Biomodels Announces Groundbreaking Uses of Gene Expression Analysis for Optimizing Clinical Trials

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Unique Analytical Platform Successfully Used To Delineate Responders and Non-Responders in Clinical Trials

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Biomodels, LLC, a Boston-based preclinical research organization, reports the success of the application of a novel gene expression analysis to optimize clinical trials in a new publication now available on-line (Alterovitz G, et al. Personalized medicine for mucositis: Bayesian networks identify unique gene clusters which predict the response to gamma-D-glutamyl-L-tryptophan (SCV-07) for the attenuation of chemoradiation-induced oral mucositis. Oral Oncology. Article in press). SCV-07 is a novel peptide being developed by SciClone Pharmaceuticals, Foster City, CA.

“The successful demonstration that learned gene clusters differentiated drug responders from non-responders in a phase 2 clinical trial is of great potential benefit to drug developers and patients as the data can be used to determine inclusion criteria for later stage pivotal studies” says Dr. Stephen Sonis, Biomodels’ CMO and partner. Such re-casting of current study development paradigms should result in study populations that are most likely to respond to investigational drugs. Consequently, studies will require fewer subjects to demonstrate efficacy, and be completed more quickly. And patients who have little chance of benefiting from the test agent will not be submitted to the potential risks inherent in clinical trials.

The unique analytical platform was developed in Biomodels’ longstanding collaboration with leading bioinformaticians. Dr. Gil Alterovitz, an Assistant Professor at Harvard Medical School (HMS) and affiliated with the HMS Center for Biomedical Informatics, notes that “Separating out responders from non-responders can really be a game-changer in drug development. New computational technologies are enabling this type of work to be possible today. While this study focused on a particular drug, the paradigm used here is widely applicable.”

Biomodels’ success in applying its unique genomics analysis platform to drug response in clinical trials is especially timely as industry reports estimate that the market for personalized medicine will grow significantly through 2015 due to increased concentration on cost savings and early disease detection. This component of Biomodels’ business is especially positioned for rapid expansion as the technique is applied to approved drugs. Biomodels is working closely with SciClone to apply this cutting-edge technology to its SCV-07 drug development program.

As Dr. Edward Fey, Biomodels’ managing partner, notes “Our successful responder/non-responder differentiation is not only important in clinical trial design, but ultimately in optimizing the likelihood that patients will only receive medicines that will benefit them”.

Summary of Study
Gamma-d-glutamyl-l-tryptophan (SCV-07) demonstrated an overall efficacy signal in ameliorating oral mucositis (OM) in a clinical trial of head and neck cancer patients. However, not all SCV-07-treated subjects responded positively. Here we determined if specific gene clusters could discriminate between subjects who responded to SCV-07 and those who did not.

Microarrays were done using peripheral blood RNA obtained at screening and on the last day of radiation from 28 subjects enrolled in the SCV-07 trial. An analytical technique was applied that relied on learned Bayesian networks to identify gene clusters which discriminated between individuals who received SCV-07 and those who received placebo, and which differentiated subjects for whom SCV-07 was an effective OM intervention from those for whom it was not.

Scientists identified 107 genes that discriminated SCV-07 responders from non-responders using four models and applied Akaike Information Criteria (AIC) and Bayes Factor (BF) analysis to evaluate predictive accuracy. AIC were superior to BF: the accuracy of predicting placebo vs. treatment was 78% using BF, but 91% using the AIC score. The ability to differentiate responders from non-responders using the AIC score was dramatic and ranged from 93% to 100% depending on the dataset that was evaluated. Predictive Bayesian networks were identified and functional cluster analyses were performed. A specific 10 gene cluster was a critical contributor to the predictability of the dataset.

The results demonstrate proof of concept in which the application of a genomics-based analytical paradigm was capable of discriminating responders and non-responders for an OM intervention.

About Biomodels
Biomodels, a preclinical drug research organization founded in 1997, develops and conducts predictive translational studies for biotechnology and pharmaceutical companies, particularly in the areas of cancer, cancer supportive care, radiation injury, and inflammatory diseases, especially those involving the gastrointestinal tract, joints and skin. The company specializes in (non-GLP) efficacy studies that optimize dose, schedule and define mechanism of action. For additional information, please visit http://www.biomodels.com

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Edward G. Fey, Ph.D.
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