Skip to main content

New Drug Approvals 2012 - Pt. XIV - Mirabegron (MyrbetriqTM)


ATC Code: G04BD (incomplete)
Wikipedia: Mirabegron


On June 28 2012, the FDA approved Mirabegron (tradename: Myrbetriq; Research Code: YM-178), a novel, first-in-class selective β3-adrenergic receptor agonist indicated for the treatment of overactive bladder (OAB) with symptoms of urge urinary incontinence, urgency, and urinary frequency. OAB syndrome is a urological condiction defined as urinary urgency, usually accompanied by frequency and nocturia, with or without urge urinary incontinence, in the absence of urinary tract infection or other obvious pathology. Mirabegron acts by relaxing the detrusor smooth muscle during the storage phase of the urinary bladder fill-void cycle by activation of β3-receptor which in turn increases bladder capacity.

Other treatments for OAB are already in the market and these include treatments with antimuscarinic drugs, such as Flavoxate (approved in 1970; tradename: Urispas; ChEMBL: CHEMBL1493), Oxybutynin (approved in 1975, tradenames: Ditropan, Ditropan XL, Oxytrol, Gelnique, Anturol; ChEMBL: CHEMBL1231), Tolterodine (approved in 1998; tradenames: Detrol, Detrol LA; ChEMBL: CHEMBL1382), Trospium (approved in 2004; tradenames: Santura, Santura XR; ChEMBL: CHEMBL1201344), Solifenacin (approved in 2004; tradenames: Vesicare; ChEMBL: CHEMBL1200803), Darifenacin (approved in 2004; tradenames: Enablex; ChEMBL: CHEMBL1346) and Fesoterodine (approved in 2008; tradenames: Toviaz; ChEMBL: CHEMBL1201764). While these drugs act by inhibiting the muscarinic action of acethylcholine, Mirabegron represents the first β3-receptor agonist to ever reach the market.

β3-receptor (ChEMBL: CHEMBL246; Uniprot: P13945) is a 408 amino-acid long G protein-coupled receptor (GPCR), belonging to Rhodopsin family (PFAM: PF00001; subfamily A17). Crystal structures of the closely related Î²1- and Î²2-receptors are known and act as good frameworks for understanding the mode of action of Mirabegron.

>ADRB3_HUMAN Beta-3 adrenergic receptor
MAPWPHENSSLAPWPDLPTLAPNTANTSGLPGVPWEAALAGALLALAVLATVGGNLLVIV
AIAWTPRLQTMTNVFVTSLAAADLVMGLLVVPPAATLALTGHWPLGATGCELWTSVDVLC
VTASIETLCALAVDRYLAVTNPLRYGALVTKRCARTAVVLVWVVSAAVSFAPIMSQWWRV
GADAEAQRCHSNPRCCAFASNMPYVLLSSSVSFYLPLLVMLFVYARVFVVATRQLRLLRG
ELGRFPPEESPPAPSRSLAPAPVGTCAPPEGVPACGRRPARLLPLREHRALCTLGLIMGT
FTLCWLPFFLANVLRALGGPSLVPGPAFLALNWLGYANSAFNPLIYCRSPDFRSAFRRLL
CRCGRRLPPEPCAAARPALFPSGVPAARSSPAQPRLCQRLDGASWGVS


Mirabegron is a synthetic chiral small-molecule, with a molecular weight of 396.51 Da, a AlogP of 2.26, 4 hydrogen bond donors and 5 hydrogen bond acceptors, and thus fully rule-of-five compliant. (IUPAC: 2-(2-amino-1,3-thiazol-4-yl)-N-[4-[2-[[(2R)-2-hydroxy-2-phenylethyl]amino]ethyl]phenyl]acetamide; Canonical Smiles: C1=CC=C(C=C1)[C@H](CNCCC2=CC=C(C=C2)NC(=O)CC3=CSC(=N3)N)O; InChI: InChI=1S/C21H24N4O2S/c22-21-25-18(14-28-21)12-20(27)24-17-8-6-15(7-9-17)10-11-23-13-19(26)16-4-2-1-3-5-16/h1-9,14,19,23,26H,10-13H2,(H2,22,25)(H,24,27)/t19-/m0/s1)

The recommended starting dosage of Mirabegron is 25 mg once daily, with or without food, and is effective for 8 weeks. Depending individual patient efficacy and tolerability, the dose may be increased to 50 mg once daily.

Mirabegron has a bioavalibity of 29% at a dose of 25 mg, which increases to 35% at a dose of 50 mg, a volume of distribution (Vd) of approximately 1670 L and a moderate plasma protein binding of ca. 71%. Mirabegron is metabolized via multiple pathways involving dealkylation, oxidation, glucuronidation and amide hydrolyis. Studies have suggested that although CYP3A4 and CYP2D6 isoenzymes play a role in the oxidative metabolism of Mirabegron, this is a limited role in the overall elimination. In addition to these isoenzymes, the metabolism of Mirabegron may also involve butylcholinesterase, uridine diphospho-glucuronosyltransferases and alcohol dehydrogenase. Two major inactive metabolites were observed in human plasma and these represent 16% and 11% of the total exposure. Mirabegron total clearance (CLtot) from plasma is ca. 57 L/h, with a terminal half-life of approximately of 50 hours. Renal clearance (CLR) is approximately 13 L/h, which corresponds to nearly 25% of CLtot. The urinary elimination of unchanged Mirabegron is dose-dependent and ranges from ca. 6% after a daily dose of 25 mg to 12.2% after a daily dose of 100 mg.

The license holder is Astellas Pharma Inc. and the full prescribing information of Mirabegron can be found here.

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