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| First Name: | Claus A. | | Last Name: | Andersen | | Title: | Principal Scientist | | Advanced Degrees: | M.Sci.Eng. Bioinf, PhD | | Affiliation: | Siena Biotech S.p.A. | | Department: | Molecular Informatics | | Street Address 1: | Strada del Petriccio e Belriguardo 35 | | City: | Siena | | State/Province: | Tuscany | | Zip/Postal Code: | 53100 | Country/Territory: | Italy | | Phone: | +39 0577 381 457 | | Fax: | +39 0577 381 303 | | Email Address: |  |
Disclosure:
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Member reports the following financial or other potential conflicts of interest: [Last Modified: 13 March 2009]
I am working for a biotech developing drugs to cure Alzheimer's Disease.
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Neuropathology, Apoptosis/Cell cycle, Protein structure/chemistry, Proteomics, Tau/Cytoskeleton, Bioinformatics/Statistics, Brain imaging, Oxidative Stress, Signal transduction, DNA microarrays, A-beta PP/A-beta, Animal Models, Molecular and Cell biology, Neurobiology, Chemistry/Pharmacology
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Working in cross-disciplinary groups with molecular biologists, neurobiologists, biochemists and lab technicians to analyse and interpret their data and relevant public data. This may be transcriptomics, proteomics, bioassay readouts or phenotypic results.
Special focus on the construction of biomolecular pathways within biotech, where disparate public as well as in-house data sources are brought together to find new drugs or bioproduction processes (systems biology). Furthermore prediction of compound related characteristics relevant for pharmacology.
Specialties:Analysing and interpreting data arriving from biological experiments. Working on drug discovery projects in the early target, hit and lead identification phases. Bringing biology, chemistry, pharmacology, statistics/algorithms and informatics together to make biotech discoveries more efficient and effective.
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PMID: 18571100
PMID: 12079362
PMID: 11839303
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Having good disease models (in-vivo and in-vitro), which are predictive of the human setting. The disease models also need to be "feasible" i.e. drugs curing neurodegenerative diseases are very difficult to develop unless they can be optimized early on in the drug discovery process. |
PMID: 15915152
PMID: 17054791
PMID: 19109536 |
Development of new disease models (ranging from very feasible to very predictive) which can be used to study the disease. Predictiveness of a disease model should be assessed based on a panel of biomarkers (transcriptomics/proteomics/metabolomics) and capture the complexity of the biological patho-physiology. The biomarker panel should be selected such that disease and healthy can be clearly and robustly separated. Furthermore the biomarker panel should ideally be such that if a compound can revert the readout from diseased to healthy, the disease should be cured. |
For AD that the disease can be caused by mutations in APP (e.g. Swedish mutation) and PSEN1. Exactly how neuronal dysfunction comes about is heavily debated, but can only be properly addressed once predictive disease models are developed.
Presently using post-mortem material you only have a look at the "accident" (Alzheimers) 10-15 years
after it actually happened. |
With the latest sequencing technology (e.g. www.completegenomics.com or www.pacificbiosciences.com) sequencing thousands of individuals completely is no longer unfeasible.
It is not unlikely that sporadic AD to a large extent is not genetically driven, but mainly determined by the environment. But by determining the genetic predispositions the main players can be individualized. Furthermore the timing aspect (i.e. the probable early disease onset years before measurable mental deficits can be measured) make it such that AD at present is difficult to study in any other way.
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If sporadic AD is completely environmentally driven, then you would need to collect data on the environment from large population groups to enable the extraction of disease correlations. With the advent of the internet this could actually be made possible as a community effort if a central cite (e.g. Alzforum) could provide the infrastructure and harmonization needed to collect personal data locally and make it comparable across studies. Setting aside the legal aspects I am convinced that many relatives to AD patients would be willing and interested in participating to such initiatives. |
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