Estimating the Extent of the Health Hazard Posed by High-Production Volume Chemicals

Abstract

the goal of providing a humane, economical, and efficient method of collecting basic toxicologic data for HPV chemicals (2-4). For this purpose, HPV chemicals are defined as those produced or imported into the United States in quantities greater than 1 million pounds per year. The program asks chemical producers and importers to voluntarily provide basic toxicologic data on HPV chemicals (5). These chemicals were identified under the Toxic Substance Control Act 1990 Inventory Update Rule (6). Overall, the HPV Chemical Challenge Program list contains 2,800 chemicals (7). The Screening Information Data Set (SIDS) of the Organization for Economic Cooperation and Development was selected as the toxicologic criteria needed to meet the goals of the HPV Chemical Challenge Program (8). SIDS includes tests for genotoxicity, acute and chronic toxicity, reproductive toxicity, ecotoxicity, and environmental fate. One of the challenges, as part of the TestSmart Program, was to assess the overall magnitude of the health hazards posed by HPV chemicals based on structure-activity relationship (SAR) modeling. The U.S. EPA will consider test result submission for the HPV Program based on SAR models that are scientifically justifiable (9). As part of this program we undertook an analysis of a random set of 200 HPV chemicals and predicted the probability of each to induce a variety of toxic effects including genotoxicity, carcinogenicity, developmental toxicity, and systemic

Structural Alerts of Mutagens and Carcinogens

“…At odds with DEREK and OncoLogic, this approach does not use a priori knowledge on the mechanisms of action or on SAs, but re-analyzes the database of chemicals in order to develop its own rules (i.e., SAs) liking the toxicity of chemicals to their structures. This approach has been implemented into a commercial computer program [29][30][31][32][33][34][35][36][37]. CASE and MULTICASE are a very characterized SAR approach, which is distinguished from other approaches by its central reliance on computer-generated substructural fragments, which are its major type of molecular descriptors, and the completely automated and unbiased manner in which these descriptors are generated and chosen for inclusion in SAR model.…”

Integration of Structure−Activity Relationship and Artificial Intelligence Systems To Improve in Silico Prediction of Ames Test Mutagenicity

“…Following these pioneer works, several mutagenicity programs were developed. White et al challenged the most commonly used programs (CASE/MULTICASE, , DEREK, , TOPKAT , ) with a test set of over 500 proprietary pharmaceuticals and reported a concordance (accuracy) between 72% and 81%. Two recent reviews , examining numerous (Q)SAR models for noncongeneric chemicals reported similar results and stressed the lack of sensitivity (the measure of the ability to correctly identify true positives) as the main limitation of these systems.…”

An Evaluation of the US High Production Volume (HPV) Chemical-testing Programme: A Study in (Ir)Relevance, Redundancy and Retro Thinking

“…One of the major assumptions underlying the HPV programme is that chemicals produced in large volumes pose a greater hazard, on average, than other substances. However, Cunningham & Rosenkranz (5) have documented, by using computerised structure-activity relationship analysis, that "for all toxic effects (including mutagenicity, genotoxicity, carcinogenicity, and developmental toxicity) except one (the in vitro induction of [sister chromatid exchanges]), the proportion of chemicals predicted to be toxic among the HPV sample was significantly less than the proportion of chemicals predicted to be toxic in the [non-HPV] reference set". Likewise, the popular assumption that substantial "knowledge gaps" exist (1) does not withstand even a cursory review of the published literature for HPV chemicals (6)(7)(8).…”