Computational Toxicology: Approaches to Predict the Toxicity of Compounds

Overview

This one-day, in-person course will provide hands on instruction on the use of computational approaches to assess the safety of chemical compounds. Although the techniques learned in the course will be broadly applicable for the safety assessment of chemical compounds used for for any purpose (e.g, industrial compounds, pesticides) the course focuses on the use of these approaches to predict the toxicity of compounds released from medical devices and pharmaceutical packaging/drug delivery devices. The course will illustrate how open source QSAR models and Read Across approaches can be used to predict toxicity, but will also address models to predict the rate at which compounds are released from polymeric materials and will explore the use of models to perform in vitro-to-in vivo extraplation (IVIVE) of dose. In addition, the course will also explain how to use these model-derived estimates in a safety or risk assessment of chemical compounds.

Who should take this course?

This course will be primarily useful for toxicologists, chemists, and biocompatibility specialists in the medical device and pharmaceutical fields who use computational models (e.g., QSAR, Read Across) to prepare safety or risk assessments of E&L compounds and drug impurities. The course may also be of interest to regulatory affairs professionals and corporate managers who review safety assessments of E&L compounds and drug impurities included in regulatory submission documents.

Why should I take this course?

The safety assessment of E&L compounds and drug impurities requires data on the dose of the compound received by the patient and the toxicity of the compounds; however, exposure and toxicity data are not available for many of these compounds. Fortunately, in silico computational approaches are available to predict the rate at which compounds leach from polymeric materials and predict the potential toxic hazard posed by patient exposure to these compounds.

What will I learn in this course?

Module 1: When to use computational approaches for the safety assessment of E&L compounds and drug impurities (0.75 hour)

Module 2: Using open-source exposure models to predict patient exposure to E&L compounds (0.25 hours)

Module 3: Using open-source QSAR models and expert systems to predict the toxicological hazards associated with E&L compounds and drug impurities (1.5 hours)

Module 4: Using Read Across methods for the safety assessment of E&L compounds (1.5 hours)

Module 5: Using model-derived predictions of exposure and toxicity in a safety assessment of E&L compounds and drug impurities (1 hour)

Module 6: Hands-on use of models, presentation of case studies, and Q&A (1 hour)

About the trainer

Ron Brown is a toxicologist with 35 years of experience in regulatory toxicology and risk assessment. He recently retired from the US FDA and currently directs a small company, Risk Science Consortium, LLC, that provides consultation and training in toxicological risk assessment and computational toxicology. At the FDA, Ron was the senior toxicologist responsible for developing and reviewing toxicological risk assessments of extractable and leachable (E&L) compounds from medical devices. While at the FDA, he served in a number of leadership roles in standards development organizations. At the international level, he served for many years as convener of ISO TC194 WG11 which is responsible for the development and revision of the ISO 10993-17 standard, Biological evaluation of medical devices-Part 17: Establishment of allowable limits for leachable substances. At the national level, he represented the United States as an expert on ISO TC194 WG11 and served as co-chair of the AAMI Biological Evaluation Committee. Prior to his position at the US FDA, Ron served as a Senior Associate at the ILSI Risk Science Institute. He is founding member and former President of the Medical Device and Combination Products Specialty Section of the Society of Toxicology and former President of the Dose-Response Specialty Section of the Society for Risk Analysis.