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Artificial intelligence and machine learning promise to transform healthcare across the board, but particularly through the use of precision medicine.

Precision medicine is often defined differently than the common phrase “personalized medicine,” which simply means tailoring treatments to the patient. Precision medicine, on the other hand, specifically applies machine learning to the genetic material of patients with less-common conditions. The AI finds patterns within material to identify common phenotypes, while pharmaceutical companies use that information to develop drugs targeted to the specific need. 

Palo Alto, California-based Endpoint Health is one player in this space looking to tap the potential machine learning has for precision medicine. Today, it announced it has received $52 million series A equity and debt financing – a big jump from its first funding round of $12 million in 2020. 

The new funding will be used, in part, to extend the company’s Precision-First platform and expand its therapeutic pipeline to include programs for chronic immune-mediated diseases. The money is also meant to advance Endpoint Health’s efforts to test the use of a  human plasma-derived medication, called Antithrombin III, for use by patients with sepsis — a life-threatening medical emergency that occurs when the body’s immune response to an infection becomes dysregulated.


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AI boosts speed and lowers costs for clinical trials

A Phase II clinical trial is intended to investigate the use of Antithrombin III in patients with sepsis. Endpoint Health used ML-driven insights to develop a blood test that will identify patients who will be most helped by Antithrombin III. To develop the test, which identifies patients with a particular form of sepsis, the Precision First platform analyzed RNA from sepsis patients, using machine learning to “look for underlying patterns that are unique and different that a normal human couldn’t see,” said Jason Springs, Endpoint Health chief executive officer.

The immune reactions of patients with sepsis differ, so the platform discovers subgroups and identifies what people in those groups have in common. 

“The immune system is very complex, so the machine learning platform lets the computer pull apart that complexity in a way no human could,” Springs said.

“Nobody could look at 10,000 data points for 1,000 patients and quickly understand if there are one or two different clusters of patients that look like each other – one person can’t process that much information at once,” he said. “But the computer will keep crunching and it can handle the amount of data required to understand these illnesses.”

This initial analysis determined whether to develop a blood test for a group of sepsis patients “that the computer told us were unique,” Springs said, adding that the test identifies patients within a sepsis subtype. Those patients can take part in the upcoming Phase II clinical trials for the sepsis drug. Identifying patients in this way makes clinical trials shorter and less expensive, as researchers can choose the participants they think most likely to respond to their therapy. 

AI analysis of genetic samples to match patients to medication

The entire process begins through the AI analysis of genetic samples from sepsis patients, who agree to submit blood samples. 

“If we get 1,000 samples, one or two months later we will know if we see some patterns,” Springs said. 

In the future, the test can determine sepsis subgroups to match a patient with the medication specific to their group. That type of matching can’t come soon enough, Springs added. Some sepsis patients develop disseminated intravascular coagulation (DIC), where small blood clots are distributed across the body. The condition is very dangerous.

“Our hope is that our therapy will have a good chance of resolving that problem,” he said. 

Other companies are also using AI tools to deliver precision medicine. According to The Journal of Precision Medicine, embracing digitization is the “the key to enable and operationalize both standardization and personalization in health care.”  Tools can help affordably create and deliver complex, patient-specific pharmaceutical or medical devices.

Other precision medicine companies using AI include Synapse, GNS Healthcare andTempus, which in 2020 announced an additional $100 million financing, bringing its financing total to $600 million since its 2015 inception. Tempus focuses on cancer treatment, though it devoted resources to COVID-19.

Endpoint Health’s own recent funding round is impressively large because the company’s technology attracts investors. 

“Investors are interested in technology that can analyze and understand large sets of unique patient data and gain insights that could speed clinical development of therapies that are much more likely to succeed in patients,” Springs says.

AI healthcare market to reach over $35 billion

In fact, the United States dominates the list of firms with highest VC funding in healthcare AI to date, and has the most completed AI-related healthcare research studies and trials, says a September 2021 report from EIT Health and McKinsey.

The global AI healthcare market spend is anticipated to reach over $35 billion by 2030, growing by 24% from $2 million in 2019, according to the BIS Research market intelligence firm.

“We are in the very early days of our understanding of AI and its full potential in healthcare, in particular with regards to the impact of AI on personalization,” according to the report, which predicts precision medicine will grow to offer medicine tailored to every patient’s unique need. 

While that is in the future, Springs can attest to the fact that precision medicine is already here. And the potential for AI tools in precision medicine will continue to skyrocket.

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