Cytocast provides an AI‑driven Digital Twin Platform™ that generates tissue‑specific safety predictions for chemical compounds, enabling rapid, mechanistic risk profiling across large libraries. Its three modular products—Screener™, Optimizer™ and Nominator™—support early‑stage triage, preclinical refinement, and lead nomination by delivering interactive reports that flag potential side effects, explain adverse‑event mechanisms, and forecast drug‑drug interactions, helping pharma and biotech teams make evidence‑based, fail‑fast decisions.
Funding
$1.8M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Drug development programs often encounter late-stage failures due to unforeseen safety liabilities, leading to costly delays and wasted resources. Early-stage discovery and preclinical teams lack rapid, interpretable methods to predict adverse‑event risk across large compound libraries, making it difficult to prioritize the most promising candidates.
Solution
Cytocast offers an AI‑driven Digital Twin Platform™ that generates biorealistic, tissue‑specific safety predictions for chemical compounds. The platform combines deep‑learning target prediction with large‑scale in‑silico simulations of 29 human tissue types, producing mechanistic risk profiles within 1–2 weeks. Three modular products support the drug development workflow: Screener™ ranks hundreds to thousands of compounds before synthesis, flagging potential side effects; Optimizer™ refines candidate selection after in vitro and in vivo testing by providing detailed adverse‑event analyses and mechanistic explanations; Nominator™ delivers interactive, comparative reports for lead and backup nomination, including drug‑drug interaction forecasts. Interactive reports enable R&D teams to make evidence‑based “fail‑fast” decisions, reduce downstream safety failures, and allocate resources to candidates with the highest probability of success.
Target Audience
Primary customers are large pharmaceutical companies, innovative biotech firms, and contract research organizations seeking to de‑risk discovery and preclinical programs; the platform also targets emerging applications in animal health, cosmetics, and food science.
Features
- Deep‑learning target prediction integrated with biophysical simulations of 29 digital tissue replicas
- Rapid turnaround (1–2 weeks) for both high‑throughput triage and detailed safety profiling
- Mechanistic insight linking predicted adverse events to specific protein complexes and signaling pathways
- Fully interactive reports that support scenario exploration, comparative analysis, and collaborative decision‑making
- Optional incorporation of secondary pharmacology data and drug‑drug interaction predictions
- Scalable workflow covering pre‑synthesis triage, early‑late preclinical refinement, and lead nomination