DeepCyte is a new biotech company that’s using artificial intelligence and single-cell biology to improve drug safety testing. The company recently raised $1.5 million in seed funding to develop its innovative tools. Co-founded by Theodore Alexandrov, Ph.D., and Shawn Owens, DeepCyte aims to help biopharma teams identify not only if a drug is toxic but also which cells it affects and why. The company is based in Delaware and Copenhagen.

Addressing a Long-Standing Problem in Drug Development

Many drugs fail during clinical trials due to toxicity or side effects that are hard to predict early on. In fact, around 30% of clinical failures are linked to these issues. Despite decades of developing new screening methods, the failure rate has stayed roughly the same. Current tools, like animal models and bulk tissue assays, only measure average responses across large tissue samples. They don’t reveal how individual cells respond to a drug, which can hide important warning signs.

This distinction is crucial because different cells can react very differently to the same drug. Some cells may be harmed while others remain unaffected. Recognizing these differences early can prevent costly failures later in the process. Regulatory agencies like the FDA are now pushing for a shift away from animal testing towards more human-relevant models that better predict how drugs will behave in people.

How DeepCyte Uses AI and Single-Cell Biology

DeepCyte’s approach combines cutting-edge single-cell biology with artificial intelligence. Single-cell biology studies each cell individually, providing a detailed picture of how different cell types react to a drug. When this detailed data is fed into AI algorithms, patterns of toxicity that standard tests might miss become visible.

Both the FDA and European regulators are working on guidelines to recognize new testing methods such as organ-on-a-chip systems and AI-based models. Organ-on-a-chip devices mimic human organs in the lab, allowing researchers to observe drug effects on human-like tissue. DeepCyte believes that this combination of human cell data and AI will become a standard part of drug safety testing in the future.

The company’s view is that mechanism-aware, cell-level evidence will be essential for safer drug development. As regulators push for more specific and human-relevant data, DeepCyte’s tools aim to meet this need by providing detailed insights into how drugs impact different cell types within tissues.

What the Funding Will Support

While DeepCyte has not detailed exactly how it will use the new funds, it plans to focus on product development, dataset expansion, and building relationships with pharmaceutical companies. The company is working on two early-stage products that leverage proprietary data to train AI models. These data atlases are vital for the AI to recognize toxicity patterns accurately.

The company emphasizes that continued investment will be needed to grow these datasets and refine its tools. The goal is to eventually give drug developers a reliable way to predict toxicity at an early stage, reducing costly failures in later clinical phases. With this seed funding, DeepCyte aims to accelerate the development of its AI-powered solutions and establish a foothold in the biopharma market.

Inspired by

• https://justainews.com/companies/funding-news/deepcyte-raises-1-5m-to-build-ai-tools-that-detect-drug-toxicity-before-clinical-trials/

Sources

• patentpc.com
• fda.gov