More than 56% of drugs fail in clinical trials because of safety issues, resulting in an annual loss of over $400 billion and delaying potentially life-saving treatments for patients. To tackle this challenge, Ignota Labs, a UK-based biotech startup, has developed an AI platform called SAFEPATH that uses deep learning, cheminformatics, and bioinformatics to uncover and resolve the root causes of drug toxicity, unlocking new opportunities for assets that would otherwise be abandoned.
Earlier this week, the startup secured $6.9M in seed funding, co-led by Montage Ventures and AIX Ventures (which invested in Perplexity and Hugging Face), with participation from Modi Ventures, Blue Wire Capital, and Gaingels. The funding will expand the pipeline by acquiring additional distressed assets and advancing the startup’s first asset, a PDE9A inhibitor, into early-stage clinical trials.
The company has raised a total of $7.9M in funding, though its valuation remains undisclosed. Sam Windsor, CEO and co-founder of Ignota Labs, said, “Traditional safety assessments reveal when something is wrong, but our platform goes further by identifying the exact molecular and biological issues to provide actionable insights to re-engineer and revive therapies. With this funding, we can expand our efforts to salvage distressed assets and accelerate the delivery of life-saving therapies to patients.”
Rescuing promising therapies that would otherwise be abandoned
Founded in 2021 in Cambridge by Sam Windsor (ex-Google DeepMind’s AlphaFold), Jordan Lane (ex-AstraZeneca), and Layla Hosseini-Gerami (ex-researcher at the University of Cambridge, where she produced several publications in collaboration with Eli Lilly), Ignota Labs aims to rescue promising drugs that have failed in clinical trials due to safety concerns.
The founders bring complementary expertise to the venture. Windsor has a decade of experience in life sciences and previously worked on DeepMind’s AlphaFold team. Dr Lane has developed five advanced assets in clinical trials, while Dr Hosseini-Gerami specialises in AI and cheminformatics.
Speaking to TFN, Hosseini-Gerami explained: “Sam and Jordan met during their first week at the University of Nottingham. After ten years in drug discovery, Jordan wanted to launch a company to improve efficiency. Sam had just completed his MBA, so they began establishing the venture together. They decided on an AI-driven drug discovery approach and needed specialised expertise, searching among top academic research groups to find me. After connecting on LinkedIn, we spent the following months building a proof-of-concept and developing a pitch for pre-seed funding, which led to the company operating full-time by October 2022.”
In just two years, Ignota Labs has demonstrated the power of its approach. The company successfully in-licensed its first asset, a first-in-class metabolic health drug with the potential to improve the lives of over 1.2 billion post-menopausal women. Using SAFEPATH, Ignota Labs resolved the safety concerns that had halted the drug’s progress, validating these findings through rodent models.
How Ignota Labs builds a pipeline of high-value therapies
Ignota Labs employs a novel strategy to build a pipeline of high-value therapies. The company identifies promising drug candidates abandoned due to safety concerns—typically 80-90% of the way to success—and uses SAFEPATH to determine the underlying issues and develop solutions. This model reduces development timelines by years, cuts costs by millions, and offers multi-billion-dollar potential for each recovered therapy.
Unlike traditional safety assessments that merely identify what went wrong, Ignota Labs’ AI platform SAFEPATH combines cheminformatics, bioinformatics, and multimodal data analysis to explain why and how safety issues occur. This delivers actionable insights that can be used to refine or repurpose drug candidates.
The SAFEPATH platform includes IgnotaNet, a state-of-the-art architecture that builds 15,000 machine-learning models that predict on- and off-target protein binding and toxicity endpoints. It also features a heterogeneous knowledge graph incorporating millions of nodes and relationships between proteins, drugs, diseases, pathways, and side effects.
In terms of competition, Layla added, “We focus on a different piece of the drug development funnel. We are not drug discovery—we only focus on those discovered to show strong therapeutic indications, which is a feat in itself, but then fall at the next hurdle of drug trials. We also combine cheminformatics and bioinformatics in ML models, which is unique among other approaches that usually focus on one or the other.”
Krish Ramadurai, Partner at AIX Ventures, said: “Ignota is redefining drug development and pioneering a new era of predictive and engineerable biology—where AI systematically de-risks therapeutic development by designing safer, more effective drugs with precision. Their approach shifts biopharmaceutical R&D from trial-and-error to a rational, data-driven process, enabling molecules to be precisely refined at scale to accelerate clinical development. I’m incredibly excited about their potential to transform AI-driven drug discovery and unlock massive value from previously failed assets to improve patient outcomes.”
Future outlook
Ignota Labs recently released a pre-print showcasing how SAFEPATH analyses hepatotoxicity mechanisms in erlotinib and gefitinib. They also have a peer-reviewed paper published in the Royal Society of Chemistry’s Digital Discovery journal, further validating their approach.
Commenting on plans, Layla outlined three key priorities: “Ignota Labs focuses on finding more promising assets to in-license, progressing our current assets further into the clinic, and doubling our team size, with roles across science, technology, and business development/ops.”
The company has recently relocated to Cambridge Science Park, positioning itself at the heart of one of the UK’s most vibrant biotech ecosystems. With the new funding secured and a growing team, Ignota Labs is well-positioned to make significant strides in rescuing failed drugs and potentially revolutionising drug development.
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