Vamstar to Add Payer Intelligence Before Drug Trials

Vamstar, a London-based life sciences AI company, has secured around £900k ($1.2m) in funding, with which to further develop software helping health tech developers anticipate payer requirements and predict launch outcomes before trial protocols are locked in.

Vamstar was founded in 2019, and already offers Polaris, a platform used by global pharmaceutical, medical technology and life sciences companies for pricing intelligence, tender and contract automation, and market access, in more than 100 countries.

The firm says most new pharma products that reach late-stage trials never become commercially viable, which means much of the total of £200 billion ($264 billion) spent worldwide on pharmaceutical R&D each year is wasted. Vamstar's tools bring pricing intelligence into the mix at a much earlier stage, using agentic AI to connect clinical trial evidence, health-economic modelling and real-world market and pricing data in one system. Development teams can make design choices in the knowledge of what evidence will be required for buyers to accept which price points.

Development to be funded by the new investment - which came from Innovate UK's Sovereign AI programme - includes the expansion of coverage across six therapeutic areas; the addition of clinical evidence, outcomes and real-world market data sources; and a proof-of-concept pilot of the tech 'at scale as a cloud service in a realistic environment'.

'The industry spends years generating evidence and only learns at the end whether payers will accept it,' says CEO and co-founder Praful Mehta. 'We are reversing that order. If developers can see the market-access verdict while the trial can still change, they design better trials, fewer good products fail for the wrong reasons, and patients get them sooner.'

CTO and co-founder Dr Richard Freeman (pictured) adds: 'This is actually the first time that a system interconnects clinical trials, regulatory market data, and the actual post-approval market and commercial worlds together at scale, allowing for a rapid feedback loop. This is powered by a massive agentic AI architecture that combines a large-scale knowledge graph, large language models, and machine learning models.'

Web site: www.vamstar.io .