It takes about 10 to 15 years and roughly 800 mln $ to bring a new drug to the market. Only 10% of drug molecules entering clinical trials succeed and only 3 out of 10 drugs generate enough profit to pay back for the investment. Drug targets may be searched by hypothesis driven modeling of molecular networks within and between cells by systems biology. However, there is the potential to simplify the search for new drugs and drug targets by an initial top-down cytomics phase. The cytomics approach i) requires no detailed a-priori knowledge on mechanisms of drug activity or complex diseases, ii) is hypothesis driven for the investigated parameters (genome, transcriptome, proteome, metabolome a.o.) and iii) is hypothesis-free for data analysis. Moreover it iv) carries the potential to uncover unknown molecular interrelations as a prerequisite for later new hypothesis driven modeling and research strategies. A set of discriminatory parameter patterns (molecular hotspots) describing the cellular model (mechanism of drug action) can be identified by differential molecular cell phenotyping. Hereby, the immediate modeling of existing complexities by bottom-up oriented systems biology is avoided. The review focuses on the fast technological developments of molecular single cell analysis in recent years. They comprise a multitude of sensitive new molecular markers as well as various new image and flow cytometric high-content screening methods as facilitators of the cytomics concept. New bioinformatic tools enable the extraction of relevant molecular hotspots in description of cellular models, being required for the subsequent molecular reverse engineering phase by systems biology.
Keywords: Cytomics, drug discovery, high-content analysis, data mining, high- throughput screening, cytometry
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