Skip to main content

New Drug Approvals 2012 - Pt. III - Axitinib (INLYTA®)





ATC Code: L01XE17
Wikipedia: Axitinib

On Jan 27th 2012, the FDA approved Axitinib (also known as AG-13736, trade name: Inlyta), a kinase inhibitor, for the treatment of advanced renal cell carcinoma after failure of a first line systemic treatment.

Renal Cell Carcinoma (RCC) is a cancer of the lining of proximal convoluted tubules, the tiny tubes through which the blood is filtered, in the kidney. It is the most common type of kidney cancer in adults and is responsible for 80% of all kidney cancers (Cancer Research UK). Over 270,000 new cases of kidney cancers are diagnosed every year and the numbers are on the rise (CRUK).

Axitinib is a tyrosine kinase inhibitor, inhibiting all subtypes of the Vascular Endothelial Growth Factor Receptor (VEGFR), VEGRF1 (Uniprot:P17948; ChEMBL1868 ; canSAR), VEGFR2 (Uniprot:P35968; ChEMBL ; canSAR) and VEGFR3 (Uniprot:P35916 ; ChEMBL; canSAR).
VEGFRs are single-pass membrane receptors that have multiple extracellular Immunoglobulin-like domains involved in growth factor binding (the ligand is VEGF); and an intracellular Tyrosine Protein Kinase catalytic domain (pfam:PF07714). Axitinib inhibits this kinase domain (rough boundaries shown as sequence alignment)




(PDB code: 1y6b; VEGFR2 kinase catalytic domain)


P17948  827   LKLGKSLGRGAFGKVVQASAFGIKKSPTCRTVAVKMLKEGATASEYKALMTELKILTHIGHHLNVVNLLGACTKQGGPLM  906
P35968  834   LKLGKPLGRGAFGQVIEADAFGIDKTATCRTVAVKMLKEGATHSEHRALMSELKILIHIGHHLNVVNLLGACTKPGGPLM  913
P35916  845   LHLGRVLGYGAFGKVVEASAFGIHKGSSCDTVAVKMLKEGATASEHRALMSELKILIHIGNHLNVVNLLGACTKPQGPLM  924

P17948  907   VIVEYCKYGNLSNYLKSKRDLFFLNKDAALHME-PKKEKMEPGLEQGKKP-RLDSVTSSESFASSGFQEDKSLSDVEEEE  984
P35968  914   VIVEFCKFGNLSTYLRSKRNEFVPYKTKGARFR-QGKDYVGAIPVDLKR--RLDSITSSQSSASSGFVEEKSLSDVEEEE  990
P35916  925   VIVEFCKYGNLSNFLRAKRDAFSPCAEKSPEQRGRFRAMVELARLDRRRPGSSDRVLFARFSKTEGGARRAS----PDQE  1000

P17948  985   DSDGFYKEPITMEDLISYSFQVARGMEFLSSRKCIHRDLAARNILLSENNVVKICDFGLARDIYKNPDYVRKGDTRLPLK  1064
P35968  991   APEDLYKDFLTLEHLICYSFQVAKGMEFLASRKCIHRDLAARNILLSEKNVVKICDFGLARDIYKDPDYVRKGDARLPLK  1070
P35916  1001  A-EDLWLSPLTMEDLVCYSFQVARGMEFLASRKCIHRDLAARNILLSESDVVKICDFGLARDIYKDPDYVRKGSARLPLK  1079

P17948  1065  WMAPESIFDKIYSTKSDVWSYGVLLWEIFSLGGSPYPGVQMDEDFCSRLREGMRMRAPEYSTPEIYQIMLDCWHRDPKER  1144
P35968  1071  WMAPETIFDRVYTIQSDVWSFGVLLWEIFSLGASPYPGVKIDEEFCRRLKEGTRMRAPDYTTPEMYQTMLDCWHGEPSQR  1150
P35916  1080  WMAPESIFDKVYTTQSDVWSFGVLLWEIFSLGASPYPGVQINEEFCQRLRDGTRMRAPELATPAIRRIMLNCWSGDPKAR  1159

P17948  1145  PRFAELVEKLGDLLQANVQQDGKDYI--PINAILTGNSGFTYSTPAFSEDFFK-ESISAPKFNSGSSDDVRYVNAFKFMS  1221
P35968  1151  PTFSELVEHLGNLLQANAQQDGKDYIVLPISETLSMEEDSGLSLPTSPVSCMEEEEVCDPKF--------HYDNTAGISQ  1222
P35916  1160  PAFSELVEILGDLLQGRGLQEEEEVCMAPRSSQ-SSEEGSFSQVSTMALHIAQADAEDSPPSLQRHSLAARYYNWVSFPG  1238

P17948  1222  L----------ERIKTFEELL---PNATSMFDDYQGDSSTLLASPMLKRFTWTDSKPKASLKIDLRVTSKS----KESGL  1284
P35968  1223  YLQNSKRKSRPVSVKTFEDIPLEEPEVKVIPDDNQTDSGMVLASEELKTL---EDRTKLSPSFGGMVPSKS----RESVA  1295
P35916  1239  CLARGAETRGSSRMKTFEEFPMTPTTYKGSVD-NQTDSGMVLASEEFEQI---ESRHRQESGFSCKGPGQNVAVTRAHPD  1314

P17948  1285  SDVSRPSF-CHSSCGHVSEGKRRFTYDHAELER----KIACCSPPPDY----NSVVLYSTPPI  1338
P35968  1296  SEGSNQTS--GYQSGYHSDDTDTTVYSSEEAELLKLIEIGVQTGSTAQILQPDSGTTLSSPPV  1356
P35916  1315  SQGRRRRPERGARGGQ-------VFYNSEYGELSEPSEEDHCSPSARVTFFTDNSY-------  1363

There are many VEGF inhibitors in development, and several launched drugs also have activity against  VEGFR (including Vandetanib, Sorafenib, Pazopanib and the broad spectrum inhibitor Sunitinib).
Axitinib (Trade name: Inlyta®; IUPAC= N-methyl-2-[3-((E)­ 2-pyridin-2-yl-vinyl)-1H-indazol-6-ylsulfanyl]-benzamide; Canonical SMILES: CNC(=O)c1ccccc1Sc2ccc3c(\C=C\c4ccccn4)n[nH]c3c2 ; InChIKey=RITAVMQDGBJQJZ-FMIVXFBMSA-N); (ChEMBL1289926; canSAR)
It has the molecular formula C22H18N4OS. Its molecular weight is 386.47, and has an AlogP of 4.49. Following single oral 5-mg dose administration, the median Tmax ranged between 2.5-4.1 hours.The mean oral bioavailability is 58%. Axitinib is highly bound (>99%) to human plasma proteins. The plasma half life (T1/2varies between 2.5 and 6.1 hours. It is metabolized primarily in the liver by CYP3A4/5 and to a lesser extent by CYP1A2, CYP2C19, and UGT1A1.

Full prescribing information can be found here.


Axitinib (Inlyta) is a product of Pfizer

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