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| About Anamika Sarkar (Novartis) |
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Anamika Sarkar received her Batchelor and Masters degrees in Applied Mathematics from Delhi University (in 1990) and the Indian Institute of Technology Delhi (in 1992), respectively. She received her PhD, in 2000, also from IIT Delhi, in Applied Mathematics. Her PhD work was mainly focused on understanding the error in measuring the pressure drop and dispersion of a drug in a catheterized artery. She then moved to the USA and received her postdoctoral experiences at the Bioengineering Department, University of Washington, Seattle. During her stay in Seattle, her work was focused on understanding the cellular response to different stimuli, with special reference to immune cells, T cells, by modeling intracellular biochemical reactions. She the moved from west to east, to New York at the Memorial Sloan Kettering Center, Computational Biology in 2004. She stayed there for a year as a postdoc, where her research was centralized on understanding the link between apoptosis and mitochondria and ATP production/consumption. She then joined the Mt. Sinai School of Medicine, New York, Pharmacology and Systems Therapeutics in 2005 as an Assistant Scientist. While she was at Mt. Sinai, her work was focused on understanding possible methods by which Interferon induction (the first line of defense as antiviral response) can be delayed and reduced during infection. In June 2009, she took the opportunity to be part of the research and development of drug discovery by joining Novartis Pharmaceutical corporation as a Visiting Scientist.
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Disease Modeling and its Applications in Model-Based Drug R&D: The Example of an Integrated Model of Hypertension, Renal Disease Progression and Therapeutic Modulation of the Renin-Angiotensin-Aldosterone System (RAAS)
Anamika Sarkar, Melissa K. Hallow, Gabriel Helmlinger, Ramprasad Ramakrishna, Ramesh Sarangapani, and Anna Georgieva (M&S, Novartis Pharmaceuticals, NJ, USA)
Arthur Lo, Hector De Leon, Jeff Trimmer, Manoj Rodrigo, Jennifer Beh, Stuart Friedman, Sergey Ermakov (Entelos Inc., CA, USA)
Objective: We present a multiorgan systems approach to modeling blood-pressure (BP) regulation and end-organ protection offered by therapies aimed at modulating the RAAS.
Background: More than 50 million Americans and close to one billion people suffer from elevated BP. Clinical data have demonstrated a strong relationship between BP elevation and increases in cardiovascular and renal events, with severe risk factors among the elderly (JNC7). A disregulated RAAS is central to BP regulation, sodium and water balance, and hypertension development .
Methods: We used a deterministic approach to model pathophysiological mechanisms of hypertension and their impact on end-organ status, based on literature and clinical observations. The RAAS hypertension model features a modular design, built using a top-down approach, with four key interconnected modules: systemic, renal, cardiovascular and nervous systems. The systemic module, for example, captures a systems-level RAAS pathway and its interactions with sodium/water regulation to achieve long-term BP control. The renal module includes fluid dynamics processes controlling glomerular filtration rate (GFR) and also represents kidney as an assembly of single nephrons linked to the glomerular and tubular RAAS pathways.
Results: Here, we present some key features of the first generation multiscale RAAS model. We briefly report on model parameterization using literature data, as well as model-based simulations of systemic and renal biomarkers in select patient phenotypes following treatment with angiotensin-converting-enzyme inhibitors (ACEI), angiotensin-receptor blockers (ARB) and calcium channel blockers (CCB).
Conclusion: Our results demonstrate that the RAAS hypertension platform is a valuable tool at 1) elucidating multi-faceted disease progression patterns in various hypertensive patient phenotypes, and 2) characterizing the effect of various pharmacological options to treat hypertension. These features illustrate that systematic disease modeling is a powerful instrument to guide clinical drug development.
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