Today, it’s an unfortunate reality, but most people have either lost a loved one to cancer or know of someone who has.
Miroculus, a precision medicine startup, wants to create widespread access to affordable early-stage cancer detection. “Having lost loved ones to cancer greatly contributed in the decision to try and tackle this problem,” said CEO Alejandro Tocigl.
The company is building a 3D-printed device called Miriam that will be able to use a small blood sample to diagnose early stage cancer.
The device uses digital microfluidics, a new technology that creates a “lab on a chip” that can be designed with a step-by-step protocol for transferring and analyzing tiny fluid samples.
The company’s first disease focus is gastric cancer. In collaboration with the NIH, Miroculus recently ran a multi-center clinical study in three countries with 650 patients to identify a stomach cancer microRNA diagnostic signature.
The idea for Miroculus was born at Singularity University’s Global Solutions Program in 2013 and has since grown significantly. Now, the company is also working to accelerate research efforts for using microRNA for disease diagnoses, and they’ve developed an open-sourced artificial intelligence (AI) tool called Loom to achieve this.
“We hope to see Loom guiding researchers and clinicians around the globe through the microRNA knowledge database,” said Tocigl.
Using microRNA as a biomarker (indicator) for cancer and disease is showing increasing promise for early-stage disease detection, and recent research has proved this in diagnosing ovarian cancer and lung cancer, among others.
We interviewed Tocigl to learn more about the company’s ambitions and how this exciting technology may advance in the next few years.
Mission: We believe everyone should have access to accurate, affordable, and minimally invasive diagnostic tools for the detection of cancer and other conditions from the earliest stages when they are still easy to treat. We are developing a simple blood test to detect disease at the molecular level on a decentralized, automated, and affordable platform.
Moonshot: Democratize access to early diagnosis.
How does the product work and what core technology is used? Is the 3D-printed Miriam device using the same technology as when it was first prototyped in 2014?
Miriam has advanced to a more sophisticated version that minimizes user intervention and automates the complete test from sample loading to test results reporting. The core technology is digital microfluidics and a proprietary microRNA detection method packaged in an affordable instrument with disposable cartridges.
When Miroculus began, the goal was to use a single blood sample to diagnose cancer. What needs to be overcome to get to this point? What timeframe are you now looking at for the kind of broad cancer detection you envision?
The goal of Miroculus remains the same: a simple blood test to detect disease at the molecular level. We are targeting gastric cancer (GC) as our first application.
GC is one of the most prevalent cancers in emerging economies where affordable and efficient healthcare is in high demand. Based on a single blood test, symptomatic patients and asymptomatic patients will be triaged into a group for further diagnostics, including endoscopy, and patients with no GC cancer indication that would not be sent to endoscopy. Currently, less than 1% of all endoscopies are cancerous.
This would result in significant health cost savings and enable much faster and more affordable testing than the current system. We are currently establishing relationships with regulatory bodies and hospitals to be on the market in 2018. After our first test hits the market, we’ll expand into other types of cancers and other conditions.
Miroculus’ 3D-printed cancer detection device Miriam is an ambitious undertaking alone. But you’ve also built Loom.bio—a microRNA research AI, which in some ways seems like an open-sourced IBM Watson for health. How central is Loom to the company’s mission? With AI growing in sophistication, how do you hope Loom will impact diagnosis five years from now?
Loom is an up-to-date snapshot of the microRNA literature landscape we built to expedite our own research. Loom is a service that not only lists but also weighs the relationship between microRNAs, genes, and diseases based on all scientific literature available in PubMed and PMC.
The Loom dataset is one of the inputs to our machine learning models to identify relevant microRNAs in a disease of interest, and we are making it accessible and open because we believe it may prove valuable in accelerating research efforts in the microRNA space.
We hope to see Loom guiding researchers and clinicians around the globe through the microRNA knowledge database.
Tools like Loom and the use of natural language processing and machine learning can catapult the diagnostics field to a new level where comprehensive, well-informed diagnosis can be made using multi-layered information and perhaps more than one type of biomarkers to accurately classify a health condition.
What was the motivation behind open sourcing Miroculus’ code?
To provide life science researchers with a tool that can facilitate their studies on microRNAs as well as keep them most up to date with publications relevant to their field. Doing so only enhances their ability to explore the potential of microRNAs and further contribute to the rapidly-growing knowledge database in the field.
In 2014 your CTO, Jorge Soto, was quoted in a Smithsonian article saying that the Miriam device is showing a critical “inflection point in microRNA research” and that using microRNA for cancer detection has a lot of scientific validation but still needs clinical validation. Two years later, where do we stand with clinical validation for using microRNA for early cancer detection?
There are already some microRNA-based diagnostic kits out there. Although the field is still young, we have seen increasing research and clinical validation studies showing very promising results. We expect to see in the near future at least five to ten new diagnostic tests based on microRNAs, not only for cancer detection but also for other conditions.
We have also contributed to the clinical validation by completing a multi-center clinical study with 650 samples in collaboration with NIH, leading to a discovery of the microRNA signature for stomach cancer.
What is something you hold to be true that many people may disagree with?
There is a way to detect cancer early at the molecular level with a simple blood test.
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