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Xtalks, Online
2024-10-31
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, 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 Leadscope’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.
Read more...
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.
Keywords: ADME, Drug Development, Drug Discovery, Pharmacovigilance, Regulatory, Translational Research, Toxicology/Safety, Extractables & Leachables
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Organized by:
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Xtalks |
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Invited Speakers:
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Kevin P. Cross, PhD, Head of Science, Instem
Candice Johnson, PhD, Senior Research Scientist, Instem
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Deadline for Abstracts:
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2024-10-31
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Registration:
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Free Registration
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E-mail:
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tristan@xtalks.com
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