Application of modern toxicology approaches for predicting acute toxicity for chemical defense [E-Book]

Title from PDF title page.

Includes bibliographical references.

The US Department of Defense (DOD) is faced with an overwhelming task in evaluating chemicals that could potentially pose a threat to its deployed personnel. There are over 84,000 registered chemicals, and testing them with traditional toxicity-testing methods is not feasible in terms of time or money. In recent years, there has been a concerted effort to develop new approaches to toxicity testing that incorporate advances in systems biology, toxicogenomics, bioinformatics, and computational toxicology. Given the advances, DOD asked the National Research Council to determine how DOD could use modern approaches for predicting chemical toxicity in its efforts to prevent debilitating, acute exposures to deployed personnel. This report provides an overall conceptual approach that DOD could use to develop a predictive toxicology system. Application of Modern Toxicology Approaches for Predicting Acute Toxicity for Chemical Defense reviews the current state of computational and high-throughput approaches for predicting acute toxicity and suggests methods for integrating data and predictions. This report concludes with lessons learned from current high-throughput screening programs and suggests some initial steps for DOD investment.

This project was supported by Contract W81K04-11-D-0017, TO#8 between the National Academies of Sciences, Engineering, and Medicine and the US Department of Defense. Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the authors and do not necessarily reflect the view of the organizations or agencies that provided support for this project.

Description based on version viewed December 14, 2015.

Introduction -- Conceptual framework and prioritization strategy -- Nontesting approaches relevant to prediction of acute toxicity and potency -- Assays for predicting acute toxicity -- Integration and decision-making for predictive toxicology -- Lessons learned and new steps.