Paper: The Functional Therapeutic Chemical Classification System

Here's an Open Access paper from Samuel in the group.



Drug repositioning is the discovery of new indications
for compounds that have already been approved and used in a
clinical setting. Recently, some computational approaches have been
suggested to unveil new opportunities in a systematic fashion, by
taking into consideration gene expression signatures or chemical
features for instance. We present here a novel method based on
knowledge integration using semantic technologies, to capture the
functional role of approved chemical compounds.



In order to computationally generate repositioning
hypotheses, we used the Web Ontology Language (OWL) to formally
define the semantics of over 20,000 terms with axioms to correctly
denote various modes of action (MoA). Based on an integration
of public data, we have automatically assigned over a thousand
of approved drugs into these MoA categories. The resulting new research resource is called the Functional Therapeutic Chemical Classification
System (FTC) and was further evaluated against the content of the
traditional Anatomical Therapeutic Chemical Classification System
(ATC). We illustrate how the new classification can be used to
generate drug repurposing hypotheses, using Alzheimers disease as
a use-case.


A web application built on the top of the resource is
freely available at https://www.ebi.ac.uk/chembl/ftc. The source code
of the project is available at https://github.com/loopasam/ftc. 

%T The Functional Therapeutic Chemical Classification System
%D 2013
%J Bioinformatics
%A S. Croset
%A J.P. Overington
%A D. Rebholz-Schuhmann

%O Open Access

Magic methyls and magic carpets

A few days ago, there was this post by Derek Lowe, reviewing a recent paper on magic methyls and their occurrence and impact in medicinal chemistry practice. They're called 'magic' because, although methyls are relatively insignificant in terms of size, polarity or lipophilicity, the addition of one in a compound can sometimes have a dramatic impact in its potency - much more that it would be attributed to any simple desolvation effects.

More generally, the 'magic methyl' phenomenon pops up in discussions about the validity of the molecular similarity principle, descriptors, QSAR - almost everything in the applied Chemoinformatics field - and belongs to the general class of 'activity cliffs'. 

Methylation is a chemical transformation, and transformations along with their impact on a property of choice can be easily mined and studied using the so-called Matched Molecular Pairs analysis (MMPA). We already have a comprehensive database of all the matched pairs and transformations in ChEMBL, so it was relatively straightforward to extract all the methylations (H>>CH3) recorded in ChEMBL_17 and analyse their impact in binding affinity. (b.t.w., MMPs are coming to the ChEMBL interface soon, so look out for this feature if you are interested in this area).

So, in more detail, I extracted all the H>>CH3 pairs and joined them with their pActivities (Ki, IC₅₀, EC₅₀) against human proteins as reported in the literature (our data validity flags were quite useful in this case). The trick here is to only consider molecule pairs tested against the same assay, so that their respective activities are directly comparable and one can safely subtract one from the other.

I ended up with 37,771 data points - much more than another recent publication that looked at this. Here's how the histogram of Delta pActivity (log units) looks like:

As you can see, the scale tilts slightly to the left of zero, meaning that methylation has overall neutral to negative effect on binding affinity. This is not the first time people see this. There are indeed, however, several examples (~2.3K out of 37.8K, to be exact) of magic methyls with more than 10-fold increase in activity. More about this later.

Some of you will ask: 'OK, but what about the context? - methylation of a carbon, nitrogen or oxygen is not the same'. You're right, it's not. So I trellised the above plot by a perception of context - i.e. whether the methylation happens next to an aromatic/aliphatic C or N or next to an oxygen:

The same trend, more or less, is observed with the exception of the aromatic carbon context, whereby methylation seems to have more favourable effect that expected by the overall distribution. Perhaps that could be explained by introducing torsional and planarity changes, etc. For a more thorough explanation of this, see here. 

Here are some examples of 'magic methyls' in the literature:

The take home message is: Magic methyls, unlike magic carpets, do exist but there are also equally as many, or even more, 'nasty' methyls. However, both of them are just a rather small minority compared to the 'boring' methyls - i.e. methyls with minimal or zero impact on potency.

It's just human nature to remember the few exceptions and outliers and forget the vast evidence to the contrary. However, isolating and understanding such edge cases and black swans is what could make the difference in drug discovery. 

George

New Drug Approvals 2013 - Pt. XVII - Flutemetamol F18 (VizamylTM)

ATC Code: V09AX04

On October 25^th, the FDA approved Flutemetamol F18 (Tradename: Vizamyl; Research Code: [¹⁸F]AH110690 ), a radioactive diagnostic agent, for intravenous (i.v.) use in Positron Emission Tomography (PET) imaging of the brain in adult patients with cognitive impairment, who are being evaluated for Alzheimer’s disease (AD) and dementia.

Alzheimer's disease is a non-treatable, progressively worsening and fatal disease, characterised by a decrease in cognitive functions, such as memory, and is usually associated with an accumulation of β amyloid (Uniprot: P05067) plaques in several brain regions. These deposits are believed to be responsible for cellular damage and ultimately cell death.

Flutemetamol F18 is the second approved diagnostic drug to estimate β-amyloid neuritic plaque density, after the approval of Florbetapir F18 in 2012. Like Florbetapir F18, Flutemetamol F18 binds to β amyloid plaques in the brain where the F-18 isotope produces a positron signal that can be detected by a PET scanner. The advantages of this compound over its predecessor are: exposure to a lower dose of radiation; and more time for PET image acquisition (20 vs. 10 minutes). In in vitro binding studies using postmortem human brain homogenates containing fibrillar β amyloid, the dissociation constant (Kd) for flutemetamol was 6.7 nM.

It is worth mentioning, that a positive scan, indicating the presence of β amyloid deposits, it's not enough to diagnose a patient with Alzheimer's disease, since these protein deposits can also be present in patients with other types of dementia, or in elderly people without any neurological disease. However, a negative scan, where little or none β-amyloid plaques can be detected, indicates that the cause for dementia is probably not due to Alzheimer's disease.

Flutemetamol F18 (IUPAC Name: 2-[3-fluoranyl-4-(methylamino)phenyl]-1,3-benzothiazol-6-ol; Canonical smiles: CNc1ccc(cc1[18F])c2nc3ccc(O)cc3s2 ; ChEMBL: CHEMBL2042122; PubChem: 15950376; ChemSpider: 13092196; Standard InChI Key: VVECGOCJFKTUAX-HUYCHCPVSA-N) is a synthetic small molecule with a radioactive isotope of fluorine (¹⁸F), with a molecular weight of 274.3 Da, 3 hydrogen bond acceptors, 2 hydrogen bond donors, and has an ALogP of 3.61. The compound is therefore fully compliant with the rule of five.

Flutemetamol F18 is available as a radioactive solution for intravenous injection and the recommended imaging dose is 185 megabecquerels (MBq) [5 millicuries(mCi)] in a total volume of 10 mL or less. Following intravenous injection, the plasma concentrations declines by approximately 75% in the first 20 minutes post-injection, and by approximately 90% in the first 180 minutes. Flutemetamol F18 metabolites are primarily excreted via the hepatobiliary (52%) and the renal system (37%).

The license holder for Vizamyl^TM is GE Healthcare, and the full prescribing information can be found here.

EU-OPENSCREEN 3rd Stakeholder Meeting, Oslo, Norway

Dear future user, partner, collaborator or supporter!

The ESFRI project EU-OPENSCREEN is an academic infrastructure initiative in Chemical Biology to serve your research needs. We are currently preparing the implementation of this pan-European infrastructure of open screening platforms to support basic and applied research. EU-OPENSCREEN will offer access to a unique compound library representing the know-how of European chemists, to a broad range of cutting-edge screening technologies, to valuable tool compounds for research, and to the knowledge that emerges from validated output of hundreds of screens stored and made publically available in a central database.

We cordially invite you to join us in Oslo for an exciting science day where we inform about the progress of the project and the planned services with reports on the design of the joint European Compound Library, the screening services and the database. In particular, we would like to share with you your own experiences from academic screening projects and thus invite you to present your projects as poster. From these, highlight projects will be selected for oral presentation.

See http://www.eu-openscreen.eu for more details.

Competition Time - Win a Raspberry Pi with ChEMBL - chempi

Here's a free to enter competition for a brand new, fully working raspberry pi running the brand new chempi implementation. It includes everything you need to get started at home with ChEMBL - a sort of in silico Breaking Bad maybe (hopefully not, thinking about it). It includes everything you need, with the exception of a power supply and ethernet cable.



We have run out of our creative juices, and cannot think of a suitable poem to mark the release of chempi - so the competition is for you to finish a limerick for us, starting with the line.



There once was a hacker with chempi....



Entries must be posted in the comments section. Obscene or defamatory entries will be removed (all comments are moderated, so it may take a few hours for you entry to appear, so do not repost twenty times!). We haven't really decided how to pronounce chempi (with a hard 'k' start or a soft 'sh' start, just as with ChEMBL, both are used in the wild; and also does it rhyme with scampi, or the irrational number pi?). All entries will be assumed to be made under CC-BY licensing. The competition will be open until noon GMT on Sunday 10th November 2013.



Entires will be judged for compliance to a standard limerick format, outrageous rhymes with chempi, gratuitous chemistry references, and finally humour.



The judges decision (i.e. mine) is final. The winning entry will be published on the ChEMBL-og.



jpo



PS Before I get asked, the competition is not open to members of the ChEMBL group, or extended family members of the ChEMBL group.

Tastypie & Chempi

One of the immediate consequences of refactoring our webservices using Django, Tastypie and related approaches (as described here) is that we can run them on almost any database backend. Django abstracts communication with database and using custom QueryManagers we were able to implement chemisty-specific opererations, such as substructure and similarity search in a database agnostic manner.



This means, that if we want, we can use only Open Source components (such as Postgres and RDKit), or elect to use optimised commercially sourced software as appropriate. However, what if we go one step further and try to use Open Hardware as well? This is exactly what we've just done! We managed to install full ChEMBL 17 on raspbery pi.



Some frequently asked questions (at lease those that have been asked internally) and technical details are below:



1. How much space does it take?



12 Gb, including OS, data and all relevant software. Unfortunately we a used 32 Gb SD card so this is size if you would like to use our cloned disk image.



EDIT: Compressed image takes 4.13 Gb.



2. What OS is it running?



Raspbian, free operating system based on Debian.



3. Is it slow?



We haven't make any benchmarks yet. Obviously it's slower than our online web services - but then it's a lot cheaper. On the other hand, performing some sample requests we can say that performance is certainly acceptable; and there is a lot room for improvements - raspberry pis can be easily overclocked from 700 MHz to 1GHz and according to some benchmarks this can give rise to doubling of application speed in some cases. The SD card we used is not the fastest one as well. Finally, all caching is disabled because we wanted to save disk space but using database caching from Django caching framework should give further major improvements - so maybe use the 32 Gb image after all.



Types of request that chempi can be slower on are:



 - Image generation, but if we replace image with JSON from which image can be generated using HTML5 canvas on the client side (the way we generated images in our game) it can be much faster. More about this topic in future blog post.

- Queries using aggregate functions such as COUNT (it seems that we need to optimise our postgres db by adding some more indexes).

- Substructure and similarity search - again, caching, over-clocking and some database and cartridge (choosing faster fingerprints) optimization should solve all the problems. "Premature optimization is a root of all evil", so we first wanted to have a proof of concept that just works, not necessarily works super fast.



4. Can I make my own chempi?



Yes, we are planning to share our SD card image, we will probably use BitTorrent protocol to do this due to image size, and some issues we have faced with distribution of the myChEMBL. We do remember that not everyone has mega-fast broadband!



5. Is chempi useful at all?



Although we think it is interesting as a proof of concept having chemical database on such small and open source hardware, we do think this may have some interesting future real-world applications:



 - plugging our chempi to local network makes it immediately accessible to other computers. So this is a zero configuration demonstration of ChEMBL.

- analogically to the thesis included in this paper, it can encourage cheminformatics education on low cost ARM hardware.

- raspberry can be easily enhanced with camera to perform image recognition. This, combined with software like OSRA can give ability so scan compound images and search them in database.

- adding some e-ink display (for example, jailbroken Kindle?) can produce very interesting small machine...



6. What are some of the technical details?



To deploy our webservices (which are just another Django application) we've used Gunicorn as a server, which in turn connects to NGINX via standard unix pipe. To make it work as a deamon and launch on machine startup, we've used Supervisor. We believe this is ideal way to deploy Django not only on raspberry but on all production machines to if you like to run chembl webservices locally in your company/academia we suggest to do it this way.

michal

Usan Watch: October 2013

The USANs for October 2013 have recently been published.



We have modified the sourcing of this data - using the new ChEMBL API to automatically parse the documents, extract and validate the mol files for the compounds. So in future, these reports should be more timely, complete and fun!

USAN	Research Code	InChIKey (Parent)	Drug Class	Therapeutic class	Target
alectinib	AF-802; CH-5424802	KDGFLJKFZUIJMX-UHFFFAOYSA-N	synthetic small molecule	therapeutic	ALK
apitolisib	GDC-0980.1, G-038390, G-038390.1, RG-7422	YOVVNQKCSKSHKT-HNNXBMFYSA-N	synthetic small molecule	therapeutic	MTOR,PI3K
cimaglermin-alfa	GGF2, rhGGF2	n/a	protein	therapeutic	ErbB
decernotinib	VX-509, VRT-831509	ASUGUQWIHMTFJL-QGZVFWFLSA-N	synthetic small molecule	therapeutic	JAK3
elobixibat	A-3309; AZD-7806	XFLQIRAKKLNXRQ-UUWRZZSWSA-N	n/a	therapeutic	SLC10A2
ipatasertib	GDC-0068; RG-7440	GRZXWCHAXNAUHY-NSISKUIASA-N	synthetic small molecule	therapeutic	AKT
lixisenatide	AVE-0010	n/a	peptide	therapeutic	GLP1R
ulocuplumab	MDX-1338, BMS-936564	n/a	monoclonal antibody	therapeutic	CXCR4