Skip to main content

New Drug Approvals 2011 - Pt. X Vandetanib (ZactimaTM)








ATC code: L01XE12

On the 6th April 2011, the FDA approved Vandetanib (trade name: ZactimaTM, ATC code: L01XE12, NDA 022405), a multi-kinase inhibitor, for the treatment of symptomatic or progressive medullary thyroid cancer in patients with unresectable locally advanced or metastatic disease. (medullary thyroid cancer; CRUK Thyroid cancer; ICD C73) Medullary thyroid cancer is a rare form of Thyroid cancer, but is associated with poorer prognosis. While the primary tumor can be successfully removed using surgery and radiotherapy, and thus can have a high 5 and 10 year survival rate (>90%), the metastatic disease remains challenging and is has a low 40% survival rate. Medullary thyroid cancer can be a sporadic or hereditary disease, and has complex underlying genetic causes. Approximately 25% of cases are associated with the RET (REarranged during Transfection) proto-oncogene. RET mutations cause Multiple Endocrine Neoplasia type 2 (MEN 2) which increases the risk of Thyroid cancer. (see OMIM for MEN 2A and MEN 2B)

Vandetanib (also known as ZD-6474 and Trade name:ZactimaTM) ( IUPAC:N-(4-bromo-2-fluorophenyl)-6-methoxy-7-[(1-methylpiperidin-4-yl)methoxy]quinazolin-4-amine); InChI:1S/C22H24BrFN4O2/c1-28-7-5-14(6-8-28)12-30-21-11-19-16(10-20(21)29-2)22(26-13-25-19)27-18-4-3-15(23)9-17(18)24/h3-4,9-11,13-14H,5-8,12H2,1-2H3,(H,25,26,27) SMILES:COc1cc2c(Nc3ccc(Br)cc3F)ncnc2cc1OCC4CCN(C)CC4 ChEMBL:24828; ) It has the molecular formula C22H24BrFN4O2 and has a molecular weight of 475.36. It has no chiral centres. Vandetanib contains an aminoquinazoline, a very common group within a large number of protein kinase inhibitors - this mimics the adenine ring of ATP.

Vandetanib has been issued with a black box warning because it can prolong QT interval (the time between the start of the Q wave and the end of the T wave in the heart's electrical cycle. A prolonged QT interval is a biomarker for ventricular tachyarrhythmias like torsades de pointes and a risk factor for sudden death.) For this reason, Vandetanib should not be used in patients with hypocalcemia, hypokalemia, hypomagnesemia, or long QT syndrome.

Vandetanib tablets for daily oral administration are available in two dosage strengths, 100 mg and 300 mg, containing 100 mg and 300 mg of vandetanib, respectively. The pharmacokinetics of vandetanib at the 300 mg dose in MTC patients are characterized by a mean clearance (Cl) of approximately 13.2 L/h, a mean volume of distribution of approximately 7450 L, and a median plasma half-life (T1/2) of 19 days.

Vandetanib has a broad activity profile, showing activity against multiple tyrosine kinases including RET (Uniprot: P07949; canSAR Target Synopsis) , EGFR (Uniprot: P00533; canSAR Target Synopsis), FGFR1 (Uniprot: P11362; canSAR Target Synopsis), FGFR2 (Uniprot: P21802; canSAR Target Synopsis), FGFR3 (Uniprot: P22607; canSAR Target Synopsis), and many others, all of which are members of the Protein Tyrosine Kinase family (PFAM:Pkinase_Tyr (PF07714)). RET mutations associated with medullary thyroid cancer include C634R germline mutation in exon 11 and an additional somatic mutation (at chromosomal position 164761.0012), but the efficacy of Vandetanib is independent of the mutation status of RET. A complex structure of Vandetanib bound to RET is available (PDB code: 2ivu @PDBe)

The prescribing information can be found here

Vandetanib is a product of AstraZeneca

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

Using ChEMBL activity comments

We’re sometimes asked what the ‘activity_comments’ in the ChEMBL database mean. In this Blog post, we’ll use aspirin as an example to explain some of the more common activity comments. First, let’s review the bioactivity data included in ChEMBL. We extract bioactivity data directly from   seven core medicinal chemistry journals . Some common activity types, such as IC50s, are standardised  to allow broad comparisons across assays; the standardised data can be found in the  standard_value ,  standard_relation  and  standard_units  fields. Original data is retained in the database downloads in the  value ,  relation  and  units  fields. However, we extract all data from a publication including non-numerical bioactivity and ADME data. In these cases, the activity comments may be populated during the ChEMBL extraction-curation process  in order to capture the author's  overall  conclusions . Similarly, for deposited datasets and subsets of other databases (e.g. DrugMatrix, PubChem), th