Apcin-A - CAS 1683617-62-0

Apcin-A is an anaphase-promoting complex (APC) inhibitor. Apcin-A interacts strongly with Cdc20, and inhibits the ubiquitination of Cdc20 substrates.

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Molecular Formula
C10H14Cl3N5O2
Molecular Weight
342.61

Apcin-A

    • Specification
      • IUPAC Name
        3-aminopropyl N-[2,2,2-trichloro-1-(pyrimidin-2-ylamino)ethyl]carbamate
        Synonyms
        Apcin-A; 1683617-62-0; 3-Aminopropyl (2,2,2-trichloro-1-(pyrimidin-2-ylamino)ethyl)carbamate; starbld0000888; SCHEMBL22567019; EX-A5236; AKOS030257613; MS-25252; HY-130841; CS-0114386
    • Properties
      • InChI Key
        JQTSJVDIFMKETH-UHFFFAOYSA-N
        InChI
        InChI=1S/C10H14Cl3N5O2/c11-10(12,13)7(17-8-15-4-2-5-16-8)18-9(19)20-6-1-3-14/h2,4-5,7H,1,3,6,14H2,(H,18,19)(H,15,16,17)
        Canonical SMILES
        C1=CN=C(N=C1)NC(C(Cl)(Cl)Cl)NC(=O)OCCCN
    • Reference Reading
      • 1. Consensus guidelines for the use and interpretation of angiogenesis assays
        Patrycja Nowak-Sliwinska, Kari Alitalo, Elizabeth Allen, et al. Angiogenesis. 2018 Aug;21(3):425-532.doi: 10.1007/s10456-018-9613-x.
        The formation of new blood vessels, or angiogenesis, is a complex process that plays important roles in growth and development, tissue and organ regeneration, as well as numerous pathological conditions. Angiogenesis undergoes multiple discrete steps that can be individually evaluated and quantified by a large number of bioassays. These independent assessments hold advantages but also have limitations. This article describes in vivo, ex vivo, and in vitro bioassays that are available for the evaluation of angiogenesis and highlights critical aspects that are relevant for their execution and proper interpretation. As such, this collaborative work is the first edition of consensus guidelines on angiogenesis bioassays to serve for current and future reference.
        2. m6A mRNA demethylase FTO regulates melanoma tumorigenicity and response to anti-PD-1 blockade
        Seungwon Yang, Jiangbo Wei, Yan-Hong Cui, Gayoung Park, Palak Shah, Yu Deng, Andrew E Aplin, Zhike Lu, Seungmin Hwang, Chuan He, Yu-Ying He0 Nat Commun. 2019 Jun 25;10(1):2782.doi: 10.1038/s41467-019-10669-0.
        Melanoma is one of the most deadly and therapy-resistant cancers. Here we show that N6-methyladenosine (m6A) mRNA demethylation by fat mass and obesity-associated protein (FTO) increases melanoma growth and decreases response to anti-PD-1 blockade immunotherapy. FTO level is increased in human melanoma and enhances melanoma tumorigenesis in mice. FTO is induced by metabolic starvation stress through the autophagy and NF-κB pathway. Knockdown of FTO increases m6A methylation in the critical protumorigenic melanoma cell-intrinsic genes including PD-1 (PDCD1), CXCR4, and SOX10, leading to increased RNA decay through the m6A reader YTHDF2. Knockdown of FTO sensitizes melanoma cells to interferon gamma (IFNγ) and sensitizes melanoma to anti-PD-1 treatment in mice, depending on adaptive immunity. Our findings demonstrate a crucial role of FTO as an m6A demethylase in promoting melanoma tumorigenesis and anti-PD-1 resistance, and suggest that the combination of FTO inhibition with anti-PD-1 blockade may reduce the resistance to immunotherapy in melanoma.
        3. Melanoma models for the next generation of therapies
        E Elizabeth Patton, Kristen L Mueller, David J Adams, et al. Cancer Cell. 2021 May 10;39(5):610-631.doi: 10.1016/j.ccell.2021.01.011.Epub 2021 Feb 4.
        There is a lack of appropriate melanoma models that can be used to evaluate the efficacy of novel therapeutic modalities. Here, we discuss the current state of the art of melanoma models including genetically engineered mouse, patient-derived xenograft, zebrafish, and ex vivo and in vitro models. We also identify five major challenges that can be addressed using such models, including metastasis and tumor dormancy, drug resistance, the melanoma immune response, and the impact of aging and environmental exposures on melanoma progression and drug resistance. Additionally, we discuss the opportunity for building models for rare subtypes of melanomas, which represent an unmet critical need. Finally, we identify key recommendations for melanoma models that may improve accuracy of preclinical testing and predict efficacy in clinical trials, to help usher in the next generation of melanoma therapies.
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Concentration (start) x Volume (start) = Concentration (final) x Volume (final)
It is commonly abbreviated as: C1V1 = C2V2

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Tip: Chemical formula is case sensitive. C22H30N4O c22h30n40
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