Skip to main content

New Drug Approvals 2012 - Pt. XXII - Perampanel (FycompaTM)



ATC Code: N03AX22
Wikipedia: Perampanel

On October 22nd 2012 the FDA approved Perampanel (research code: E2007, ER-155055-90, trade name Fycompa, CHEMBL1214124). Perampanel is an orally administered drug to be used as an adjunctive therapy for the treatment of partial-onset seizures with or without secondary generalized seizures in patients with epilepsy.

Epileptic seizures are defined as "abnormal excessive or synchronous neuronal activity in the brain". The net symptoms can be very diverse, from severe thrashing movements to a very mild brief loss of awareness. Approximately 4% of the population will have experienced a unprovoked seizure by the age of 80, with a 30-50% chance of repeat in this group. Seizures can last from a few seconds to a state of life threatening persistent seizure (known as status epilepticus).

Approximately 25 % of the people suffering from a seizure or  status epilepticus will be diagnosed to have epilepsy. Treatment may reduce the chance of a second seizure by as much as 50%.

Perampanel acts by non-competitively inhibiting the ionotropic α-amino-3-hydroxy-5-methyl-4- isoxazolepropionic acid (AMPA) glutamate receptor. This receptor consists of a heteromeric combination of 2 out of 4 known subunits GluR-1 - GluR4 (respectively: CHEMBL2009, CHEMBL4016, CHEMBL3595 and CHEMBL3190 or Uniprot 42261, 42262, 42263 and 48058). Of these, the combinations GluR-1/GluR-2 and GluR-2/GluR-3 are the most frequent. The specific mechanism by which Perampanel exerts its antiepileptic effect in humans has not been fully elucidated.



Fycompa is a small molecule drug with a molecular mass of 349.4 g/mol, an AlogP of 3.57 , 3 rotatable bonds and does not violate the rule of 5. Canonical SMILES : O=C1N(C=C(C=C1c2ccccc2C#N)c3ccccn3)c4ccccc4
InChi: InChI=1S/C23H15N3O/c24-15-17-8-4-5-11-20(17)21-14-18(22-12-6-7-13-25-22)16-26(23(21)27)19-9-2-1-3-10-19/h1-14,16H

The recommended starting dose of Perampanel in the absence of other CYP3A4 enzyme-inducing antiepileptic drugs is 2 mg once daily taken orally at bedtime (and can be incremented to the recommended dose range of 8 mg to 12 mg once daily). The recommended starting dose of Perampanel in the presence of CYP3A4 enzyme-inducing antiepileptic drugs is 4 mg and patients should be monitored closely for response.

Perampanel is extensively metabolized via initial oxidation and sequential glucuronidation. Oxidative metabolism is mediated by CYP3A4 and/or CYP3A5 based on results of in vitro studies using recombinant human CYPs and human liver microsomes. Other CYP enzymes may also be involved.

The license holder is Eisai Inc. and the 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

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

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 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