EN

Marcel Leist

doi:10.14573/altex.2013.2.227

Abstract

Modern toxicology has embraced in vitro methods, and major hopes are based on the omics technologies and systems biology approaches they bring along (Hartung and McBride, 2011; Hartung et al., 2012). A culture of stringent validation has been developed for such approaches (Leist et al., 2010, 2012a,b), while the quality and usefulness of animal experiments have been little scrutinized. A new study (Seok et al., 2013) now shows the low predictivity of animal responses in the field of inflammation. These findings corroborate earlier findings from comparisons in the fields of neurodegeneration, stroke and sepsis. The low predictivity of animal experiments in research areas allowing direct comparisons of mouse versus human data puts strong doubt on the usefulness of animal data as key technology to predict human safety.

Highlights

Regulatory toxicology is involved with the prediction of human risk and with regulatory approaches to limit such ‘assumed/predicted’ risks to humans
This is a very particular form of science, in that it deals mostly not with facts, but with assumptions and predictions derived from models
The art of predictive toxicology lies in its construction of an intricate web of cross-relationships, to anchor the assumed human hazard to sets of real data

Summary

Introduction

Prominent examples of case studies that suggest poor predictivity are the experience with thalidomide, or with the TG1412 drug candidate, which caused terrible effects in man that had not been predicted from the available animal data (Stebbings et al, 2007). In many areas of toxicology (for instance in the field of pesticides) such comparative data are hardly available. (b) can one show, or reasonably assume, that the predictivity of animals for man does not differ fundamentally in different fields of biomedical research?

Results

Conclusion