Skip to main content

Drug safety information: Boxed warnings and Withdrawn drugs

Updated drug safety information is available (as of ChEMBL 28) for drugs with boxed warnings and for withdrawn drugs. 

Boxed warnings (also know as black box warnings) are provided on medicinal product labels for FDA approved drugs if the medicinal product can cause severe or life-threatening side effects. They are free text descriptions, enclosed within a black box, hence the name! For example, Oxaprozin is used to treat osteoarthritis but carries a boxed warning.

Our recent work has classified the type of adverse effect described in boxed warnings on a per drug basis. For medicinal products that contain one active pharmaceutical ingredient, a boxed warning can be directly linked to a drug. Therefore, toxicity class(es) have been assigned to approved drugs with boxed warning information described on medicinal product labels (e.g. Cardiotoxicity, Hepatotoxicity etc). Clickable links to examples of medicinal product labels with boxed warning text descriptions have been retained to allow database users to drill down through the information “audit trail” to examine the source information. Further details are available from Hunter et al., 2021

As part of this effort, source references for previously curated withdrawn drugs have also been publicly exposed, ie drugs that have been approved but subsequently withdrawn from one or more markets of the world for safety reasons. See our blog on Withdrawn Drugs.

All safety information can be accessed in the ChEMBL web interface via the Drugs view (which is grouped by parent compound) or via the Compound view (for either view, filter on the left hand side by 'withdrawn flag' or 'black box warning'). For an individual drug, detailed drug warning information and source references are available on the Compound Report Card, e.g. Tolcapone, CHEMBL1324, is approved to treat Parkinson's Disease but carries a warning of Hepatotoxicity:
  





Programatic access is also available via our new Drug_Warning API endpoint, e.g. a search for hepatotoxic drugs (either withdrawn or those with a boxed warning) could apply this syntax: 
https://www.ebi.ac.uk/chembl/api/data/drug_warning.json?warning_class=Hepatotoxicity

The drug safety information allows drugs that cause similarly reported toxicities to be easily grouped, analyzed, and visualized. The ChEMBL resource contains a wide range of bioactivity data types, from early “Discovery” stage preclinical data for individual compounds through to postclinical data on marketed drugs; the inclusion of the drug safety information within this framework can support a wide range of safety-related drug discovery questions. The drug safety information will be updated in future database releases. 

This work has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 116030. This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA. 




Comments

Popular posts from this blog

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

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

Multi-task neural network on ChEMBL with PyTorch 1.0 and RDKit

  Update: KNIME protocol with the model available thanks to Greg Landrum. Update: New code to train the model and ONNX exported trained models available in github . The use and application of multi-task neural networks is growing rapidly in cheminformatics and drug discovery. Examples can be found in the following publications: - Deep Learning as an Opportunity in VirtualScreening - Massively Multitask Networks for Drug Discovery - Beyond the hype: deep neural networks outperform established methods using a ChEMBL bioactivity benchmark set But what is a multi-task neural network? In short, it's a kind of neural network architecture that can optimise multiple classification/regression problems at the same time while taking advantage of their shared description. This blogpost gives a great overview of their architecture. All networks in references above implement the hard parameter sharing approach. So, having a set of activities relating targets and molecules we can tra