A Novel Multispecies Toxicokinetic Modeling Approach in Support of Chemical Risk Assessment

Fish Physiologically Based Toxicokinetic Modeling Approach for In Vitro-In Vivo and Cross-Species Extrapolation of Endocrine-Disrupting Chemicals in Risk Assessment

High-throughput in vitro assays combined with in vitro-in vivo extrapolation (IVIVE) leverage in vitro responses to predict the corresponding in vivo exposures and thresholds of concern. The integrated approach is also expected to offer the potential for efficient tools to provide estimates of chemical toxicity to various wildlife species instead of animal testing. However, developing fish physiologically based toxicokinetic (PBTK) models for IVIVE in ecological applications is challenging, especially for plausible estimation of an internal effective dose, such as fish equivalent concentration (FEC). Here, a fish PBTK model linked with the IVIVE approach was established, with parameter optimization of chemical unbound fraction, pH-dependent ionization and hepatic clearance, and integration of temperature effect and growth dilution. The fish PBTK-IVIVE approach provides not only a more precise estimation of tissue-specific concentrations but also a reasonable approximation of FEC targeting the estrogenic potency of endocrine-disrupting chemicals. Both predictions were compared with in vivo data and were accurate for most indissociable/dissociable chemicals. Furthermore, the model can help determine cross-species variability and sensitivity among the five fish species. Using the available IVIVE-derived FEC with target pathways is helpful to develop predicted no-effect concentration for chemicals with similar mode of action and support screening-level ecological risk assessment.

Implementing comprehensive machine learning models of multispecies toxicity assessment to improve regulation of organic compounds

Machine learning has made significant progress in assessing the risk associated with hazardous chemicals. However, most models were constructed by randomly selecting one algorithm and one toxicity endpoint towards single species, which may cause biased regulation of chemicals. In the present study, we implemented comprehensive prediction models involving multiple advanced machine learning and end-to-end deep learning to assess the aquatic toxicity of chemicals. The generated optimal models accurately unravel the quantitative structure-toxicity relationships, with the correlation coefficients of all training sets from 0.59 to 0.81 and of the test sets from 0.56 to 0.83. For each chemical, its ecological risk was determined from the toxicity information towards multiple species. The results also revealed the toxicity mechanism of chemicals was species sensitivity, and the high-level organisms were faced with more serious side effects from hazardous substances. The proposed approach was finally applied to screen over 16,000 compounds and identify high-risk chemicals. We believe that the current approach can provide a useful tool for predicting the toxicity of diverse organic chemicals and help regulatory authorities make more reasonable decisions.

Trends and prospects in graphene and its derivatives toxicity research: A bibliometric analysis

The purpose of this paper is to explore the current research status, hot topics, and future prospects in the field of graphene and its derivatives toxicity. In the article, the Web of Science Core Collection database was used as the data source, and the CiteSpace and VOSviewer were used to conduct a visual analysis of the last 10 years of research on graphene and its derivatives toxicity. A total of 8573 articles were included, and we analyzed the literature characteristics of the research results in the field of graphene and its derivatives toxicity, as well as the distribution of authors and co-cited authors; the distribution of countries and institutions; the situation of co-cited references; and the distribution of journals and categories. The most prolific countries, institutions, journals, and authors are China, the Chinese Academy of Sciences, RSC Advances, and Wang, Dayong, respectively. The co-cited author with the most citations was Akhavan, Omid. The five research hotspot keywords in the field of graphene and its derivatives toxicity were "nanomaterials," "exposure," "biocompatibility," "adsorption," and "detection." Frontier topics were "facile synthesis," "antibacterial activity," and "carbon dots." Our study provides perspectives for the study of graphene and its derivatives toxicity and yields valuable information and suggestions for the development of graphene and its derivatives toxicity research in the future.

A Generalized Physiologically Based Kinetic Model for Fish for Environmental Risk Assessment of Pharmaceuticals

An increasing number of pharmaceuticals found in the environment potentially impose adverse effects on organisms such as fish. Physiologically based kinetic (PBK) models are essential risk assessment tools, allowing a mechanistic approach to understanding chemical effects within organisms. However, fish PBK models have been restricted to a few species, limiting the overall applicability given the countless species. Moreover, many pharmaceuticals are ionizable, and fish PBK models accounting for ionization are rare. Here, we developed a generalized PBK model, estimating required parameters as functions of fish and chemical properties. We assessed the model performance for five pharmaceuticals (covering neutral and ionic structures). With biotransformation half-lives (HLs) from EPI Suite, 73 and 41% of the time-course estimations were within a 10-fold and a 3-fold difference from measurements, respectively. The performance improved using experimental biotransformation HLs (87 and 59%, respectively). Estimations for ionizable substances were more accurate than any of the existing species-specific PBK models. The present study is the first to develop a generalized fish PBK model focusing on mechanism-based parameterization and explicitly accounting for ionization. Our generalized model facilitates its application across chemicals and species, improving efficiency for environmental risk assessment and supporting an animal-free toxicity testing paradigm.

In vitro-in vivo biotransformation and phase I metabolite profiling of benzo[a]pyrene in Gulf killifish (Fundulus grandis) populations with different exposure histories

Chronic exposure to pollution may lead populations to display evolutionary adaptations associated with cellular and physiological mechanisms of defense against xenobiotics. This could result in differences in the way individuals of the same species, but inhabiting different areas, cope with chemical exposure. In the present study, we explore two Gulf killifish (Fundulus grandis) populations with different exposure histories for potential differences in the biotransformation of benzo[a]pyrene (BaP), and conduct a comparative evaluation of in vitro and in vivo approaches to describe the applicability of new approach methodologies (NAMs) for biotransformation assessments. Pollution-adapted and non-adapted F. grandis were subjected to intraperitoneal (IP) injections of BaP in time-course exposures, prior to measurements of CYP biotransformation activity, BaP liver concentrations, and the identification and quantification of phase I metabolites. Additionally, substrate depletion bioassays using liver S9 fractions were employed for measurements of intrinsic hepatic clearance and to evaluate the production of metabolites in vitro. Pollution-adapted F. grandis presented significantly lower CYP1A activity and intrinsic clearance rates that were 3 to 4 times lower than non-adapted fish. The metabolite profiling of BaP showed the presence of 1‑hydroxy-benzo[a]pyrene in both the in vitro and in vivo approaches but with no significant population differences. Contrarily, 9‑hydroxy-benzo[a]pyrene and benzo[a]pyrene-4,5-dihydrodiol, only identified through the in vivo approach, presented higher concentrations in the bile of pollution-adapted fish relative to non-adapted individuals. These observations further the understanding of the evolutionary adaptation of F. grandis inhabiting heavily polluted environments in the Houston Ship Channel, TX, USA, and highlight the need to consider the evolutionary history of populations of interest during the implementation of NAMs.