A novel method based on similarity and triangulation for predicting the toxicities of various binary mixtures

An artificial intelligence-based model for predicting reproductive toxicity of bisphenol analogues mixtures to the rotifer Brachionus calyciflorus

The joint toxicity effects of mixtures, particularly reproductive toxicity, one of the main causes of aquatic ecosystem degradation, are often overlooked as it is impractical to test all mixtures. This study developed and evaluated the following models to predict the concentration response curve concerning the joint reproductive toxicity of mixtures of three bisphenol analogues (BPA, BPF, BPAF) on the rotifer Brachionus calyciflorus: concentration addition (CA), independent action (IA), and two deep neural network (DNN) models. One applied mixture molecular descriptors as input variables (DNN-QSAR), while the other applied the ratios of chemicals in the mixtures (DNN-Ratio). Descriptors related to molecular mass were found to be of greater importance and exhibited a proportional relationship with toxic effects. The results indicate that the range of correlation coefficients (R2) between predicted and measured values for various mixture rays by CA and IA models is 0.372 to 0.974 and - 0.970 to 0.586, respectively. The R2 values for DNN-Ratio and DNN-QSAR were 0.841 to 0.984 and 0.834 to 0.991, respectively, demonstrating that models developed by DNN significantly outperform traditional models in predicting the joint toxicity of mixtures. Furthermore, DNN-QSAR not only predicts mixture toxicity but also provides accurate toxicity predictions for BPA, BPF, and BPAF, with R2 values of 0.990, 0.616, and 0.887, respectively, while DNN-Ratio yields values of 0.920, 0.355, and - 0.495. The study also found that the joint effects of mixtures are primarily influenced by the total concentration of the mixtures, and an increase in total concentration shifts the joint effects towards addition. This study introduces a novel approach to predict joint toxicity and analyze the influencing factors of joint effects, providing a more comprehensive assessment of the ecological risk posed by mixtures.

Time-dependent hormetic dose responses of skin care product mixtures to Vibrio qinghaiensis sp.-Q67: Appearance and quantification

Hormesis is a widely recognized and extensively studied phenomenon. However, few studies have described the quantitative characteristics of hormesis required for appropriate risk assessment. Although skin care product (SCP) mixtures and their active ingredients can induce the hormesis of Vibrio qinghaiensis sp.-Q67 (Q67), the quantitative characteristics of time-dependent hormetic dose responses in SCPs have not yet been investigated. In this study, 28 SCP mixtures were tested for luminescence toxicity against Q67 after five exposure durations (0.25, 3, 6, 9, and 12 h). With increasing exposure duration, the concentration response curves (CRCs) were classified as constant monotonic nonlinear (S-shaped) for four SCPs, S- to hormetic (J-shaped) for 13 SCPs, and constant J-shaped for 11 SCPs. Of 140 CRCs, 98 were J-shaped. An increased frequency of SCPs inducing hormesis was observed. The toxicity (pEC50) of the SCPs was independent of the exposure duration and product type. The maximum stimulatory effect (Emin) of the 12 SCPs increased with exposure duration. We proposed a modified parameter, the width of inhibition dose zone (WIDZ; EC50/EC10), to depict the width of inhibition dose zone. The WIDZ of S-shaped CRCs were significantly larger than that of J-shaped CRCs. In addition, the characteristic parameters reported in the general literature were analyzed. The good linear relationship between EC50 and the maximum stimulatory effective concentration (ECmin) indicated that toxicity may be transformed into stimulatory effects over exposure durations. The width of stimulation dose zone (WSDZ) and Emin of the seven SCPs had the same increasing trends with increasing exposure duration. The combination of WIDZ with other characteristic parameters (e.g., zero effective concentration point, ECmin, etc.) could better depict hormesis with low-dose stimulation and high-dose inhibition. The quantitative characteristics of the dose-responses of hormesis-inducing SCPs could provide reference basis for the risk assessment of SCP mixtures.

Predictive in silico models for aquatic toxicity of cosmetic and personal care additive mixtures

As emerging environmental contaminants, cosmetic and personal care additives (CPCAs) may have less oversight than other consumer products. Their continuous release and pseudopersistence could cause long-term harm to the aquatic environment. Since CPCAs generally exist in the form of mixtures in the environment, prediction and analysis of their mixture toxicity are crucial for ecological risk assessment. In this study, the acute toxicity of five typical CPCA mixtures to Daphnia magna was tested. The combined toxicity of binary mixtures was examined with the traditional concentration addition (CA) and independent action (IA) model. Overall, the synergistic effect of the five CPCAs may be caused mainly by methylparaben. In addition, reliable approaches for quantitative structure-activity relationship (QSAR) model development were explored. Specifically, 18 QSAR models were developed by three dataset partitioning techniques (Kennard-Stone's algorithm division, Euclidean distance based division, and sorted activity based division), two descriptor filtering methods (genetic algorithm and stepwise multiple linear regression) and three regression methods (multiple linear regression, partial least squares and support vector machine). Sixteen equations were applied for the calculation of the mixture descriptors to screen the functional expression of the mixture descriptors with the largest contribution to the mixture toxicity. A new comprehensive parameter that integrates internal and external validation was proposed for QSAR models evaluation. The mixture toxicity is mainly related the 3D distribution of atomic masses and the spatial distribution of the molecule electronic properties. Rigorously validated and externally predictive QSAR models were developed for predicting the toxicity of binary CPCAs mixtures with any ratio, in the applicability domain. The best possible work frame for construction and validation of QSAR models to provide reliable predictions on the mixture toxicity was proposed.

Environmental pollution impacts: Are p values over-valued?

An examination revealed the dominance of the published literature of environmental science by p values. Meanwhile, the use of effect size has been neglected in publications reporting primary data, yet the size of effect is often more informative than p values inference in assessing the effects of pollution on living organisms, comparing susceptibility/resistance among organisms, and ranking pollutants according to their potency, among others. Statistical significance does not necessarily mean biological, practical, or scientific significance, and its use based on (often misinterpreted) p values reflects the average response or effect at average conditions based on an assumed linear model fit to the entire sample. However, pollution impacts and organismal responses are rarely characterized by linear and symmetric features, and dichotomous 'statistical significance' based on p values is inadequate to fully describe data and findings. Considering 'the fallacy of the average', variance, and differential response of different population percentiles in new studies would provide otherwise wasted biologically, practically, or scientifically significant information. Since p values often inform as to whether some findings warrant further examination, journals should consider mandating the reporting of effect sizes and confidence intervals, together with p values (should they be used), to provide more integrated information regarding pollution impacts. Moreover, replacing 'statistical significance' with language of evidence, especially in key components of publications, such as abstracts and conclusions, could help preventing potential misleading of the public and decision and policy makers.

US EPA: Is there room to open a new window for evaluating potential sub-threshold effects and ecological risks?

With a rule published on 6 January 2021, the US Environmental Protection Agency (EPA) considers for the first time sub-threshold responses, abandoning the use of default dose-response models. This may affect worldwide scientific research, in terms of research design and methodology, and regulatory actions in China and other countries.

BNNmix: A new approach for predicting the mixture toxicity of multiple components based on the back-propagation neural network

The chemical mixtures in various environmental media not only have concentration diversity but also mixture-ratio diversity. It is impossible to experimentally determine the toxicities of all mixtures; therefore, it is necessary to develop effective methods based on models to predict mixture toxicity. In this study, a new approach (BNNmix) based on the back-propagation neural network (BPNN) was developed and used to predict the toxicities of seven-component mixtures (consisting of two substituted phenols, two pesticides, two ionic liquids, and one heavy metal) on Caenorhabditis elegans. We found that the combined toxicities of various mixtures used in the experiments were neither global concentration-additive nor global response-additive, which implied that it was impossible to accurately predict the toxicities of such mixtures by using common models such as concentration addition (CA) and response addition (independent action, IA). Using the BNNmix approach to estimate or predict the toxicities of the mixtures under test, it was found that the predictive toxicities of various mixtures with different mixture ratios and concentrations were almost in accordance with those observed experimentally. Unlike the CA and IA models, the BNNmix approach can predict not only the toxicities of mixtures having toxicological interactions but also those with global concentration or response additivities.

Alteration in concentration-response curves of four N-alkylpyridinium chloride by exposure concentration, time and in their mixtures by uniform design

The concentration-response curves (CRCs) of chemicals are important in extrapolating their effects from laboratory studies to their risk assessment in the field. Yet, the CRCs can be altered by exposure concentration and mixture conditions, and also by exposure time in recent reports. Presently, ionic liquids (N-alkylpyridinium chloride, [apyr]Cl) were used for CRC-alteration studies. In individual effects on Vibrio qinghaiensis sp. Q67 (Q67) from 0.25 to 24 h, the CRCs of [epyr]Cl and [bpyr]Cl changed from S- to J-shaped with decreases in inhibition and increases in stimulation, while the CRCs of [hpyr]Cl changed from S- to flat-shape with decreases in inhibition but without stimulation. In mixture effects on Q67, the CRCs all changed from S- to J-shaped from 0.25 to 24 h. By means of the variable selection and modeling method based on the prediction (VSMP), the CRC-alterations of mixtures were positively contributed by [epyr]Cl but negatively contributed by [bpyr]Cl. Furthermore, a parameter was developed by the area of a triangular that combined acute inhibition (EC_50,0.25h) and chronic stimulation (Zero-effect Point, i.e., ZEP_24h and the minimum inhibition effect, i.e., E_min,24h). This parameter successfully evaluated the CRC-alterations in both individual and mixture effects over time, and indicated potential interactions in CRC-alteration in mixtures.