The application of human-derived cell lines in neurotoxicity studies of environmental pollutants.

As industrial and societal advancements progress, an increasing number of environmental pollutants linked to human existence have been substantiated to elicit neurotoxicity and developmental neural toxicity. For research in this field, human-derived neural cell lines have become excellent in vitro models. This study examines the utilization of immortalized cell lines, specifically the SH-SY5Y human neuroblastoma cell line, and neural cells derived from human pluripotent stem cells, in the investigation of neurotoxicity and developmental neural toxicity caused by environmental pollutants. The study also explores the culturing techniques employed for these cell lines and provides an overview of the standardized assays used to assess various biological endpoints. The environmental pollutants involved include a variety of organic compounds, heavy metals, and microplastics. The utilization of cell lines derived from human sources holds significant significance in elucidating the neurotoxic effects of environmental pollutants and the underlying mechanisms. Finally, we propose the possibility of improving the in vitro model of the human nervous system and the toxicity detection methods.

QSAR-QSIIR-based prediction of bioconcentration factor using machine learning and preliminary application.

Bioconcentration factor (BCF) is one of the important parameters for developing human health ambient water quality criteria (HHAWQC) for chemical pollutants. Traditional experimental method to obtain BCF is time-consuming and costly. Therefore, prediction of BCF by modeling has attracted much attention. QSAR (Quantitative Structure-Activity Relationship) model based on molecular descriptor is often used to predict BCF, however, in order to improve the accuracy of prediction, previous models are only applicable for prediction for a single category of substance and a single species, and cannot meet the needs of BCF prediction of pollutants lacing toxicity data. In this study, optimized 17 traditional molecular descriptor and five kinds of bioactivity descriptor were selected from more than 200 molecular descriptor and 25 kinds of biological activity descriptors. A QSAR-QSIIR (Quantitative Structure In vitro-In vivo Relationship) model suitable for multiple chemical substances and whole species is constructed by using optimized 4-MLP machine learning algorithm with selected molecular and bioactivity descriptors. The constructed model significantly improves the prediction accuracy of BCF. The R2 of verification set and test set are 0.8575 and 0.7924, respectively, and the difference between predicted BCF and measured BCF is mostly less than 1.5 times. Then, BCF of BTEX in Chinese common aquatic products is predicted using the constructed QSAR-QSIIR model, and the HHAWQC of BTEX in China are derived using the predicted BCF, which provides a valuable reference for establishment of China's BTEX water quality standards.

The Application of Reference Dose Prediction Model to Human Health Water Quality Criteria and Risk Assessment.

Oral reference dose (RfD) is a key parameter for deriving the human health ambient water quality criteria (AWQC) for non-carcinogenic substances. In this study, a non-experimental approach was used to calculate the RfD values, which explore the potential correlation between toxicity and physicochemical characteristics and the chemical structure of pesticides. The molecular descriptors of contaminants were calculated using T.E.S.T software from EPA, and a prediction model was developed using a stepwise multiple linear regression (MLR) approaches. Approximately 95% and 85% of the data points differ by less than 10-fold and 5-fold between predicted values and true values, respectively, which improves the efficiency of RfD calculation. The model prediction values have certain reference values in the absence of experimental data, which is beneficial to the advancement of contaminant health risk assessment. In addition, using the prediction model constructed in this manuscript, the RfD values of two pesticide substances in the list of priority pollutants are calculated to derive human health water quality criteria. Furthermore, an initial assessment of the health risk was performed by the quotient value method based on the human health water quality criteria calculated by the prediction model.

Chinese Technical Guideline for Deriving Water Quality Criteria for Protection of Freshwater Organisms.

In recent years, China has determined the national goal of "developing national environmental criteria", thereby promoting the rapid development of environmental quality criteria research in China. In 2017, the Ministry of Ecology and Environment of China (MEEC, formerly the Ministry of Environmental Protection of China) issued the technical guideline for deriving water quality criteria (WQC) for protection of freshwater organisms (HJ 831-2017), and in 2022, they organized the guideline revision and issued an updated version (HJ 831-2022). The primary contents of the revision included the following. The minimum toxicity data requirements were upgraded from 6 to 10, and the species mean toxicity value was replaced by the same effect toxicity value for the criteria calculation. It is now required that the tested organisms must be distributed in China's natural fresh waters, and the toxicity data of non-native model species will no longer be used. The list of freshwater invasive species in China that cannot be used as test species was added into the guideline. The acute/chronic ratio (ACR) method for the criteria derivation and the extreme value model were deleted, and the provisions for testing the toxicity data distribution were also deleted. The exposure time of the toxicity test of various tested organisms was refined, and the priority of the toxicity data was clearly specified. This paper introduces the framework and specific technical requirements of HJ 831-2022 in detail, including data collection, pre-processing of toxicity data, criteria derivation, fitting models, and quality control. This introduction is helpful for international peers to understand the latest research progress of China's WQC.

Reference dose prediction by using CDK molecular descriptors: A non-experimental method.

Reference dose (RfD) is an estimate of a daily dose that individual can be exposed chronically without obvious deleterious effects during a lifetime. In the area of toxicology, researchers always use the traditional approach by employing NOAEL/LOAEL or the benchmark dose (BMD) and other dose-response approaches to estimate RfD. These methods have, despite their typicalness, certain limitations. In this study, we present a novel method of the estimation of reference dose without experiments. The information of the organic chemicals is available from the Integrated Risk Information System (IRIS) of USEPA. Molecular descriptors for each molecular structure were calculated by an integrated platform, and the chemicals were classified into four categories based on molecular similarity: 128 contained benzene rings, 47 were heteroaromatics, 104 contained halogen substituents and 44 were halogenated aliphatic hydrocarbons. The predictive model of RfD was constructed by the multiple linear stepwise regression (MLR) method. Approximately 95% and 82% of the data points differ by less than 10-fold and 5-fold between the predicted values and the true values respectively. The non-experimental method improves the estimation efficiency and has a certain reference value to predict.