PR-104 is a selective hypoxia-activated DNA cross-linking agent and can be used for the research of multiple tumor xenograft models. PR-104, as a nitrogen mustard pre-proagent, is converted efficiently to the more lipophilic dinitrobenzamide mustards alco
Storage Temp
Protected from light,Argon charged,Store at -80°C
Shipped In
Ice chest + Ice pads
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Product Description
PR-104 is a selective hypoxia-activated DNA cross-linking agent and can be used for the research of multiple tumor xenograft models. PR-104, as a nitrogen mustard pre-proagent, is converted efficiently to the more lipophilic dinitrobenzamide mustards alcohol PR-104A.
In Vitro
PR-104 (80 μM; 1 hour; SiHa cells) shows greater suppression of radiation-induced DNA single-strand breaks under hypoxic than aerobic conditions. PR-104 (100 μM; 1 hour; SiHa cells) results in phosphorylation of Ser139 of histone H2AX (gH2AX). PR-104 (0.266 mmol/kg; 18 h; SiHa cells) shows activity against hypoxic cells after irradiation. PR-104 varies in potency between cell lines, with the lowest IC 50 (0.51 μmol/L) in H460 cells and highest (7.3 μmol/L) in PC3 prostate cells. MCE has not independently confirmed the accuracy of these methods. They are for reference only.
In Vivo
PR-104 (0.56 mmol/kg; i.v. or i.p.; 0~2 hours) makes the plasma area under the curve. PR-104 (0.23 mmol/kg; i.p.; 100 days) shows antitumor activity . MCE has not independently confirmed the accuracy of these methods. They are for reference only. Animal Model: CD-1nu/nu mice Dosage: 0.56 mmol/kg (Pharmacokinetics Analysis) Administration: I.v. or i.p. Result: The plasma area under the curve. Animal Model: CD1-Foxn1nu mice Dosage: 0.23 mmol/kg Administration: I.p. Result: Showed antitumor activity.
This compound belongs to the class of organic compounds known as dinitroanilines. These are organic compounds containing an aniline moiety, which is substituted at 2 positions by a nitro group.
External Descriptors
Not available
1. Djoumbou Feunang Y, Eisner R, Knox C, Chepelev L, Hastings J, Owen G, Fahy E, Steinbeck C, Subramanian S, Bolton E, Greiner R, and Wishart DS. ClassyFire: Automated Chemical Classification With A Comprehensive, Computable Taxonomy. Journal of Cheminformatics, 2016, 8:61.
