Effective analysis of biomedical big data for the optimization of drugs for rare and...
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Transcript of Effective analysis of biomedical big data for the optimization of drugs for rare and...
effective analysis of biomedical BIG DATA the optimization of drugs for rare and
neurodegenerative diseases, and cancer
FINISTERRAE
FINISTERRAE Big Data for the
optimization of drugs for rare and neurodegenerative
diseases, and Cancer.
“This generation has a historic opportunity and responsibility to transform medicine by
using systematic approaches in the biological sciences to dramatically accelerate the understanding and
treatment of disease” The Eli and Edythe L. Broad Institute
Of MIT and Harvard
FINISTERRAE means “the end of the
world”. In our case, it is referred to genomic and
biomedical BIG DATA principles with
translational means. Our goal is to make big data
smaller, completely reducing its complex
nature.
... making it easier to find new
therapeutic targets and orphan drugs for RARE DISEASES.
...and also in neurodegenerative
diseases (Parkinson,
Alzheimer, ALS,...) and Cancer.
What’s more, we want to make early diagnosis of various types of CANCER more accurate, as well as make the optimization of personalized treatment possible (precision translational medicine)
Also, the FINISTERRAE platform will
contribute to the OPTIMIZATION OF
MEDICAL DECISIONS and the providing
additional value to
hospital data.
Regarding drug optimization, SECONDARY EFFECTS MINIMIZATION would be very interesting so that patients do not have to go through unnecessary suffering.
LET’S TALK NUMBERS
7000: number of RARE DISEASES that affect over 30 million people in the USA and some more in Europe. Over the past
few years, small pharmaceutical companies have
launched various dozens of drugs.
NEURODEGENERATIVE DISEASES are one of the biggest issues of today’s
society. By itself, Alzheimer affects more
than 7 million Europeans, and it is thought that this
number will be duplicated in less than
20 years.
Despite all the billions of dollars invested in
CANCER investigation, the obtained results for
preventing it and for carrying out advanced treatments are highly
disappointing.
Only 500 out of 10.000 drugs found in
investigation period pass on to preclinical
period, and from there only 1 of them goes on
to clinical use. www.acrohealth.org
BREAST CANCER
MAMMAPRINT ONCOTYPE
MAMMOSTRAT are really expensive,
although trustworthy tests
possible metastasis prediction during
diagnosis
The goal is to come up with simple, solid, trustworthy and non-expensive methods to ameliorate diagnosis and Breast Cancer treatment, significantly reducing mortality.
And this methodology can also be applied to
other types of CANCER:
COLORECTAL, LUNG,
PROSTATE, PANCREAS…
We model biomedical data and we obtain information
that would be impossible to find using traditional
methods. We are opened for proofs of concepts to show
our capabilities.
We work using a win-win methodology.
S O M E F I N I S T E R R A E ’ S
R E F E R E N C E S
S O M E F I N I S T E R R A E ’ S
R E S U LT S
CORRELATION NETWORK for
CHRONIC LYMPHOCITIC LEUKEMIA (CLL)
Main Mutations in CLL and how they impact expression
CORRELATION NETWORK for MYOSITIS
CORRELATION NETWORK for ALS
• We have found very innovative results about the disease
mechanisms in Alzheimer, Parkinson, ALS and Multiple
Sclerosis, for instance. • We are modelling different rare
diseases that have genetic data available in public data
repositories. • We are able to fusion different
kind of data for Treatment Optimization.
Ignacio Fdez.-Alberti IIRR and Corporate Communications
Juan Luis Fdez.-Martínez
Professor in Applied Mathematics.
CTO.
Zulima Fernández-Muñiz
PhD in Applied Mathematics.
Ana Cernea-Cobernau PhD in Applied Mathematics.
Enrique J. De Andrés Galiana
PhD in Applied Mathematics.
The TEAM
Juan Luis Fernández Martínez
Inverse Problems Group [email protected]
+34 682202198
Ignacio Fdez. Alberti Salud Social Media
CONTACT TO DISCOVER NEW OPPORTUNITIES