PITTSBURGH, Oct. 25 /PRNewswire/ -- A class of drugs that has repeatedly failed to obtain approval for the treatment of severe sepsis would achieve markedly higher efficacy under conditions specified by a novel in silico model of inflammation, according to an article in the current edition of Critical Care Medicine.
The computer model was developed by a University of Pittsburgh team encompassing critical care medicine, surgery, mathematics, and physics and is being commercialized by Immunetrics Inc., also of Pittsburgh. The CCM publication is believed to represent the first demonstration of the utility of mathematical models in evaluating clinical trials strategy for anti-sepsis compounds. Sepsis is the leading cause of mortality in U.S. intensive care units.
The article describes a computer-based trial of 1,000 virtual patients with randomized bacterial loads submitted to a neutralizing antibody directed at tumor necrosis factor. Numerous compounds utilizing this approached have failed in Phase III trials despite strong therapeutic rationales and promising animal and early phase human studies.
The in silico trial found that greatest benefit was obtained using low doses of anti-TNF therapy for 48 hours; higher and longer doses resulted in much higher mortality rates. It also demonstrated the degree to which ratios of pro- and anti-inflammatory circulating cytokines, among other biomarkers identified by the model, are likely to improve patient selection over trials using simple, static biomarkers or no biomarkers at all.
In particular, the model identified a subset of 265 virtual patients predicted to respond favorably to treatment, based on a complex panel of time- dependent pro-inflammatory and anti-inflammatory mediators. Of this subset, 233, or 87.2%, were helped by treatment while only two, or 0.2%, were harmed. These results provided significantly improved outcomes over recent human trials in which a single biomarker, IL-6, was used to select candidates for therapy.
"The published results indicate the power of using in silico models to help optimize clinical trial design in complex indications," said Steven Chang, CEO of Immunetrics. "As the paper describes, in silico trial design is a method coming of age."
Notably, the U.S. Food and Drug Administration has stated that computer- based predictive models are "urgently needed to improve predictability and efficiency along the critical path from laboratory concept to commercial product."
In addition to holding an exclusive license to the model described in the CCM paper, Immunetrics is collaborating in the development of enrichments to the model. For example, more recent versions of the model encompass key adaptive elements of the immune response as well as a more comprehensive coagulation cascade. The company has also developed proprietary fitting and other optimization technologies that dramatically accelerate the model development process. The current version of the company's modeling platform and clinical trials simulation engine for inflammation indications is available for licensing.
The CCM paper was authored by Gilles Clermont M.D. of the University of Pittsburgh Department of Critical Care Medicine; John Bartels of Immunetrics; Rukmini Kumar of the University of Pittsburgh Physics Department; Greg Constantine of the university's Department of Mathematics; Yoram Vodovotz of the university's Department of Surgery; and Carson Chow of the university's Department of Mathematics. Drs. Clermont, Vodovotz and Chow are co-founders of and consultants to Immunetrics, Inc.
About Immunetrics Inc.
Immunetrics is a Pittsburgh-based biosimulation company providing a platform to model complex biological systems. Immunetrics possesses the first known comprehensive model of the acute inflammatory response, which is now enhanced with key elements of the adaptive immune system and coagulation cascade. With this model, Immunetrics can help drug companies prioritize and design preclinical and clinical programs for new therapies. Immunetrics was founded by LaunchCyte LLC and has been funded by Innovation Works, the University of Pittsburgh Medical Center, the University of Pittsburgh, the Pittsburgh Life Sciences Greenhouse, and individual investors.