Skip to main content

New Drug Approvals 2014 - Pt. VI - Florbetaben F18 (Neuraceq™)




ATC Code: Unavailable
Wikipedia: Florbetaben_F18
ChEMBL: CHEMBL1908906

On March 19th the FDA approved  Florbetaben F18 (Neuraceq™) as a radioactive diagnostic agent for Positron Emission Tomography (PET) imaging of the brain to estimate β-amyloid (βA) neuritic plaque density in adult patients with cognitive impairment who are being evaluated for Alzheimer’s disease or other causes of cognitive decline.

Alzheimer's disease is the most common form of dementia, can currently not be cured and is characterised by a progressive disease pattern that usually leads to death.  Alzheimer's is predicted to affect 1 in 85 people globally by 2050.

Target(s)
Florbetaben binds with high affinity to βA in brain homogenates and selectively labels βA plaques and cerebral amyloid angiopathy. βA (PDB ; Uniprot P05067) denotes 36-43 length peptides that are believed to be crucially involved in the Alzheimer's disease mechanism. βA aggregates in the brain of Alzheimer's patients and is derived from amyloid precursor protein which is cut by certain enzymes. βA and the resulting plaques are toxic to neurons. Following intravenous administration, Florbetaben F18 crosses the blood brain barrier and shows differential retention in brain regions that contain βA deposits. Differences in signal intensity between brain regions showing specific and non­ specific Florbetaben F18 uptake form the basis for the image interpretation method.



Florbetaben F18 (CHEMBL1908906Pubchem : 53257383) is a small molecule drug with a molecular weight of 359.4 Da, an AlogP of 3.75, 12 rotatable bonds, and no rule of 5 violations. Florbetaben F18 
is administered intravenously.

Canonical SMILES: CNc1ccc(\C=C\c2ccc(OCCOCCOCCF)cc2)cc1
InChi: InChI=1S/C21H26FNO3/c1-23-20-8-4-18(5-9-20)2-3-19-6-10-21(11-7-19)26-17-16-25-15-14-24-13-12-22/h2-11,23H,12-17H2,1H3/b3-2+

Dosage
The recommended dose of Neuraceq is 300 MBq (8.1 mCi), maximum 30 mcg mass dose, administered as a single
slow intravenous bolus (6 sec/mL) in a total volume of up to 10 mL. PET images should subsequently be acquired approximately 45 - 130 minutes after injection over a period of 15-20 minutes. 

Warning / limitations of use
A positive Neuraceq scan does not establish the diagnosis of AD or any other cognitive disorder.
Safety and effectiveness of Neuraceq have not been established for:
  • Predicting development of dementia or other neurologic conditions;
  • Monitoring responses to therapies.
Neuraceq, similar to other radiopharmaceuticals, contributes to a patient's overall long-term cumulative radiation exposure. Long-term cumulative radiation exposure is associated with an increased risk of cancer.

Pharmacokinetics
Ten minutes after intravenous bolus injection of 300 MBq of Neuraceq in human volunteers, approximately 6% of the injected radioactivity was distributed to the brain. Florbetaben F 18 plasma concentrations declined by approximately 75% at 20 minutes post-injection, and by approximately 90% at 50 minutes.

Elimination
Florbetaben F18 is mainly eliminated via the hepatobiliary route with a mean half-life of approximately 1 hour. 

Metabolism
Forbetaben F18 is metabolized mainly by CYP2J2 and CYP4F2.

License holder
The license holder is Piramal Imaging, the highlights of the 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

Using ChEMBL activity comments

We’re sometimes asked what the ‘activity_comments’ in the ChEMBL database mean. In this Blog post, we’ll use aspirin as an example to explain some of the more common activity comments. First, let’s review the bioactivity data included in ChEMBL. We extract bioactivity data directly from   seven core medicinal chemistry journals . Some common activity types, such as IC50s, are standardised  to allow broad comparisons across assays; the standardised data can be found in the  standard_value ,  standard_relation  and  standard_units  fields. Original data is retained in the database downloads in the  value ,  relation  and  units  fields. However, we extract all data from a publication including non-numerical bioactivity and ADME data. In these cases, the activity comments may be populated during the ChEMBL extraction-curation process  in order to capture the author's  overall  conclusions . Similarly, for deposited datasets and subsets of other databases (e.g. DrugMatrix, PubChem), th