SESSION 4: I.T.S. for Safety Assessment

Constructing and evaluating Integrated Testing Strategies (ITS) for Safety Assessment
Thomas Hartung, Johns Hopkins University
PRESENTING AUTHOR: 

Thomas Hartung, MD, PhD

INSTITUTION / COMPANY : 

Johns Hopkins University

AUTHOR(S): 

Thomas Hartung, Johns Hopkins University, Bloomberg School of Public Health, CAAT, Baltimore, USA; University of Konstanz, CAAT-Europe, Germany

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ABSTRACT CONTENT / DETAILS: 

Despite the fact that toxicology uses many stand-alone tests, a systematic combination of several information sources very often is required: Examples include: when not all possible outcomes of interest (e.g., modes of action), classes of test substances (applicability domains), or severity classes of effect are covered in a single test; when the positive test result is rare (low prevalence leading to excessive false-positive results); when the gold standard test is too costly or uses too many animals, creating a need for prioritization by screening.

Similarly, tests are combined when the human predictivity of a single test is not satisfactory or when existing data and evidence from various tests will be integrated. Increasingly, kinetic information also will be integrated to make an in vivo extrapolation from in vitro data.

Integrated Testing Strategies (ITS) offer the solution to these problems. ITS have been discussed for more than a decade, and some attempts have been made in test guidance for regulations. Despite their obvious potential for revamping regulatory toxicology, however, we still have little guidance on the composition, validation, and adaptation of ITS for different purposes.

Similarly, Weight of Evidence and Evidence-based Toxicology approaches require different pieces of evidence and test data to be weighed and combined.

ITS also represent the logical way of combining pathway-based tests, as suggested in Toxicology for the 21st Century.

The presentation describes the state of the art of ITS and makes suggestions as to the definition, systematic combination, and quality assurance of ITS. Examples of machine learning approaches to optimize ITS for skin sensitization are given.