Computational Models
ITR Laboratories offers predictive power built on data, expertise, and regulatory alignment.
Computational Models Behind The In Silico Analysis
- Quantitative Structure–Activity Relationship ((Q)SAR) models are used to analyze molecular structures and predict toxicological outcomes based on patterns in large datasets of known compounds
- Structural features linked to acute toxicity using curated expert rules are identified, enabling early detection of potential safety concerns during compound design
- We predict toxicological properties of novel or under-studied chemicals by comparing them to structurally similar, well-characterized analogues, supporting scientifically sound extrapolations
- The In-silico analysis combines statistical models, expert systems, and curated databases to deliver robust, transparent, and reproducible toxicity predictions
- The models align with international guidelines (e.g., ICH M7), providing scientifically defensible outputs suitable for regulatory submissions
De-risk Your Pipeline with Predictive AI Toxicology Solutions
The in silico toxicology services are designed to predict the potential toxicity and adverse human clinical effects of various chemicals, including pharmaceuticals, cosmetics, and food ingredients.
We employ sophisticated artificial intelligence and machine-learning algorithms to predict potential safety outcomes.
A database of well over 500,000 toxicology studies for more than 200,000 chemicals is used to make the predictions.
The database
The database is constantly growing using toxicology data from various sources such as:
- Public Databases: National Toxicology Program (NTP) and other public genetic toxicity databases.
- FDA Submissions: Non-confidential portions of FDA submissions, including data from the Food Additive Resource Management system (FARM) and Priority-based Assessment of Food Additives (PAFA).
- Research Articles
- Regulatory Agencies: Study reports from regulatory agencies like the FDA.