U.S. flag

An official website of the United States government

Dot gov

The .gov means it's official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you're on a federal government site.


The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.


ICCVAM Biennial Report 2018-2019

Biennial Progress Report 2018-2019 Interagency Coordinating Committee on the Validation of Alternative Methods

Computational Tools Applications

ICCVAM and its member agencies are exploring how computational approaches can be applied to reduce animal use for toxicity testing. These approaches have potential application for acute oral toxicity and eye irritation testing, and for predicting whether chemicals could cause cardiotoxicity, developmental toxicity, or neurotoxicity.

In Silico Screening Approaches for Assessing Cardiovascular Safety

NIEHS scientists are using two approaches to develop computational models for predicting the potential of substances to cause toxicity to the heart and vascular system.

In one approach, a weighted gene coregulation network analysis was applied to rat heart gene expression data from the DrugMatrix database to map gene expression relationships associated with cardiotoxic stress. The map revealed sets of genes linked to biological processes with potential relevance to cardiotoxicity. Applied to known cardiotoxic substances such as anthracyclines, corticosteroids, and kinase inhibitors, the map may provide an approach for exploring mechanisms of cardiotoxicity. An abstract describing this project (Rahman et al.) was accepted for presentation at the SOT 2020 annual meeting.

The second approach used in silico tools and in vitro HTS data to generate bioactivity scores for environmental chemicals against molecular and cellular targets known to mediate bioactivity in the cardiovascular system. These scores support a visualization tool and ranking system that can be used to screen and prioritize chemicals with limited or no toxicity information for further assessment. An abstract describing this project (Krishna et al.) was accepted for presentation at the SOT 2020 annual meeting.

Statistical Models for Classification of Eye Irritants

NICEATM developed statistical models that could potentially be used to classify chemicals as eye corrosives, irritants, or non-irritants according to EPA and GHS hazard classification endpoints. Models were developed using machine learning approaches combined with historical in vivo eye irritation data, chemical structural information, and physicochemical properties. These models were used to predict hazard classifications for a database of over 500 substances, including many mixtures. Results suggest that these models are useful for screening substances for eye irritation potential. Future efforts to increase the models’ utility will focus on expanding their applicability domains and using the models in conjunction with other input variables in a defined approach for eye irritation testing. A paper describing this work is in preparation.

Adverse Outcome Pathway for Embryonic Vascular Development

Work to identify alternative methods for developmental toxicity testing has focused on understanding and predicting disruption of key mechanisms in embryonic and fetal development. AOPs provide a useful framework for integrating the evidence derived from in silico and in vitro systems to inform chemical hazard characterization. An ongoing collaboration between NICEATM and EPA has built and applied an AOP for developmental toxicity through a mode of action linked to embryonic vascular disruption. A 2019 publication (Saili et al. 2019) reviewed the model for quantitative prediction of developmental vascular toxicity from ToxCast HTS data and compared the HTS results to functional vascular development assays in complex cell systems, virtual tissues, and small model organisms. Results increased confidence in the capacity to predict adverse developmental outcomes from HTS in vitro data and model computational dynamics for in silico reconstruction of developmental systems biology.

Additivity Approaches to Predicting Toxicity of Formulations

The EPA Office of Pesticide Programs has been accepting voluntary submissions of oral and inhalation toxicity data for agrochemical formulations under a pilot program to evaluate the usefulness and acceptability of a mathematical tool that estimates the toxicological classification of a chemical mixture. The submitted data were paired with toxicity calculations done in accordance with the GHS additivity equation based on the individual components of the formulation. NICEATM is evaluating the extent to which predictions of acute toxicity for a formulation derived using the additivity equation compare to in vivo test results. The evaluation will be finalized in 2020 along with a report describing the results.

CATMoS and Additivity Approaches to Predict Toxicity of Mixtures

While exposure of humans to environmental hazards often occurs with complex chemical mixtures, most existing toxicity data and tools are for single compounds. An approach to estimating toxicity of mixtures is provided by the GHS additivity formula, which is based on the acute toxicity estimate of ingredients. The concentration-addition method assumes that all components in the mixture share the same mechanism of toxicity and the toxicity of the mixture is sum of their concentration and potency. Air Force researchers used data in the NICEATM Integrated Chemical Environment (ICE) for assessment of acute oral toxicity of mixtures. The ICE database contains in vivo acute oral toxicity data for about 10,000 chemicals and more than 500 mixtures. By using the available experimental data for single compounds, the GHS category could be calculated for 273 mixtures. Use of CATMoS predictions available via OPERA enabled toxicity estimates for 487 mixtures with 69% accuracy for GHS classification. For 172 mixtures with two or more active ingredients, the accuracy rate was 78%. These results demonstrate that CATMoS together with the additivity formula can be used to predict GHS category for chemical mixtures.

Use of a PBPK Model to Derive a Human-equivalent Dose

CCDC CBC has successfully used physiologically based PK modeling to derive a high-fidelity human-equivalent dose of the ultra-potent opioid carfentanil. This effort included validating the pharmacokinetics of carfentanil in an in vivo rabbit model with the rabbit PBPK in silico model, converting to a human physiology of interest, and then calculating an equivalent dose by optimizing the maximal plasma concentration and area under the curve of the PK profile. CCDC CBC’s predicted human-equivalent lethal dose of carfentanil differed from that of the U.S. Drug Enforcement Administration by only 50 ng. CCDC CBC’s PK profile is also supported by a Canadian report of an overdose of carfentanil by inhalation administration in which periodic blood samples were analyzed for carfentanil concentration from admission into the emergency department until discharge.

Current efforts aim to convert this intravenous human equivalent dose into an inhaled dose by using the PBPK model’s pulmonary administration module. Additionally, CCDC CBC aims to predict dermal absorption in a reliable way using the transdermal module of their PBPK software suite.