tightly to a target because it can form lots of interactions. Put another way, the complex molecule can present a number of diverse molecular recognition elements to a target. Sulfate anions and water molecules don’t have the same options although you’ll have ‘seen’ both binding to proteins if you’ve looked at enough crystal structures. There is a catch, however. The catch is that the complex molecule has to position all of those molecular recognition elements exactly where they are needed if that binding potential is to be realised.

Let’s take a look at Figure 3 from the article (the success landscape) in which three probabilities are plotted as a function of ligand complexity. The red line represents the probability of measuring binding assuming that the ligand uses all of its molecular recognition elements. This probability increases with complexity but can’t exceed 1. This tells us that if we just want to observe binding, nanomolar is likely to work just as well as picomolar. The green line is the really interesting one and it represents the probability of the ligand matching one way. It is this requirement for a one way match that gives this curve its maximum. Multiply the probability of a one way match by the probability of measuring binding and you get the probability of a useful event (yellow line) which also has a maximum. This tells us that there is an optimum complexity when you’re selecting compounds for your screening library. This optimum is a function of your assay system (i.e. target + detection technology) and improving your assay will shift the red line to the left.

This molecular complexity model is a somewhat abstract and it’s not easy to place an arbitrary molecule in Figure 3 for an arbitrary assay system. I’m not convinced of the importance of a unique binding mode for fragments because one fragment binding at two locations counts as two fragment hits. This is not a big deal because relaxing the requirement for unique binding leads gives a curve that decreases with complexity and we still end up with a maximum in the probability of a useful event.

I’ve used a different view of molecular complexity when designing compound libraries for fragment screening. This view is conceptually closer to ‘needle screening’ which was described by a group at Roche (11 authors, all with surnames in first half of the alphabet) in 2000. The needles are low molecular weight compounds which can ‘penetrate into deep and narrow channels and subpockets of active sites like a fine sharp needle probing the surface of an active site’. The needles are selected to be ‘devoid of an unnecessary structural elements’. My view of molecular complexity is that it increases with the extent to which a molecule is substituted. Substituents in molecules can be specified (and counted) using SMARTS notation so low complexity molecules can be identified by restricting the extent of substitution in addition to size. I’ve prepared a cartoon graphic which shows why you might want to do this.

This is a probably a good point to stop although it’s likely that I’ll return to this theme in future posts. Before that I’ll need to take a look at Ligand Efficiency…

Literature reviewed
Hann et al, J. Chem. Inf. Comput. Sci., 2001, 41, 856–864. | DOI
Boehm et al, J. Med. Chem. 2000, 43, 2664-2674. | DOI

FBDD: Examples

2008-12-20T17:59:00.027Z

Many examples of fragment-based approaches have been described in the literature. Here's a sampling, organised by institution:

Astex
Howard et al, Fragment-Based Discovery of the Pyrazol-4-yl Urea (AT9283), a Multitargeted kinase Inhibitor with Potent Aurora Kinase Activity, J. Med. Chem. 2009, 52, 379-388 DOI | Dan's review

Wyatt et al, Identification of N-(4-Piperidinyl)-4-(2,6-dichlorobenzoylamino)-1H-pyrazole-3-carboxamide (AT7519), a Novel Cyclin Dependent Kinase Inhibitor Using Fragment-Based X-Ray Crystallography and Structure Based Drug Design. J. Med. Chem. 2008, 51, 4986–4999. DOI | Dan's review

Roche
Boehm et al, Novel Inhibitors of DNA Gyrase: 3D Struture Based Biased Needle Screening, Hit Validaton by Biophysical Methods, and 3D Guided Optimization. A promising Alternative to Random Screening. J. Med. Chem. 2000, 43, 2664-2674. DOI | Pete's review

X-ray Crystallography

2008-12-02T20:50:00.018Z

X-ray crystallography can be used to screen fragments.

Hartshorn et al, Fragment-Based Lead Discovery Using X-ray Crystallography J. Med. Chem. 2005, 48, 403-413 DOI

Nienaber et al, Discovering novel ligands from macromolecules using X-ray crystallographic screening. Nature Biotech. 2000, 18, 1105-1108. DOI

Allen et al, An Experimental Approach to Mapping the Binding Surfaces of Crystalline Proteins J. Phys. Chem., 1996, 100, 2605–2611 DOI

Biochemical Assay

2008-12-02T20:37:00.019Z

Biochemical assays can have high throughput and are relatively inexpensive to run.

Shapiro, Walkup and Keating Correction for Interference by Test Samples in High-Throughput Assays. J. Biomol. Screen. 2009, 14, 1008-1016 | DOI | Review

Hesterkamp et al, Fragment based drug discovery using fluorescence correlation spectroscopy techniques: Challenges and solutions. Curr. Top. Med. Chem. 2007, 7, 1582-1591 Link

Barker et al, Fragment screening by biochemical assay. Expert Opin. Drug Discov. 2006, 1, 225-236 DOI

FBDD Theory

2008-12-01T22:59:00.015Z

These articles describe the theoretical basis of FBDD

Ligand Efficiency

Bembenek et al, Ligand efficiency and fragment-based drug discovery. Drug. Discov. Today, In press DOI | Mel's review

Reynolds et al, The role of molecular size in ligand efficiency. Bioorg. Med. Chem. Lett. 2007 17, 4258-61 DOI | Mel's review

Hajduk, Fragment-Based Drug Design: How Big Is Too Big? J. Med. Chem. 2006, 49, 6972-6976 DOI | Pete's review

Kuntz et al, The maximal affinity of ligands. PNAS 1999, 96, 9997-10002. Link to free article | Mel's review

Molecular Complexity

Hann, Leach & Harper, Molecular Complexity and Its Impact on the Probability of Finding Leads for Drug Discovery. J. Chem. Inf. Comput. Sci. 2001, 41, 856-864 DOI | Pete's review

Screening Library Literature

2008-12-01T22:32:00.012Z

The first law of computation (garbage in, garbage out) applies equally well to FBDD. These articles discuss the selection of compounds for fragment screening.

Blomberg et al, Design of compound libraries for fragment screening, J. Comput.-Aid. Mol. Des., 2009, 23, 513-525 DOI

Schuffenhauer et al, Library Design for Fragment Screening. Curr. Top. Med. Chem. 2005, 5, 751-762 link

Baurin et al, Design and Characterization of Libraries for Use in NMR Screening against Protein Targets. J. Chem Inf. Comp. Sci. 2004, 44, 2157-2166 DOI

A few more general review articles on FBDD

2008-11-20T20:06:00.004Z

Current Opinion in Chemical Biology has published several mini reviews in FBDD. This one by the folks at Astex is short, but touches on both NMR and X-ray crystallographic approaches to FBDD:

Jhoti H, et al. Fragment-based screening using X-ray crystallography and NMR spectroscopy. Curr Opin Chem Biol. (2007) 11:485-493

In the same vein is a paper from Teddy Zartler at Merck that also discusses the concept of ligand efficiency in FBDD:

Zartler ER and MJ Shapiro. Fragonomics: fragment-based drug discovery. Curr Opin Chem Biol. (2005) 9:366-370

Tethering, an alternate approach to FBDD pioneered by Sunesis Pharmaceuticals, is discussed in this review:

Erlanson DA and SK Hansen. Making drugs on proteins: site-directed ligand discovery for fragment based assembly. Curr Opin Chem Biol. (2004) 8:399-406.

NMR

2008-11-11T21:19:00.033Z

A variety of NMR approaches can be used to detect and quantify binding of fragments to their targets.

Lepre, Moore & Peng, Theory and Applications of NMR-Based Screening in Pharmaceutical Research Chem Rev 2004, 104, 3641-3675 DOI

Stockman & Dalvit, NMR screening techniques in drug discovery and drug design. Prog. NMR Spec. 2002, 41, 187-231. DOI

Dalvit et al, WaterLOGSY as a method for primary NMR screening: Practical aspects and range of applicability, J. Biomol. NMR 2001, 21, 349-359 DOI

Fielding, Determination of association constants (Ka) from solution NMR data, Tetrahedron 2000, 56, 6151-6170 DOI

Shuker et al, Discovering High-Affinity Ligands for Proteins: SAR by NMR, Science 1996, 274, 1531-1534 DOI

General FBDD Reviews: Pete's selections

2008-11-10T19:42:00.023Z

There is plenty of review literature on FBDD and the following articles should provide a good introduction to the field.

Hesterkamp & Whittaker, Fragment-based activity space: Smaller is better. Curr. Opin. Chem. Biol. 2008, 12, 260-268. DOI

Congreve et al, Recent Developments in Fragment-Based Drug Discovery, J. Med. Chem. 2008, 51, 3661-3680 DOI

Hajduk & Greer, A decade of fragment-based drug design: strategic advances and lessons learned Nat. Rev. Drug Discov. 2007, 6, 211-219 DOI

Albert et al, An Integrated Approach to Fragment Based Lead Generation: Philosophy, Strategy and Case Studies from AstraZeneca's Drug Discovery Programs Curr. Top. Med. Chem. 2007, 7, 1600-1629 link

Erlanson et al, Fragment-Based Drug Discovery J. Med. Chem. 2004, 47, 3463-3482 DOI