Article

Anti-proliferative and anti-angiogenic effects of CB2R agonist (JWH-133) in non-small lung cancer cells (A549) and human umbilical vein endothelial cells: an in vitro investigation.

Department of Pharmacology, Pavol Jozef Šafárik University, Košice, Slovak Republic.
Folia biologica (Impact Factor: 1.22). 01/2012; 58(2):75-80.
Source: PubMed

ABSTRACT Non-small cell lung cancer has one of the highest mortality rates among cancer-suffering patients. It is well known that the unwanted psychotropic effects of cannabinoids (CBs) are mediated via the CB(1) receptor (R), and selective targeting of the CB(2)R would thus avoid side effects in cancer treatment. Therefore, the aim of our study was to evaluate the effect of selective CB(2)R agonist, JWH-133, on A549 cells (non-small lung cancer) and human umbilical vein endothelial cells (HUVECs). Cytotoxicity assay and DNA fragmentation assay were employed to evaluate the influence of JWH-133 (3-(1,1-dimethylbutyl)- 1-deoxy-Δ8-tetrahydrocannabinol) on investigated cancer cells. In addition, migration assay and gelatinase zymography were performed in HUVECs to asses JWH-133 anti-angiogenic activity. Our study showed that JWH-133 exerted cytotoxic effect only at the highest concentration used (10(-4) mol/l), while inhibition of colony formation was also detected at the non-toxic concentrations (10(-5)-10(-8) mol/l). JWH-133 was also found to be able to induce weak DNA fragmentation in A549 cells. Furthermore, JWH-133 at non-toxic concentrations inhibited some steps in the process of angiogenesis. It significantly inhibited endothelial cell migration after 17 h of incubation at concentrations of 10(-4)-10(-6) mol/l. In addition, JWH-133 inhibited MMP-2 secretion as assessed by gelatinase zymography. The present study demonstrates the in vitro anti-proliferative and anti-angiogenic potential of CB(2)R agonist, JWH-133, in nonsmall lung cancer cells and HUVECs. Our results generate a rationale for further in vivo efficacy studies with this compound in preclinical cancer models.

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