QSAR study of toxic organic compounds for algae开题报告

1. 研究目的与意义（文献综述包含参考文献）

文 献 综 述 In recent decades, there has been significant increase in study of organic chemicals as potential environmental pollutants. Enormous amount of synthetic chemicals has been produced by the present industry which are used in daily life and becoming pharmaceutical waste. They are released to the environment via several routes, playing the role of toxicants to microorganisms and threatening to human and animals. For the safety evaluation and data validation of that substances, acute toxicity is fundamental ecotoxicological information. Therefore, experimental toxicity data of these compounds to aquatic ecosystem must be conducted in order to assess their hazard and risk. Microscopic algae are the most important group of primary producers of biomass in aquatic ecosystem and thereby create an ecological base for higher life forms. Toxic responses of this aquatic organisms may damage the whole ecosystem as the fundamentals of the food chain can be harmed leading to secondary effects at higher trophic levels. Thus, understanding of how toxic compounds affect on algae is essential. However, ecotoxicological tests are expensive and time consuming and this the reason for the slow risk assessment process. Methods like QSAR (Quantitative Structure Activity Relationships) are rising in use to supplement experimental data. QSAR models are regression or classification models used for in silico predictive modelling. As a regression model, QSAR relate set of predictor variables (x) to the potency of the response variable(y), however classification QSAR models associate predictor variable to several categories of response variable. The predictors(x) consist of physico-chemical properties or molecular descriptors while response variables could be a biological activity. QSAR equations form a quantitative connection between chemical structure and (biological) activity: Log(1/C) = k1P1 k2P2 .. b where P is the quantity of descriptor. Random example for the correlation to this formula: QSAR have been proved to be significant tool in toxicity prediction of organic chemicals, and REACH (Registration, Evaluation, and Authorization of Chemical) system has given support to develop, validate and apply QSARs in toxicology. Many QSARs models have been successfully designed and used in the studies of toxicological effects of organic chemicals on algae. For instance, Zeng et al. (2011) designed QSAR model based on Kow partition coefficient for toxicity prediction on algae; Aruoja et.al (2013) derived MLR(Mutilinear regression) QSAR model for non-polar and polar narcotic compounds; Chul-Woong Cho et al. designed linear free energy relationship(LFER) based QSAR to measure the adsorption of toxicants to Chlorella vulgaris.(see references).One of the major indexes to measure toxicity range and widely used index in QSAR predictions is EC50(Half maximum effective concentration. EC50 is the concentration of toxicant which induces response halfway between the baseline and maximum after certain exposure time. EC50 is a measure of concentration, indicated as molar units(M)=mol/L. In ecotoxicity, it is the concentration of the chemical substance which results in a 50 percent algae growth reduction.During one of the studies (Zeng et. al 2011), logKow found to be highly correlated with EC50.Kow (octanol/water partitioning coefficient) is very important toxicological and biological index. It is defined as the ratio of chemicals concentration in the octanol phase to its concentration in water:Kow= [concentration in octanol]/ [concentration in water]. This parameter, also called logP, originates from research into QSAR. The most important is that LogPValue can describe hydrophilicity or lipophilicity of compounds: Hydrophilic -4

2. 研究的基本内容、问题解决措施及方案

2.1 Data collection The experimental data were cited from the reference below. The data were determined Kusk, K.O., Christensen, A.M. and Nyholm, N. Algal growth inhibition test results of 425 organic chemical substances. Chemosphere 2018;204:405-412.2.2 Method Linear correlations will be performed for these experimental data and someselected parameters. R2 is also calculated. 2.3 Results and DiscussionBased on the method mentioned above, different models will be performed. It is possible that the data in the referent will be divided into different groups to improve the correlations.