Historical control data in reproductive and developmental toxicity studies

Abstract

Reproductive and developmental toxicity studies in laboratory animals are conducted as part of the process of evaluating the risk of pharmaceuticals and chemicals to human reproduction and development. In these studies, comparison of data from groups dosed with the test article to a concurrent control group is considered the most relevant approach for the interpretation of adverse effects. However, differences between the concurrent control and treated groups may arise by chance alone, and in some instances may even appear to be dose-related. These limitations of the concurrent control group are of particular concern when interpreting fetal malformation data because malformations are rare events that can be better characterized when incidences in both concurrent control and treated groups are compared to a larger set of control values. Historical control data can be useful not only to understand the range of normal for a given endpoint but also to monitor the biological variability over time due to various external factors (e.g., genetic changes in a strain, changes at the breeding facility). It can also serve to track the performance of the laboratory and identify any changes in the data that may be the result of internal factors at the performing laboratory due to modification in animal diet, seasonal changes, or even the proficiency of the technicians in handling animals and recording fetal and offspring observations. This chapter will provide the reader with guidance on building a laboratory historical control database and applying it to the scientific interpretation of reproductive and developmental toxicity data. Information on sources of external historical control data will be provided and some perspective given on the utility of this data. A discussion of the presentation of historical control data with descriptive statistics will be accompanied by examples of tabulation of the data. Supernumerary rib will be used as an example of how historical control data can be used for data interpretation. © 2013 Springer Science+Business Media, LLC.