04.30.24-Startup-Ecosystem-Conference-Startups-WKD-SMRT

MICHAEL DAVIES: We're wicked smart. Apologies for the terrible Boston accent. This is what happens if a bunch of senior lecturers who are scholar practitioners go out drinking together. We end up going, well, we think we could build a better smart home. And we decide to name it after the line from Good Will Hunting in which Chuckie says, "my friend, he's wicked smart." We're now in the startup or nano program at MIT.nano working on verification validation.

Here's the problem we're now focused on. Innovation in pharma is slow, costly, and risky because of weak data from conventional approaches. And the bottleneck. 2/3 of all of the money and the overwhelming majority of the time is not in drug discovery, but it's in trying to figure out whether or not they actually work. Moreover, it's a huge market in terms of the amount of money that gets spent on it, about $100 billion a year. Interestingly, the FDA, rather than being a laggard, is actually pushing towards having more meaningful data about patient function in real life.

And we've just reached within the last year a couple of critical inflection points. Digital endpoints have now in peer reviewed work been demonstrated to have proven ROI. We've just seen the first ever clinical trial with a digital primary endpoint, the criterion that is reported to the FDA to determine whether or not the drug works. Our solution is an in-home Edge AI platform that can halve the time and cost of clinical trials for diseases that manifest in some form of motor dysfunction.

Patients just get to live their lives. We use sophisticated sensing, AI processing at the edge, network hubs and complete connectivity to span from sensor nodes through our processing to make the data available to pharma companies through AI. This accelerates decisions. It minimizes the risk of weak data. About one third of all clinical trials fail because they don't have data with sufficient statistical power. It reduces trial size and duration, and ultimately, at the point at which we become a primary endpoint, significantly reduces the cost per patient.

We go from eight to 10 years and hundreds of millions to half that cost in half that time. Our end-to-end solution outperforms the competition, both the conventional analog approaches, wearables, which are the most widely used solutions at present, and even the other complete motion sensing by being higher resolution, giving us higher sensitivity and higher ease of use. We've de-risked and demoed our tech. Our launch customer was Biogen. We built a set of working prototypes that are complete from end to end.

We help Mass General, the neurology department there, win in $1 million grant for a bits to bytes program, which is building digital profile for 30,000 patients. We came through PharmStars and learned that we didn't know what we didn't know about the pharmaceutical industry, pivoted to disease states that involve motor dysfunction, and providing a full stack solution all the way up to and including digital biomarkers. We've responded to several pharma RFIs and RFPs. And we're now at the point where we're pivoting to our long-term stable hardware.

Interestingly, and something I found very foreign, it turns out that the pharma industry really does not want a shiny new gadget every year. They actually want something that is stable for the lifetime of the development and testing of the drug, something that's stable for five to seven years. We know the engineering work we need to do to ship that. We're in the immersion lab at MIT.nano.

And we're part of a consortium with a variety of other people focused on this for the society for digital medicine building the business case for digital endpoints. We're really interested in looking for partners in pharma, elsewhere in health care, and in complementary technologies, such as sensors, wearables, algorithms and models, data pipelines, provenance, and access. Here are my contact details. I'll be next door. And we're wicked smart. Thank you.