Every month, MassBio spotlights a member company and the great work they’re doing to advance the life sciences industry and support the patients we serve. This month we spoke with Sridhar Iyengar, CEO, Elemental Machines. Sridhar holds over 50 US and international patents and received his Ph.D. from Cambridge University as a Marshall Scholar. A serial entrepreneur, he has started three companies that have revolutionized multiple industries.
Tell us about your organization and your current initiatives.
Elemental Machines is building a soup to nuts platform for automating data collection and analysis. Many of us know from our own experiences that R&D and manufacturing workflows, especially in the life sciences, are lengthy and complex. There are a lot of nuances that contribute to this: (1) managing and integrating different data types from different machines/instruments all made by different manufacturers, (2) poor user interfaces that are not designed for people of various skill levels and various disciplines, (3) and of course working in a regulated environment.
We are leveraging new technologies such as IoT (internet of things), informatics tools, machine learning (ML), and artificial intelligence (AI) to address many of these challenges and make scientific workflows faster and cheaper than ever before. With IoT data collection tools, we effortlessly get data from lab equipment and ambient lab environments. New informatics tools are readily available and capable of handling the big data sets collected by IoT. And lastly ML and AI quickly and effectively recognize patterns and data trends to extract relevant information that would have traditionally taken weeks or months to uncover, or may possibly never even have been uncovered.
How does your organization’s activities help patients now and into the future?
We know there are a lot of patients waiting for new therapies to treat some pretty debilitating diseases. Unfortunately, cures don’t just happen overnight. From early stage R&D all the way through clinical trials, there are a lot of risks throughout the process, and hopes for new drugs may fall through because results are just not reproducible. Sometimes, despite our best efforts, it’s difficult to pinpoint the root cause behind irreproducible data. But what if we had better tools to find and tease out the unknown unknowns that just may be the root cause behind the problem? Our platform specifically addresses this challenge. Much like how software engineers have tools to debug code, the technologies behind our platform help scientists ‘debug’ their work faster, the end result being finding the right treatment sooner for those who really need it.
What do you see as the biggest challenge facing the life sciences industry today?
There’s a lot of new technologies out there that have been used by many industries outside of life sciences, that should be leveraged *within* life sciences. Unfortunately, there are additional challenges in our industry that make adoption of new tech more difficult (e.g. regulatory constraints). This completely makes sense given that there is so much is at stake (ie. the well-being of patients, the time/cost of clinical trials, etc) – it’s tough to be bold and try out new approaches, especially when you may not an expert on the subject matter. That’s why we are developing products in concert with regulatory requirements and with close partnership with industry leaders in our space. Ultimately, the coolest technology won’t make any impact unless it can actually be used in a compliant and safe manner, and that’s something we understand and embrace when we design our products.
What’s next for your organization / what are you focused on in the coming year?
We have some ambitious projects in the pipeline for the coming year. We’ve spent the last few years building out robust foundation for data collection from a number of different environments, machines, and instruments (and we’re expanding that data collection network to have even more granularity) so that we can create a platform for aggregating and analyzing large amounts of heterogeneous process data. I’m most excited about now taking that large volume of data and coupling it with AI/ML models to help scientists and engineers optimize research and process outcomes. We’re also building out features that are required for quality and regulatory compliance. These are a big deal in the life sciences, so we don’t take this lightly. If you would like to learn more about our initiatives, please reach out to us at email@example.com.
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