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ConcertAI Growing Presence in Real-World Data Analytics Space With Focus on Diversifying Trials

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NEW YORK – In an increasingly crowded real-world data analytics space, ConcertAI seeks to set itself apart through a fully integrated data pipeline with end-to-end capabilities and services for clinical development, as well as a strong focus on helping customers diversify clinical trials and mitigate access disparities in precision cancer care.

The Boston-based company, part of the SymphonyAI Group and founded in 2018, raised $150 million in its last round of fundraising in 2020. The firm recently expanded a collaboration with Janssen Research & Development, in which the drugmaker is using ConcertAI's real-world data and machine-learning algorithms to bring its drug trials to communities that traditionally find themselves underrepresented in clinical research.

The collaboration with Janssen is one of several partnerships ConcertAI is engaged in aimed at improving accessibility and equity in clinical research through real-world data. "This is an area of significant research emphasis and investment at ConcertAI," Jeff Elton, the company's CEO, said in a statement.

ConcertAI collects a variety of real-world data — including genomic and clinical — from patients. The company's clinical research clients use its Patient Care Monitor platform, for example, to gather deidentified patient-reported outcomes, which can be correlated with other clinical factors such as cancer stage at diagnosis, sociodemographic factors, and genetic test results to create a detailed picture of a patient's experience.

The company also draws on data within electronic medical records, the multiomic CancerLinQ database, and from multiple regional health systems and community providers. ConcertAI uses proprietary machine-learning algorithms to analyze and glean insights from this data, for example, which drugs work better or worse in molecularly defined patient groups, which patients are most likely to benefit from a clinical study, and which patients aren't but should receive genomic testing and associated treatments.

Real-world data of this type can also be valuable to researchers searching for patients to participate in clinical studies, particularly if they are aiming to enroll a diverse cohort.

Relatively few cancer patients participate in clinical trials and those who do tend to be white and have the means to travel to research sites at academic facilities. And, according to one estimate, a quarter of cancer trials fail to enroll the required number of patients.

Recent analysis of real-world data has revealed access disparities in precision cancer care, which involves biomarker testing and access to investigational drugs based on the results. For example, there are now a handful of precision oncology drugs on the market for non-small cell lung cancer patients who harbor certain genomic tumor alterations. Even so, data presented at the recent America Society of Clinical Oncology meeting suggest that less than half of NSCLC patients are being tested for all guidelines-backed biomarkers for which there are approved drugs, and that Black patients have more limited access to biomarker testing and clinical trials compared to their white counterparts.

These disparities have persisted for decades despite being well known, but ConcertAI and its collaborators believe that their real-world data-driven approach can help mitigate them.

Last year, Janssen began using ConcertAI's Genome360 platform, which includes in-depth treatment profiles on patients, as well as information about the molecular features of their tumors based on next-generation sequencing. The drugmaker is applying this real-world data within several of its oncology therapy trials, although it did not specify exactly which ones.

Najat Khan, chief data science officer and global head of strategy and operations at Janssen R&D, is also optimistic that with the help of real-world data, Janssen can improve racial disparities in its drug studies. The drugmaker's collaboration with ConcertAI is helping it decentralize studies, essentially bringing the trial to the patients, rather than requiring that they travel to far-away study sites.

"It's important to start with the right type of data," Khan said, explaining that the drugmaker is particularly interested in real-world data that can help it select trial sites in locations that "ensure[s] access for patients from underserved populations." Using real-world data, Janssen has homed in on locations where a clinical trial site would bolster minority enrollment, Khan noted.

Beyond Janssen, ConcertAI works with approximately 35 life sciences customers, including the US Food and Drug Administration, the Quality Cancer Care Alliance, and ASCO.

Past and ongoing real-world data projects include characterizing the differences in clinical symptoms between Black and white women with hormone receptor-positive breast cancer prior to chemotherapy, developing COVID-19 resources for providers and researchers in the oncology field, and supporting more inclusive clinical trial designs through a series of grants under the company's Engaging Research to Achieve Cancer Care Equality initiative.

ConcertAI, however, is operating in a crowded field and competes with companies such as Syapse and Roche subsidiary Flatiron, which are also working with drugmakers, health systems, and regulatory bodies to identify missed opportunities in precision medicine and genomic testing.

Syapse, for example, worked with the Henry Ford Health System to identify breast cancer patients who had not received adequate genomic testing and understand why, so that information could be used to improve future patient care.

Against these and other competitors, ConcertAI said it aims to differentiate itself by delivering a more fully integrated end-to-end suite of services, from data acquisition through analysis. Elton is optimistic about ConcertAI's position and trajectory.

"The field remains very early, but very promising," he said, highlighting that in the future there will be "enormous opportunities" to use real-world data and AI solutions to improve understanding of the physiology and genetics underpinning treatment-associated adverse events and why patients don't respond to certain drugs.

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