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🔬 Axiom Bio – Raising 💲15M to Replace Animal Testing Using Human Cell Models
Freshly launched Axiom Bio has raised 💲15M to develop AI models that replace animal 🐭 testing, focusing initially on drug-induced liver injury (DILI) prediction (Axiom’s $15 Million Series Seed Funding Round).
Axiom helps scientists eliminate molecular toxicity by providing the most accurate and affordable predictive models. Their proprietary dataset includes more than 100,000+ molecules tested in pooled primary human liver cells, tens of thousands of molecules with pharmacokinetic measurements, and thousands of molecules with curated clinical outcome data (115,000+ Unique small molecules, 1,200+ Unique targets, 50,000+ Unique scaffolds, 3,300+ Macrocyclic compounds, 9,500+ PROTACs and molecular glues, and 700+ Clinical molecules from FDA's DILIrank). Axiom's AI models offer higher accuracy than advanced in vitro systems like 3d spheroids, deep mechanistic understanding which untangles mitochondrial toxicity, ER stress, ROS formation, cytotoxicity, and more, and precise risk assessment for any molecule at relevant clinical dosage and clinical exposure levels.
Innovation Highlights 🧵:
🪡 Utilizes human liver cells and high-content imaging to generate large-scale datasets.
🪡 Machine learning models trained on this data predict how new compounds will affect liver function—more accurately than animal models.
Axiom Bio is well-aligned with the FDA’s shift to replace animal testing moving away from requiring animal models for investigational new drug (IND) applications for new monoclonal antibodies and some other drug candidates, while animal testing will be reduced, refined or potentially replaced by a suite of new approach methodologies (NAMs), including computational models, and human cell lines and organoids (FDA plans to end animal testing requirements for monoclonal antibody drugs). For a more detailed list of 📍AI-driven Toxicity Models, 📍Lab-Grown Human Organoids, and 📍Organ-On-Chip Systems (The New Approach Methods, NAMs):
