Silicon Valley-based Vium, formerly Mousera, has introduced what the company called “the first living informatics approach to transforming preclinical drug research.”
The platform includes Vium’s Digital Vivarium™ and Vium Cloud, which use unique Silicon Valley technology in order to enable in vivo research by applying life sciences, digital technology, and large-scale interpretive methods to living systems.
The company’s co-founders Timothy L. Robertson, Ph.D. and Joe Betts-Lacroix will demonstrate the platform at the BIO International Convention June 6 to 9 in San Francisco.
“There are brilliant scientists driving drug development forward by creating advanced biologic animal models, identifying novel disease targets and molecules, and a lot more,” Robertson told Outsourcing-Pharma.com.
Yet, the drug development process takes too long and is extremely expensive – according to Tufts, the product lifecycle cost per approved drug is around $2.87bn.
Preclinical research, specifically, has struggled recently, as the studies are too idiosyncratic and often don’t provide enough information.
“We can improve this by enabling researchers to get more information out of in vivo studies, so they can make better and faster decisions on which compounds to stop, modify, or expedite,” said Robertson, which would allow for less time and resources to be spent on clinical trials.
Bridging the gap
With their backgrounds in science and a desire to improve human health, Betts-Lacroix and Robertson explained they saw a “cultural gap in drug development,” seemingly overlooked by others.
There have been advances in in vivo drug development, “but not in the application of 21st century technology,” said Robertson.
Vium hopes to bridge that gap by introducing Silicon Valley technology, such as intelligent sensors, complex and integrated software/hardware systems, and high definition camera networks into in vivo research,and in doing so, optimize important scientific advances.
According to Betts-Lacroix and Robertson, Vium collects gigabytes of continuous and objective data from each research subject on a daily basis, as opposed to traditional in vivo research, which gathers much less data – often less than a kilobyte.
“Traditional preclinical data is also collected subjectively, manually recorded by technicians at intervals through the day,” added Betts-Lacroix. “This process is often arduous, prone to human error, and causes stress to the animals, which can impact study results.”
The Vium Digital Vivarium™ and Vium Cloud automate measurements and give researchers 24/7 online access to their data, which can be monitored and analyzed at any time.
According to the co-founders, the Digital Vivarium also provides a more humane, natural environment for research subjects, as it acquires more data from each animal, enabling fewer to be used than in traditional research labs, thus creating “the most humane, ‘low touch’ possible environment.”
The company is fully accredited by AAALAC and it a recipient of a Letter of Commendation from AAALAC’s Council on Accreditation, including a commendation for upholding the 3Rs: Replacement, Reduction, and Refinement, the gold standard framework for humane animal research.
“At Vium, we begin by validating against conventional in vivo measures, demonstrating that results generated by our systems match or exceed industry standards,” said Robertson.
The Vium Arthritis Index was recently presented at the American Association of Immunologists annual meeting. According to Betts and Robertson, the Index showed that the standard measure of efficacy in arthritis drugs could be accurately measured against conventional methods with less human labor and error, and much less animal handling.
The company is currently focused on validating indices in MS, lupus, phenotyping, and aging with more in the works and is engaging researchers to run studies that will further demonstrate the technology.
As for the company’s challenges, “any new technology that disrupts an old way of working can take time to adopt,” said Betts-Lacroix, who added that ultimately, “the integration of new technologies like machine learning and computer vision to preclinical research brings more evidence, value and consistency.”