On November 13, 2013, the FDA approved Ibrutinib (ImbruvicaTM) for the treatment of patients with mantle cell lymphoma (MCL) who have received at least one prior therapy. MCL is a subtype of B-cell lymphoma and accounts for 6% of non-Hodgkin's lymphoma cases. In an open-label, multi-center, single-arm trial of 111 previously treated patients, Ibrutinib showed a 65.8% response rate.
Ibrutinib is an irreversible inhibitor of the Tyrosine-protein kinase BTK (Uniprot:Q06187; ChEMBL:CHEMBL5251; canSAR target synopsis) and is the first approved targeted BTK inhibitor. It forms a covalent bond with a cysteine residue via a Michael acceptor mechanism, in the BTK active site, leading to inhibition of BTK enzymatic activity
Ibrutinib (ChEMBL:CHEMBL1873475; canSAR drug synopsis; also known as CRA-032765 and PCI-32765) has the formula C25H24N6O2 and a molecular weight 440.50. It is absorbed after oral administration with a median Tmax of 1-2 hours. After administration of 560 mg dose, the observed AUC is 953 ± 705 ng⋅h/mL. The apparent volume of distribution at steady state (Vd,ss/F) is approximately 10000 L and the half-life is 4 to 6 hours.
On November 1, 2013 the FDA approved obinutuzumab (GazyvaTM) for use in combination with chlorambucil (a nitrogen mustard alkylating agent) for the treatment of patients with previously untreated chronic lymphocytic leukemia (CLL). CLL is the most common type of Leukaemia accounting for 35% of all reported Leukaemias (See CRUK CLL page). In a randomized three-arm clinical study, the combination of obinutuzumab (in combination with chlorambucil) improved the progression-free survival (PFS) of patients to 23.0 months compared to 11.1 months for chlorambucil alone.
Obinutuzumab (CHEMBL1743048) is a humanized anti-CD20 monoclonal antibody of ca. 150 kDa molecular weight. Its target, the B-lymphicyte antigen CD20, is the product of the gene MS4A1 (Uniprot: P11836; ChEMBL: CHEMBL2058; canSAR target synopsis. The CD20 antigen is expressed on the surface of pre B- and mature B-lymphocytes. Obinutuzumab mediates B-cell lysis through three main routes:
Engagement of immune effector cells, resulting in antibody-dependent cellular cytotoxicity and antibody-dependent cellular phagocytosis
Direct activation of intracellular death signaling pathways
The geometric mean (CV%) volume of distribution of obinutuzumab at steady state is approximately 3.8L. The terminal clearance is 0.09 (46%) L/day and the terminal half-life is ~28.4 days.
Obinutuzumab has been issued with a boxed warning because of the following observed events: Reactivation of Hepatitis B Virus (HBV), in some cases resulting in fulminant hepatitis, hepatic failure, and death; and causing Progressive Multifocal Leukoencephalopathy (PML) resulting in death.
GazyvaTM is produced by Genentech, Inc.
The full Prescribing Information is here.
We are very pleased to announce the release of two new datasets on the ChEMBL-NTD portal.
The first dataset is provided by the Drug for Neglected Diseases initiative (DNDi) and is focused on the selection and optimization of hits from a high-throughput phenotypic screen against Trypanosoma cruzi. The paper describing the dataset in more detail can be accessed here and the data can be downloaded from here.
The second dataset from the DeRisi Lab UCSF and is focused on the screening of MMVs Malaria Box compounds in Plasmodium falciparum, to understand if anti-malarial compounds target the apicoplast organelle. More details about the dataset can be found here and the data can be downloaded from here.
Both datasets will be loaded into the next version of ChEMBL, which will be due out early next year.
The ChEMBL - Neglected Tropical Disease portal is a repository for Open Access primary screening and medicinal chemistry data directed at neglected diseases. If you would like to deposit your data here please get in contact.
The ChEMBL group had the honour of hosting the second RDKit UGM. It was a great way to catch up with the RDKit community, find out about what they are working and learn about new features the toolkit offers. We gave two talks during the meeting, so if you want to know how Clippy can make interacting with different chemical formats on your desktop easier, go here, and if you want to learn about wrapping RDKit up in a RESTful Web Service a.k.a. Beaker (to be described in future blog post), go here. Many discussions about new features RDKit could offer were had throughout the meeting and one which caught my attention was support for plotting compound images on HTML5 Canvas.
Unable to participate in a hackathon held on the final day, I set about hosting my own small hackathon during the weekend (only 1 attendee). The result of this weekend coding effect was a pull request made against RDKit github repo, introducing the new class called JSONCanvas.
Technical Details
As a general rule of the past, the model for generating image relies on
the server to sending a binary representation of the compound (e.g.
.png, .jpeg) to the client. With advances in browser technologies, it is
now feasible to rely on the client to generate the graphical
representation of the compound as it now has access to many methods,
which allows it to handle geometrical primitives. It can decide if those primitives should be rendered as SVG, VML or even HTML5 Canvas (check out Kinetic.js for HTML5 canvas rendering, as it knows how to draw some core shapes on canvas).
My solution uses Raphael.js - a JavaScript library for drawing vector graphics in the browser. For displaying the graphic is uses SVG on browsers that support this format. On older browsers it will fail over to VML. In the library documentation we can find a very interesting method called Paper.add(). This method accepts JSON containing an array of geometrical objects (such as circle, rectangle, path) to be displayed and returns a handle for manipulating (moving, rotating, scaling) the object as a whole. This means that if we could create a JSON object, which uses shapes to represent a chemical compound, we could draw it or manipulate the compound directly. The new JSONCanvas class produces the previously described JSON object for any* given RDKit compound.
(*I am sure we might find a couple of exceptions) But why? 1. Cost - reduced server processing required to raster image and often third-party drawing libraries are also required. 2. Bandwidth - reduced bandwidth required to transfer JSON representation of compounds. Also, as it is text-based you can employ further compression (by configuring your server to send gziped JSON which most modern browsers understand) or using AMF. 3. Accuracy - improved scaling quality made possible with vector graphics. 4. Interactivity - compounds rendered using JSON on the client side can handle standard events such as click, hover, etc. Complex operations (animating, sorting, dragging,...), can also be applied to these objects.
Usage
As an example usage of this technique please look at our chemical game. To give you some idea of scale and performance the game loads 1000 compounds when page first loads. If you want to see raw example please explore source of my demo page. Other examples can involve:
1. Online compound cloud (similar to tag cloud but with compound images instead of words). Such a cloud can be used to visualise compound similarity.
2. Compound stream - substructure search can sometimes return very large number of results. Such results can be represented as pseudo-infinite stream of compounds - only small portion of results is presented on the screen but scrolling down causes more results to be rendered when older one are discarded.
How can I use it?
1. You can download my fork or RDKit containing all relevant changes.
2. Today Greg Landrum, RDKit creator made his own branch containing modified version of the original pull request, so hopefully this is on it's way to be accepted in master branch in future.
As a group we are happy to participate in such a great open source library!
The USANs for September, 2013 have recently been published. We actually missed September, due to switch over in service for the INNs, but now they're here.
An update to what has happen to the Wellcome Trust funded database ChEMBL over the past few years has just been published - it seems odd, that we've been around long enough to achieve our 2nd NAR Database paper - so much more to do though! This paper contains features and content up to ChEMBL 17.
This could put you in a difficult position which NAR paper to cite in your own publications using ChEMBL; so we suggest both! ;)
Oh, and it's Open Access, of course.
%J Nucleic Acids Research
%D 2013
%P 1–8
%O doi:10.1093/nar/gkt1031
%T The ChEMBL bioactivity database: an update
%A A.P. Bento
%A A. Gaulton
%A Anne Hersey
%A L.J. Bellis,
%A J. Chambers
%A M. Davies
%A F.A. Krueger
%A Y. Light
%A L. Mak
%A S. McGlinchey
%A M. Nowotka
%A G. Papadatos
%A R. Santos
%A J.P. Overington
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
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.
