Skip to main content

New Drug Approvals 2012 - Pt. XVIII - Teriflunomide (AubagioTM)

ATC Code: L04AA13
Wikipedia: Teriflunomide

On September 12th the FDA approved Teriflunomide (tradename AUBAGIO, ChEMBL973), an orally administered drug for the treatment of relapsing forms of Multiple Sclerosis (MS). Teriflunomide is an inhibitor of of pyrimidine synthesis by dihydroorotate dehydrogenase (DHODH, Uniprot: Q02127) but is it not certain if this explains the effect of the drug on MS lesions. Teriflunomide inhibits rapidly dividing cells, which includes activated T lymphocytes thought to drive the MS disease process. The net effect of the inhibition of DHODH is that lymphocytes cannot accumulate sufficient pyrimidines for DNA synthesis. Additionally, Teriflunomide has been shown to inhibit the activation of nuclear factor kappaB and tyrosine kinases, but at doses higher than needed for the observed anti-inflammatory effects. Teriflunomide is the active metabolite of an already approved drug Leflunomide (tradename Arava, ChEMBL960) indicated for the treatment of rheumatoid and psoriatic arthritis.

MS is an inflammatory disease characterised by damaging of the myelin sheaths surrounding the axons of the brain and spinal cord. This demyelation results in a broad number of symptoms scarring. The prevalence ranges between 2 – 150 per 100.000 and the disease onset usually occurs in young adults. MS cannot currently be cured and the prognosis is difficult to predict, depending on the subtype of the disease. The United States National Multiple Sclerosis Society characterised four clinical courses, two of which are classified as relapsing forms of MS namely 'relapsing remitting' and 'progressive relapsing'.

Currently there are six other disease-modifying treatments for MS approved by regulatory agencies. These are: Fingolimod (trade name Gilenya, CHEMBL314854), interferon beta-1a (trade names Avonex, CinnoVex, ReciGen and Rebif, CHEMBL1201562) and interferon beta-1b (U.S. trade name Betaseron, in Europe and Japan Betaferon, CHEMBL1201563), glatiramer acetate (trade name Copaxone, CHEMBL1201507), mitoxantrone (trade name Novantrone, CHEMBL58) and natalizumab (trade name Tysabri). Of these drugs, only Fingolimod is orally administered, the others are injected intravenously or subcutaneously, hence Terfiflunomide is the second oral treatment option for MS.

Terfiflunomide is a small molecule drug with a molecular mass of 270.20 g/ml, an AlogP of 2.09 , 3 rotatable bonds and does not violate the rule of 5.
 Canonical SMILES : C\C(=C(/C#N)\C(=O)Nc1ccc(cc1)C(F)(F)F)\O
 InChi: InChI=1S/C12H9F3N2O2/c1-7(18)10(6-16)11(19)17-9-4-2-8(3-5-9)12(13,14)15/h2-5,18H,1H3,(H,17,19)/b10-7-

The structure of the drug can interconvert between Z and E stereoisomers with the Z enol being the most stable and the active form.

DHODH (EC:, Uniprot: Q02127, PDB: 1D3G, CHEMBL: ChEMBL1966, IntAct: EBI-3928775 ), is a 395 amino acid monomer located at the mitochondrion inner membrane. The protein is a single-pass membrane protein with the catalytic site located in the mitochondrial inter-membrane space.

>sp|Q02127|PYRD_HUMAN Dihydroorotate dehydrogenase (quinone)

The recommended dose of AUBAGIO is 7 mg or 14 mg orally once daily. AUBAGIO can be taken with or without food.

The median time to reach maximum plasma concentrations is between 1 and 4 hours post-dose following and oral administration. The half life is approximately 18-19 days after repeated doses of 7 mg and 14 mg respectively. It takes approximately 3 months respectively to reach steady-state concentrations.

Teriflunomide is mainly eliminated through direct biliary excretion of unchanged drug and renal excretion of metabolites.

The drug comes with a box warning to alert prescribers to the risk of liver problems, including death, and a risk of birth defects. Physicians are advised to do a blood test for liver function prior to prescribing Terfiflunomide and periodically during the course of treatment. Based on animal studies, the drug may cause fetal harm.

The license holder is the Genzyme Corporation and the full prescribing information can be found here.


Anonymous said…
new useful drug.
thanks for share.

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 . In addition to it, the most popular legacy REST endpoints will also remain implemented in the new web services: 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

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: Please see ChEMBL_30 release notes for full details of all changes in this release: 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

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

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