Cancer Treatment for Everyone

Massive Bio Announces First Patient Enrolled in Its Pivotal Global SYNERGY-AI: Artificial Intelligence Based Precision Oncology Clinical Trial Matching and Registry

March 15, 2018

Massive Bio, Inc., a leader in providing simplified and affordable access to precision oncology to cancer patients treated at community-based oncology practices, announced today that the first patient has been enrolled in a global registry for cancer patients evaluating the feasibility and clinical utility of an Artificial Intelligence-based precision oncology clinical trial matching tool powered by a virtual tumor board (VTB) program. This registry will assess its clinical impact on world-wide patients with advanced cancer to facilitate clinical trial enrollment (CTE), as well as financial impact. 

The SYNERGY-AI registry is the first of its kind to combine artificial intelligence, genomic biomarkers and multi-variate analysis to accelerate clinical trial matching and promote access to promising cancer therapies. SYNERGY-AI is a part of Massive Bio’s global strategic collaboration with community-based oncology practices. Established in 2014, the goal of the collaboration is to promote precision oncology approaches at the point-of-care, and to ease access to clinical trials, while reducing patient and provider burden and overall cancer care costs.

“Today’s announcement marks a one-of-a-kind clinical program which utilizes a precision medicine approach to clinical trial matching that could potentially catalyze accelerated enrollment from a patient-driven perspective, while promoting access and innovation,” said Dr. Arturo Loaiza-Bonilla, MD, MSEd, Chief Medical Advisor and Co-Founder of Massive Bio. “Initiation of this study also represents a significant milestone for Massive Bio, as we have the opportunity to help over 1,500 patients to enroll in therapeutic cancer trials. Most importantly, we believe this program has the potential to change the paradigm of how we approach clinical trial screening and enrollment and help many more patients in the future.”

Groundbreaking research by Massive Bio’s team (presented at the European Society for Medical Oncology Molecular Analysis for Personalized Therapy 2017, and published in Annals of Oncology) established for the first time that a patient-activated VTB and trial matching system can effectively optimize clinical trial enrollment while reducing costs.

“We expect that this registry, which is a part of Massive Bio’s Clinical Trial Matching System (CTMS) platform, will generate important insights about the utility of AI to optimize clinical trial enrollment, particularly in difficult-to-treat cancer patients, biomarker-driven and rare tumors, for whom are in need of entering into a novel targeted or immunotherapy-based study. This research accelerates the field of integrated biomedical informatics and precision medicine and advances potential treatment options for patients whose cancers harbor genetic abnormalities,” said Selin Kurnaz, PhD., CEO and Co-Founder of Massive Bio. Dr. Kurnaz added that “It is well described that only approximately 3% to 5% of adult cancer patients participate in clinical trials. In the era of value-based care, patient-centered approaches and innovation, Massive Bio’s VTB and Clinical Trial Matching System (CTMS) platforms are extremely well positioned to optimize clinical trial enrollment”.

For more information call +1-844-627-7246 or email

About the Study

The SYNERGY-AI Registry is an international prospective, observational cohort study of adult and pediatric patients with advanced solid and hematological malignancies. Using a proprietary application programming interface (API) linked to existing electronic health records (EHR) platforms, individual clinical data is extracted, analyzed and matched to a parametric database of existing institutional and non-institutional CT. Machine learning algorithms allow for optimized matching based on CT allocation and availability. Enrollment is ongoing, with a target of ≥1,500 patients. Please refer to for details. 

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