Skip to main content

New Drug Approvals - Pt. XIII - Saxagliptin (Onglyza)

On the 31st July 2009 Saxagliptin (tradename Onglyza) was approved for the treatment of Type II diabetes - Type 2 Diabetes is also known as adult-onset diabetes, and also non-insulin-dependent diabetes melittus (NIDDM). It is the type of diabetes that is often associated with obesity, and so is an increasingly common disease/condition in our well-fed western and also developing world cultures.

Saxagliptin (previously known by the research code BMS-477118) is the third orally-dosed Dipeptidyl peptidase-IV (or DPP-IV) inhibitor to market, and is in the same mechanistic class as other 'gliptins' - Sitagliptin (tradename Januvia) and Vildagliptin (tradename Galvus/Eucreas) which are both launched and also others such as Alogliptin (aka SYR322) and Linagliptin (aka BI-1356, and expected tradename Ondero), which are in late stage clinical trials. The DPP-IV drug class has had quite a complex development and commercial history, as web searches will readily show.

Saxagliptin is a small molecule drug (Molecular Weight of 315.4 g.mol-1 for Saxagliptin itself, and 334.43 g.mol-1 for the Saxagliptin monohydrate dosed ingredient), and has low aqueous solubility. Saxagliptin is well absorbed and has low plasma protein binding (<30%),>ca. 15.8 µmol) once a day. The full prescribing information can be found here.

The Saxagliptin structure is (1S,3S,5S)-2-[(2S)-2-Amino-2-(3-hydroxytricyclo[3.3.1.13,7]dec-1-yl)acetyl]-2-azabicyclo[3.1.0]hexane-3-carbonitrile. It contains a number of interesting chemical groups, and a clear underlying similarity to a dipeptide can be seen in the 2-D structure (the enzyme DPP-IV, a proteinase, cleaves the two N-terminal amino acids of its substrate peptides)). Normally DPP-IV is involved in the inactivation of two endogenous peptides, GLP-1 and GIP, by DPP-4, blocking this degradation potentiates the secretion of insulin in the beta cells and suppress glucagon release by the alpha cells of the islets of Langerhans located in the pancreas. The first functional group of note is the nitrile (the triple bonded nitrogen-carbon unit) - this is essential to the inhibitory activity and is found in several of the other 'gliptins. This group forms a reversible, covalent bond with the residue Ser 630 of DPP-IV. Secondly, there is the bulky, hydrophobic adamantane (or (tricyclo[3.3.1.13,7]decane) group (this is the 3-D cage like portion of the molecule. Simple substituted adamantanes are sometimes drugs in their own right, for example amantadine, memantine and rimantadine. Within the 'gliptins though, the large bulky adamantyl group blocks an intramolecular cyclisation, which inactivates the inhibitor. These nitrile and adamantyl groups are linked via an amide bond, and an unusual 5,3 fused ring system pyrollidine (which resembles the amino-acid proline, found in the corresponding position of natural substrates).

Saxagliptin canonical SMILES: C1CC2(CC3CC1C(C3)(C2)O)C(C(=O)N4C(CC5C4C5)C#N)N Saxagliptin InChI: InChI=1S/C18H25N3O2/c19-8-13-4-11-5-14(11)21(13)16(22)15(20)17-2-1-12-3-10(6-17)7-18(12,23)9-17/h10-15,23H,1-7,9,20H2/t10?,11?,12?,13-,14-,15+,17?,18?/m0/s1 Saxagliptin InChIKey: SBBHGAZNWZOMBJ-TXTOARCRSA-N Saxagliptin CAS registry: 361442-04-8 Saxagliptin ChemDraw: Saxagliptin.cdx

The license holder for Saxagliptin is Bristol Myers Squibb and the product website is www.onglyza.com.

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