Skip to main content

New Drug Approvals - Pt. XVIII - Ustekinumab (Stelara)

Recently approved by the FDA, on September 25th 2009, was Ustekinumab, marketed under the trade name Stelara. Ustekinumab, previously known as CNTO-1275, is a first-in-class injectable biological drug blocking signalling of two distinct interleukins (IL-12 and IL-23) and is indicated for the treatment of adults with moderate to severe plaque psoriasis. Psoriasis (ICD-10: L40) is a complex autoimmune disease, typically leading to the formation of scaly red or silvery-white plaques on the skin. Another drug with the same mechanism as Ustekinumab (blocking IL-12 and IL-23 signalling) is ABT-874, ABT-874 is currently still in clinical trials.
Ustekinumab is dosed as a subcutaneous injection given at weeks 0 and 4, followed subsequently by every-12-week maintenance dosing. The recommended starting dose of is 45 mg for patients weighing 100 kg or less, and 90 mg for patients weighing more than 110 kg (the 45mg dose corresponds to a 0.31 umol dose).
Ustekinumab is is a human IgG1қ monoclonal antibody and is the first drug to target IL-12 and IL-23 cytokines, these are two naturally occurring secreted, heterodimeric proteins (two distinct proteins (themselves derived from two distinct genes) bound in a specific complex), they are involved in inflammatory and immune responses, such as the activation of CD4+ and Natural Killer T-cells. Ustekinumab binds with high affinity and specificity to the p40 protein shared as a common subunit by both IL-12 and IL-23. Through targeting the shared p40 component, Ustekinumab is able to block signalling by both interleukins. IL-12 and IL-23 eventually signal via master inflammatory system regulators tumor necrosis factor-α (TNF-α) and nuclear factor κB (NFκB).
<CHEMBL_DRUG>
<DRUG_NAME="Ustekinumab" TRADEMARK_NAME="Stelara" RESEARCH_CODE="CNTO1275" APPROVAL_DATE="25-SEPT-2009" DRUG_MOLECULAR_WEIGHT="145650">
<DRUG_SUBUNIT="Heavy gamma-1 chain">
EVQLVQSGAEVKKPGESLKISCKGSGYSFTTYWLGWVRQMPGKGLDWIGI
MSPVDSDIRYSPSFQGQVTMSVDKSITTAYLQWNSLKASDTAMYYCARRR
PGQGYFDFWGQGTLVTVSSSSTKGPSVFPLAPSSKSTSGGTAALGCLVKD
YFPEPVTVSWNSGALTSGVHTFPAVLQSSGLYSLSSVVTVPSSSLGTQTY
ICNVNHKPSNTKVDKRVEPKSCDKTHTCPPCPAPELLGGPSVFLFPPKPK
DTLMISRTPEVTCVVVDVSHEDPEVKFNWYVDGVEVHNAKTKPREEQYNS
TYRVVSVLTVLHQDWLNGKEYKCKVSNKALPAPIEKTISKAKGQPREPQV
YTLPPSRDELTKNQVSLTCLVKGFYPSDIAVEWESNGQPENNYKTTPPVL
DSDGSFFLYSKLTVDKSRWQQGNVFSCSVMHEALHNHYTQKSLSLSPGK
</DRUG_SUBUNIT>
<DRUG_SUBUNIT NAME="Light kappa chain">
DIQMTQSPSSLSASVGDRVTITCRASQGISSWLAWYQQKPEKAPKSLIYA
ASSLQSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQYNIYPYTFGQ
GTKLEIKRTVAAPSVFIFPPSDEQLKSGTASVVCLLNNFYPREAKVQWKV
DNALQSGNSQESVTEQDSKDSTYSLSSTLTLSKADYEKHKVYACEVTHQG
LSSPVTKSFNRGEC
</DRUG_SUBUNIT>
<DRUG_TARGET UNIPROT="P29460" TARGET_NAME="p40 subunit of IL-12 and IL-23">
IWELKKDVYVVELDWYPDAPGEMVVLTCDTPEEDGITWTLDQSSEVLGSG
KTLTIQVKEFGDAGQYTCHKGGEVLSHSLLLLHKKEDGIWSTDILKDQKE
PKNKTFLRCEAKNYSGRFTCWWLTTISTDLTFSVKSSRGSSDPQGVTCGA
ATLSAERVRGDNKEYEYSVECQEDSACPAAEESLPIEVMVDAVHKLKYEN
YTSSFFIRDIIKPDPPKNLQLKPLKNSRQVEVSWEYPDTWSTPHSYFSLT
FCVQVQGKSKREKKDRVFTDKTSATVICRKNASISVRAQDRYYSSSWSEW
ASVPCS
</DRUG_TARGET>
</DRUG>
</CHEMBL_DRUG>
The license holder for Ustekinumab is Johnson & Johnson. and the product website is www.stelarainfo.com.

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