Design and Synthesis of 1,2-Bis(hydroxymethyl)pyrrolo[2,1- a]phthalazine Hybrids as Potent Anticancer Agents that Inhibit Angiogenesis and Induce DNA Interstrand Cross-links.
Sue-Ming ChangVicky JainTai-Lin ChenAnilkumar S PatelHima Bindu PiduguYi-Wen LinMing-Hsi WuJiao-Ren HuangHan-Chung WuAnamik ShahTsann-Long SuTe-Chang LeePublished in: Journal of medicinal chemistry (2019)
Hybrid molecules are composed of two pharmacophores with different biological activities. Here, we conjugated phthalazine moieties (antiangiogenetic pharmacophore) and bis(hydroxymethyl)pyrrole moieties (DNA cross-linking agent) to form a series of bis(hydroxymethyl)pyrrolo[2,1- a]phthalazine hybrids. These conjugates were cytotoxic to a variety of cancer cell lines by inducing DNA damage, arresting cell cycle progression at the G2/M phase, triggering apoptosis, and inhibiting vascular endothelial growth factor receptor 2 (VEGFR-2) in endothelial cells. Among them, compound 29d encapsulated in a liposomal formulation (e.g., 29dL) significantly suppressed the growth of small-cell lung cancer cell (H526) xenografts in mice. Based on immunohistochemical staining, the tumor xenografts in mice treated with 29dL showed time-dependent decreases in the intensity of CD31, a marker of blood vessels, whereas the intensity of γ-H2AX, a marker of DNA damage, increased. The present data revealed that the conjugation of antiangiogenic and DNA-damaging agents can generate potential hybrid agents for cancer treatment.
Keyphrases
- vascular endothelial growth factor
- dna damage
- endothelial cells
- cell cycle
- circulating tumor
- oxidative stress
- cell free
- single molecule
- ionic liquid
- single cell
- cell proliferation
- dna repair
- high fat diet induced
- high intensity
- photodynamic therapy
- nucleic acid
- cell death
- molecular dynamics
- papillary thyroid
- circulating tumor cells
- endoplasmic reticulum stress
- adipose tissue
- young adults
- mesenchymal stem cells
- type diabetes
- machine learning
- newly diagnosed
- cancer therapy
- nk cells
- deep learning