Monday, 26 September 2011

Dans la merde

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

Thursday, 15 September 2011

A SMARTS way to do things?

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.


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.



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.


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