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65th Annual Meeting of the German Association for Medical Informatics, Biometry and Epidemiology (GMDS), Meeting of the Central European Network (CEN: German Region, Austro-Swiss Region and Polish Region) of the International Biometric Society (IBS)

06.09. - 09.09.2020, Berlin (online conference)

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A multivariate Bayesian approach for incorporating historical data into the assessment of test items in reproductive toxicology

Meeting Abstract

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  • Ludger Sandig - Faculty of Statistics, TU Dortmund University, Dortmund, Germany
  • Bernd Baier - Reproductive Toxicology, Nonclinical Drug Safety, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riß, Germany
  • Katja Ickstadt - Faculty of Statistics, TU Dortmund University, Dortmund, Germany
  • Bernd-Wolfgang Igl - Nonclinical Statistics, Biostat. and Data Sciences, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riß, Germany

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie. 65th Annual Meeting of the German Association for Medical Informatics, Biometry and Epidemiology (GMDS), Meeting of the Central European Network (CEN: German Region, Austro-Swiss Region and Polish Region) of the International Biometric Society (IBS). Berlin, 06.-09.09.2020. Düsseldorf: German Medical Science GMS Publishing House; 2021. DocAbstr. 313

doi: 10.3205/20gmds353, urn:nbn:de:0183-20gmds3530

Published: February 26, 2021

© 2021 Sandig et al.
This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 License. See license information at http://creativecommons.org/licenses/by/4.0/.

Outline

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Background: Nonclinical developmental and reproductive toxicology (DART) studies are required by regulatory bodies to support human clinical trials and market access of new pharmaceuticals.

Methods: The studies are usually performed in rats and a non-rodent species and aim to reveal any effect of the test substance on reproduction relevant for human risk assessment. Recommendations on study design and conduct are given in ICH Guideline S5 and allow for various phase-dependent designs and collection of a huge number of parameters.

Results: From a statistical point of view, an extremely crucial point are the complex correlation structures between mother and offspring, e.g. maternal weight development, fetus weight, ossification status and number of littermates all in dependence of different test item doses.

Conclusion: We propose a multivariate statistical model to involve certain correlation structures between different target parameters and a Bayesian approach to incorporate historical data for specifying a dose dependency of an effect caused by the test item.

The authors declare that they have no competing interests.

The authors declare that an ethics committee vote is not required.