Cambridge, Mass.-based startup Trinetx Inc. has raised a $40 million series D to better match patients with clinical trials and real-world evidence efforts. Merck Global Health Innovation Fund, the $500 million digital health-focused strategic fund of Merck & Co. Inc., led the financing. It boosted the total raised by the company to more than $102 million. New investors Mitsui & Co., Itochu Technology Ventures, Itochu Corp. and existing investors MPM Capital, F2 Ventures and Deerfield Management also participated in the latest round.

The startup has clinical and claims data on more than 300 million patients via its network of health care organizations across more than 17 countries, including the U.S., U.K., Germany, Italy, Japan, Singapore, India and Brazil. Trinetx already has 29 customers, including nine of the top 15 pharmaceutical companies, including Novartis AG, Sanofi SA and Pfizer Inc., as well as five of the top contract research organizations (CROs).

Creating better cohorts

"For a phase II or phase III study that pharma is designing – which patients are going to qualify for the study? They have inclusion/exclusion criteria, for example, patients with Crohn's disease and diabetes and didn't take the drugs for the last nine months, ages 40 to 60, female only," Trinetx CEO Gadi Lachman noted to BioWorld.

"These cohorts were designed using opinion; they were done manually before trying Trinetx, which led to more than 50 percent of the protocols requiring anywhere from two to three amendments on average," he continued. "So, this is why it takes 12 years to develop a new drug because a lot of the protocols get amended, which delays the whole thing. Now, Sanofi, Pfizer and Novartis and all our big pharma customers, they are using the Trinetx platform to build that inclusion/exclusion cohort with machine learning and real-time insight into how best to optimize this course."

He offered examples of altering the included age range to 40 to 70 years from 40 to 60 years or changing the allowance of a particular lab measure to 3 percent vs. 1 percent. The Trinetx platform can offer a pharma insight into what cohort definition is optimal for its purposes, rather than defining that solely through the long-standing practice of trial-and-error.

Trinetx can identify for its customers the number of de-identified patients that meet those specific criteria at any given hospital in its network, which can help a pharma or a CRO work with the right hospitals to find the patients it needs.

For their part, hospitals also gain access to more potential clinical trials that could benefit their patients and researchers. Lachman noted that a hospital recently added to the Trinetx network connected to two clinical studies it aimed to participate in within just the first six hours of joining.

The startup has focused thus far on working with the large academic medical centers as well as pediatric hospitals, where a lot of cancer and rare disease are congregated. But it's also focused on adding community hospitals, where a lot of chronic disease patients are routinely treated.

Global goals

Most importantly, the hospital network at Trinetx is global. Continuing to expand its hospital network and access to patient data is the primary purpose for the current financing. "This is the main reason why we raised the D round," Lachman said citing fast growth in Europe in Germany, the U.K., Italy and other countries, as well as expansion in Asia Pacific in Japan, India, Singapore and Australia and in South America in Brazil.

"We continue to grow internationally because the nature of the clinical researchers at pharma that we serve – they're interested in global trials, so you have got to serve them globally," he continued. "Second, and probably more importantly, everything we do has to be around accelerating the launch of new therapies to market to save patient lives."

Lachman noted the firm expects that the Japanese conglomerates it counts among its investors, Mitsui and Itochu, will aid in its expansion efforts in and around Asia. Merck was drawn to the deal by the uniqueness of the Trinetx offering, which combines a global reach with machine learning analytics, Lachman said.

Competitors, such as Flatiron and others, don't typically span the globe. But it can be useful to use a few sources of clinical trial and real-world evidence analytics in tandem, in order to best triangulate the results.

Digital clinical trials

After just five years since its inception in 2013, Trinetx lays claim to the largest network of global health care providers amongst the competition. It integrates all sorts of de-identified patient data, including electronic medical record, genomic, insurance claim and disease registry data, that can be united for a single patient but is provided to pharma and CRO customers only at an aggregate level. Trinetx started to monetize its platform in 2016 and since then has annually doubled both its bookings and its revenues.

In addition to geographic expansion, Trinetx aims to use the financing to improve its technology and further expand its offerings. Beyond clinical trials, real-world patient data offer a vast opportunity. The FDA has offered some initial guidance on how those data should be used – and real-world data (RWD) are expected to streamline the process of gaining subsequent indication approvals, since physicians are able to and often do prescribe any medication for an off-label indication. RWD will also enable much more useful safety and efficacy tracking, including better understanding of which patients really benefit.

Digital clinical trials, which track actual cohorts of patients who are matched and monitored over time, are an actual format that a RWD study can take. "If you think about it, we have access to data in the real world, real patients, real drugs, real situations, real successes, real failures, surgeries, ER admissions, we see it all," Lachman explained. "So, any pharma can come to us and ask us to conduct for them a digital study. Let's select this cohort of patients in those 20 or 30 or 40 or 50 sites and follow them over a period of time and see how well they're doing.

"Let's cut and slice the data to understand that our drug is very, very effective on this cohort or subcohort, less effective on this subcohort. And, by the way, look at that cohort three, that are completely not taking our drug but could definitely benefit from it. Maybe they're not being diagnosed with a disease, but they should be," he added. "So, to take the real-time nature of the data, to take the depth of the data, and the quality of the data – and allow pharma to figure out what's going on out there in the real world on a real-time basis over the years – this is something very, very exciting to us."