Skip to main content

Drug Side Effect Prediction and Validation


There's a paper just published in Nature getting a lot of coverage on the internet at the moment from Novartis/UCSF, and for good reason - but as the cartoon above states, it will probably have less impact than news on Justin Bieber's new haircut, or the latest handbags from Christian Lacroix. It uses the SEA target prediction method, trained using ChEMBL bioactivity data in order to predict new targets (and then by association side effects) for existing drugs. These are then experimentally tested, and the results confirmed in a number of cases - this experimental validation is clearly complex and expensive, so it is great news that in silico methods can start to generate realistic and testable hypotheses for adverse drug reactions (there are also positive side effects too, and these are pretty interesting to look for using these methods as well).

The use of SEA as the target prediction method was inevitable given the authors involved, but following up on some presentations at this springs National ACS meeting in San Diego. There would also seem to be clear benefits in including other methods into linking a compound to a target - nearest neighbour using simple Tanimoto measures, and naive Bayes/ECFPP type approaches. The advantage of the SEA approach is that it seems to generalise better (sorry I can't remember who gave the talk on this), and so probably can make more comprehensive/complete predictions, and be less tied to the training data (in this case ChEMBL) - however as databases grow, these predictions will get a lot better. There will also be big improvements possible if other data adopts the same basic data model as ChEMBL (or something like the services in OpenPHACTS), so methods can pool across different data sources, including proprietary in-house data.

There are probably papers being written right now about a tournament/consensus multi-method approach to target prediction using an ensemble of the above methods. (If such a paper uses random forests, and I get asked to review it, it will be carefully stored in /dev/null) ;)

So some things I think are useful improvements to this sort of approach.

1) Inclusion of the functional assays from ChEMBL in predictions (i.e. don't tie oneself to a simple molecular target assay). The big problem here though is that although pooling of target bioassay data is straightforward - pooling/clustering of functional data is not.
2) Where do you set affinity thresholds, and how do the affinities related to the pharmacodyamics of the side-effects. My view is that there will be some interesting analyses of ChEMBL that maybe, just maybe, allow one to address this issue. Remember, we know quite a lot about the exposure of the human body, to  a given drug at a given dose level...
3) Consideration of (active) metabolites. It's pretty straightforward now to predict structures of likely metabolites (not at a quantitative level though) and this may be useful in drugs that are extensively metabolised in vivo.

Anyway, finish off with some eye-candy, a picture from the paper (hopefully allowed under fair use!).


And here's a reference to the paper, in good old Bell AT&T labs refer format - Mendeley-Schmendeley as my mother used to say when I was a boy.

%T Large-scale prediction and testing of drug activity on side-effect targets
%A E. Lounkine
%A M.J. Keiser
%A S. Whitebread
%A D. Mikhailov
%A J. Hamon
%A J.L. Jenkins
%A P. Lavan
%A E. Weber
%A A.K. Doak
%A S. Côté
%A B.K. Shoichet
%A L. Urban
%J Nature
%D 2012
%O doi:10.1038/nature11159

Comments

Popular posts from this blog

A python client for accessing ChEMBL web services

Motivation The CheMBL Web Services provide simple reliable programmatic access to the data stored in ChEMBL database. RESTful API approaches are quite easy to master in most languages but still require writing a few lines of code. Additionally, it can be a challenging task to write a nontrivial application using REST without any examples. These factors were the motivation for us to write a small client library for accessing web services from Python. Why Python? We choose this language because Python has become extremely popular (and still growing in use) in scientific applications; there are several Open Source chemical toolkits available in this language, and so the wealth of ChEMBL resources and functionality of those toolkits can be easily combined. Moreover, Python is a very web-friendly language and we wanted to show how easy complex resource acquisition can be expressed in Python. Reinventing the wheel? There are already some libraries providing access to ChEMBL d

LSH-based similarity search in MongoDB is faster than postgres cartridge.

TL;DR: In his excellent blog post , Matt Swain described the implementation of compound similarity searches in MongoDB . Unfortunately, Matt's approach had suboptimal ( polynomial ) time complexity with respect to decreasing similarity thresholds, which renders unsuitable for production environments. In this article, we improve on the method by enhancing it with Locality Sensitive Hashing algorithm, which significantly reduces query time and outperforms RDKit PostgreSQL cartridge . myChEMBL 21 - NoSQL edition    Given that NoSQL technologies applied to computational chemistry and cheminformatics are gaining traction and popularity, we decided to include a taster in future myChEMBL releases. Two especially appealing technologies are Neo4j and MongoDB . The former is a graph database and the latter is a BSON document storage. We would like to provide IPython notebook -based tutorials explaining how to use this software to deal with common cheminformatics p

ChEMBL 30 released

  We are pleased to announce the release of ChEMBL 30. This version of the database, prepared on 22/02/2022 contains: 2,786,911 compound records 2,157,379 compounds (of which 2,136,187 have mol files) 19,286,751 activities 1,458,215 assays 14,855 targets 84,092 documents Data can be downloaded from the ChEMBL FTP site: https://ftp.ebi.ac.uk/pub/databases/chembl/ChEMBLdb/releases/chembl_30/ Please see ChEMBL_30 release notes for full details of all changes in this release:  https://ftp.ebi.ac.uk/pub/databases/chembl/ChEMBLdb/releases/chembl_30/chembl_30_release_notes.txt New Deposited Datasets EUbOPEN Chemogenomic Library (src_id = 55, ChEMBL Document ID CHEMBL4689842):   The EUbOPEN consortium is an Innovative Medicines Initiative (IMI) funded project to enable and unlock biology in the open. The aims of the project are to assemble an open access chemogenomic library comprising about 5,000 well annotated compounds covering roughly 1,000 different proteins, to synthesize at least

New Drug Approvals 2011 - Pt. XXVI - Icatibant (FirazyrTM)

ATCC: C01EB19 Wikipedia: Icatibant On the August 25th 2011, the FDA approved Icatibant (trade name: Firazyr TM ), a bradykinin B2 receptor (B2R) antagonist indicated for the treatment of acute attacks of hereditary angioedema (HAE) in patients aged 18 or older. HAE is a rare genetic disease and is caused by low levels of C1-esterase inhibitor (C1-INH) , the major endogenous inhibitor and regulator of the protease plasma kallikrein and the key regulator of the Factor XII/kallikrein cascade. One component this cascade is the production of bradykinin by plasma kallikrein. During HAE attacks, disregulated activity of plasma kallikrein leads to excessive bradykinin production; bradykinin is a potent vasodilator, which s thought to be responsible for the characteristic HAE symptoms of localised swelling, inflammation and pain. Icatibant treats the clinical symptoms of HAE attack by selective- and competitively binding, as an antagonist, to the B2 bradykinin receptor (B2

UniChem 2.0

UniChem new beta interface and web services We are excited to announce that our UniChem beta site will become the default one on the 11th of May. The new system will allow us to better maintain UniChem and to bring new functionality in a more sustainable way. The current interface and web services will still be reachable for a period of time at https://www.ebi.ac.uk/unichem/legacy . In addition to it, the most popular legacy REST endpoints will also remain implemented in the new web services: https://www.ebi.ac.uk/unichem/api/docs#/Legacy Some downtime is expected during the swap.  What's new? UniChem’s current API and web application is implemented with a framework version that’s not maintained and the cost of updating it surpasses the cost of rebuilding it. In order to improve stability, security, and support the implementation and fast delivery of new features, we have decided to revamp our user-facing systems using the latest version of widely used and maintained frameworks, i