SAR—The U.S. Regulatory Perspective

Abstract

Structure-Activity Relationships (SAR) have been used for over a decade by the U.S. EPA's Office of Pollution Prevention and Toxics (OPPT) in their new chemicals program. The development and use of SAR resulted from the need to make rapid risk-based decisions on thousands of new chemicals per year while seldom receiving data on chemical properties, potential exposures, or hazards to humans or organisms in the environment. Qualitative SAR and quantitative SAR methods (QSAR) have been used to fill some of these data gaps by estimating the potential properties and hazards of such chemicals. SAR has been used to assess chemical hazards, identify testing needs, and set priorities. Validation of these SAR assessment tools is an ongoing process.     

The Application of Structure-Activity Relationships (SARs) in the Aquatic Toxicity Evaluation of Discrete Organic Chemicals

Abstract

The Office of Pollution Prevention and Toxics (OPPT), United States Environmental Protection Agency (USEPA) routinely uses structure-activity relationships (SAR) for the aquatic hazard assessment of new chemicals submitted under Section 5 of the Toxic Substances Control Act (TSCA). With 15 years of experience and the general acceptance of toxicity predictions based on SARs, OPPT has expanded the use and application of the methodology to include existing chemicals used in printing, dry cleaning, and paint stripping. SAR analysis has also been used in the hazard evaluation of the U.S. and EU/OECD high production volume (HPV) chemicals. This paper describes the assumptions, limitations, and methodology for the use of SARs to evaluate large sets of discrete organic chemicals.     

In silico exploratory study using structure–activity relationship models and metabolic information for prediction of mutagenicity based on the Ames test and rodent micronucleus assay

The mutagenic potential of chemicals is a cause of growing concern, due to the possible impact on human health. In this paper we have developed a knowledge-based approach, combining information from structure–activity relationship (SAR) and metabolic triggers generated from the metabolic fate of chemicals in biological systems for prediction of mutagenicity in vitro based on the Ames test and in vivo based on the rodent micronucleus assay. In the first part of the work, a model was developed, which comprises newly generated SAR rules and a set of metabolic triggers. These SAR rules and metabolic triggers were further externally validated to predict mutagenicity in vitro, with metabolic triggers being used only to predict mutagenicity of chemicals, which were predicted unknown, by SARpy. Hence, this model has a higher accuracy than the SAR model, with an accuracy of 89% for the training set and 75% for the external validation set. Subsequently, the results of the second part of this work enlist a set of metabolic triggers for prediction of mutagenicity in vivo, based on the rodent micronucleus assay. Finally, the results of the third part enlist a list of metabolic triggers to find similarities and differences in the mutagenic response of chemicals in vitro and in vivo.     

Development,Characterization and Application of Predictive-Toxicology Models

Abstract

The adoption of SAR techniques for risk assessment purposes requires that the predictive performance of models be characterized and optimized. The development of such methods with respect to CASE/MULTICASE are described. Moreover the effects of size, informational content, ratio of actives/inactives in the model on predictivity must be determined.

Characterized models can provide mechanistic insights: nature of toxicophore, reactivity, receptor binding. Comparison of toxicophores among SAR models allows a determination of mechanistic overlaps (e.g., mutagenicity, toxicity, inhibition of gap junctional intercellular communication vs. carcinogenicity).

Methods have been developed to combine SAR submodels and thereby improve predictive performance.

Now that predictive toxicology methods are gaining acceptance, the development of Good Laboratory Practices is a further priority, as is the development of graduate programs in Computational Toxicology to adequately train the needed professional.     

Computational Toxicology and the Generation of Mechanistic Hypotheses: γ-Butyrolactone

Abstract

In this study, we use SAR approaches in an attempt to elucidate the action of γ-butyrolactone (GBL), an illicit drug and a dietary supplement, that can cause coma and deaths in humans while exhibiting low systemic toxicity towards rodents.

The lack of systemic toxicity of GBL and of its metabolite(s) was also predicted by validated SAR models. In fact using diverse SAR models, the only significant SAR prediction was that GBL had the potential for inhibiting human cytochrome P4502D6 (CYP2D6). However, inhibition of that isozyme is not necessarily associated with toxicity. It is suggested that GBL users also abuse other substances. When GBL inhibits CYP2D6 this may prevent the CYP2D6-mediated detoxification of other toxicants simultaneously consumed by the GBL user.     

Integrated approach to assess the domain of applicability of some commercial (Q)SAR models1

An integrated framework of data analysis has been proposed to systematically address the determination of the domain of applicability (DA) of some commercial Quantitative Structure Activity Relationship ((Q)SAR) models based on the structure of test chemicals. This framework forms one of the important steps in dealing with the growing concerns on reliability of model-based predictions on toxicity of chemicals specifically in the regulatory context. The present study uses some of the well-known mutagenicity and carcinogenicity models that are available within the Casetox (MultiCASE Inc.) and TOPKAT (Accelrys Software Inc.) programs. The approach enumerated in this paper employs chemoinformatics tools that facilitate comparisons of key structural features as well as application of cluster analysis techniques. The approach has been illustrated using a set of eleven chemical structures selected from the Canadian Domestic Substances List (DSL) that are not present in the model training sets, and the efficacy of the approach has also been assessed using seven chemicals with known toxicities. The methodologies presented here could help address the issue of DA of complex (Q)SAR models and at the same time, serve as useful tools for regulators to make a preliminary assessment of (Q)SAR based systems thereby helping the process of hazard-based regulatory assessments of chemicals.     

7-羟基异黄酮及其衍生物的合成与选择性雌激素受体结合的构效关系研究

以多酚类化合物和多取代苯乙酸类化合物为原料,用一锅法合成了24个7-羟基异黄酮类化合物;并从一锅法制得的3d,3g经脱除甲基得到两个2'-羟基取代的异黄酮化合物4a和4b.雌激素受体(Estrogen receptors,ERs)的选择性结合活性试验表明:26个化合物(包括4个新化合物3r,3s,3u和3v)中,9个化合物与ERβ相对于ERα的选择性作用强于染料木素(Genistein);发现7,8-二羟基异黄酮类化合物与ERβ相对结合能力高于相应7-羟基异黄酮类化合物;4'取代基对化合物与ERβ结合相对于ERα的选择性影响从大到小为:H>Cl>F>OH;2',3'及5'位取代基降低异黄酮对ERα和ERβ的亲和性.     

Recovering laboratory doses using fine-grained quartz from Chinese loess

Here we report the results of dose recovery experiments carried out on fine-grained quartz from a Holocene sample in Chinese loess. Optical bleaching prior to giving a 9.35 Gy dose in the laboratory was carried out using artificial light sources, namely blue light emitting diodes (LEDs) and a solar simulator, followed by more than 24 h storage at room temperature. Stimulating the quartz with blue LEDs at room temperature resulted in overestimation of the recovered dose, whereas using the same stimulation time at temperatures above 150 °C resulted in the correct value for the recovered dose. Exposure to the solar simulator for times less than 200 min resulted in overestimation of the dose, whereas progressive underestimation was found for longer bleaching times. When doses of up 5333 Gy were given ahead of a 200 min exposure to the solar simulator, the dose recovered depended upon the magnitude of the previous dose, thus questioning the general application of a simple dose recovery experiment.     

A Dual Attention Encoding Network Using Gradient Profile Loss for Oil Spill Detection Based on SAR Images

Marine oil spills due to ship collisions or operational errors have caused tremendous damage to the marine environment. In order to better monitor the marine environment on a daily basis and reduce the damage and harm caused by oil pollution, we use marine image information acquired by synthetic aperture radar (SAR) and combine it with image segmentation techniques in deep learning to monitor oil spills. However, it is a significant challenge to accurately distinguish oil spill areas in original SAR images, which are characterized by high noise, blurred boundaries, and uneven intensity. Hence, we propose a dual attention encoding network (DAENet) using an encoder–decoder U-shaped architecture for identifying oil spill areas. In the encoding phase, we use the dual attention module to adaptively integrate local features with their global dependencies, thus improving the fusion feature maps of different scales. Moreover, a gradient profile (GP) loss function is used to improve the recognition accuracy of the oil spill areas’ boundary lines in the DAENet. We used the Deep-SAR oil spill (SOS) dataset with manual annotation for training, testing, and evaluation of the network, and we established a dataset containing original data from GaoFen-3 for network testing and performance evaluation. The results show that DAENet has the highest mIoU of 86.1% and the highest F1-score of 90.2% in the SOS dataset, and it has the highest mIoU of 92.3% and the highest F1-score of 95.1% in the GaoFen-3 dataset. The method proposed in this paper not only improves the detection and identification accuracy of the original SOS dataset, but also provides a more feasible and effective method for marine oil spill monitoring.