Skip to main content

New Drug Approvals 2012 - Pt. XVI - Aclidinium bromide (TudorzaTM PressairTM)






ATC Code: R03BB05
Wikipedia: Aclidinium bromide

On July 23th, the FDA approved Aclidinum bromide (Tradename: Tudorza PressairTM; Research Codes: LAS-34273, LAS W-330), a muscarinic acetylcholine M3 receptor antagonist, for the long-term maintenance treatment of bronchospasm associated with chronic obstructive pulmonary disease (COPD).

Chronic obstructive pulmonary disease (COPD) is characterised by the occurrence of chronic bronchitis or emphysema, a pair of commonly co-existing diseases of the lungs in which the airways become narrowed. Bronchial spasms, a sudden constriction of the muscles in the walls of the bronchioles, occur frequently in COPD.

Aclidinum bromide is a long-acting antimuscarinic agent that through the inhibition of the muscarinic acetylcholine M3 receptors present in the airway smooth muscle, leads to bronchodilation, and consequently eases the symptoms of COPD.

The muscarinic acetylcholine M3 receptor (Uniprot: P20309, ChEMBL: CHEMBL245) belongs to the G-protein coupled receptor (GPCR) type 1 family, and binds the endogenous neurotransmitter acethylcoline. Since it is coupled to a Gq protein, its inhibition leads to a decrease of intracellular calcium levels, and consequently smooth muscle relaxation.

>ACM3_HUMAN Muscarinic acetylcholine receptor M3
MTLHNNSTTSPLFPNISSSWIHSPSDAGLPPGTVTHFGSYNVSRAAGNFSSPDGTTDDPL
GGHTVWQVVFIAFLTGILALVTIIGNILVIVSFKVNKQLKTVNNYFLLSLACADLIIGVI
SMNLFTTYIIMNRWALGNLACDLWLAIDYVASNASVMNLLVISFDRYFSITRPLTYRAKR
TTKRAGVMIGLAWVISFVLWAPAILFWQYFVGKRTVPPGECFIQFLSEPTITFGTAIAAF
YMPVTIMTILYWRIYKETEKRTKELAGLQASGTEAETENFVHPTGSSRSCSSYELQQQSM
KRSNRRKYGRCHFWFTTKSWKPSSEQMDQDHSSSDSWNNNDAAASLENSASSDEEDIGSE
TRAIYSIVLKLPGHSTILNSTKLPSSDNLQVPEEELGMVDLERKADKLQAQKSVDDGGSF
PKSFSKLPIQLESAVDTAKTSDVNSSVGKSTATLPLSFKEATLAKRFALKTRSQITKRKR
MSLVKEKKAAQTLSAILLAFIITWTPYNIMVLVNTFCDSCIPKTFWNLGYWLCYINSTVN
PVCYALCNKTFRTTFKMLLLCQCDKKKRRKQQYQQRQSVIFHKRAPEQAL

There is one partially resolved 3D structure for this protein (2CSA), but there are now several relevant homologous structures of other closely related members of the family (see here for a current list of rhodopsin-like GPCR structures).

The -ium USAN/INN stem covers quaternary ammonium compounds. Members of these class include for example tiotropium bromide (ChEMBL ID: CHEMBL1182657), and ipratropium bromide (ChEMBL ID: CHEMBL1615433, which are also anthicholinergic drugs approved for the treatment of COPD.




Aclidinum bromide (IUPAC: [1-(3-phenoxypropyl)-1-azoniabicyclo[2.2.2]octan-3-yl]2-hydroxy-2,2-dithiophen-2-ylacetate bromide; Canonical smiles (for active quaternary amine): OC(C(=O)O[C@H]1C[N+]2(CCCOc3ccccc3)CCC1CC2)(c4cccs4)c5cccs5 ; PubChem: 11467166; Chemspider: 9609381; ChEMBLID: CHEMBL1194325; Standard InChI Key: ASMXXROZKSBQIH-VITNCHFBSA-N) is a synthetic quaternary ammonium compound with one chiral center, a molecular weight of 484.7 Da, 7 hydrogen bond acceptors, 1 hydrogen bond donor, and has an ALogP of 3.4. The compound is therefore fully rule-of-five compliant.

Aclidinum bromide is available as a dry powder inhaler and the recommended daily dose is two oral inhalations of 400 mcg. It has an apparent volume of distribution of 300 L following intravenous administration of 400 mcg, and its absolute bioavailability is approximately 6%. The estimated effective half-life of Aclidinum (t1/2) is 5 to 8 hours.

The major route of metabolism for aclidinum bromide is non-enzymatic and esterases-mediated hydrolysis, being rapidly and extensively converted to its alcohol and dithienylglycolic acid derivatives, neither of which binds to muscarinic receptors - this leads to very low systemic exposure of the active aclidinium species. Excretion of aclidinium bromide is mainly through the urine (54 - 65%) and faeces (20 - 30%), where only 1% is excreted as unchanged aclidinium. The total clearance is approximately 170 L/h after an intravenous dose of aclidinium bromide in young healthy volunteers.

The license holder for TudorzaTM PressairTM is Forest Pharmaceuticals, and the full prescribing information can be found here.

Comments

Andrew said…
In addition to the structure of the peptide mentioned in the post, there's also a recent structure of the M3 receptor in complex with the drug tiotropium. The PDB ID is 4DAJ, and Pubmed ID for the associated publication is 22358844.

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