A comprehensive model for reproductive and developmental toxicity hazard identification: II. Construction of QSAR models to predict activities of untested chemicals

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Abstract

This report describes the construction, optimization and validation of a battery of quantitative structure–activity relationship (QSAR) models to predict reproductive and developmental (reprotox) hazards of untested chemicals. These models run with MC4PC software to predict seven general reprotox classes: male and female reproductive toxicity, fetal dysmorphogenesis, functional toxicity, mortality, growth, and newborn behavioral toxicity. The reprotox QSARs incorporate a weight of evidence paradigm using rats, mice, and rabbit reprotox study data and are designed to identify trans-species reprotoxicants. The majority of the reprotox QSARs exhibit good predictive performance properties: high specificity (>80%), low false positives (<20%), significant receiver operating characteristic (ROC) values (>2.00), and high coverage (>80%) in 10% leave-many-out validation experiments. The QSARs are based on 627–2023 chemicals and exhibited a wide applicability domain for FDA regulated organic chemicals for which they were designed. Experiments were also performed using the MC4PC multiple module prediction technology, and ROC statistics, and adjustments to the ratio of active to inactive (A/I ratio) chemicals in training data sets were made to optimize the predictive performance of QSAR models. Results revealed that an A/I ratio of ∼40% was optimal for MC4PC. We discuss specific recommendations for the application of the reprotox QSAR battery.

Introduction

This report describes part II of an investigation to develop a model for the identification of human reproductive and developmental toxicity hazards. This investigation was conducted by the Food and Drug Administration (FDA), Center for Drug Evaluation and Research (CDER), Office of Pharmaceutical Science (OPS), Informatics and Computational Safety Analysis Staff (ICSAS), which is an applied regulatory research unit that compiles databases of the results of the clinical and toxicological studies submitted by the pharmaceutical industry and develops quantitative structure–activity relationships (QSARs) based on these results. (http://www.fda.gov/cder/Offices/OPS_IO/default.htm).

The primary goal of this investigation was to develop a weight of evidence (WOE)-based battery of QSARs to estimate the potential reprotox activities of untested chemicals. Furthermore, it was the objective of this investigation to validate and optimize the predictive performance of the QSARs based upon OECD (Q)SAR guidelines and make them available for regulatory and industry applications. The reprotox QSAR battery was constructed for general reprotox classes using clusters of related specific reprotox categories identified in the companion report (Matthews et al., 2007). Rather than developing hundreds of individual QSAR models for every site-specific reprotox endpoint in each mammalian species, we optimized the QSAR models for the identification of trans-species reprotoxicants that are most likely to have potential human risk and thus high regulatory significance. Our working hypothesis is that chemicals that induce trans-species reproductive toxicity findings in female and male mammals, and trans-species developmental toxicity findings in the mammalian fetus are less influenced by genetic variability and are most likely to be reprotoxicants in humans. Conversely, chemicals that induce reprotox in a single mammalian tissue, gender, and/or species are more influenced by genetic variability among mammalian species and are less likely to be reprotoxicants in humans based upon the results of the animal studies.

We elected to use the MC4PC software for this investigation because we have already demonstrated that it is able to accurately predict the potential carcinogenicity (Matthews and Contrera, 1998) and genetic toxicity (Matthews et al., 2006a, Matthews et al., 2006b) of chemicals. We hypothesized that MC4PC might also be able to identify trans-species structural alerts for general reprotox classes. In addition, other laboratories have used earlier versions of this program (MCASE and CASE) to screen chemicals for potential teratogenicity (Ghanooni et al., 1997, Gomez et al., 1999, Cunningham and Rosenkranz, 2001) and to predict teratogenicity of restricted classes of chemicals (Klopman and Ptchelintsev, 1993).

The contents of this report and the companion report are designed to fulfill the five OECD principles to facilitate the consideration of a (Q)SAR model for regulatory purposes (OECD Environment Health and Safety Publications. XX, 2006). This investigation had: (1) defined endpoints; (2) an unambiguous algorithm; (3) a defined domain of applicability; (4) appropriate measures of goodness-of-fit, robustness and predictivity; and (5) a mechanistic interpretation. Because reprotox is associated with numerous chemical mechanisms, we developed more general (Q)SARs based upon the same observed toxic effect associated with non-congeneric classes of chemicals and different toxicity mechanisms in order to achieve the largest possible applicability domain (AD). The QSAR battery developed in this study is designed to provide QSAR estimates for the reproductive toxicity and developmental toxicity endpoints associated with OECD test guidelines and thereby be useful to REACH (van der Jagt et al., 2003; http://www.europa.eu.int/comm/environmental/chemicals/reach.htm; http://ecb.jrc.it/REACH/), the 7th Amendment of the Cosmetics Directive (http://www.eceae.org/english/cosmetics.html), and other regulatory applications.

Section snippets

Data sources and defined endpoints

A detailed description of the sources of the reproductive and developmental toxicity (reprotox) study data used in this investigation is provided in the companion report (Matthews et al., 2007). Briefly, the study findings were subdivided into seven general reprotox classes: (1) male reproductive toxicity, (2) female reproductive toxicity, (3) fetal dysmorphogenesis, (4) fetal functional toxicity, (5) fetal and newborn mammal mortality, (6) fetal growth, and (7) newborn behavioral toxicity. The

Optimization of QSAR models for general reprotox classes

QSAR validation experiments were performed to optimize detection of the general reproductive and developmental toxicity classes using single-species mammalian models for the rat, mouse, rabbit, as well as composite trans-species QSAR models. Because the reprotox data sets had low percentages of active chemicals and the MC4PC program is optimized for training data sets containing approximately 50% active molecules, all of the reprotox experiments assessed the impact of increasing the ratio of

Comprehensive battery of reprotox QSARs for untested chemicals

In this report, we describe the construction, optimization and validation of a comprehensive battery of QSAR models to predict seven different general reprotox classes: male and female reproductive toxicity; fetal dysmorphogenesis, functional toxicity, mortality, growth, and newborn behavioral toxicity. These reprotox QSARs are unique in that they incorporate a WOE paradigm that uses data from as many as three mammalian species (rats, mice, and rabbits), and are designed to identify

Acknowledgments

We thank Dr. Roustem Saiakhov for checking the accuracy of the chemical structures used in the experiments described in this paper and Mr. Matt Fuller for prompt technical support for MC4PC during the course of this investigation. We also thank Ms. Susan Matthews for assistance in data collection and Ms. Amie Rodgers for performing some of the validation experiments used in this study. Lastly, we thank Drs. Ruth Merkatz and Mariatta Anthony of the FDA’s Office of Women’s Health (OWH) and Dr.

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    This report is not an official US Food and Drug Administration guidance or policy statement. No official support or endorsement by the US Food and Drug Administration is intended or should be inferred.

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