Triomics secures $15M in Series A funding to streamline cancer trial participant matching.

In the complex landscape of cancer treatment, clinical trials represent a beacon of hope, offering new therapies that could potentially extend or even save lives. Despite this, the rate of enrollment in these trials remains startlingly low, with only 3% to 5% of eligible patients participating annually in the United States. The reasons behind this are multifaceted, but a significant barrier has been the time-intensive process required for medical professionals to match patients with appropriate trials. This is where Triomics, a generative AI startup, steps in with a promise to transform this critical aspect of cancer care.

The Challenge of Clinical Trial Enrollment

Clinical trials are crucial for the development of new cancer treatments. They offer patients access to cutting-edge therapies that are not yet available on the market. However, the enrollment process is fraught with challenges. One major hurdle is the sheer amount of time it takes for oncologists and nurses to identify suitable trials for their patients. These medical professionals are often overwhelmed by their day-to-day responsibilities and struggle to keep up with the vast array of ongoing clinical trials.

The task of matching a patient with a trial is not straightforward. Most trials have stringent eligibility criteria, including the stage of cancer, specific mutations, and prior treatments the patient has undergone. Reviewing these factors against a patient’s medical records to find a suitable trial is a time-consuming process, often requiring several hours of meticulous work.

Personal Stories Highlight the Struggle

The difficulties of finding the right clinical trials are not just statistics—they affect real people. Many individuals, like myself, have experienced the challenge firsthand while trying to find trials for loved ones. I spent countless hours navigating to find options for my father and, more recently, for a friend with stage IV cancer. In many cases, doctors may only suggest one trial, if any, leaving patients and their families to do their own research for additional options.

Triomics: A New Hope with AI

Founded by Sarim Khan, a former MIT biotech researcher, and Hrituraj Singh, an AI scientist previously with Adobe, Triomics aims to tackle these issues head-on. The duo, friends since their college days, launched the startup in 2021 after recognizing how advances in generative AI and large language models (LLMs) could revolutionize the process of trial matching.

Triomics developed a specialized LLM, OncoLLM, designed to integrate seamlessly with the electronic health records (EHR) systems used by cancer centers and oncology departments. This AI-driven tool can rapidly parse through a patient’s medical history and match them with suitable clinical trials in minutes—a task that previously took several hours.

Table: Impact of OncoLLM on Clinical Trial Matching

Feature Before OncoLLM With OncoLLM
Time to Match a Patient Several hours Minutes
Number of Trials Considered Limited Extensive
Personalization of Matches Low High
Accessibility of Trial Matches Poor Improved

Expanding Impact and Market Growth

Since its inception, Triomics has seen significant adoption of its technology. Six cancer centers and hospitals are currently using or piloting OncoLLM, with plans to double this number by the end of the year. This expansion has been supported by a robust $15 million Series A funding round from prominent investors including Lightspeed, Nexus Venture Partners, General Catalyst, and Y Combinator.

Triomics is not merely a clinical trials company. The data processed by OncoLLM can also assist medical staff in preparing for patient visits and submitting detailed cancer progression reports to state regulatory agencies. The potential applications of Triomics’ technology extend beyond just clinical trials, touching various aspects of oncological care.

Competing in a Crowded Field

Triomics is not alone in the AI-driven clinical trial matching space. Other startups like Deep 6 AI, QuantHealth, and Trajectory are also developing technologies to improve the efficiency and effectiveness of trial matching. However, Khan believes that Triomics sets itself apart by processing extensive datasets specifically tailored for cancer centers.

Key Points

  • Low Enrollment Rates: Despite the availability of numerous clinical trials, enrollment rates among eligible cancer patients remain low.
  • Time Constraints: Oncologists and medical staff face significant time constraints, making it difficult to stay informed about available trials.
  • AI Solutions: Triomics utilizes generative AI to dramatically reduce the time required to match patients with clinical trials.
  • Broader Applications: Beyond trial matching, Triomics’ technology helps in preparing for patient visits and regulatory reporting.

In conclusion, Triomics represents a significant step forward in leveraging AI to address long-standing inefficiencies in clinical trial enrollment. By reducing the time and effort required to match patients with trials, Triomics not only enhances the accessibility of potentially life-saving treatments but also paves the way for a more responsive and patient-centric approach to cancer care.

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