Skip to main content

New Drug Approvals 2011 - Pt. IX Ipilimumab (YervoyTM)



ATC code:L01XC11

On 25th March 2011, the FDA approved Ipilimumab (trade name YervoyTM, ATC code:L01XC11), a human cytotoxic T-lymphocyte antigen 4 (CTLA-4)-blocking antibody indicated for the treatment of unresectable or metastatic melanoma. (MelanomaCRUK melanoma; OMIM: 155600; ICD: C43).

Malignant melanoma is diagnosed in an estimated 160,000 new patients each year and, despite being less common than other skin neoplasms, it is responsible for 75% of skin cancer-related deaths. Current available treatment options for melanoma are limited to surgery, chemotherapy, radiotherapy and immunotherapy, although there are a number of targetted agents in the clinical development at the moment. Ipilimumab effect in melanoma is indirect and probably due to enabling a T-cell mediated immune response

In a randomised clinical study that assessed the response of unresectable or metastatic melanoma patients to Ipilimumab, alone and in combination with investigational peptide vaccine adjuvant, gp100, the combination showed increased survival time (median survival of 10 months, compared with 6.4 months for patients receiving the vaccine alone) as well as a near doubling of the rates of survival at 12 months (46% vs 25%) and 24 months (24% vs 14%) as compared to the peptide alone.


Ipilimumab's molecular target is CTLA-4 (Uniprot: P16410canSAR ; PFAM: P16410), a negative regulator of T-cell activation. Ipilimumab augments T-cell activation and proliferation by binding to CTLA-4 and preventing its interaction with its ligands (CD80 and CD86). CTLA-4 is a membrane-bound, 223 amino acid long, T-cell protein. It contains an immunoglobulin V-type domain (PFAM:PF07686). The structure of CTLA-4 is determined (see e.g. PDBe:3osk)Ipilimumab has been issued with a Black Box warning as it can result in severe and fatal immune-mediated adverse reactions due to T-cell activation and proliferation, particularly enterocolitis, hepatitis, dermatitis (including toxic epidermal necrolysis), neuropathy, and endocrinopathy.


Ipilimumab is administered intravenously, and the recommended dose is 3 mg/kg administered over 90 minutes every 3 weeks for a total of four doses. The terminal half-life (t1/2) is 14.7 days (30.1%); systemic clearance (CL) is 15.3 mL/h (38.5%); and volume of distribution at steady-state (Vss) is 7.21 L (10.5%). 


The full prescribing information can be found hereYervoy™ is a product of Bristol-Myers Squibb


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

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

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

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