London-based Multiomic Health, a company building an AI-enabled precision therapeutics discovery platform for metabolic syndrome diseases, announced on Thursday, May 4, that it has secured £5M (approximately €5.71M) in a Seed extension round of funding.
MultiOmic has raised a total of $8.6M since its founding in May 2021, including a previous round that was funded by the San Francisco-based venture capital firm Fifty Years.
The current investment round was led by Hoxton Ventures, with participation from Ada Ventures, MMC Ventures and Verve Ventures.
Rob Kniaz, Partner at Hoxton, says, “We are very excited to be partnering with Robert and the team at MultiOmic. Metabolic syndrome and its resultant medical conditions are the most significant cause of death globally, and this is forecast to increase since COVID survivors are at a much higher risk of being affected.”
“We couldn’t be more bullish about the transformational impact that innovations at the intersection of tech and bio are having on patients – we see tremendous potential in the space. Via our investment in MultiOmic, we see a very exciting opportunity to use technology to address an area of high unmet need where conventional approaches have historically failed.”
About MultiOmic Health
Founded in 2021 by Robert Thong, MultiOmic Health focuses on metabolic syndrome-related disorders using AI-enabled drug discovery (AIDD). It collaborates with chosen healthcare providers to collect and evaluate de-identified patient data and bio-samples.
The MOHSAIC platform from MultiOmic combines integrated multi-omics data, computational systems biology modelling, and focused wet laboratory trials to generate unique precision treatment ideas that MultiOmic commercialises through collaborations with existing biopharma businesses.
Thong says, “Existing treatments for metabolic syndrome-related conditions merely reduce risk or delay onset of serious consequences such as heart attack, stroke, kidney failure, blindness, nerve damage, foot amputation, liver failure and premature death.”
“Patients’ disease journeys exhibit immense heterogeneity, reflecting potentially hundreds of different disease variants that result not only from inherited genetics but also diet, lifestyle and other environmental factors.”
“Unlike cancer however, there are no existing precision medicines to tackle the specific molecular-level drivers of each metabolic syndrome disease variant. We will address this huge unmet medical need by systematically discovering, at scale, unique treatment concepts for these different disease variants.”
“Our human-centric platform originates therapeutics from high-fidelity patient data, reversing the traditional drug discovery models that initiate treatments from test tube and animal experiments. We are going from bedside to lab bench, rather than from lab bench to bedside!” adds Thong.
Capital utilisation
MultiOmic says it will use the funds to show proof-of-concept for MultiOmic’s MOHSAIC platform in diabetic kidney disease.
According to the company, a $300B yearly expense for global healthcare is now attributed to the 30 to 40 per cent of diabetics who later acquire chronic renal disease.
MultiOmic’s MOHSAIC platform, optimised for metabolic syndrome, combines:
- analysing complicated multi-omics patient data and associated longitudinal clinical data using machine learning and other AI approaches;
- Simulations of disease pathways in systems biology; and
- Iterative refinement and validation of in-silico findings using targeted wet lab experiments.
Alasdair Thong, co-founder of MultiOmic, says, “Recent declining omics data generation costs, coupled with accelerating deployment of AI-enabled data science and digital simulation in disease biology, represent a unique opportunity to bring the immense benefits of precision medicine to metabolic syndrome patients.”
“We created this venture to address a hitherto under-exploited space that can now be tackled affordably with the right tools. We leveraged our experience of startups and biopharma R&D to design a data strategy and operating model that avoids many of the pitfalls that befell earlier-generation AI-enabled startups,” he adds.
