Current Medicinal Chemistry, 2010, 17, 1719-1729 1719 0929-8673/10 $55.00+.00 © 2010 Bentham Science Publishers Ltd. Potential of a Cytomics Top-Down Strategy for Drug Discovery A. Tárnok* ,1 , A. Pierzchalski 2 and G. Valet 3 1 Dept. Paediatric Cardiology, Cardiac Centre, University Leipzig, Germany 2 Translational Center for Regenerative Medicine, Leipzig, Germany 3 Max-Planck-Institut für Biochemie, Martinsried, Germany Abstract: It takes about 10 to 15 years and roughly 800 mln $ to bring a new drug to the market. Only 10% of drug mole- cules 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 cy- tomics phase. The cytomics approach i) requires no detailed a-priori knowledge on mechanisms of drug activity or com- plex 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 interrela- tions 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 com- prise 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 mo- lecular 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. A. THE PROBLEM – WHERE WE ARE IN THE DRUG DISCOVERY PROCESS Pharmaceutical and biotech companies try to develop new drugs that have a high chance to reach the market and to fund their research. The current disease models used in drug discovery and preclinical development have difficulties to predict failure in drug development (clinical phase I to III and IV) in 80–90% of drugs entering clinical trials. It requires about 10 to 15 years and between US$ 500– 800 million to bring a new drug to the market [1, 2]. Only a 10% overall success rate of drug molecules entering clinical trials [3] is typically reached. In addition, only 3 out of 10 drugs generate enough profit to pay back for the investment [4]. One of the reasons for this is that currently used disease models show a correlation deficit to clinical reality, because of the underestimation of the complexity and variability of clinical disease processes in man [5]. To improve the overall efficiency and profitability new technologies and parameter screening approaches supporting drug discovery and devel- opment are being introduced in the following. The emerging potential to gain detailed quantitative data from biological specimens has become increasingly impor- tant in the new fields of high-content and high-throughput single-cell analysis for systems biology and cytomics [6, 7]. Genomics, proteomics and metabolomics provide important technical contributions to cell biology but become limited when single or scarce cells are examined or fast cellular processes have to be followed kinetically [8]. *Address correspondence to this author at the Dept. Paediatric Cardiology, Heart Centre, University Leipzig, Strümpellstr. 39, 04289 Leipzig, Germany; Tel: +49-341-8652430; Fax: +49-341-8651143; E-mail: tarnok@medizin.uni-leipzig.de B. CYTOMICS It is important to keep in mind that single cells constitute the elementary building units of living organisms. Cytomics as bioinformatic knowledge extraction from molecular cell phenotype analysis of many single cells of cell systems (cy- tomes), tissues, organs, and organisms by image or flow cy- tometry [9] provides a new potential to unwind the cellular biocomplexity of organisms starting from the cellular level. In analogy with other -omics like genome/genomics, pro- teome/proteomics, metabolome/metabolomics, the scope of cytome/cytomics concerns heterogeneous cellular systems. Cytomics is the broadest approach of any cell-based -omics and harbors among others (cellular) metabolomics, lipidom- ics, location proteomics and toponomics [6]. The functional heterogeneity of cytomes results from both the genome and external environmental influences. Cytomics can be consid- ered as a discipline that links genomics and proteomics to cell and tissue phenotype and function, as modulated by ex- ternal influences. Of special importance is the cell-by-cell basis of cytomics analysis. This approach allows resolving heterogeneous systems by avoiding the loss of information that characterizes bulk technologies where average values are obtained from large number of cells or from tissue ho- mogenates [9]. The cytomics approach reflects the reality that cells and their inter-relationship and not genes or bio- molecules represent the elementary function units of organ- isms. Typical cytomes are the system of leukocytes in the pe- ripheral blood or the cell system of an organ. For drug dis- covery such cytomes are difficult to obtain in large entities. That is why appropriate cellular models like hepatocytes are being introduced and are treated as representative cell sys-