Using Mathematical Modeling to Guide Drug Selection in Advanced Cancer Cases 

 
Optimata's technology, underlying PrediCare - the diagnostic test for navigation of individualized patient treatment - is based on the development of Mechanistic Mathematical Models. These models describe pathological or physiological processes on multiple scales—e.g., the molecular/cellular level, the tissue level, and the level of a physiological system.
 
The models are calibrated using vast amounts of data from large patient populations, including data on patient biomarkers, imaging information, medical measurements, etc. The calibrated models can then receive input data on a single patient, and simulate that patient's course of disease or response to a given treatment regimen. The models' output is a personalized prediction for the prognosis or treatment response of that patient. Such predictions can be used, for example, to select the most effective treatment option among available alternatives, or to identify the most effective dosing schedule for a selected treatment. 
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Using Mathematical Modeling to Guide Drug Development  
 
Optimata's solutions can also be used in drug development, to identify subpopulations that might benefit from a given drug (even if other subpopulations do not). In the standard approach, the development of drugs is completed only if they show efficacy in sufficiently large patient populations. Under this approach, drugs that are expected to affect only small subpopulations of patients may be rejected as inefficacious. Optimata's personalized simulations and prediction approach can be used to identify potentially life-saving compounds that otherwise might "slip through the cracks."
 
 
Using Mathematical Modeling to Select Optimal Regimens for Patient Populations/Subpopulations    
 
Optimata's Virtual Patient® platform, OVP, is a unique platform, developed and used for tailoring drug regimens to patient populations and subpopulations. It is based on a system of mechanistic mathematical models describing pathological, physiological and pharmacological processes on multiple scales. The models are interactive and adjusted to reflect the population dynamics. They can be used to simulate different treatment protocols and to optimize the treatment schedules for a given patient population, according to criteria defined by the user. 
 
The OVP platform has already undergone a proof of concept  in breast cancer patients and in drug development ,and has been employed by  biotech and pharmaceutical companies worldwide.