Skip to main content

2010 New Drug Approvals - Pt. III - Liraglutide (Victoza)

ATC code: A10BX07

Also approved in January is Liraglutide, on January 25th, under the trade name Victoza. Liraglutide, previously known as NN2211, is a glucagon-like peptide-1 (GLP-1) receptor agonist indicated as an adjunct to diet and exercise to improve glycemic control in adults with type 2 diabetes mellitus. People with this type of diabetes are often overweight, have high blood pressure and/or cholesterol and become insensitive to insulin. Type 2 diabetes is the commonest form of diabetes, and is a major health problem in 'developed' economies. Liraglutide ATC code is A10BX07

Liraglutide works by activating the GLP-1 receptor (GLP-1R), which in turn stimulates the adenylyl cyclase pathway leading to insulin release in the presence of elevated glucose concentrations. GLP-1R is a class-II GPCR (also known as secretin receptor family). Liraglutide is the second GLP-1 agonist approved for the treatment of type 2 diabetes, after Exenatide (marketed as Byetta), which reached the market in 2005. Other -glutides in development include Taspgolutide (R1583 or BIM-507) developed by Roche, and Abiglutide (a stabilized GLP-1 analogue fused to serum albumin) from GSK. Liraglutide is an engineered form of the natural human GLP-1 peptide (one amino acid difference to the major circulating form of GLP-1, GLP-1(7-37) - an arginine replaces a lysine at position 34), an additional modification is the addition of palmitic acid attached via a glutamic acid spacer at position 26. The molecular weight of Liraglutide is 3751.2 Da.

Each standard dose contains 1.8mg of Liraglutide (equivalent to 480 nmol). Dosing is as a once daily subcutaneous (s.c.) injection. Liraglutide has a plasma half-life of ~13 hr (far longer than the ca. 2 min half life of the natural GLP-1 peptide). Slowing the fast degradation of GLP-1 is the basis of the therapeutic mechanism of the -gliptin class of DPP-IV inhibitors, e.g. Saxagliptin and Sitagliptin). The absolute bioavailability of Liraglutide, after s.c. dosing is 55%, and has high plasma protein binding > 98%. The volume of distribution Vd is 0.07L/kg, with a clearance of 1.2 L/h. Liraglutide has a boxed warning (risk of thyroid C-cell tumours). Victoza is marketed by Novo Nordisk and the product website is


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

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: 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

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