Challenges in constructing statistically based structure‐activity relationship models for developmental toxicity

Abstract

Regulatory agencies are increasingly called upon to review large numbers of environmental contaminants that have not been characterized for their potential to pose a health risk. Additionally, there is special interest in protecting potentially sensitive subpopulations and identifying developmental toxicants that may be present in the environment. Thus, there is an urgent need for efficient methods to screen large numbers of chemicals for their potential to pose a developmental hazard. One potential screening method involves the use of statistically based structure-activity relationship (SAR) tools to predict activity of untested chemicals. Such systems rely on statistical analyses to discern relationships between structure and activity for a training set of substances. Predictions can then be made for an untested substance as long as its structural features are encompassed by chemicals of the training set. In theory, such systems could assist regulatory agencies in their screening efforts; however, to date, there has been little independent evaluation of these tools for this use. To contribute to such an evaluation, the International Life Sciences Institute Risk Science Institute (ILSI RSI) convened a Working Group to examine methodology used to construct statistically based SAR systems for developmental toxicity. This document reports on the deliberations of the Working Group, which concluded that an improved process is needed for utilizing developmental toxicity data in the construction of statistically based SAR models. The process must be objective, reproducible, rational and transparent. Moreover, it must be informed by the expertise of developmental toxicologists and biologists and must be subject to peer review.

A roadmap for the development of alternative (non-animal) methods for systemic toxicity testing

“…The results in the second attempt (Benigni and Zito, 2004) ranged from 25 to 64%. In remarkable contrast, mere internal validations can show results of 75-89% predictivity for carcinogenicity (Matthews et al, 2006;Julien et al, 2004).…”

“…A working group of ILSI/HESI assessed structure/activity relationships (SAR) (Julien et al, 2004) This highlights the need for "virtual models" in which a toolbox of dynamic models can be used to interpret HTS data and pathway-based information. Latest studies from ToxCast have demonstrated the feasibility of predictive modeling of fertility, blood vessel development, and prenatal developmental toxicity (Sipes et al, 2011a).…”

Applicability of QSAR analysis to the evaluation of the toxicological relevance of metabolites and degradates of pesticide active substances for dietary risk assessment

“…This need has been acknowledged by the International Life Sciences Institute Risk Science Institute (ILSI RSI), who convened a working group to review methodology used to construct statistically based SAR systems for developmental toxicity (Julien et al, 2004). It was concluded that an improved process is needed for utilisng developmental toxicity data in the construction of statistically based SAR models.…”

“…It was concluded that an improved process is needed for utilisng developmental toxicity data in the construction of statistically based SAR models. As result of the ILSI RSI report (Julien et al, 2004), ILSI is developing a QSAR-ready and peer-reviewed database with the assistance of Leadscope Inc. (Columbus, Ohio, USA), and with data contributions coming from a range of governmental and academic organisations, as well as contract research laboratories and major pharmaceutical companies. At the time of writing, this database is not yet available.…”

Food for thought … on in silico methods in toxicology

“…the results in the second attempt (Benigni and Zito, 2004) ranged from 25 to 64%. In remarkable contrast, mere internal validations can show results of 75-89% predictivity (Matthews et al, 2006) for carcinogenicity and >80% for reproductive toxicity (Matthews et al, 2007), considered one of the most difficult areas for in silico predictions (Julien et al, 2004;Bremer et al, 2007).…”