Conventional models for predicting drug-induced liver injury (DILI) often lack the accuracy, mechanistic insight, clinical relevance and scalability needed to guide modern drug development. These limitations contribute to costly trial failures and market withdrawals across the industry.
Axiom is introducing a new paradigm. By combining the world’s largest dataset of primary human hepatocytes with advanced AI/ML, our platform delivers clinically relevant and interpretable DILI risk assessment. High-content imaging captures subtle phenotypic responses, while Axiom’s models connect these features to patient outcomes, providing scientists not just with predictions, but with transparent mechanistic explanations that can inform decision-making and medicinal chemistry design.
Key Takeaways:
- How Axiom leverages real-world biological and clinical data at unprecedented scale to generate precise, and interpretable clinical risk predictions.
- Best practices for model validation against real-world outcomes.
- Strategies for embedding AI/ML into preclinical workflows to improve safety decisions.
- Insights into how mechanistic imaging informs medicinal chemistry and risk mitigation.