Cell Biochemistry Martinsried

Predictive Medicine by Cytomics

(Evidence Based Medicine at the Cellular Level)

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    1. Potential and Challenges
    - Predictive Medicine by cytomics (molecular cell systems research) represents a new concept for the correct prediction of future disease course in individual patients by data pattern profiles with an accuracy of >95%, permitting the pretherapeutic identification of risk patient or individualized pretherapeutic risk assessments.
    - Individualised or personalised disease course predictions by cytomics are dynamic because of therapy dependance. Patients with prediction for "disease aggravation" may convert under therapy within some time into "no complication" patients such as e.g. in intensive care medicine. The early detection of disease aggravation or amelioration provides a lead time for preventive therapy onset or therapy reduction.
    - The lead time may increase overall therapeutic efficiency by reduction of irreversible tissue damage or unwanted therapeutic side effects and adverse drug reactions (ADRs). Scientific feedback is obtained by the sequential monitoring of therapy associated molecular alterations in disease associated cellular systems like e.g. granulo- and monocytes in sepsis or remission cells in leukemias.
    - The use of molecular alterations in disease associated cellular systems (cytomes) by flow cytometry or other single cell oriented methods makes this approach to a substantial degree independent of the exact knowledge on the ultimate molecular cause of disease. This facilitates disease course predictions in complex malignant, infectious, inflammatory, metabolic or degenerative diseases.
    - New hypotheses on disease generation may be developed by the interpretation of the predictive molecular data patterns (e.g. overtraining syndrome) thus providing better access to the molecular causes of complex diseases. This bottom-up molecular reverse engineering strategy like analysis of the apparent molecular phenotype of all cell types in cytomes resulting from genotype and exposure takes advantage of deductive experimental hypothesis for data generation, inductive evaluation of the entire collected multiparameter information by data sieving (CLASSIF1) or data mining followed by deductive result interpretation, modelling of predictive parameter or by additional rounds of experimentation and data sieving.
    - The potential of the concept consists in its general applicability in various areas of clinical or ambulant medicine. This is illustrated below by a number of collaborative projects with individual hospitals and institutions as well as within the framework of the European Working Group on Clinical Cell Analysis ( EWGCCA) in the context of clinical cytomics.
    - The evident challenge is to advance this effort to the patient level in a multistep effort of scientists, clinicians and industry for example by recent the efforts to conceptualize a human cytome project.

    2. Individual Patient Disease Course Prediction and Diagnosis (Medical Cytomics, Clinical Cytomics)

    3. Non Medical Data Classification

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    2004 G.Valet
    Max-Planck-Institut für Biochemie, Am Klopferspitz 18a, D-82152 Martinsried, Germany, Tel: +49/89/8578-2518, -2525, Fax: +49/89/8578-2563, INTERNET: http://www.biochem.mpg.de/valet/cellbio.html
    Last Update: Apr.10,2004