AI-Based Toxicity & Safety Prediction

Ensuring drug safety is one of the most critical challenges in drug development. Predicting toxicity risks, adverse effects, and off-target interactions early in the process can significantly reduce late-stage failures, regulatory hurdles, and costly withdrawals. However, traditional toxicity assessment relies on preclinical animal testing, in vitro assays, and retrospective analysis of clinical data, which are time-consuming, expensive, and often fail to predict human-specific toxicities.

AI4CR is building the foundations for AI-driven toxicity prediction by developing disease-specific knowledge graphs and agentic AI workflows that extract safety-relevant entity relationships from scientific literature, regulatory filings, and multi-omics datasets. These living knowledge graphs will provide the basis for future AI-based toxicity assessment and risk modeling, enabling a more data-driven, scalable approach to safety evaluation.

Challenges in Traditional Toxicity & Safety Assessment

• Limited Early-Stage Predictive Models – Toxicity risks are often identified too late in development, after significant resources have already been invested in preclinical and early clinical trials.

• Reliance on Animal Testing – Preclinical animal models do not always translate to human responses, leading to false negatives or false positives in toxicity prediction.

• Fragmented Safety Data – Adverse event reports, toxicogenomic studies, and drug safety databases are scattered across multiple sources, making it difficult to derive comprehensive safety insights.

• High Cost & Attrition in Drug Development – Late-stage toxicity failures are a major driver of R&D cost escalation, forcing companies to abandon promising candidates after significant investment.

  
  

AI4CR’s Approach

AI4CR is actively working towards an AI-powered toxicity prediction framework, starting with real-time biomedical knowledge graphs that structure safety-relevant data across multiple sources.

• Building Disease-Specific Safety Knowledge Graphs – AI4CR is designing real-time, continuously updated knowledge graphs that integrate toxicology studies, molecular interactions, adverse event data, and regulatory insights.

• Automating Multi-Source Toxicity Data Extraction – Agentic AI workflows extract toxicity-related insights from scientific literature, clinical trial reports, FDA/EMA filings, and toxicogenomic datasets, laying the groundwork for future predictive safety modeling.

• Mapping Drug-Target & Off-Target Interactions – By establishing structured relationships between drugs, targets, and toxicological pathways, AI4CR is creating the infrastructure for early detection of off-target risks.

• Preparing for AI-Driven Predictive Toxicology – As AI4CR’s data pipelines evolve, the goal is to develop machine learning models that assess dose-response relationships, mechanistic toxicity risks, and patient-specific safety profiles.

  
  

Key Benefits (Current & Future Goals)

✔️ Structured Safety Intelligence – AI4CR’s knowledge graphs provide a centralized, structured view of toxicology data, making safety insights more accessible.
✔️ Early Identification of Potential Risks – By mapping drug-toxicity relationships, AI4CR aims to support earlier risk mitigation in development pipelines.
✔️ Reducing Late-Stage Failures – Over time, predictive safety models will help pharma teams prioritize low-risk candidates, improving clinical success rates.
✔️ Scalable AI-Powered Safety Assessment – While still in development, AI4CR’s approach is designed to evolve into a fully automated toxicity prediction system that integrates multi-omics, preclinical, and real-world safety data.

Conclusion

AI4CR is building the foundation for AI-driven toxicity and safety prediction by developing knowledge graphs and automated research pipelines that will serve as a basis for future predictive toxicology models. While full AI-based risk prediction is a long-term goal, the immediate focus is on structuring and integrating safety-relevant data to improve early-stage decision-making in drug development.