How Well do Toxicology Studies Predict Clinical Safety Outcome? – A Translational Safety Big Data Analysis

Time: 14:00 - 15:00 (UK Time)

Chair: Dr. Bernd-Wolfgang Igl (Boehringer Ingelheim Pharma GmbH & Co. KG)

Published in: Regulatory Toxicology and Pharmacology 96 (2018) 94–105; https://doi.org/10.1016/j.yrtph.2018.04.018)


Matthew Clark, Scientific Services, R&D Solutions, Elsevier, Philadelphia, USA & Thomas Steger-Hartmann, Investigational Toxicology, Bayer AG, Berlin, Germany


Attrition of drug candidates in clinical trials due to safety issues still contributes to a significant part of project closures besides other reasons such as the lack of efficacy, PK issues or strategic reasons. While failure of a candidate during preclinical development is a reflection of the primary task of the functions involved in this phase (i.e. toxicology, safety pharmacology and DMPK), failures during the later clinical phases often raise the question whether the preclinical safety studies are sufficiently predictive for the human outcome. Due to the fact that the First-in-Man study requires pivotal animal studies normally performed in two species, the focus of analysis of the debated predictivity centers around these animal studies. After the seminal study from Olson et al. (2000) numerous publications have shown that animal toxicity studies are predictive to a certain extent and that the predictivity varies among endpoints, some of them such as hematological, gastrointestinal, and cardiovascular events being better predicted than others (e.g. cutaneous adverse events). Most of these analyses compared the preclinical – clinical correlation for a rather limited set of compounds (<150) or for specific field of indications. The authors will present the results of this purely statistical approach based on data available for 3290 compounds in the commercial database Pharmapendium. The work provides answers to the implication of an observation in an animal for human risk and more specifically to the question whether concordance, i.e. the translatability of an observation from animal to human is dependent on the animal species. The statistical methods and procedures will be described in detail.


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