Integrated testing strategies (ITS) for safety assessment
Author(s) -
Costanza Rovida,
Nathalie Alépée,
Anne M Api,
David A. Basketter,
Frédéric Y. Bois,
F. Caloni,
Emanuela Corsini,
Mardas Daneshian,
Chantra Eskes,
Janine Ezendam,
Horst W. Fuchs,
Patrick Hayden,
Christa HegeleHartung,
Sebastian Hoffmann,
Bruno Hubesch,
Miriam N. Jacobs,
Joanna Jaworska,
André Kleensang,
Nicole Kleinstreuer,
J. Lalko,
Robert Landsiedel,
Frédéric Lebreux,
Thomas Luechtefeld,
Monica Locatelli,
Annette Mehling,
Andreas Natsch,
Jonathan W. Pitchford,
Donald Prater,
Pilar Prieto,
Andreas Schepky,
Gerrit Schüürmann,
Lena Smirnova,
Colleen Toóle,
Erwin van Vliet,
Dirk Weisensee,
Thomas Härtung
Publication year - 2014
Publication title -
altex
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.975
H-Index - 51
eISSN - 1868-8551
pISSN - 1868-596X
DOI - 10.14573/altex.1411011
Subject(s) - computer science , test strategy , risk analysis (engineering) , outcome (game theory) , test (biology) , data science , machine learning , business , software , paleontology , mathematics , mathematical economics , biology , programming language
Integrated testing strategies (ITS), as opposed to single definitive tests or fixed batteries of tests, are expected to efficiently combine different information sources in a quantifiable fashion to satisfy an information need, in this case for regulatory safety assessments. With increasing awareness of the limitations of each individual tool and the development of highly targeted tests and predictions, the need for combining pieces of evidence increases. The discussions that took place during this workshop, which brought together a group of experts coming from different related areas, illustrate the current state of the art of ITS, as well as promising developments and identifiable challenges. The case of skin sensitization was taken as an example to understand how possible ITS can be constructed, optimized and validated. This will require embracing and developing new concepts such as adverse outcome pathways (AOP), advanced statistical learning algorithms and machine learning, mechanistic validation and "Good ITS Practices".
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