Current status and future directions for a neurotoxicity hazard assessment framework that integrates in silico approaches.

Neurotoxicology is the study of adverse effects on the structure or function of the developing or mature adult nervous system following exposure to chemical, biological, or physical agents. The development of more informative alternative methods to assess developmental (DNT) and adult (NT) neurotoxicity induced by xenobiotics is critically needed. The use of such alternative methods including in silico approaches that predict DNT or NT from chemical structure (e.g., statistical-based and expert rule-based systems) is ideally based on a comprehensive understanding of the relevant biological mechanisms. This paper discusses known mechanisms alongside the current state of the art in DNT/NT testing. In silico approaches available today that support the assessment of neurotoxicity based on knowledge of chemical structure are reviewed, and a conceptual framework for the integration of in silico methods with experimental information is presented. Establishing this framework is essential for the development of protocols, namely standardized approaches, to ensure that assessments of NT and DNT based on chemical structures are generated in a transparent, consistent, and defendable manner.

IVIVE: Facilitating the Use of In Vitro Toxicity Data in Risk Assessment and Decision Making.

During the past few decades, the science of toxicology has been undergoing a transformation from observational to predictive science. New approach methodologies (NAMs), including in vitro assays, in silico models, read-across, and in vitro to in vivo extrapolation (IVIVE), are being developed to reduce, refine, or replace whole animal testing, encouraging the judicious use of time and resources. Some of these methods have advanced past the exploratory research stage and are beginning to gain acceptance for the risk assessment of chemicals. A review of the recent literature reveals a burst of IVIVE publications over the past decade. In this review, we propose operational definitions for IVIVE, present literature examples for several common toxicity endpoints, and highlight their implications in decision-making processes across various federal agencies, as well as international organizations, including those in the European Union (EU). The current challenges and future needs are also summarized for IVIVE. In addition to refining and reducing the number of animals in traditional toxicity testing protocols and being used for prioritizing chemical testing, the goal to use IVIVE to facilitate the replacement of animal models can be achieved through their continued evolution and development, including a strategic plan to qualify IVIVE methods for regulatory acceptance.

In Silico Assessment of Acute Oral Toxicity for Mixtures.

While exposure of humans to environmental hazards often occurs with complex chemical mixtures, the majority of existing toxicity data are for single compounds. The Globally Harmonized System of chemical classification (GHS) developed by the Organization for Economic Cooperation and Development uses the additivity formula for acute oral toxicity classification of mixtures, which is based on the acute toxicity estimate of individual ingredients. We evaluated the prediction of GHS category classifications for mixtures using toxicological data collected in the Integrated Chemical Environment (ICE) developed by the National Toxicology Program (United States Department of Health and Human Services). The ICE database contains in vivo acute oral toxicity data for ∼10,000 chemicals and for 582 mixtures with one or multiple active ingredients. By using the available experimental data for individual ingredients, we were able to calculate a GHS category for only half of the mixtures. To expand a set of components with acute oral toxicity data, we used the Collaborative Acute Toxicity Modeling Suite (CATMoS) implemented in the Open Structure-Activity/Property Relationship App to make predictions for active ingredients without available experimental data. As a result, we were able to make predictions for 503 mixtures/formulations with 72% accuracy for the GHS classification. For 186 mixtures with two or more active ingredients, the accuracy rate was 76%. The structure-based analysis of the misclassified mixtures did not reveal any specific structural features associated with the mispredictions. Our results demonstrate that CATMoS together with an additivity formula can be used to predict the GHS category for chemical mixtures.

Predicting passive and active tissue:plasma partition coefficients: interindividual and interspecies variability.

A mechanistic tissue composition model incorporating passive and active transport for the prediction of steady-state tissue:plasma partition coefficients (K(t:pl)) of chemicals in multiple mammalian species was used to assess interindividual and interspecies variability. This approach predicts K(t:pl) using chemical lipophilicity, pKa, phospholipid membrane binding, and the unbound plasma fraction, together with tissue fractions of water, neutral lipids, neutral and acidic phospholipids, proteins, and pH. Active transport K(t:pl) is predicted using Michaelis-Menten transport parameters. Species-specific biological properties were identified from 126 peer reviewed journal articles, listed in the Supporting Information, for mouse, rat, guinea pig, rabbit, beagle dog, pig, monkey, and human species. Means and coefficients of variation for biological properties were used in a Monte Carlo analysis to assess variability. The results show K(t:pl) interspecies variability for the brain, fat, heart, kidney, liver, lung, muscle, red blood cell, skin, and spleen, but uncertainty in the estimates obscured some differences. Compounds undergoing active transport are shown to have concentration-dependent K(t:pl). This tissue composition-based mechanistic model can be used to predict K(t:pl) for organic chemicals across eight species and 10 tissues, and can be an important component in drug development when scaling K(t:pl) from animal models to humans.

A physiologically based pharmacokinetic model for the oxime TMB-4: simulation of rodent and human data.

Multiple oximes have been synthesized and evaluated for use as countermeasures against chemical warfare nerve agents. The current U.S. military and civilian oxime countermeasure, 2-[(hydroxyimino)methyl]-1-methylpyridin-1-ium chloride (2-PAM), is under consideration for replacement with a more effective acetylcholinesterase reactivator, 1,1'-methylenebis{4-hydroxyiminomethyl}pyridinium dimethanesulfonate (MMB-4). Kinetic data in the scientific literature for MMB-4 are limited; therefore, a physiologically based pharmacokinetic (PBPK) model was developed for a structurally related oxime, 1,1'-trimethylenebis{4-hydroximinomethyl}pyridinium dibromide. Based on a previous model structure for the organophosphate diisopropylfluorophosphate, the model includes key sites of acetylcholinesterase inhibition (brain and diaphragm), as well as fat, kidney, liver, rapidly perfused tissues and slowly perfused tissues. All tissue compartments are diffusion limited. Model parameters were collected from the literature, predicted using quantitative structure-property relationships or, when necessary, fit to available pharmacokinetic data from the literature. The model was parameterized using rat plasma, tissue and urine time course data from intramuscular administration, as well as human blood and urine data from intravenous and intramuscular administration; sensitivity analyses were performed. The PBPK model successfully simulates rat and human data sets and has been evaluated by predicting intravenous mouse and intramuscular human data not used in the development of the model. Monte Carlo analyses were performed to quantify human population kinetic variability in the human evaluation data set. The model identifies potential pharmacokinetic differences between rodents and humans, indicated by differences in model parameters between species. The PBPK model can be used to optimize the dosing regimen to improve oxime therapeutic efficacy in a human population.