The International Conference on Engineering and Applied Sciences
February 18-20, 2016 at Peninsula Tower, Peninsula Excelsior Hotel, Singapore
Faculty of Biotechnology and Biomolecular Sciences
Universiti Putra Malaysia
Development of Cell-Based Assays for Measuring Hypoxia-Inducible Factor Activities
Cell-based assay systems have become increasingly popular in modern drug discovery. Currently, these assays are used for lead identification, dose optimization as well as target validation. The use of miniaturized high-throughput techniques makes these cell-based assays more efficient and cost-effective for drug screening. These assays are preferred over other in-vitro methods since they give direct cellular functional responses to the drug target of interest. This allows for clearer understanding of physiological and pharmacological responses to the target. Hypoxia inducible factor (HIF) has been shown to be involved in various cellular regulations from stem cell differentiation, proliferation, cellular apoptosis to cancer development. Discovery of its central roles in normal cell functions as well as disease development and progression has raised the need for a more robust cell-based assay system to measure HIF activities. Unfortunately, the currently available systems offer limited options. They are very costly with limited sensitivity. However, due to the lack of alternative options, researchers are confined with these assays and their limitations. In the present study, we developed a highly sensitive and robust cell-based assay system that can be used to evaluate HIF activities in drug discovery. A selected hypoxia response element of a HIF target gene was utilized to drive the expression of a firefly luciferase reporter gene. The correlation of luciferase expression and HIF activities was confirmed by examining their specificity, linearity, precision, accuracy, robustness and sensitivity in various experimental conditions. Findings from this study will contribute towards accelerating the screening process of HIF-related drug candidates leading to faster identification of new therapeutic drugs.