Skip to main content

New Drug Approvals 2013 - Pt. VI - Gadoterate Meglumine (DotaremTM)






ATC Code: V08CA02
Wikipedia: Gadoteric Acid

On March 20th 2013, FDA approved Gadoteric Acid (as the meglumine salt; tradename: Dotarem; research code: P 449; CHEMBL: CHEMBL2219415), a gadolinium-based contrast agent (GBCA) indicated for intravenous use with magnetic resonance imaging (MRI) in brain (intracranial), spine and associated tissues of patients ages 2 years and older, to detect and visualize areas with disruption of the blood brain barrier (BBB) and/or abnormal vascularity of the central nervous system (CNS).

When placed in a magnetic field, Gadoteric Acid develops a magnetic moment. This magnetic moment enhances the relaxation rates of water protons in its vicinity, leading to an increase in signal intensity (brightness) of tissues. Gadoteric Acid enhances the contrast in MRI images, by shortening the spin-lattice (T1) and the spin-spin (T2) relaxation times.

Other GBCAs have already been approved by FDA for use in patients undergoing CNS MRI and these include Gadopentetate Dimeglumine (approved in 1988 under the tradename Magnevist; ChEMBL: CHEMBL1200431; PubChem: CID55466; ChemSpider: 396793), Gadoteridol (approved in 1992 under the tradename Prohance; ChEMBL: CHEMBL1200593; PubChem: CID60714; ChemSpider: 54719), Gadodiamide (approved in 1993 under the tradename Omniscan; ChEMBL: CHEMBL1200346; PubChem: CID153921; ChemSpider: 135661), Gadoversetamide (approved in 1999 under the tradename Optimark; ChEMBL: CHEMBL1200457; PubChem: CID444013; ChemSpider: 392041), Gadobenate Dimeglumine (approved in 2004 under the tradename Multihance; ChEMBL: CHEMBL1200571; PubChem: CID49799998; ChemSpider: 25046318) and Gadobutrol (approved in 2011 under the tradename Gadavist; ChEMBL: CHEMBL2218860; PubChem: CID15814656; ChemSpider: 26330337).



Gadoteric Acid is a macrocyclic ionic contrast agent, consisting of the chelating agent DOTA and gadolinium (Gd3+).
IUPAC: gadolinium(3+);2-[4,7,10-tris(carboxymethyl)-1,4,7,10-tetrazacyclododec-1-yl]acetic acid
Canonical Smiles: [Gd+3].OC(=O)CN1CCN(CC(=O)[O-])CCN(CC(=O)[O-])CCN(CC(=O)[O-])CC1
InChI: InChI=1S/C16H28N4O8.Gd/c21-13(22)9-17-1-2-18(10-14(23)24)5-6-20(12-16(27)28)8-7-19(4-3-17)11-15(25)26;/h1-12H2,(H,21,22)(H,23,24)(H,25,26)(H,27,28);/q;+3/p-3

The recommended dose of Gadoteric Acid is 0.2 mL/kg (0.1 mmol/kg) body weight administrated as an intravenous bolus injection at a flow rate of approximately 2 mL/second for adults and 1-2 mL/second for pediatric patients. Gadoteric Acid has a volume of distribution of 179 mL/kg and 211 mL/kg in female and male subjetcs, respectively, roughly equivalent to that of extracellular water, and an elimination half-life of about 1.4 hr and 2.0 hr in female and male subjects, respectively. Gadoteric Acid does not undergo plasma protein binding and it is not known to be metabolized. It is excreted primarily in the urine with 72.9% and 85.4% eliminated within 48 hours in female and male subjects, respectively. In healthy subjects, the renal and total clearance rates are comparable, with a renal clearance of 1.27 mL/min/kg and 1.40 mL/min/kg in female and male subjects, respectively, and a total clearance of 1.74 mL/min/kg and 1.64 mL/min/kg in female and male subjects, respectively.

All GBCAs, including Gadoterate Meglumine, carry a boxed warning about the risk of nephrogenic systemic fibrosis (NSF), a condition associated with the use of GBCAs in certain patients with kidney disease.

The license holder for Gadoterate Meglumine is Guerbet LLC and the prescribing information can be found here (Gadoteric Acid is also approved in Europe and the SPC can be found here).

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