Skip to main content

New Drug Approvals 2012 - Pt. VIII - Peginesatide (OmontysTM)






ATC code: B03XA (incomplete)

On March 27, the FDA approved Peginesatide for the treatment of anemia due to chronic kindney disease (CDK) in patients on dialysis. CDK is a slow but progressive loss of kidney function that can be caused by diabetes mellitushypertension and glomerulonephritis, among others. One of the symptoms in the advanced stages of CDK is anemia, a decrease in the number of red blood cells and hence the hemoglobin (adult hemoglobin: heterotetramer of two copies of P69905 and two copies of P68871) content of the blood. Anemia in patients suffering from CDK is caused by reduced production of erythropoetin, a hormone that regulates the levels of red blood cells and is synthesized predominantly in the cortex of the kidney.

Anemia induced by CDK can be treated by supplying exogenous erythroepotin or analogs of this hormone (e.g. Methoxy polyethylene glycol-epoetin beta, CHEMBL1201829). The collective term for these substances is erythropoiesis-stimulating agent (ESAs). Like endogenous erythropoetin, ESAs exert their effect through binding of the erythropeotin receptor (EpoR, Uniprot P19235) and subsequent activation of the JAK2 (Uniprot O60674) STAT5A (Uniprot P42229) pathway, which results in increased survival of erythrocyte progenitors.

Peginesatide is an ESA with no sequence homology to erythropoetin. Instead, it is composed of two synthetic 21 amino-acid peptides that are linked through a lysine branched PEG chain, as shown below.


The dimeric peptide has a molecular weight of about 4.9 kDa, and the PEG chain has a molecular weight of approximately 40kDa. Peginasetide is dosed as an acetate salt. The empirical formula of the free base is C2031H3950N62O958S6 and total molecular weight ~45 kDa.

Peginesatide does not induce any cytochrome P450s and according to in-vitro protein-binding studies does not bind serum albumin or lipoproteins. The half-life of Peginesatide following intravenous administration is 25.0 ± 7.6 hours in healthy subjects and 47.9 ± 16.5 hours in dialysis patients. Clearance is 0.5 ± 0.2 mL/hr.kg and the mean volume of distribution is 34.9 ± 13.8 mL/kg. Peginesatide is mainly cleared through the urine and a study using radio-labelled Peginesatide indicates that it is not excreted unchanged.

Peginesatide has a black box warning and adverse reactions include increased risk for death, myocardial infarcts, stroke, venous thromboembolism, thrombosis of vascular access and tumor progression or recurrence.

An advantage of Peginesatide over other ESAs is that it can be administered at monthly intervals. Given the adverse reactions, the dosage recommendation is to individualize dosing and give the lowest dose that is sufficient to reduce the need for blood transfusions. 0.04 mg/kg is the recommended dose for probing patient response.

Peginesatide was developed by Affymax and Tekeda Pharmaceuticals and is marketed under the trade name Omontys.

Full prescribing information 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

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

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