In this free webinar, learn how artificial intelligence/machine learning-based technology is already being used in a regulatory setting for predictive toxicology. Attendees will learn how ICH M7 submissions represent the most prevalent application of QSAR models. The featured speakers will discuss why scientific understanding of mechanisms is important in creating reliable QSAR models. The speakers will share how the assessment of extractables and leachables is an emerging area for predictive toxicology.

TORONTO, Oct. 15, 2024 /PRNewswire-PRWeb/ — The application of artificial intelligence (AI) technologies, such as machine learning models (e.g., QSAR) and expert rules-based systems, is a well-established practice in regulatory predictive toxicology, where they are used to support drug safety assessments.

The implementation of these methods in regulatory predictive toxicology is partly due to their adherence to principles defined in the OECD QSAR Assessment Framework (QAF) that outline criteria (based on earlier validation principles for models) to help ensure data quality; objective measures of QSAR model performance and robustness; a domain of applicability, documentation and transparency; and an underlying mechanistic interpretation where possible.

Recently, advancements in technology, such as algorithmic innovation, have led to a surge in new AI modalities. New guidelines need to be developed to ensure data reliability for models to be suitable to support regulatory assessments. In this webinar, the expert speakers will illustrate how Instem’s QSAR models and structural alerts adhere to the QAF and support regulatory safety assessments. They will identify current and emerging regulatory guidelines and demonstrate the applicability of structure-based predictive models.

Register for this webinar today to explore the application of advanced AI technologies such as QSAR models and expert rules-based systems in supporting drug safety assessments.

Join experts from Instem, Kevin P. Cross, PhD, Head of Science; and Candice Johnson, PhD, Senior Research Scientist, for the live webinar on Thursday, October 31, 2024, at 12pm EDT (9am PDT).

For more information, or to register for this event, visit AI Applications in Predictive Toxicology Supporting Safety Assessments.

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