Skip to main content

2010 New Drug Approvals - Pt. XIII - Fingolimod (Gilenya)



ATC code: L04AA27

On September 21st, the FDA approved Fingolimod (previously known as FTY-720) for the treatment of relapsing forms of multiple sclerosis (MS). Fingolimod is marketed under the name Gilenya and is the first oral drug that can slow the progression of MS.

MS is a chronic autoimmune disorder affecting the central nervous system (CNS) and causing a broad spectrum of neurological symptoms ranging from numbness of the limbs and muscle weakness to cognitive impairment, depression, and a broad spectrum of other disorders. People suffering from MS experience inflammatory reactions which damage the myelin sheath surrounding the axons of nerve cells. The inflicted lesions impair the transmission of action potentials and ultimately perturb normal function of the CNS.

Upon administration, Fingolimod is phosphorylated by sphingosine kinase (Uniprot: Q9NRA0) to form the the active metabolite Fingolimod-phosphate - Fingolimod is therefore a prodrug. Fingolimod-phosphate binds the sphingosine 1-phosphate receptors S1PR-1, S1PR3, S1PR4 and S1PR5 (Uniprot: P21453,  Q99500, O95977, Q9H228) with high affinity and thereby blocks the capacity of leukocytes to migrate from lymph nodes into the peripheral blood. These receptors are also known as EDG receptors, and are all members of the rhodospin-like GPCR family (PFAM: PF00001), the largest single historical successful family of drug targets (GPCR SARfari: S1PR-1 (aka. EDG1)). The curative mechanism underlying Fingolimod's therapeutic effect is unknown but may involve a reduced migration of lymphocytes into the CNS.

The chemical structure of Fingolimod was derived from the natural product Myriocin (a natural product, first isolated from the fungus Isaria sinclairii), which is known as an immune suppressor and inhibitor of the sphingosine biosynthesis. Myriocin is a structural analogue of sphingosine.

The approved medication Gilenya is an oral capsule containing 0.56mg of the hydrochloride salt of Fingolimod which is equivalent to 0.5mg of Fingolimod. Dosage is 0.5mg per day - equivalent to 1.6 umol.

The peak concentration of Fingolimod in the blood is reached after 12 to 17 hours (TMAX) after oral administration and steady-state concentrations are reached after 1-2 months of daily oral administration, and are around ten-fold higher than from a single dose. Fingolimod is highly absorbed, having a bioavailability of 93%, and is also highly protein bound >99.7%, and has a high volume of distribution (Vd) of 1200L. The apparent half-life is 6 to 9 days, with a clearance of 6.3L.hr-1. Metabolisation of Fingolimod follows a main route of fatty acid-like degradation after oxidative biotransformation mainly by CYP4F2 (Uniprot: P78329).

Adverse side effects include headaches, diarrhea, reduced heart rate and atriventricular blocks, a higher risk of infections, macular edema, resipiratory effects and hepatic effects.



IUPAC: 2-amino-2-[2-(4-octylphenyl)ethyl]propane-1,3-diol


SMILES: CCCCCCCCc1ccc(CCC(N)(CO)CO)cc1

InChI: 1S/C19H33NO2/c1-2-3-4-5-6-7-8-17-9-11-18
(12-10-17)13-14-19(20,15-21)16-22/h9-12,21-22H,2
-8,13-16,20H2,1H3

Fingolimod is marketed under the name Gilenya by Novartis.

Full prescribing information can be found here.

Comments

Popular posts from this blog

New SureChEMBL announcement

(Generated with DALL-E 3 ∙ 30 October 2023 at 1:48 pm) We have some very exciting news to report: the new SureChEMBL is now available! Hooray! What is SureChEMBL, you may ask. Good question! In our portfolio of chemical biology services, alongside our established database of bioactivity data for drug-like molecules ChEMBL , our dictionary of annotated small molecule entities ChEBI , and our compound cross-referencing system UniChem , we also deliver a database of annotated patents! Almost 10 years ago , EMBL-EBI acquired the SureChem system of chemically annotated patents and made this freely accessible in the public domain as SureChEMBL. Since then, our team has continued to maintain and deliver SureChEMBL. However, this has become increasingly challenging due to the complexities of the underlying codebase. We were awarded a Wellcome Trust grant in 2021 to completely overhaul SureChEMBL, with a new UI, backend infrastructure, and new f

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

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

ChEMBL 26 Released

We are pleased to announce the release of ChEMBL_26 This version of the database, prepared on 10/01/2020 contains: 2,425,876 compound records 1,950,765 compounds (of which 1,940,733 have mol files) 15,996,368 activities 1,221,311 assays 13,377 targets 76,076 documents You can query the ChEMBL 26 data online via the ChEMBL Interface and you can also download the data from the ChEMBL FTP site . Please see ChEMBL_26 release notes for full details of all changes in this release. Changes since the last release: * Deposited Data Sets: CO-ADD antimicrobial screening data: Two new data sets have been included from the Community for Open Access Drug Discovery (CO-ADD). These data sets are screening of the NIH NCI Natural Product Set III in the CO-ADD assays (src_id = 40, Document ChEMBL_ID = CHEMBL4296183, DOI = 10.6019/CHEMBL4296183) and screening of the NIH NCI Diversity Set V in the CO-ADD assays (src_id = 40, Document ChEMBL_ID = CHEMBL4296182, DOI = 10.601