Skip to main content

New Drug Approvals 2013 - Pt. 1 - Alogliptin (NesinaTM)


ATC Code: A10BH04
Wikipedia: Alogliptin

On January 25th 2013, FDA approved Alogliptin (as the benzoate salt; tradename: Nesina; research code: SYR-322, TAK-322; CHEMBL: CHEMBL376359), a dipeptidyl peptidase-4 (DPP-4) inhibitor indicated as an adjunct to diet and exercise to improve glycemic control in adults with type 2 diabetes mellitus (also known as noninsulin-dependent diabetes mellitus (NIDDM)).

NIDDM is a chronic disease characterized by high blood glucose. In response to meals, increased concentrations of incretin hormones such as glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic polypeptide (GIP) are released into the bloodstream from the small intestine. These hormones cause insulin release from the pancreatic beta cells in a glucose-dependent manner but are inactivated by the DPP-4 enzyme within minutes. GLP-1 also lowers glucagon secretion from pancreatic alpha cells, reducing hepatic glucose production. In patients with NIDDM, concentrations of GLP-1 are reduced but the insulin response to GLP-1 is preserved. Alogliptin exerts its therapeutic action by inhibiting DPP-4, thereby slowing the inactivation of the incretin hormones and increasing their bloodstream concentrations.

Image from Wikipedia

Other DPP-4 inhibitors are already available on the market (some of which have already been covered here on the ChEMBL-og) and these include Linagliptin (approved in 2011 under the tradename Tradjenta; ChEMBL: CHEMBL237500; PubChem: CID10096344; ChemSpider: 8271879), Saxagliptin (approved in 2009 under the tradename Onglyza; ChEMBL: CHEMBL385517; PubChem: CID11243969; ChemSpider: 9419005) and Sitagliptin (approved in 2006 under the tradename Januvia; ChEMBL: CHEMBL1422; PubChem: CID4369359; ChemSpider: 3571948). Several other DPP-4 inhibitors are in clinical trials such as Trelagliptin (ChEMBL: CHEMBL1650443; research code: SYR-472), Omarigliptin (ChEMBL: CHEMBL2105762; research code: MK-3102), Carmegliptin (ChEMBL: CHEMBL591118; research code: R-1579, RO-4876904), Gosogliptin (ChEMBL: CHEMBL515387; research code: PF-734200), Dutogliptin (research code: PHX1149), Denagliptin (ChEMBL: CHEMBL2110666; research code: GW823093). Vildagliptin (ChEMBL: CHEMBL142703) has been approved in Europe and Japan, but not in the United States.

DPP-4 (ChEMBL: CHEMBL284; Uniprot: P27487) is 766 amino acid-long enzyme, which is responsible for the removal of N-terminal dipeptides sequentially from polypeptides having unsubstituted N termini, provided that the penultimate residue is proline. It belongs to the Dipeptidyl peptidase IV family (PFAM: PF00930).

>DPP4_HUMAN Dipeptidyl peptidase 4
MKTPWKVLLGLLGAAALVTIITVPVVLLNKGTDDATADSRKTYTLTDYLKNTYRLKLYSL
RWISDHEYLYKQENNILVFNAEYGNSSVFLENSTFDEFGHSINDYSISPDGQFILLEYNY
VKQWRHSYTASYDIYDLNKRQLITEERIPNNTQWVTWSPVGHKLAYVWNNDIYVKIEPNL
PSYRITWTGKEDIIYNGITDWVYEEEVFSAYSALWWSPNGTFLAYAQFNDTEVPLIEYSF
YSDESLQYPKTVRVPYPKAGAVNPTVKFFVVNTDSLSSVTNATSIQITAPASMLIGDHYL
CDVTWATQERISLQWLRRIQNYSVMDICDYDESSGRWNCLVARQHIEMSTTGWVGRFRPS
EPHFTLDGNSFYKIISNEEGYRHICYFQIDKKDCTFITKGTWEVIGIEALTSDYLYYISN
EYKGMPGGRNLYKIQLSDYTKVTCLSCELNPERCQYYSVSFSKEAKYYQLRCSGPGLPLY
TLHSSVNDKGLRVLEDNSALDKMLQNVQMPSKKLDFIILNETKFWYQMILPPHFDKSKKY
PLLLDVYAGPCSQKADTVFRLNWATYLASTENIIVASFDGRGSGYQGDKIMHAINRRLGT
FEVEDQIEAARQFSKMGFVDNKRIAIWGWSYGGYVTSMVLGSGSGVFKCGIAVAPVSRWE
YYDSVYTERYMGLPTPEDNLDHYRNSTVMSRAENFKQVEYLLIHGTADDNVHFQQSAQIS
KALVDVGVDFQAMWYTDEDHGIASSTAHQHIYTHMSHFIKQCFSLP

The image above shows a crystal structure of DPP-4 (in this example, two copies of DPP-4 are displayed - PDBe: 3g0b). Information on the active site of DPP-4 can be found here.


Alogliptin is an oral small-molecule with a molecular weight of 339.4 Da (461.51 Da as the benzoate salt). The image on the right shows Alogliptin in the active site of DPP-4. Important features of its chemical structure are the aminopiperidine motif, which provides a salt bridge to the glutamic acids residues 205/206 in the active site of DPP-4, the cyanobenzyl group which interacts with the arginine residue 125, the carbonyl group from the pyrimidinedione moiety which participates in an hydrogen bond with the backbone NH of tyrosine 631 and the uracil ring which π-stacks with tyrosine 547.
IUPAC: 2-[[6-[(3R)-3-aminopiperidin-1-yl]-3-methyl-2,4-dioxopyrimidin-1-yl]methyl]benzonitrile
Canonical Smiles: CN1C(=O)C=C(N2CCC[C@@H](N)C2)N(Cc3ccccc3C#N)C1=O
InChI: InChI=1S/C18H21N5O2/c1-21-17(24)9-16(22-8-4-7-15(20)12-22)23(18(21)25)11-14-6-3-2-5-13(14)10-19/h2-3,5-6,9,15H,4,7-8,11-12,20H2,1H3/t15-/m1/s1

The recommended dosage of Alogliptin is 25 mg once daily. Alogliptin has good oral bioavailability F (approximately 100% bioavailable), with a volume of distribution Vd of 417 L and a low plasma protein binding (20%). Excretion is mainly renal (76% of the dose recovered in urine) and mostly as the parent compound (60% to 71%). Alogliptin is metabolized by CYP2D6 and CYP3A4 to two minor metabolites, M-I (N-demethylated alogliptin - &gt1% of the parent drug), which is an active metabolite and is an inhibitor of DPP-4 similar to the parent molecule and M-II (N-acethylated alogliptin - &gt6% of the parent drug), which does not display any inhibitory activity towards DPP-4 or other DPP-related enzymes. The renal clearance of Alogliptin is 9.6 L/hr and the systemic clearance is 14.0 L/hr.

The license holder is Takeda Pharmaceuticals America, Inc. and the prescribing information of Alogliptin can be found here.

Patricia

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

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

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