Utility of the QSAR modeling system for predicting the toxicity of substances on the European inventory of existing commercial chemicals∗

Many chemicals are in common commercial use for which no information on the environmental fate or toxicity exists. Recent legislation requires that many substances be assessed for their toxicity to aquatic organisms within a very short time and determine which of these chemicals need to be studied in greater detail. It would be impossible to measure the acute and chronic effects of all of these compounds on a single organism, let alone a battery of different types of organisms, communities or ecosystems. Initially, the chemicals on the European Inventory of Existing Commercial Chemical Substances (EINECS) need to be screened and relative hazard to the environment determined. In response to OECD directives, there has been a great deal of activity by government and industry scientists. At the International Workshop on Advances in Environmental Hazard and Risk Assessment it was concluded that quantitative structure activity relationships (QSAR) could and should be used in the hazard assessment process. Papers published in that volume outline the advantages, disadvantages, limitations, advances and research requirements.

The QSAR, structure‐activity based chemical modeling and information system, which was developed by the US‐Environmental Protection Agency was used to predict the acute toxicity of 113 substances from the “Old Substances”; list of the German government to the four commonly used aquatic toxicity test organisms: Daphnia magna (DM), fathead minnow (FHM), rainbow trout (RBT), and blue‐gill sunfish (BG).

Of these compounds the QSAR system predicted the acute toxicity of 87 substances towards fathead minnow. For the other three species examined the QSAR system could be used to predict toxicity for 78 compounds.

The predicted toxicities were compared to observed toxicities of compounds which have been evaluated and stored in the “Aquire”; data base. Observed toxicity values were available for at least one species for 38 compounds. The toxicities of some compounds are well predicted while those of other compounds were not well predicted. Overall, the QSAR system accurately classified the acute toxicity ranges of 50%, 64%, 56% and 56% of the compounds investigated for DM, FHM, RBT and BG, respectively. Of the compounds studied 10 were very poorly predicted, of these the QSAR system overpredicted the toxicity of three, while underpredicting the toxicity of seven. Of these seven compounds, five contained amino groups.     

农药类化合物对大型溞的毒性作用模式:与基线毒性化合物比较研究

农药在控制有害生物的同时,对水生生态系统产生较大的毒性效应。本文通过实验获得了25种基线化合物对大型溞的急性毒性数据,并与57种农药类化合物对大型溞的急性毒性数据进行比较研究,同时根据体外浓度LC_(50)、生物富集因子BCF和体内临界浓度CBR的关系,计算了这些化合物在大型溞体内的临界浓度,研究了农药类化合物对大型溞的毒性作用机理。结果表明,基线化合物在大型溞体内的临界浓度log1/CBR值在一个很窄的范围波动,而农药类化合物在大型溞体内的临界浓度log1/CBR值范围广且较高。这说明多数农药类化合物对大型溞为反应性毒性作用模式。其中,除草剂对大型溞的毒性显著低于杀菌剂和杀虫剂对大型溞的毒性。这可能是因为除草剂主要通过干扰植物光合作用、植物激素或植物分子合成发挥毒性效应,从而导致其对大型溞的生理系统难以发生反应性毒性效应。而杀虫剂和杀菌剂主要通过干扰生物神经系统、生殖系统、呼吸作用或大分子合成发挥毒性效应,因此易与大型溞生理系统发生生物化学反应,从而具有较高的毒性效应。本文分别建立了除草剂、杀菌剂和杀虫剂对大型溞的急性毒性QSAR模型。除草剂对大型溞的急性毒性机理较简单,其毒性与化合物疏水性程度和离子化程度有关;而杀菌剂对大型溞的急性毒性主要与化合物的标准生成热和极性表面积有关;杀虫剂对大型溞的急性毒性作用机理较复杂,它们对大型溞的毒性效应与其和生物分子之间的氢键和范德华力有关。     

基于形态修正的描述符构建可电离化合物对大型溞急性毒性的QSAR模型

在环境水体中,可电离有机化合物(IOCs)可解离为分子和离子形态。研究表明,IOCs离子形态的环境行为、毒性效应等都与其分子形态存在较大差异,因而在研究IOCs环境行为、毒性效应时不应忽略离子化的影响。在构建IOCs相关预测模型时如何表征离子化的影响是当前研究的重要内容之一。探讨了采用基于形态修正的描述符构建IOCs水生毒性预测模型的可行性。具体而言,采用逐步多元线性回归(MLR)方法,构建了可预测63种取代酚、取代苯甲酸和取代苯胺等IOCs对大型溞急性毒性的定量结构-活性关系(QSAR)模型。与仅采用分子形态描述符的模型相比,使用基于形态修正描述符的模型决定系数(R2)、去一法交叉验证系数(Q2LOO)、外部验证系数(Q2EXT)等参数从0.622～0.705提高到了0.840～0.875,表明基于形态修正描述符的模型具有更好的拟合优度、稳健性和预测能力。因此,在将来的研究中,可采用基于形态修正的描述符构建IOCs水生毒性效应预测模型。     

Acute toxicity of industrial endocrine-disrupting chemicals,natural and synthetic sex hormones to the freshwater planarian,Dugesia japonica

Many chemicals commonly occurring in surface water are found to be hormonally active. Fourteen compounds including four sex hormones, three synthetic hormones, one non-steroidal anti-androgen, and six industrial endocrine-disrupting chemicals were selected to examine their acute toxicities on the freshwater planarian, Dugesia japonica. Among all test chemicals, diethylstilbestrol had the highest toxicity. Relatively, the toxicities of the synthetic hormones were higher than the natural ones. Among the six industrial endocrine-disrupting chemicals tested, bisphenol A was the most toxic, with a 48-h LC₅₀ of 8.3 mg L^?1. Overall, the natural or synthetic hormones, other than estrone and estriol, were more acutely toxic to planarians than the industrial endocrine disruptors tested. The acute toxicities of the test chemicals to Dugesia japonica were in the same order of magnitude as those to Daphnia magna based on available published data. Despite this study used non-environmentally relevant levels, such kinds of chemicals do not exist singly in the aquatic environment, but typically act in synergistic and/or additive ways in the complex environmental mixtures, and the total estrogen equivalents are much higher than a single chemical. This study stands as a starting point for other acute and chronic assays, namely using mixtures.     

Comparative quantitative structure–activity relationship (QSAR) study on acute toxicity of triazole fungicides to zebrafish

We employed two-dimensional quantitative structure–activity relationship (2D-QSAR) and hologram QSAR (HQSAR) methods to quantitatively investigate the mechanism and active site of toxicity for Danio rerio exposed to triazole fungicides. Our results showed that 2D-QSAR models constructed using the energy of the lowest unoccupied molecular orbit, the net C atom charges, the octanol–water partition coefficient and the molecular shape factor had higher predictive abilities. HQSAR models containing the fragment distinctions atoms (As), bonds (Bs), chirality (Ch) and donors and acceptors (D&A) had higher reliability. It was found that 2D-QSAR results explaining the toxicity mechanism were consistent with HQSAR. In summary, the hydrophobicity and shape/size of the molecule were the important factors influencing the toxic effect of these chemicals against D. rerio. In addition, electron exchange may occur between these fungicides and the target. The study provided a method to evaluate the environmental risk of chemicals with a similar structure, based on the QSAR models obtained.     

Indoor air pollution: A review∗

Relationships between toxicities of substituted aromatic hydrocarbons to Daphnia magna and to Fathead minnows or Photobacterium phosphoreum were studied based on structure‐activity equations we established. Good relationships between toxicities of chemicals to Daphnia magna and to Fathead minnows, Photobacterium phosphoreum were found for a wide range of chemicals. The toxicity data of fish can be predicted by the toxicity data of Daphnia magna or Photobacterium phosphoreum. It is probable that the toxicities of chemicals to the three organisms are similar.     

Water Quality Criteria for European Freshwater Fish

1. For water pollution control purposes, the concentration-addition model for describing the joint effects of mixtures of toxicants on aquatic organisms is appropriate; in this model the contribution of each component in the mixture is expressed as a proportion of the aqueous concentration producing a given response in a given time (e.g. p 96-h LC50).

2. Examination of available data using this model shows that for mixtures of toxicants found in sewage and industrial effluents, the joint acutely-lethal toxicity to fish and other aquatic organisms is close to that predicted, assuming simple addition of the proportional contribution from each toxicant. The observed median value for the joint effect of these toxicants on fish is 0.95 of that predicted, and the corresponding collective value for sewage effluents, river waters, and a few industrial wastes, based on the toxicity of their constituents, is 0.85, while that for pesticides is 1.3.

3. The less-than-predicted effect of commonly-occurring toxicants in some mixtures may be partly attributable to small fractions of their respective LC50 values having a less-than-additional effect. However, recent research has shown that for some organic chemicals which have a common quantitative structure-activity relationship (QSAR), their joint action as determined by acute toxicity is additive at all concentrations.

4. The few (unpublished) data available for the long-term lethal joint effect on fish of toxicants in mixtures suggest that they may be markedly more than additive, a phenomenon that needs confirmation and further investigation.

5. In the few studies on the sub-lethal effects on fish (eg growth), the joint effect of toxicants has been consistently less-than-additive which suggests that as concentrations of toxicants are reduced towards the levels of no effect, their potential for addition is also reduced. There appear to be no marked and consistent differences between the response of different species to mixtures of toxicants.

6. Field studies have shown that reasonably accurate toxicity predictions based on chemical analysis can be made if the waters which are polluted are acutely lethal to fish, and that a fish population of some kind can exist where the median 2 p t LCSOs (rainbow trout) is < 0.2. It is not known whether this condition is equivalent to a C p NOEC of 4.0 (ie the sum of the individual fractions of the NOEC for the species present), or to a NOEC of < 1.0 for each individual toxicant (i.e. fractions of the NOEC are not summed).

7. In general, the joint effect of the common toxicants on lethal and sub-lethal responses of fish is not explained by variations in the uptake of the individual toxicants concerned; this may not apply for those chemicals with a common QSAR, although there is little experimental evidence in this field.

8. There is an immediate need for more empirical studies on the joint effect of mixtures of toxic units of individual components, and the relation between long- and short-term lethal and non-lethal joint effects. This applies to mixtures of commonly occurring toxicants as well as to mixtures of organic chemicals with a common QSAR. The data obtained should be reinforced by studies on the mechanisms of interaction of toxicants. More field studies which relate water quality to the structure and productivity of fish populations are also required, involving direct measurements of fractional toxicity of the river water wherever possible.

9. The concentration-addition model appears to be adequate to describe the joint effect of commonly-occurring constituents of sewage and industrial wastes, and for tentative predictions of the joint effect on fish populations of toxicants present at concentrations higher than the EIFAC recommended values. However, concentrations lower than the EIFAC recommended values may make an increasingly lesser contribution to the toxicity of mixtures of toxicants and there may be a need to adjust the tentative water quality criteria downwards where two or more toxicants are present at concentrations close to these values. For toxicants with a common QSAR, their additive joint action may necessitate the setting of water quality criteriafor this group as a whole and not on the basis of individual compounds. However, too little is known of their precise joint action where the combined concentration produces a sub-lethal response.     

有机毒物对水蚤的急性毒性

对水蚤的毒笥试验数据是被广泛用于评价化合物水生生态效应的重要依据，检测有机化合物的毒性时，需特别注意待测物的难溶性，挥发性及易光解性，检测了多种氯代芳烃的ＥＣ５０值，其１８ｈ的结果与２４ｈ的结果相近，检测了２４种硝基芳烃的毒性，不同方法计算的ＥＣ５０值一致。     

基于QSAR模型的磺胺与群体感应抑制剂对枯草芽孢杆菌联合慢性毒性规律初探

群体感应抑制剂是抗生素最有可能的替代品,两者在环境中的共存会对生物造成联合毒性影响。以革兰氏阳性菌枯草芽孢杆菌(Bacillus subtilis,B.subtilis)为模式生物,3种群体感应抑制剂(呋喃酮、吡咯酮和吡咯)和磺胺类药物为研究对象,测定了20 h单一和联合毒性。结果显示,3种群体感应抑制剂和磺胺的联合毒性分别表现为相加和拮抗。同时根据不同的联合毒性效应,以药物和蛋白分子的对接结合能(Ebinding)作为结构参数分别构建了联合毒性的QSAR模型,并分析了不同毒性效应下混合物中各组分的相互作用关系。结果表明,无论是相加还是拮抗,在二元混合体系中磺胺与其靶蛋白DHPS的有效结合浓度总是高于群体感应抑制剂与Lux S的有效结合浓度;但当产生拮抗作用时,磺胺与DHPS的有效结合浓度相对较低,推测可能是群体感应抑制剂的存在使得磺胺由分子态变为离子态,从而使其难以穿过细胞壁与DHPS结合导致的。本研究为建立和分析联合毒性的QSAR模型提供了一定的理论基础。     

Toxicity of substituted benzaldehydes to photobacterium phosphoreum and quantitative structure‐activity relationship

Acute toxicities (15min‐EC₅₀) of 21 substituted benzaldehydes to Photobacterium phosphoreum were determined. A quantitative structure‐activity relationship (QSAR) was developed using molecular connectivity indices and quantum chemical parameters, which show that the toxicity is influenced mainly by the difference of the simple and valence‐corrected fourth path molecular connectivity, polarizability, dipole moment, and the most negative net atomic charge on an atom. This study further shows that benzaldehydes are reactive chemicals, which exhibit bioreactive toxicity.     

Kombinationswirkungen von Umweltchemikalien in der Ökotoxikologie

Ecosystems and biocoenoses are exposed to multiple mixtures of environmental pollutants, but the usual risk assessment of chemical toxicities is focussed only on the judgement of single substance toxicity. With the two biometrical models concentration-addition and independent action known from pharmacology and toxicology, a pragmatic way for the analysis of combined effects is possible using the experimental knowledge of single substance toxicity. A short introduction to the models is given and an appropriate experimental design for mixture toxicity analysis is outlined. The principal suitability of the concepts was verifed in two different bioassays (green alga; luminescent bacterium) with the analysis of binary and multiple mixture toxicities of environmental chemicals. In this paper we present the results obtained with the green algae bioassay. Congruent results from the bioluminescence inhibition assay can be found in Grimme (1998). The results obtained indicate that the toxicities of mixtures of chemicals can be studied experimentally, even at low concentrations of the individual components. Mixture toxicities were detected at low, statistically non-significantly acting concentrations of the single compounds. These results force one to take mixture toxicities into account when environmental standards are established.     

有机污染物对藻类毒性的测定

藻类是研究水生毒理学的很好的材料之一，对其毒性是评价化合物危害的基本数据，也是研究化合物结构与生物效应的手段之一，通过测定工业废水，数种硝基芳烃及多种有机锡对藻类的毒性，发现剂量与效应时间有很好的相关性，化合物结构与效应之间也有一定的规律。     

组合指数在环境混合物联合毒性研究中的初步应用

化学品在实际环境中总是以组分繁杂多变的混合物形式存在,其混合物的毒性评估与预测一直是环境毒理学研究重点。在环境毒理学领域,浓度加和(concentration addition,CA)及独立作用(independent action,IA)是评估与预测化学混合物联合毒性的经典模型,一般认为CA适用于作用模式相似的混合物体系而IA适用于作用模式相异的混合物体系,但如何使用CA与IA一直存在争议。组合指数(combination index,CI)是在半数效应方程基础上发展起来的不依赖于作用模式的用于混合物联合毒性评估的混合物毒性指数,具有坚实的理论基础,不仅能定性地评估毒理学相互作用,也能定量地评估相互作用的大小,已在药物组合研究中得以广泛应用,近年来已引起环境毒理学研究者兴趣。本文就组合指数及药物组合应用、进入环境毒理学领域、与CA及IA的关系、存在的问题等几个方面进行评述,以期推进CI在化学混合物毒性评估与预测领域中的应用。     

卤代苯对江水细菌的慢性毒性

毒性数据是对化学品进行环境风险评价的基础,慢性毒性数据更能真实的反映毒物对水生生物生长、繁殖及生理功能产生的影响。本文采用细菌生长抑制实验,测定了17种卤代苯对长江水中混合细菌的慢性毒性,得到-lgNOEC值,毒性范围在4.27(氯苯)~5.22(1,2,4-三氯苯)之间。选用量化参数对毒性数据进行定量结构活性关系(QSAR)研究,结果表明,卤代苯对江水细菌的慢性毒性主要与化合物分子的空间大小有关,量化参数范德华面积(SVdW)及分子生成热(Hf)能够很好的描述卤代苯对江水细菌的慢性毒性。     

页岩气压裂返排液的水生生态毒性效应研究

页岩气压裂作业过程产生大量压裂返排液,这类废液富含有毒且致癌的芳香烃、有毒金属和铀、钍、镭等放射性物质、石油类添加剂等,具有可生化性差和难处理的特点。这类废液若不经任何处理就进行排放,将给环境和人群健康带来潜在的危害和风险。目前尚不清楚它们的生态毒性效应。论文从涪陵页岩气开采基地采集了4口开采井(2号井、6号井-1、6号井-2和9号井)的压裂返排液,采用国家废水测试标准分析4种压裂返排液的水质理化指标,采用斜生栅藻(Scenedesmus obliquus)、大型溞(Daphnia magna)等标准毒性试验生物探究压裂返排液污染物体系的水生生态毒性效应,并研究水质理化指标与压裂返排液综合毒性的关系。结果表明,采集的所有压裂返排液样品均具备较大的水生生态毒性效应。以斜生栅藻的96 h的半数效应浓度(96 h-EC_(50))或大型溞的96 h的半数致死浓度(96 h-LC_(50))和毒性单位(TUa)为评价指标进行生态毒性分级,4个位点压裂返排液的生态毒性均为中毒及以上。压裂返排液的重金属、阴阳离子及有机物含量与其综合毒性的相关性结果表明,这些污染物成分与其藻类综合毒性无显著相关性。但Li、V、Cu、Ga、Rb、Ba等6种重金属及Na+、Cl-等2种阴阳离子对压裂返排液的溞类综合毒性有显著影响,影响程度依次为LiGaBaCl-VNa+RbCu。因此,在对压裂返排液进行处理时,这些与毒性相关的污染物是需要重点考虑的化学成分。     

Organochlorine pesticides and chlorinated hydrocarbon solvents in the blood of chemically sensitive patients [corrected]. A statistical comparison with therapeutic medication and natural hormones

The blood levels of organochlorine pesticides and chlorinated hydrocarbon solvents were measured in 200 and 114 chemically sensitive patients respectively, and compared with blood concentrations of standard medication (non- chlorinated substances. Clonidine, Haloperidol) of comparable toxicity after therapeutically effective dosage, and with reference levels of highly potent chemicals in the blood such as hormones. It was shown that the average blood levels of the most toxic environmental pollutants are comparable with the therapeutic steady state average blood levels of medications which have similar toxicities in the animal model. In addition the toxicity levels of xenoestrogens are at least an order of magnitude higher than normal plasma estrogen or progesterone levels. These findings suggest the possibility of additive or synergistic effects of these chlorinated compounds and the aforementioned medications. Also, these findings suggest the possibility of hormone deregulation from exposure to the aforementioned toxic chlorinated compounds.     

Nano-QSAR: 纳米毒理学领域的新方法

随着纳米毒理学实验研究的不断深入,反映纳米材料生物毒性效应的数据也不断丰富,以这些数据为基础建立的定量结构活性关系(QSAR)模型开始发挥其在纳米材料潜在毒性研究和预测方面的作用。纳米材料的QSAR(Nano-QSAR)研究以经典QSAR模型为指导,结合纳米材料特殊的物理化学性质,提供了一种对纳米材料快速筛选和优先测试的新途径。本文就Nano-QSAR的前期研究现状,从纳米材料结构描述符、毒性效应数据和建模方法3个方面分析了模型的构建流程和框架;通过列举部分研究成果和主要的模型指标,初步探讨了建模方法的选择和结构描述符的识别;最后指出目前Nano-QSAR研究面临的挑战和今后努力的方向。     

Abschätzung des umweltchemischen und ökotoxikologischen Verhaltens von Stoffen durch computergestützte Analyse von Struktur und Verhalten sowie von Struktur und Wirkung

Risk assessment of chemicals requires comprehensive data material, which often is not available. To provide substitutes for the lacking experimental ecotoxicological and physico-chemical data, a software-system (SAR-System) has been developed comprising more than 90 estimation models for relevant endpoints. The approach is based on quantitative structure-activity relationships (QSAR). Two major aspects characterize the SAR-System:

1. The implemented models were tested for their validity and application range.
2. The QSARs are accessible by a menu-driven programme package.

The following endpoints are included: Thysico-Chemical Data: 1-Octanol/water-partition-coefficient log P_ow, vapour pressure, water solubility, pK_a-value, boiling point Biological Data: Toxicity towards fish, daphnia, tetrahymenae, algae, bacteria and mammals, mutagenicity. Distribution: soil sorption, bioconcentration, Henry-Constant, Mackay (Level I). Degradation: photodegradation, biodegradability.     

微塑料与有机污染物的相互作用研究进展

微塑料(粒径小于5 mm的塑料)作为海洋中一种新型的污染物正受到越来越多的关注。微塑料在全球多个海域均有检出,根据其来源分为原生微塑料和次生微塑料。原生微塑料由人工直接制造所得,常见于日常生活用品中;次生微塑料由大块塑料制品长期风化、磨损和光解形成。塑料自身含有多种有机添加剂,不断向环境中释放,污染海洋环境;微塑料表面还可吸附有机污染物,此吸附作用受两者的物理化学性质和环境条件影响,吸附污染物后的微塑料生物毒性增强。另外,聚合物复合光催化材料可加快有机污染物如染料的光降解反应速率,因而微塑料可能会促进有机污染物的光解。针对目前微塑料对有机物光降解的贡献、机理鲜见研究的问题,未来应加强以下3方面的研究:(1)微塑料对不同有机污染物光降解是否存在影响?(2)微塑料类型、尺寸以及反应条件对有机污染物光降解如何影响?(3)微塑料对有机污染物光降解影响的内在机制是什么?     

AQUATOX: Modeling environmental fate and ecological effects in aquatic ecosystems

AQUATOX combines aquatic ecosystem, chemical fate, and ecotoxicological constructs to obtain a truly integrative fate and effects model. It is a general, mechanistic ecological risk assessment model intended to be used to evaluate past, present, and future direct and indirect effects from various stressors including nutrients, organic wastes, sediments, toxic organic chemicals, flow, and temperature in aquatic ecosystems. The model has a very flexible structure and provides multiple analytical tools useful for evaluating ecological effects, including uncertainty analysis, nominal range sensitivity analysis, comparison of perturbed and control simulations, and graphing and tabulation of predicted concentrations, rates, and photosynthetic limitations. It can represent a full aquatic food web, including multiple genera and guilds of periphyton, phytoplankton, submersed aquatic vegetation, invertebrates, and fish and associated organic toxicants. It can model up to 20 organic chemicals simultaneously. (It does not model metals.) Modeled processes for organic toxicants include chemodynamics of neutral and ionized organic chemicals, bioaccumulation as a function of sorption and bioenergetics, biotransformation to daughter products, and sublethal and lethal toxicity. It has an extensive library of default biotic, chemical, and toxicological parameters and incorporates the ICE regression equations for estimating toxicity in numerous organisms. The model has been implemented for streams, small rivers, ponds, lakes, reservoirs, and estuaries. It is an integral part of the BASINS system with linkage to the watershed models HSPF and SWAT.