The application of acute oral toxicity computational models in dangerous goods classification.

Acute oral toxicity (AOT) data inform the acute toxicity potential of a compound and guides occupational safety and transportation practices. AOT data enable the categorization of a chemical into the appropriate AOT Globally Harmonized System (GHS) category based on the severity of the hazard. AOT data are also utilized to identify compounds that are Dangerous Goods (DGs) and subsequent transportation guidance for shipping of these hazardous materials. Proper identification of DGs is challenging for novel compounds that lack data. It is not feasible to err on the side of caution for all compounds lacking AOT data and to designate them as DGs, as shipping a compound as a DG has cost, resource, and time implications. With the wealth of available historical AOT data, AOT testing approaches are evolving, and in silico AOT models are emerging as tools that can be utilized with confidence to assess the acute toxicity potential of de novo molecules. Such approaches align with the 3R principles, offering a reduction or even replacement of traditional in vivo testing methods and can also be leveraged for product stewardship purposes. Utilizing proprietary historical in vivo AOT data for 210 pharmaceutical compounds (PCs), we evaluated the performance of two established in silico AOT programs: the Leadscope AOT Model Suite and the Collaborative Acute Toxicity Modeling Suite. These models accurately identified 94% and 97% compounds that were not DGs (GHS categories 4, 5, and not classified (NC)) suggesting that the models are fit-for-purpose in identifying PCs with low acute oral toxicity potential (LD50 >300 mg/kg). Utilization of these models to identify compounds that are not DGs can enable them to be de-prioritized for in vivo testing. This manuscript provides a detailed evaluation and assessment of the two models and recommends the most suitable applications of such models.

Setting impurity limits for endogenous substances: Recommendations for a harmonized procedure and an example using fatty acids.

Endogenous substances, such as fatty, amino, and nucleic acids, are often purposefully used in parenterally pharmaceuticals, but may be present as impurities. Currently, no consensus guidance exists on setting impurity limits for these substances. Specific procedures are needed, as the amount and types of toxicity data available for endogenous substances are typically far less than those for other chemical impurities. Additionally, the parenteral route of administration of these substances is inherently non-physiological, resulting in potentially different or increased severity of toxicity. Risk Assessment Process Maps (RAPMAPs) are proposed as a model to facilitate the development of health-based exposure limits (HBELs) for endogenous substances. This yielded a framework that was applied to derive HBELs for several fatty acids commonly used in parenteral pharmaceuticals. This approach was used to derive HBELs with further vetting based on anticipated perturbations in physiological serum levels, impacts of dose-rate, and consideration of intermittent dosing. Parenteral HBELs of 100-500 mg/day were generated for several fatty acids, and a proposed class-based limit of 50 mg/day to be used in the absence of chemical-specific data. This default limit is consistent with the low toxicity of this chemical class and ICH Q3C value for Class 3 solvents.

Genetic toxicology in silico protocol.

In silico toxicology (IST) approaches to rapidly assess chemical hazard, and usage of such methods is increasing in all applications but especially for regulatory submissions, such as for assessing chemicals under REACH as well as the ICH M7 guideline for drug impurities. There are a number of obstacles to performing an IST assessment, including uncertainty in how such an assessment and associated expert review should be performed or what is fit for purpose, as well as a lack of confidence that the results will be accepted by colleagues, collaborators and regulatory authorities. To address this, a project to develop a series of IST protocols for different hazard endpoints has been initiated and this paper describes the genetic toxicity in silico (GIST) protocol. The protocol outlines a hazard assessment framework including key effects/mechanisms and their relationships to endpoints such as gene mutation and clastogenicity. IST models and data are reviewed that support the assessment of these effects/mechanisms along with defined approaches for combining the information and evaluating the confidence in the assessment. This protocol has been developed through a consortium of toxicologists, computational scientists, and regulatory scientists across several industries to support the implementation and acceptance of in silico approaches.

Principles and Procedures for Assessment of Acute Toxicity Incorporating In Silico Methods.

Acute toxicity in silico models are being used to support an increasing number of application areas including (1) product research and development, (2) product approval and registration as well as (3) the transport, storage and handling of chemicals. The adoption of such models is being hindered, in part, because of a lack of guidance describing how to perform and document an in silico analysis. To address this issue, a framework for an acute toxicity hazard assessment is proposed. This framework combines results from different sources including in silico methods and in vitro or in vivo experiments. In silico methods that can assist the prediction of in vivo outcomes (i.e., LD50) are analyzed concluding that predictions obtained using in silico approaches are now well-suited for reliably supporting assessment of LD50-based acute toxicity for the purpose of GHS classification. A general overview is provided of the endpoints from in vitro studies commonly evaluated for predicting acute toxicity (e.g., cytotoxicity/cytolethality as well as assays targeting specific mechanisms). The increased understanding of pathways and key triggering mechanisms underlying toxicity and the increased availability of in vitro data allow for a shift away from assessments solely based on endpoints such as LD50, to mechanism-based endpoints that can be accurately assessed in vitro or by using in silico prediction models. This paper also highlights the importance of an expert review of all available information using weight-of-evidence considerations and illustrates, using a series of diverse practical use cases, how in silico approaches support the assessment of acute toxicity.

Development of quantitative structure-activity relationship (QSAR) models to predict the carcinogenic potency of chemicals. II. Using oral slope factor as a measure of carcinogenic potency.

The overall risk associated with exposure to a chemical is determined by combining quantitative estimates of exposure to the chemical with their known health effects. For chemicals that cause carcinogenicity, oral slope factors (OSFs) and inhalation unit risks are used to quantitatively estimate the carcinogenic potency or the risk associated with exposure to the chemical by oral or inhalation route, respectively. Frequently, there is a lack of animal or human studies in the literature to determine OSFs. This study aims to circumvent this problem by developing quantitative structure-activity relationship (QSAR) models to predict the OSFs of chemicals. The OSFs of 70 chemicals based on male/female human, rat, and mouse bioassay data were obtained from the United States Environmental Protection Agency's Integrated Risk Information System (IRIS) database. A global QSAR model that considered all 70 chemicals as well as species and/or sex-specific QSARs were developed in this study. Study results indicate that the species and sex-specific QSARs (r(2)>0.8, q(2)>0.7) had a better predictive abilities than the global QSAR developed using data from all species and sexes (r(2)=0.77, q(2)=0.73). The QSARs developed in this study were externally validated, and demonstrated reasonable predictive abilities.

Development of quantitative structure-activity relationship (QSAR) models to predict the carcinogenic potency of chemicals I. Alternative toxicity measures as an estimator of carcinogenic potency.

Determining the carcinogenicity and carcinogenic potency of new chemicals is both a labor-intensive and time-consuming process. In order to expedite the screening process, there is a need to identify alternative toxicity measures that may be used as surrogates for carcinogenic potency. Alternative toxicity measures for carcinogenic potency currently being used in the literature include lethal dose (dose that kills 50% of a study population [LD(50)]), lowest-observed-adverse-effect-level (LOAEL) and maximum tolerated dose (MTD). The purpose of this study was to investigate the correlation between tumor dose (TD(50)) and three alternative toxicity measures as an estimator of carcinogenic potency. A second aim of this study was to develop a Classification and Regression Tree (CART) between TD(50) and estimated/experimental predictor variables to predict the carcinogenic potency of new chemicals. Rat TD(50)s of 590 structurally diverse chemicals were obtained from the Cancer Potency Database, and the three alternative toxicity measures considered in this study were estimated using TOPKAT, a toxicity estimation software. Though poor correlations were obtained between carcinogenic potency and the three alternative toxicity (both experimental and TOPKAT) measures for the CPDB chemicals, a CART developed using experimental data with no missing values as predictor variables provided reasonable estimates of TD(50) for nine chemicals that were part of an external validation set. However, if experimental values for the three alternative measures, mutagenicity and logP are not available in the literature, then either the CART developed using missing experimental values or estimated values may be used for making a prediction.

Human health risk assessment database, "the NHSRC toxicity value database": supporting the risk assessment process at US EPA's National Homeland Security Research Center.

The toxicity value database of the United States Environmental Protection Agency's (EPA) National Homeland Security Research Center has been in development since 2004. The toxicity value database includes a compilation of agent property, toxicity, dose-response, and health effects data for 96 agents: 84 chemical and radiological agents and 12 biotoxins. The database is populated with multiple toxicity benchmark values and agent property information from secondary sources, with web links to the secondary sources, where available. A selected set of primary literature citations and associated dose-response data are also included. The toxicity value database offers a powerful means to quickly and efficiently gather pertinent toxicity and dose-response data for a number of agents that are of concern to the nation's security. This database, in conjunction with other tools, will play an important role in understanding human health risks, and will provide a means for risk assessors and managers to make quick and informed decisions on the potential health risks and determine appropriate responses (e.g., cleanup) to agent release. A final, stand alone MS ACESSS working version of the toxicity value database was completed in November, 2007.