How long does it take to get FDA approval for a heart-failure drug?
It sounds like a simple question, but without the help of an artificial intelligence (AI) powered MedTech cloud-based platform, it could take months and millions of dollars to find out. The market size for AI in healthcare is projected to reach $187.95 billion by 2030, according to Precedence Research.
When Michelle Wu was first asked this question, global clinical and regulatory healthcare information was publicly available, but it was scattered around the world in different databases and languages. Worse yet, keywords were misspelled or there were handwritten notes included in the databases, making what should be searchable unsearchable.
Using AI, big data, and machine learning, Wu launched Nyquist to provide business, clinical, and regulatory intelligence and analytics on medical devices and pharmaceuticals across major markets, such as the U.S., Japan, the E.U., and China—within seconds.
When Wu was the global strategy manager at Novartis, the CEO asked her about the length of time it takes the FDA to approve a new heart medication. “It’s the #1 cause of death in the U.S.,” she said. Heart disease costs the U.S. about $363 billion annually, according to the CDC.
Over the next three months, Wu read tons of FDA approvals that were 5,000 pages long in which maybe there were one or two relevant paragraphs. She also spoke with many experts. Then Wu compiled the data into Excel spreadsheets so that she could answer that simple question.
“This is insane,” exclaimed Wu. Vital healthcare information that could be mined to provide insights into how to develop life-saving medical innovations faster and at a lower cost was available but in arcane, black-box computer systems. “There has to be a better way,” Wu thought.
This project was a light-bulb moment for her. The financial industry had Bloomberg to analyze content and data to help investors uncover opportunities and minimize risk, and pharmaceutical, biotech, and medical device companies needed something similar.
Wu left Novartis to attend Stanford Graduate School of Business (GSB) to work on the idea. She also worked at a couple of startups before starting Nyquist.
In 2020, Wu and her cofounder KK (Qiang Kou), raised $523,000 in pre-seed funding from former Google, Amazon, big pharma, and MedTech executives and launched Nyquist. The startup aggregated medical data worldwide and then connected the dots, making the information valuable to analysts, R&D departments, and commercial teams in pharmaceutical and medical device companies.
By using Nyquist platform, pharma and medical device companies can speed up the process of medical innovations in the U.S., Japan, and the E.U. reaching China, India, Africa, and other emerging markets and vice versa. Companies in emerging markets are developing many cheaper and effective healthcare innovations. The world needs to know about these. The process of getting approval from one country to another can take 2 to 7 years.
Over the course of two years, Nyquist has developed the largest AI data platform for medical devices. “We have about 30 customers from seven countries,” said Wu.
Working collaboratively with prospects and customers like Medtronic, Nyquist has discovered new uses for its platform. During a pitch call with Medtronic, an executive asked if the platform could be used to research supply-chain factories. Within a few moments, Medtronic had a list. There was silence and Wu thought the video had frozen. Then she heard mumbling and paper rustling from the Medtronic side of the call. Shortly after the call, Nyquist had a new customer.
When raising Nyquist’s seed round, Wu experienced the typical naysayers. Old white men who told her no one would pay more than $50 for publicly available information.
Then she was introduced to Ilana Stern of Peterson Ventures, who quickly saw how arcane, manual, messy, costly, and lengthy the current process was. “They’re helping medical device companies, and eventually pharma companies accelerate and increase the success of clinical trials and bringing products to market,” said Stern. “What’s most important is the lives impacted in getting innovation to market more quickly.”
“It’s the way they’re harnessing natural-language processing, AI, and machine learning to ingest and organize data to surface insights is incredibly powerful,” said Stern. “With the click of a button, you can look at the equivalent of FDA data in Japan, China [and many more countries].”
Stern was also impressed that Wu raised half a million dollars in pre-seed funding, considered a small amount of money. Yet, she built out a platform used by customers, including Medtronic, and Becton, Dickinson and Company, also known as BD.
The startup raised $6 million in March of 2022. Peterson Ventures led the round with participation from GSR Ventures, Lightspeed Ventures Partners, and Village Global. “We will soon launch our pharma platform,” said Wu. Nyquist will also expand its global MedTech platform to include 108 clinical sites worldwide.
Another challenge is constantly aggregating all the global insight and data. One way they have overcome this challenge is that the team is geographically diverse, coming from Asia, Switzerland, Germany, and the U.S. But it is also diverse in gender and sexual orientation. “More than 50% of our employees are women and we have a lot of queer moms.” Diversity improves performance and outcomes.
In addition, “We just graduated from Google Accelerator,” said Wu. “It’s like learning to paint from Leonardo da Vinci. They bring the creme de la creme of AI experts [for participants to learn from].”
Still, this young company is doing more. “There are a lot of clinical trials and medical device companies that have suffered during the Russian-Ukrainian war,” said Wu. “We are helping a couple of medical device companies in Eastern Europe pro-bono move their clinical trials and manufacturers outside the war zone.”
Source: forbes.com
