Entelos Announces Expanded Research with Pfizer

FOSTER CITY, Calif., November 13, 2007 – Entelos, Inc. (LSE:ENTL) a leading life sciences company building predictive computer models of human physiology and "virtual patients" for drug discovery and development, announced today that Pfizer, Inc. (“Pfizer”, NYSE:PFE) has signed an agreement with Entelos to expand and continue research using the Entelos® Cardiovascular PhysioLab® platform. The expanded research will focus on identifying biomarkers and interpretation of clinical study data.  Financial terms are not disclosed.

The Entelos Cardiovascular PhysioLab platform is an innovative and predictive computer model that simulates patients and drug effects, and represents key biological mechanisms and disease processes that can help to explain the underlying causes of heart attacks, strokes, and other serious cardiovascular conditions in multiple patient types.

The platform mathematically represents the relative contribution of circulating lipids, systemic inflammation and local vessel inflammation to the progression of atherosclerosis, a key driver leading to heart disease. Simulations test the effects of specific therapies on “virtual patients” by computing changes in plaque geometry and composition that can then be used to predict the long-term risk of a major cardiovascular event for each patient type. The predictive capabilities of the platform have been rigorously validated by demonstrating consistency with a wide range of published clinical trial results.

Entelos has supported a variety of preclinical and clinical research in prior work conducted with Pfizer over multiple stages.

“We believe that our cardiovascular disease platform can help Pfizer gain even greater value from the tremendous amount of information available in their historical clinical trial databases,” commented James Karis, president and CEO of Entelos. “Rather than just 'data mining', our powerful, engineering-based, dynamic simulation models of human physiology can do a much better job of identifying which patients would benefit most from a specific treatment. More importantly, we can often explain why certain patients respond better than others, enabling not only better patient care, but helping to keep effective drugs on the market that are safe for a targeted subpopulation. We look forward to continuing our work with Pfizer and to conducting research on their highest priority projects in cardiovascular disease, cholesterol regulation, and atherosclerosis.”

For further information please contact:

Entelos, Inc.
  
James Karis, President and CEO

Jill Fujisaki, VP Investor Relations

 
 Tel: +1 650 572 5400
 
Evolution Securities
 
 
Bobbie Hilliam, Associate Director

 
 Tel +44 (0) 20 7071 4300
 
Buchanan Communications
 
 
Lisa Baderoon / Mary-Jane Johnson
 Tel +44 (0) 20 7466 5000
 

 

Notes for Editors

About Entelos

Entelos, Inc. (www.entelos.com) is a US-based life sciences company applying next-generation predictive technologies to revolutionize the way medicines are discovered, developed, and utilized. The Company leverages its proprietary in silico disease models, “virtual patients”, and toxicology reference systems to develop safer and more effective drugs and support pharmaceutical R&D and commercialization. In addition to internal drug programs in rheumatoid arthritis and women's health, Entelos provides customized technology and research services to global pharmaceutical and health-care companies in cardiovascular diseases, asthma, obesity, diabetes, hematopoeisis (anemia), cholesterol metabolism, and skin sensitization. The company is also developing a model in oncology and is collaborating with the FDA to build a model of drug-induced liver injury. Entelos offers cost-effective drug development capabilities through its strategic alliance with India-based Jubilant Biosys.

 

About Entelos® Cardiovascular PhysioLab® Platform

Entelos® Cardiovascular PhysioLab® platform is a large-scale mathematical model of human physiology that supports research in the area of cardiovascular disease. The platform includes the physiology and pathophysiology of cholesterol metabolism, atherosclerosis, and plaque rupture, and has been developed to support drug discovery research, biomarker identification, and clinical development of cholesterol-lowering and anti-inflammatory therapies that could treat and ultimately prevent atherosclerotic plaque formation. The platform can be thought of as a dynamic “flight simulator” that enables rapid testing in silico (i.e. in a computer) to see the effects of multiple interacting factors, particularly lipid levels and inflammatory processes, and their contribution to the risk of cardiovascular events.

The Cardiovascular PhysioLab platform includes four submodels:

·         Cholesterol metabolism – includes both “good” and “bad” cholesterol, high density lipoprotein (HDL) and low density lipoprotein (LDL), respectively.  Represents the dynamic regulation of lipoprotein particle size, number, and composition, all of which have a significant impact on disease progression and prognosis, as well as their altered synthesis and uptake by the liver. 

·         Atherogenesis – represents the mechanisms of lipid retention, modification, uptake, and processing by vascular and inflammatory cells, the regulation of which leads to plaque development and progression. 

·         Plaque stability – integrates changes in plaque composition, size, and geometry by calculating peak stresses in the plaque wall that can lead to unstable plaque and rupture.

 

·         Cardiovascular risk – employs statistical methods and longitudinal patient data to translate the plaque stability readouts into the likelihood of cardiovascular risk.

                                                                      

Risk factors (e.g., smoking, diabetes, and hypertension) and known or hypothesized genetic variants are represented across the physiologic submodels of the platform.  In this manner, a broad range of “virtual patients” can be generated in the platform, tested against existing clinical data, and then used for simulations of novel therapies and clinical protocols.

Posted: November 2007


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