Novel modes of capillary electrophoresis for the determination of endocrine disrupting chemicals

The synthesis and usage of a wide range of organic chemicals has increased dramatically over the last five decades. These compounds sometimes termed endocrine disrupting chemicals include agricultural pesticides, industrial solvents, dyes, plasticisers, detergents and heat exchangers. Concerns have been raised about the potential adverse effects of these compounds on humans and wildlife species. Our objectives are to develop a method to identify, using novel capillary electrophoretic techniques, the endocrine disrupting compounds that are reported to be present in environmental samples. The CE modes, capillary zone electrophoresis, micellar electrokinetic chromatography (MEKC), cyclodextrin-modified MEKC (CD-MEKC) and electroosmotic flow-suppressed CD-MEKC were investigated for the determination of a range of endocrine disrupting chemical compounds. This paper shows some initial results obtained.     

Design and automated generation of artificial estrogen receptor as potential endocrine disruptor chemical binders

We here report on our results concerning the systematic design and synthesis of a new type of artificial estrogen receptor candidates. Our interest herein originates from the known estrogenic activity of endocrine disrupting chemicals, environmental pollutants that have attracted increased attention due to their interference with the endocrine and reproductive system of wildlife and humans. An automated protocol for the generation of libraries of potential artificial receptors is described. The resulting structures could find application in novel SPE cartridges for EDC-preconcentration.     

卵黄蛋白原的分离测定及其在环境内分泌干扰物质筛选中的应用

环境内分泌干扰物的存在直接威胁野生动物的生存和人类的健康，对其作用机制及筛选方法的研究，已经成为环境科学研究的热点领域。近年来，卵黄蛋白原作为环境内分泌干扰物的“生物标志物”，得到了较深入的研究。本文讨论了卵黄蛋白原的分离测定方法及其在内分泌干扰物筛选中应用的最新进展，为建立更有效的卵黄蛋白分离测定方法及发展新的环境内分泌干扰物筛选技术提供参考。     

Effects-based chemical category approach for prioritization of low affinity estrogenic chemicals

Regulatory agencies are charged with addressing the endocrine disrupting potential of large numbers of chemicals for which there is often little or no data on which to make decisions. Prioritizing the chemicals of greatest concern for further screening for potential hazard to humans and wildlife is an initial step in the process. This paper presents the collection of in vitro data using assays optimized to detect low affinity estrogen receptor (ER) binding chemicals and the use of that data to build effects-based chemical categories following QSAR approaches and principles pioneered by Gilman Veith and colleagues for application to environmental regulatory challenges. Effects-based chemical categories were built using these QSAR principles focused on the types of chemicals in the specific regulatory domain of concern, i.e. non-steroidal industrial chemicals, and based upon a mechanistic hypothesis of how these non-steroidal chemicals of seemingly dissimilar structure to 17ß-estradiol (E2) could interact with the ER via two distinct binding types. Chemicals were also tested to solubility thereby minimizing false negatives and providing confidence in determination of chemicals as inactive. The high-quality data collected in this manner were used to build an ER expert system for chemical prioritization described in a companion article in this journal.     

Comparative molecular field analysis (CoMFA) model using a large diverse set of natural, synthetic and environmental chemicals for binding to the androgen receptor

A large number of natural, synthetic and environmental chemicals are capable of disrupting the endocrine systems of experimental animals, wildlife and humans. These so-called endocrine disrupting chemicals (EDCs), some mimic the functions of the endogenous androgens, have become a concern to the public health. Androgens play an important role in many physiological processes, including the development and maintenance of male sexual characteristics. A common mechanism for androgen to produce both normal and adverse effects is binding to the androgen receptor (AR). In this study, we used Comparative Molecular Field Analysis (CoMFA), a three-dimensional quantitative structure-activity relationship (3D-QSAR) technique, to examine AR-ligand binding affinities. A CoMFA model with r2 = 0.902 and q2 = 0.571 was developed using a large training data set containing 146 structurally diverse natural, synthetic, and environmental chemicals with a 10(6)-fold range of relative binding affinity (RBA). By comparing the binding characteristics derived from the CoMFA contour map with these observed in a human AR crystal structure, we found that the steric and electrostatic properties encoded in this training data set are necessary and sufficient to describe the RBA of AR ligands. Finally, the CoMFA model was challenged with an external test data set; the predicted results were close to the actual values with average difference of 0.637 logRBA. This study demonstrates the utility of this CoMFA model for real-world use in predicting the AR binding affinities of structurally diverse chemicals over a wide RBA range.     

Molecularly imprinted polymer film grafted from porous silica for efficient enrichment of steroid hormones in water samples

Steroid hormones as endocrine disrupting compounds can interfere with the functioning of hormonal systems of organisms and thus affect the health and reproduction of humans and wildlife. Unfortunately, these types of harmful endocrine disrupting compounds have been found in a variety of environmental samples at very low concentrations. Therefore, a simple, fast, and efficient method for enrichment of water samples is needed. A molecularly imprinted solid‐phase extraction combined with high performance liquid chromatography coupled with diode array detection was developed for the determination of six steroid hormones, such as estrone, 17‐β‐estradiol, estriol, 17‐α‐ethinylestradiol, progesterone, and testosterone in water samples. The recoveries obtained in the proposed method were in the range of 78.7–101.3%. Matrix effect below 20% suggests that the quantitative and qualitative results of the analysis were not significantly affected by the matrix. The results show that molecularly imprinted polymers based on spherical silica gel had the potential to be a highly innovative and selective sorbent. The proposed method was proved to be applicable for molecularly imprinted solid‐phase extraction in selective and reliable extraction and enrichment of steroid hormones in environmental water samples.     

内分泌干扰剂的研究进展

内分泌干扰剂(EDC) 正在成为生态环境研究的前沿课题, 并受到各国政府的密切关注。本文综述了内分泌干扰剂的危害、作用机理、化合物类型及研究进展, 特别强调了化合物低剂量长期暴露潜在危害的新概念, 详述了传统的环境毒理学和环境分析化学所遇到的挑战及生物分析、化学仪器分析和生物传感器技术在内分泌干扰剂筛选过程中的重要战略地位。     

酚类环境雌激素的分析研究进展

环境中分布或残留的化学物质对人类和野生动物的内分泌系统的不良影响已引起广泛关注。在已发现的数十种环境内分泌干扰物中，壬基苯酚、辛基苯酚、双酚A和2，4－二氯酚等以其相似的结构特征和类雌激素活性逐渐被划分为一类新的分析研究对象，称为酚类环境雌激素。本文综述了这些物质的主要来源和分布及其分析测定的研究进展，重点评述了一些新的分析技术如基体固相分散萃取法（MSPD）、分子印迹聚合物法（MIP）、荧光分子传感法（FMS）和生物识别－化学分析联用法等在上述物质的分析测定中的应用。比较了GC－MS和LC－MS等手段用于分析此类物质的优势和各自的局限性，并展望了这一领域未来的研究发展趋势。本文引用文献共54篇。     

Screening of endocrine disrupting chemicals using a surface plasmon resonance sensor.

Because concern over endocrine disrupting reactions caused by chemicals to humans and animals is growing, a rapid and reliable screening assay for endocrine disrupting chemicals is required. We have developed an in vitro screening assay based on a hormone receptor mechanism using a surface plasmon resonance (SPR) sensor. The interaction between an estrogen receptor alpha (ER) and an estrogen response element (ERE) is monitored in real time, when ER is injected over the SPR sensor chip on which a DNA fragment containing ERE is immobilized. In the presence of a chemical with estrogenic activity, the ER-ERE interaction is enhanced and the kinetic parameters are altered. We have validated the assay in terms of its specificity, dose dependency, optimal reaction conditions and reproducibility. It has been shown that the assay is very reliable as a rapid and quantitative screening method to judge the estrogenic activities of chemicals.     

An integrated "4-phase" approach for setting endocrine disruption screening priorities--phase I and II predictions of estrogen receptor binding affinity

Recent legislation mandates the US Environmental Protection Agency (EPA) to develop a screening and testing program for potential endocrine disrupting chemicals (EDCs), of which xenoestrogens figure prominently. Under the legislation, a large number of chemicals will undergo various in vitro and in vivo assays for their potential estrogenicity, as well as other hormonal activities. There is a crucial need for priority setting before this strategy can be effectively implemented. Here we report an integrated computational approach to priority setting using estrogen receptor (ER) binding as an example. This approach rationally integrates different predictive computational models into a "Four-Phase" scheme so that it can effectively identify potential estrogenic EDCs based on their predicted ER relative binding affinity (RBA). The system has been validated using an in-house ER binding assay dataset for 232 chemicals that was designed to have both broad structural diversity and a wide range of binding affinities. When applied to 58,000 chemicals identified by Walker et al. as candidates for endocrine disruption screening, some 9100 chemicals were predicted to bind to ER. Of these, only 3600 were expected to bind to ER at RBA values up to 100,000-fold less than that of 17beta-estradiol. The method ruled out 83% of the chemicals as non-binders with a very low rate of false negatives. We believe that the same integrated scheme will be equally applicable to endpoints of other endocrine disrupting mechanisms, e.g. androgen receptor binding.     

An integrated "4-phase" approach for setting endocrine disruption screening priorities--phase I and II predictions of estrogen receptor binding affinity

Recent legislation mandates the US Environmental Protection Agency (EPA) to develop a screening and testing program for potential endocrine disrupting chemicals (EDCs), of which xenoestrogens figure prominently. Under the legislation, a large number of chemicals will undergo various in vitro and in vivo assays for their potential estrogenicity, as well as other hormonal activities. There is a crucial need for priority setting before this strategy can be effectively implemented. Here we report an integrated computational approach to priority setting using estrogen receptor (ER) binding as an example. This approach rationally integrates different predictive computational models into a "Four-Phase" scheme so that it can effectively identify potential estrogenic EDCs based on their predicted ER relative binding affinity (RBA). The system has been validated using an in-house ER binding assay dataset for 232 chemicals that was designed to have both broad structural diversity and a wide range of binding affinities. When applied to 58,000 chemicals identified by Walker et al. as candidates for endocrine disruption screening, some 9100 chemicals were predicted to bind to ER. Of these, only 3600 were expected to bind to ER at RBA values up to 100,000-fold less than that of 17 g -estradiol. The method ruled out 83% of the chemicals as non-binders with a very low rate of false negatives. We believe that the same integrated scheme will be equally applicable to endpoints of other endocrine disrupting mechanisms, e.g. androgen receptor binding.     

Pesticides as estrogen disruptors: QSAR for selective ERα and ERβ binding of pesticides

Evidence suggests that environmental exposure to estrogen-like compounds can cause adverse effects in humans and wildlife. The Endocrine Disruptor Screening and Testing Advisory Committee (EDSTAC) has advised screening of 87,000 compounds in the interest of human safety. This may best be accomplished by pre-screening using quantitative structure-activity relationship (QSAR) modelling. The present study aimed to develop in silico QSARs based on natural, semi-synthetic, synthetic, and phytoestrogens, to predict the potential estrogenic toxicity of pesticides. A diverse set of 170 compounds including steroidal-, synthetic- and phytoestrogens, as well as pesticides was used to construct the QSAR models using artificial neural networks (ANNs). Mean correlation coefficients between experimentally measured and predicted binding affinities were all greater than 0.7 and models had few false negative results, an important consideration for screening tools. This study demonstrated the utility of ANNs as QSAR models for pre-screening of potential endocrine disruptors.     

Identification of Endocrine Disrupting Chemicals using a Virus‐Based Colorimetric Sensor

A simple and portable colorimetric sensor based on M13 bacteriophage (phage) was devised to identify a class of endocrine disrupting chemicals, including benzene, phthalate, and chlorobenzene derivatives. Arrays of structurally and genetically modified M13 bacteriophage were fabricated so as to produce a biomimetic colorimetric sensor, and color changes in the phage arrays in response to several benzene derivatives were characterized. The sensor was also used to classify phthalate and chlorobenzene derivatives as representatives of endocrine disrupting chemicals. The characteristic color patterns obtained on exposure to various benzene derivatives enabled similar chemical structures in the vapor phase to be classified. Our sensing approach based on the use of a genetically surface modified M13 bacteriophage offers a promising platform for portable, simple environmental monitors that could be extended for use in numerous application areas, including food monitoring, security monitoring, explosive risk assessment, and point of care testing.     

Novel Extraction for Endocrine Disruptors in Atmospheric Particulate Matter

A rapid and efficient sample preparation method, which is called microwave-assisted microsolid phase extraction, was developed for the determination of endocrine disrupting chemicals in atmospheric particulate matter. The endocrine disrupting chemicals included bisphenol A, diethyl phthalate, dibutyl phthalate, and di(2-ethylhexyl) phthalate. The endocrine disrupting chemicals were isolated by microwave-assisted extraction following adsorption by copper(II) isonicotinate using microsolid phase extraction. The endocrine disrupting chemicals were subsequently determined by high performance liquid chromatography with an ultraviolet detector. The extraction was optimized for temperature, time, desorption time, and desorption solvent. Limits of detection (in the range of 2.0–8.5 nanograms per liter), limits of quantification (in the range of 6.6–28.0 nanograms per liter), and repeatability of the procedure (less than 10 percent) were established. Diethyl phthalate, diethyl phthalate, and di(2-ethylhexyl) phthalate were determined at values from 0.57 to 68.8 nanograms per cubic meter in atmospheric particulate matter collected from an urban area, a business center, and an industrial site in Dongguan, China. The concentration of bisphenol A was below the detection limit in these samples.     

Development of a nanomechanical biosensor for analysis of endocrine disrupting chemicals

A nanomechanical transducer is developed to detect and screen endocrine disrupting chemicals (EDCs) combining fluidic sample injection and delivery with bioreceptor protein functionalized microcantilevers (MCs). The adverse affects of EDCs on the endocrine system of humans, livestock, and wildlife provides strong motivation for advances in analytical detection and monitoring techniques. The combination of protein receptors, which include estrogen receptor alpha (ER-alpha) and estrogen receptor beta (ER-beta), as well as monoclonal antibodies (Ab), with MC systems employing modified nanostructured surfaces provides for excellent nanomechanical response sensitivity and the inherent selectivity of biospecific receptor-EDC interactions. The observed ranking of binding interaction of the tested EDCs with ER-beta is diethylstilbestrol (DES) > 17-beta-estradiol > 17-alpha-estradiol > 2-OH-estrone > bisphenol A > p,p'-dichlorodiphenyldichloroethylene (p,p'-DDE) with measurements exhibiting intra-day RSDs of about 3%. A comparison of responses of three EDCs, which include 17-beta-estradiol, 17-alpha-estradiol, and 2-OH-estrone, with ER-beta and ER-alpha illustrates which estrogen receptor subtype provides the greatest sensitivity. Antibodies specific to a particular EDC can also be used for analyte specific screening. Calibration plots for a MC functionalized with anti-17-beta-estradiol Ab show responses in the range of 1 x 10(-11) through 1 x 10(-7) M for 17-beta-estradiol with a linear portion extending over two orders of magnitude in concentration.