Modeling approaches for dose-response data in toxicology

Abstract

Dose-response modeling occurs in many application areas and has a rich research history. An extensively studied application feld is clinical studies, where dose-response modeling is used in Phase II studies to identify the dose closest to a pre-defned effect. Many non-clinical, toxicological studies also aim at identifying a dose-response relationship. However, for non-clinical or toxicological studies there are fewer regulations or guidelines. This leads to a gap between nowadays research advances in statistical modeling and the use of these methods in practice in toxicology. In addition, toxicological doseresponse studies differ from clinical studies in various technical aspects. For example, cells might be studied instead of human patients, and administered doses are constrained due to laboratory, and technical reasons rather than ethical considerations. Therefore, the transfer of clinical methodological knowledge into toxicological applications is only possible to a limited extent and tailored methodologies are required that match the specifc data structure of toxicological studies. This cumulative thesis is based upon four works that all present approaches for modeling toxicological dose-response data. The frst manuscript reveals the potential of applying the Multiple Comparison Testing and Modeling (MCP-Mod) approach by Bretz et al. (2005) developed for Phase II clinical studies on toxicological, gene-expression doseresponse data. In the second manuscript, a parametric, mechanistically motivated model for toxicological dose-time-response data is developed. The third manuscript is application-focused and explains the use of interaction effects when analyzing doseresponse gene expression in a two-factor setting. At last, a non-parametric Bayesian dose-response modeling approach was developed that performs functional shrinkage for non-linear function spaces. While the frst three manuscripts are published, the fourth work is attached in its current version.