NEW YORK--(BUSINESS WIRE)--Oncology is leading the precision medicine revolution, as clinical researchers leverage and identify biomarkers to target patient subsets with more effective treatments, and the promise of improving Phase I success rate probability by three times (25.9% versus 8.4%)1. Despite these advancements, the industry continues to struggle with issues arising from the increase in clinical complexity and patient scarcity for precision trials. To discuss these challenges and new opportunities, Medidata (NASDAQ: MDSO) is hosting a webinar with FiercePharma, “Optimize and Transform Clinical Oncology Research,” March 20 at noon (Eastern US Time).
During the webinar, special guest speakers will further explore today’s biggest challenges in precision medicine, including Reinhard Von Roemeling, Board Certified Oncologist and former SVP of Global Oncology at Merck KGaA, Robert Ford, M.D., Founder of Clinical Trials Imaging Consulting, LLC, Jason Mezey, PhD, Statistical Genomicist and Professor at Cornell University and Weill Cornell Medical College, and Michele Becci, VP of Industry Strategy at Medidata. The discussion will explore how increasingly transformative technology and advanced analytics are enabling clinical oncology investigators to:
Optimize study design and streamline operations, minimizing site and patient burden
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“Technical advances including monitoring devices with direct data transmissions into large, growing databases and analysis of disparate data will enable the creation of increasingly precise and relevant historical controls for decision making,” said Reinhard Von Roemeling, Board Certified Oncologist and former SVP of Global Oncology at Merck KGaA. “The integration of validated predictive biomarkers with other data streams is essential as it will influence novel designs and enable the success of precision oncology trials.”
In addition, Medidata has made available a white paper, Precision Oncology Trials: A Look Ahead, which outlines major challenges the drug developers are facing with precision oncology, including scarcity of patients available for targeted therapy trials. As large datasets are used to cluster patients into more precise subsets, investigators will have to measure patients on several dimensions, ranging from genotype to phenotype (including, genotype, laboratory data, digital biomarkers, sensor data, behavioral data).
