Biomodels Scientists Develop Novel Model for Cancer Treatment-Related Fatigue - Clinical Trials Arena
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Biomodels Scientists Develop Novel Model for Cancer Treatment-Related Fatigue

15th November 2009

Scientists at Biomodels have successfully developed the first model that accurately duplicates human cancer treatment-related fatigue (CTRF). The new model shows that, as in human cancer patients, fatigue is independent of anaemia or weight loss and persists long after cancer treatments end.

The new model and research findings were presented at the American Association for Cancer Research-NCI-EORTC Molecular Targets and Cancer Therapeutics meeting in Boston.

Lingering fatigue is a debilitating problem in patients who have been treated for cancer and its etiology is poorly understood. Fatigue is a common side effect of both chemotherapy and radiation therapy that impacts almost all individuals who undergo cancer treatment.

A step towards effective treatment

“There is currently no therapy for cancer treatment-induced fatigue,” says Dr. Stephen Sonis, a cancer support care expert and one of the study’s authors, who also serves on the staff of Brigham & Women’s Hospital and the Dana-Farber Cancer Institute. “The new model developed by Biomodels mimics the human condition, shedding light on the biological mechanisms involved and opens the door for developing and testing treatments to effectively prevent cancer treatment-related fatigue.”

Biomodels, a preclinical drug research organisation founded in 1997, develops and conducts predictive translational studies for biotechnology and pharmaceutical companies, particularly in the areas of cancer, cancer supportive care, radiation therapy and inflammatory diseases.

The company, based in Watertown, MA, specialises in (non-GLP) efficacy studies that optimise dose, schedule and define mechanism of action. Biomodels also has expertise in modelling inflammatory diseases, including those conditions affecting the gastrointestinal tract. Biomodels’ studies enable organisations to more readily identify lead compounds, decrease the time to a clinical trial and increase the chance of clinical success.

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