Project Scope
A computational pipeline to build models to predict target organs of toxicity from SEND datasets has been developed and published on GitHub under PHUSE. Project team members will evaluate the feasibility and performance of this pipeline when run on data from within their organisations. The pipeline will be updated to improve compatibility with different database systems, and efforts will be made to improve its performance across disparate data sources. Additional study interpretations – e.g. adversity of findings, NOAEL determination, clinical translatability, structure activity relationship – will be explored for development of predictive models. Successful modeling approaches will be published in peer-reviewed scientific journal articles.

Project Scope

A computational pipeline to build models to predict target organs of toxicity from SEND datasets has been developed and published on GitHub under PHUSE. Project team members will evaluate the feasibility and performance of this pipeline when run on data from within their organisations. The pipeline will be updated to improve compatibility with different database systems, and efforts will be made to improve its performance across disparate data sources. Additional study interpretations – e.g. adversity of findings, NOAEL determination, clinical translatability, structure activity relationship – will be explored for development of predictive models. Successful modeling approaches will be published in peer-reviewed scientific journal articles.

Problem Statement
Expert toxicologists spend hours interpreting the results of multiple studies to support the safety of new drug products. SEND data has enabled the construction of large databases of toxicology study results for building predictive models. These models can be trained using expert interpretations of old study data to make predictions that will streamline the interpretation of future studies.

Problem Impact
Many hours are spent by both industry toxicologists and regulatory authority reviewers attempting to properly integrate and interpret the results of toxicology studies conducted to support drug safety. SEND data can be used to train models to use prior interpretations to predict likely interpretations of current studies. This approach will decrease time spent reviewing study results and increase study interpretation across toxicologists because the models will be trained on their interpretive conclusions. The models may also provide valuable insight into which toxicology study endpoints are most relevant for prediction of specific interpretations, e.g. hepatotoxicity, nephrotoxicity.

Problem Impact

Many hours are spent by both industry toxicologists and regulatory authority reviewers attempting to properly integrate and interpret the results of toxicology studies conducted to support drug safety. SEND data can be used to train models to use prior interpretations to predict likely interpretations of current studies. This approach will decrease time spent reviewing study results and increase study interpretation across toxicologists because the models will be trained on their interpretive conclusions. The models may also provide valuable insight into which toxicology study endpoints are most relevant for prediction of specific interpretations, e.g. hepatotoxicity, nephrotoxicity.

Project Leads	Email
Kevin Snyder, FDA	Kevin.Snyder@fda.hhs.gov
Lennart Anger, Genentech	Anger.lennart@gene.com
Alex Pearce, PHUSE Project Assistant	Alexandra@phuse.global

CURRENT STATUS Q1 2025
No update.

Objectives & Deliverables	Timelines
Evaluate the feasibility and performance of target organ prediction models developed by the FDA on data from other project members’ organisations	Q4 2024
Identify and prioritise a list of modeling endpoints – i.e. study interpretations – which would be most useful for additional predictive modeling approaches.	Q4 2024
Iteratively improve upon the FDA modeling approach and develop, implement and test additional modeling approaches	Q3 2025
Draft a manuscript describing successful modeling approaches	Q3 2025

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