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Massive Bio Announces Plans to Expand AI Use in Oncology, from Clinical T**** Matching to Drug Matching and Beyond

Massive Bio, a leader in AI-powered cancer treatment, announced plans to launch a new AI-powered drug matching product in 2023 that will enable oncologists to proactively identify more cancer treatment options for their patients, including recently approved d**** as well as active clinical trials. The new drug matching product will also help biotech and pharmaceutical companies better target which patients and physicians need the latest d****, improving their go-to-market efficiency. Massive Bio plans to launch its first drug matching product in first half 2023 and will deliver general availability later this year.

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@MassiveBio announces plans for new AI-powered drug matching product in 2023 to enable oncologists to identify more #cancertreatment options for patients #oncology #clinicaltrials #AI #drugmatching

The new product will expand usage of artificial intelligence in oncology, building on Massive Bio’s oncology-focused AI platform, which has onboarded more than 100,000 patients for its clinical t**** matching service. The platform currently helps cancer patients identify relevant clinical trials using AI, empowering them to find treatment options faster while enabling life sciences companies to conduct more inclusive population-based recruitment rather than traditional site-specific recruitment. Massive Bio also helps patients remove logistical constraints once they are matched to a specific treatment, improving the patient’s success of treatment.

“We believe there’s so much more that can be done to advance the field of oncology,” said Selin Kurnaz, co-founder and CEO of Massive Bio. “At the end of the day, we want to ‘amazonize’ the entire patient journey which includes patient identification, clinical decision support and last mile. This allows scale and a data-driven approach to finding the best treatments, including FDA-approved d**** and active clinical trials for patients and oncologists. Right now, there is no universal system that looks at millions of data points in the patient journey to provide i****** clinical decision support. We want to reverse engineer the process using real-time clinical data – not outdated financial c***** data – to match the right patients with the right treatments at the right time. Moreover, we have 18 pharmaceutical customers, and they spend millions of dollars every year to identify the right patients for their d****, but success is extremely limited due to significant reliance on c***** information. Our new product is like a breath of fresh air for them, and we are already seeing their validation of the need and excitement.”

In 2022, roughly 1.9 million people were diagnosed with cancer in the United States. A record 30 novel oncology treatments were launched globally in 2021, bringing the total to 104 in the past five years and 159 since 2012. Many were approved for more than one indication, and the use of precision biomarkers have become the standard of care for dozens of tumors. A recent oncology pipeline report showed roughly 55% of all cancer clinical trials involved the use of biomarkers.

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For patients, many do not receive the most effective personalized treatments due to challenges integrating biomarker testing into clinical care. Patients are lost at various steps along the precision oncology pathway due to operational inefficiencies, limited understanding of biomarker strategies, inappropriate testing result usage and access barriers. The largest dataset from over 500,000 patients with non–small-cell lung cancer (NSCLC) in the United States found that most patients with a NSCLC (~64%) could have, but did not, benefit from a personalized treatment appropriate for their disease.

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For oncologists, it’s often too time-consuming and complex to manually review each patient’s data and track all the various treatment options, whether approved d**** or clinical trials in progress. By using AI to structure the clinical, genomic, and emerging drug information and match it to a patient individual needs, Massive Bio can significantly reduce the amount of time an oncologist spends on back-end research while enabling clinical teams to provide precision therapies to patients.

For diagnostic and pharmaceutical companies, Massive Bio surfaces consented patients that can benefit from appropriate testing and delivery of biomarker-driven oncology d****, rather than providing outdated c***** data, which is the ultimate standard in the patient identification industry, to inform commercial strategies. By utilizing real-world data collected in real-time, diagnostic and pharmaceutical companies can streamline their go-to-market approaches for each drug, dedicating resources to high-yield data-enriched target practices and health systems. Massive Bio’s drug matching platform will also be able to identify and provide recommendations for non-biomarker-based d**** such as chemotherapies, i.e., not limited with biomarker-driven oncology d****.

Massive Bio’s AI technology also improves equity and access to oncology care, providing t**** options, value-based pathways and a framework for payers to authorize treatments, promoting adherence and reducing costs. “These therapies need to be reimbursed to prevent financial toxicity, and insurance companies and providers need reassurance there is a digital tool in place to allow precision oncology, value-based matching, instead of the current data-poor environment, where patients get less individualized yet more expensive options, off label use, instead of a pathways-oriented, value-based biomarker driver agent, in addition to clinical t**** options; we are that best-in-class solution,” said Cagatay Culcuoglu, co-founder and CTO/COO of Massive Bio.

“With the advent of big data, genomics, analytics and real-time insights, the oncology community needs to reimagine cancer patient care, clinical t**** design, and the entire drug discovery process,” said Arturo Loaiza-Bonilla, Massive Bio’s co-founder and Chief Medical Officer. “Our team of researchers and engineers has spent years developing this technology, which applies advances in machine learning to analyze a patient’s medical history, genetic profile and current condition to map their entire journey and identify the most appropriate treatment options in real-time at scale. As an oncology researcher, this is an extremely exciting time as we work with physicians, patients and life sciences companies to advance the research and development ecosystem.”

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