High-Content Analysis in Preclinical Drug Discovery

Philip      Denner; Janine      Schmalowsky; Stefan      Prechtl

Abstract

High-Content Analysis (HCA) has developed into an established tool and is used in a wide range of academic laboratories and pharmaceutical research groups. HCA is now routinely proving to be effective in providing functionally relevant results. It is essential to select the appropriate HCA application with regard to the targeted compounds cellular function. The cellular impact and compound specificity as revealed by HCA analysis facilitates reaching definitive conclusions at an early stage in the drug discovery process. This technology therefore has the potential to substantially improve the efficiency of pharmaceutical research. Recent advances in fluorescent probes have significantly boosted the success of HCA. Auto-fluorescent proteins which minimally hinder the functioning of the living cell have been playing a decisive role in cell biology research. For companies the severely restricted license conditions regarding auto-fluorescent proteins hamper their general use in pharmaceutical research. This has opened the field for other solutions such as selflabeling protein technology, which could potentially replace the well established methods that utilize auto-fluorescent proteins. In addition, direct labeling techniques have improved considerably and may supersede many of the approaches based on fusion proteins. Following sample preparation, treated cells are imaged and the resulting multiple fluorescent signals are subjected to contextual and statistical analysis. The extraordinary advantage of HCA is that it enables the large-scale and simultaneous quantification and correlation of multiple phenotypic responses and physiological reactions using sophisticated software solutions that permit assay-specific image analysis. Hence, HCA once more has demonstrated its outstanding potential to significantly support establishing effective pharmaceutical research processes in order to both advance research projects and cut costs.

Keywords: High-content analysis, high-content screening, target discovery, target validation, lead discovery, lead optimization, functional pathway analysis, image analysis algorithms