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Cell Biochemistry Martinsried |
(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.
3. Non Medical Data Classification
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