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Model patients, virtually
January 2009
by David Hutton  |  Email the author
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FOSTER CITY, Calif.—Entelos Inc. recently revealed that the U.S. Food and Drug Administration (FDA) will be using its Cardiovascular PhysioLab simulation platform to aid agency scientists assessing the cardiovascular risks of drugs with the help of simulated "virtual patients." 

The agreement covers a specific drug class and a set of drugs within that class.
"Unfortunately we are not a liberty to disclose what class or classes of molecules are to be evaluated—however, drugs to treat cardiovascular disease are used by millions of people in the U.S. and globally so the implications are profound," says Mikhail Gishizky, CSO for Entelos.

Entelos' Cardiovascular PhysioLab platform is a large-scale computer simulation of cholesterol regulation, atherogenesis and cardiovascular risk. It has been used by Entelos for multiple pharmaceutical customers to simulate and predict the effects of drugs in patients and patient populations, evaluate novel drug targets, test combination therapies, identify and interpret biomarker patterns, and predict a drug's long-term biological effects.

Janet Woodcock, director of the FDA's Center for Drug Evaluation and Research (CDER), points out that while the modern controlled clinical trial is still the international gold standard for evaluating safety and efficacy of new therapies, rare and serious adverse events may only appear after a drug has been administered to a large heterogeneous population, long after it has been approved.

"Having information that may be predictive of likely adverse events or that can help to explain the biological mechanisms leading to adverse events in certain patient types could be extremely valuable," Woodcock says in a statement.

Under the agreement, a broad range of "virtual patients" will be generated using Entelos' cardiovascular platform and used for simulations to test the cardiovascular safety and efficacy of multiple drugs. Results will be compared to existing clinical trial data collected by the FDA from multiple drug sponsors. Insights will help to inform the FDA on key decisions concerning the effects of novel drugs on cardiovascular disease processes.

The FDA agreement is just another step in a bigger plan for Entelos.

"This is part of an overall strategy to apply our capabilities in the area of human safety sciences," says Gishizky. "The FDA-Entelos agreement is an important first step to determine how mechanistic computer models can best assist regulators in the evaluation of drug safety and efficacy."

"We can literally simulate and test thousands of virtual patients, drugs, drug combinations, and conditions to customize applications to customers' specific questions about optimal dosing, best responders, or best combination therapies to pursue. We can determine the risks and benefits to the patient and assist in the development of adaptive trial protocols."

Gishizky also points out that the long-term implications of the agreement do not signal a future where computer simulations eliminate the need to perform clinical trials. Instead, the use of mechanistic modeling and biosimulations can help regulators and sponsors assess and understand what the data they collect is telling them with respect to the efficacy and safety of the drug.

"Overall, we see the routine use of mechanistic modeling and biosimulation as something that will help patients, drug developers and regulators to develop therapeutic treatments more efficiently and minimize the risk and maximize the benefit to patients," says Gishizky.

Often the thought of simulators sparks images of jet pilots in mock flight. In the drug research arena, Gishizky says the use of modeling and simulation has been gaining acceptance, though he acknowledges there still is a long way to go.

"PK/PD modeling is now considered a standard practice in drug development," he says. "Mechanistic modeling of the type Entelos is doing has been demonstrating value to drug developers and convincing skeptics that a computer model can help them better predict human response than what they are currently doing does."

Predictive models also can have a far-reaching impact on predicting and assisting in combating post-approval adverse events.

"We see mechanistic bio-simulation modeling playing an important role in this issue," Gishizky says. "A major challenge for regulators, sponsors and physicians is how you take safety and efficacy information you have from controlled clinical trials that have maybe exposed several thousand patients to the drug and project out the real world setting where hundreds of thousands of patients will be using the drug in a controlled setting."

As Gishizky points out, it is impractical to require sponsors to run clinical trials of the size and scope that would be required to address the issue directly. Predictive modeling makes it possible to test the efficacy and safety of the drug on hundreds of thousands to millions of virtual patients prior to approval.

"This process can help identify those patients that are potentially at risk for developing adverse events so that appropriate monitoring strategies can be put in place or that these patients are not exposed to the drug in the first place," Gishizky says.

As a result, the use of predictive models in clinical trials can be a key component in decreasing post-approval adverse events.

"Being able to evaluate the effect of the drug on hundreds of thousands of virtual patients and identifying  biomarkers for those patients that can be used in the post approval drug monitoring setting will go a long way to help mitigate the risk to patients," says Gishizky. DDN
 
Code: E010903

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