Accenture Ventures and an unnamed “leading” CRO have invested in artificial intelligence (AI) focused trial design services firm QuantHealth.
The backing is the latest QuantHealth has attracted in a $17 million Series A financing round. According to the firm, the new funding will enable it to enhance its platform.
QuantHealth’s AI technology is trained on a massive dataset of 350 million patients, large biomedical knowledge-graphs, and clinical trial data.
The system is a simulation platform that, the firm claims, can predict trial outcomes with 86 percent accuracy on the binary endpoint metric.
According to QuantHealth, the system can test thousands of protocol variations and discover the optimal trial design for success, helping research and development (R&D) teams more accurately and rapidly develop and optimize trial protocols.
The advantages of the approach are potentially significant according to CEO Orr Inbar, who said: “Clinical trials are a costly and uncertain process, where the R&D cost of a new drug can be upwards of $1 billion.
“QuantHealth has created a solution that uses AI to transform how pharmaceutical companies approach their clinical trials. We’re able to seamlessly integrate data and cloud technology into the clinical trial process, not only saving time and money for pharmaceutical companies, but also increasing the chance of success in drug development.”
Inbar also predicted its new backer’s experience would help QuantHealth grow. “Accenture’s long-standing experience in this industry and leadership in data and AI will help us continue to scale our platform globally.”
Tom Lounibos, global lead of Accenture Ventures, agreed. “In addition to accelerating and enhancing global drug discovery efforts, we will work alongside QuantHealth, our clients and ecosystem partners to expand medicine and treatment options and find new opportunities to bolster patient care.”
Completion of the Series A round follows shortly after QuantHealth launched Katina, an AI-guided workflow technology that simulates protocol combinations of patient groups, treatment parameters and different endpoints, to maximize trial success probability.