Skip to main content

2010 New Drug Approvals - part VIII - Cabazitaxel (Jevtana)



ATC code (partial): L01CD

The FDA approved Cabazitaxel on June 17th. Cabitaxel is used in combination with prednisone to treat advanced, hormone-refractory prostate cancer.

Typically, prostate cancer occurs in older males and is among the most common forms of cancer affecting men with around 200,000 new cases per year diagnosed in the United States. Prostate cancer is a glandular cancer that is generally slow-growing. However, prostate cancer develops the ability to penetrate into other tissues and to form metastases.

Cabazitaxel is a new antineoplastic agent that inhibits the function of microtubules. Like other taxanes, it binds to beta-tubulin and promotes and maintains its incorporation in the assembled microtubule. As a consequence the dynamic structure of the microtubule cytoskeleton is 'frozen' and the concentration of free tubulin decreased. Mitotic cells, which depend on microtubules to restructure their shape and organelle organization, undergo apoptosis or stop progressing through the cell cycle. Thus, tumor growth is stalled.

Chemically, cabazitaxel is a dimethylated variant of the state-of-the-art treatment docetaxel. Cabazitaxel is an example of a semin-synthetic natural product, with the key raw ingredient isolated from yew leaves. Cabazitaxel is a lipophillic molecule with a high molecular weight 835.93 g.mol-1).
The volume of distribution (Vss) is 4,864 L. In vitro, the binding of cabazitaxel to human serum proteins was 89 to 92%. Cabazitaxel is mainly bound to human serum albumin (82%) and lipoproteins (88% for HDL, 70% for LDL, and 56% for VLDL). Cabazitaxel is extensively metabolized in the liver (> 95%), mainly by the CYP3A4/5 isoenzyme, and to a lower extent by CYP2C8. Clearance (primarily as metabolites) is via both urine and feces. Cabazitaxel has a plasma clearance of 48.5 L.hr-1.
Clinical trials with showed a statistically significant increase in survival rate by 2.4 month.
Cabazitaxel is to be administered as an intravenous infusion of 25mg/mm2(body surface) every three weeks in combination with a daily regiment of 10mg prednisone.
Cabazitaxel comes with a black box warning (fatal neutropenia). Patients have to be monitored for changes in neutrophil counts and treatment appropriately adjusted if a drop below 1500 neutrophils/mm3 is observed. Other adverse side effects of cabazitaxel are hypersensivity reactions, gastrointestinal symptoms and renal failure.

Cabazitaxel is marketed by Sanofi-Aventis under the name Jevtana. This is a link to the full prescribing information.




SMILES:
CO[C@H]1C[C@H]2OC[C@@]2(OC(C)=O)[C@H]3[C@H](OC(=O)c4ccccc4)[C@]5(O)C
[C@H](OC(=O)[C@H](O)[C@@H](NC(=O)OC(C)(C)C)c6ccccc6)C(=C([C@@H](OC)C
(=O)[C@]13C)C5(C)C)C


InChI:
1S/C45H57NO14/c1-24-28(57-39(51)33(48)32(26-17-13-11-14-18-26)46-40
(52)60-41(3,4)5)22-45(53)37(58-38(50)27-19-15-12-16-20-27)35-43(8,
36(49)34(55-10)31(24)42(45,6)7)29(54-9)21-30-44(35,23-56-30)59-25(2)
47/h11-20,28-30,32-35,37,48,53H,21-23H2,1-10H3,(H,46,52)/t28-,29-,30+,
32-,33+,34+,35-,37-,43+,44-,45+/m0/s1

InChI-Key:
BMQGVNUXMIRLCK-OAGWZNDDSA-N
    

Chemdraw: cabazitaxel.cdx

Comments

Popular posts from this blog

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 29 Released

  We are pleased to announce the release of ChEMBL 29. This version of the database, prepared on 01/07/2021 contains: 2,703,543 compound records 2,105,464 compounds (of which 2,084,724 have mol files) 18,635,916 activities 1,383,553 assays 14,554 targets 81,544 documents Data can be downloaded from the ChEMBL FTP site:   https://ftp.ebi.ac.uk/pub/databases/chembl/ChEMBLdb/releases/chembl_29 .  Please see ChEMBL_29 release notes for full details of all changes in this release: https://ftp.ebi.ac.uk/pub/databases/chembl/ChEMBLdb/releases/chembl_29/chembl_29_release_notes.txt New Deposited Datasets EUbOPEN Chemogenomic Library (src_id = 55, ChEMBL Document IDs CHEMBL4649982-CHEMBL4649998): 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 synthesiz

Identifying relevant compounds in patents

  As you may know, patents can be inherently noisy documents which can make it challenging to extract drug discovery information from them, such as the key targets or compounds being claimed. There are many reasons for this, ranging from deliberate obfuscation through to the long and detailed nature of the documents. For example, a typical small molecule patent may contain extensive background information relating to the target biology and disease area, chemical synthesis information, biological assay protocols and pharmacological measurements (which may refer to endogenous substances, existing therapies, reaction intermediates, reagents and reference compounds), in addition to description of the claimed compounds themselves.  The SureChEMBL system extracts this chemical information from patent documents through recognition of chemical names, conversion of images and extraction of attached files, and allows patents to be searched for chemical structures of interest. However, the curren

Julia meets RDKit

Julia is a young programming language that is getting some traction in the scientific community. It is a dynamically typed, memory safe and high performance JIT compiled language that was designed to replace languages such as Matlab, R and Python. We've been keeping an an eye on it for a while but we were missing something... yes, RDKit! Fortunately, Greg very recently added the MinimalLib CFFI interface to the RDKit repertoire. This is nothing else than a C API that makes it very easy to call RDKit from almost any programming language. More information about the MinimalLib is available directly from the source . The existence of this MinimalLib CFFI interface meant that we no longer had an excuse to not give it a go! First, we added a BinaryBuilder recipe for building RDKit's MinimalLib into Julia's Yggdrasil repository (thanks Mosè for reviewing!). The recipe builds and automatically uploads the library to Julia's general package registry. The build currently targe

New Drug Warnings Browser

As mentioned in the announcement post of  ChEMBL 29 , a new Drug Warnings Browser has been created. This is an updated version of the entity browsers in ChEMBL ( Compounds , Targets , Activities , etc). It contains new features that will be tried out with the Drug Warnings and will be applied to the other entities gradually. The new features of the Drug Warnings Browser are described below. More visible buttons to link to other entities This functionality is already available in the old entity browsers, but the button to use it is not easily recognised. In the new version, the buttons are more visible. By using those buttons, users can see the related activities, compounds, drugs, mechanisms of action and drug indications to the drug warnings selected. The page will take users to the corresponding entity browser with the items related to the ones selected, or to all the items in the dataset if the user didn’t select any. Additionally, the process of creating the join query is no