A method of assessing reproductive exposure risk of neonicotinoids and metabolites thereof

Abstract

The application relates to a method for evaluating the reproductive exposure risk of neonicotinoids and metabolites thereof, and belongs to the environmental field. The application comprises the following steps: collecting molecular descriptors which can reflect the physicochemical properties of neonicotinoids and metabolites thereof and toxicity equivalent factors (TEF) indicating reproductive health, establishing a multiple linear regression model to predict the mixed toxicity equivalent factor (TEF) of neonicotinoids and metabolites thereof, and according to the occurrence concentration of neonicotinoids and metabolites thereof in the actual environment, realizing the evaluation of the reproductive exposure risk of the complex combination of neonicotinoids and metabolites thereof. Compared with the traditional method, the application considers the interaction between neonicotinoids and metabolites thereof and the nonlinear and non-additive effect on the reproductive health effect endpoint when evaluating the reproductive exposure risk of the complex combination of neonicotinoids and metabolites thereof, and provides guiding opinions for limiting the concentration level of neonicotinoids and metabolites thereof.

Description

Technical Field

[0001] This invention belongs to the field of environmental science and technology, specifically relating to an assessment of the reproductive exposure risk of neonicotinoids and their metabolites based on the environmental concentration of neonicotinoids and their metabolites. Background Technology

[0002] Neonicotinic acid (NEOs) is a new type of widely used insecticide. These insecticides possess broad-spectrum insecticidal activity, rapid insecticidal effect, and long-lasting efficacy. They can effectively control a variety of pests, including aphids and termites; and can rapidly kill pests in a short time while maintaining a long-lasting control effect. Their main applications include agriculture, horticulture, forestry, households, and public health, and they have become the most widely used insecticides globally. While the widespread use of neonicotinic acid insecticides has effectively controlled pests, it has also caused a series of health-related problems. Studies have confirmed that neonicotinic acid is widely present in the air, dust, water, and some consumer products in our daily environment, and can enter the human body through exposure via hand-to-mouth contact, inhalation, skin absorption, food, and drinking water, causing reproductive, neurological, immune, and genetic toxicity. Infertility is one of the major health problems affecting human beings in the 21st century, therefore, the reproductive toxicity of neonicotinic acid has received more attention. Studies have shown that imidacloprid can damage the reproductive system of rats, and thiamethoxam can interfere with the endocrine system of rats.

[0003] In recent years, the comprehensive environmental impact of neonicotinoids on human reproductive health has received increasing attention. Past research has primarily focused on the effects of neonicotinoids themselves on reproductive health. However, in the real environment, neonicotinoids are readily biotransformed and metabolized, existing in mixtures with their metabolites. The toxicity and concentration of some metabolites are even far greater than their parent compounds. Traditional risk assessment systems are typically based on acute or chronic toxicity tests of single substances under simulated laboratory conditions, and do not consider the presence of metabolites, often failing to accurately reflect the environmental and health hazards of pollutants. Therefore, early reproductive exposure risk assessments based on neonicotinoid parent residues may have significantly underestimated total exposure and reproductive risk. Assessing the reproductive exposure risk of neonicotinoids and their metabolites is therefore essential. However, due to the lack of toxicity data on neonicotinoids and their metabolites, their complex interactions, and individual differences in humans and different environments, developing a method for assessing the reproductive exposure risk of neonicotinoids and their metabolites presents a significant challenge.

[0004] Current methods for assessing the reproductive exposure risk of neonicotinoids have the following problems:

[0005] (1) The metabolic transformation of neonicotinoids in the environment is not considered. Current methods for assessing the reproductive exposure risk of neonicotinoids typically only evaluate the reproductive exposure risk of the neonicotinoid parent. However, neonicotinoids are readily metabolized in the actual environment, leading to a significant decrease in its concentration in the environment. Current assessment methods do not consider the reproductive exposure risk of metabolites.

[0006] (2) The assessment results neglect the interactions (e.g., synergistic and antagonistic) between neonicotinoids and their metabolites in reproductive toxicity effects. Current methods generally assess the exposure risk of individual neonicotinoids first, and then perform a simple summation to obtain the combined exposure risk of neonicotinoids. However, there are interactions (e.g., synergistic and antagonistic) between neonicotinoids and their metabolites, which may promote or inhibit the expression of reproductive toxicity of neonicotinoids and their metabolites, thus making the assessment results inaccurate and unable to reflect the true reproductive exposure risk. Current assessment methods neglect the interactions between pollutants.

[0007] (3) The health endpoint chosen is usually the lowest observed adverse effect level (LOAEL) or the lethal dose (LD50) that causes 50% mortality in rats, instead of the no observed adverse effect level (NOAEL) to replace reproductive toxicity. The health endpoint obtained by this method may have some uncertainty and cannot well assess the risk of reproductive exposure.

[0008] (4) The assessment results are subject to chance and cannot reflect the actual exposure environment. Current assessment methods usually use the average or median concentration of neonicotinoid and its metabolites when calculating daily intake, without using Monte Carlo models to simulate all possible exposure concentrations in the actual environment, which may lead to assessment results that are too high or too low. Summary of the Invention

[0009] To address the shortcomings of existing technologies, this invention provides a method for assessing the reproductive exposure risk of neonicotinoids and their metabolites in the environment. Based on molecular descriptors reflecting the physicochemical properties of neonicotinoids and their metabolism, and a two-dimensional simplified molecular input streaked system (SMILES) representing compound structures, it predicts the toxicity equivalent factor (TEF) of the individual reproductive toxicities of neonicotinoids and their metabolites using an Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) model. Then, a multiple linear regression model is used to predict the mixed reproductive toxicity of neonicotinoids and their metabolites, and the toxicity equivalent (TEQ) of the mixed reproductive toxicity of neonicotinoids and their metabolites is given. Finally, based on the actual concentrations of neonicotinoids and their metabolites in the environment, the reproductive exposure risk is assessed.

[0010] To achieve the above objectives, the present invention adopts the following technical solution: a method for assessing reproductive exposure risk of neonicotinoids and their metabolites, the specific steps of which include the following:

[0011] 1. Based on the quantity and types of neonicotinoids and their metabolites detected in the environment, a list of neonicotinoids and their metabolites for reproductive exposure risk assessment models was determined. Histochemical Abstracts Service (CAS) codes, SMILE structures, and molecular structures of neonicotinoids and their metabolites were collected, and this information was input into the molecular descriptor software (PaDEL) to obtain molecular descriptors for neonicotinoids and their metabolites.

[0012] 2. The TEF associated with reproductive toxicity of individual neonicotinoids and their metabolites was calculated using the ADMET model, and used to calculate the TEQ. TEF was obtained from ADMETlab2.0 and the admetSAR website. The types of toxicity equivalent factors included androgen receptor (NR-AR), estrogen receptor (NR-ER), and reproductive toxicity.

[0013] 3. The 1444 molecular descriptors of the single neonicotinoid and its metabolites collected in step 1 were screened using quantitative structure-activity relationship software (QSARINS). The screening principles were as follows: (1) Descriptors with a standard deviation of less than 0.0001 were removed; (2) Any descriptor with missing values ​​was removed; (3) Descriptors with a correlation greater than or equal to 0.8 were removed; (4) Descriptors with an absolute value of Pearson correlation coefficient with toxicity equivalent factor (TEF) less than 0.3 were removed.

[0014] 4. Monte Carlo simulation was performed using the Crystal Ball software nested within EXCEL to simulate (10,000 times) the concentration ratio of neonicotinoids and their metabolites in the actual environment, and the mixed molecular descriptors and mixed TEFs of neonicotinoids and their metabolites were calculated using formulas (1)-(9):

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[0024] Where i represents the number of a single pollutant, n represents the total number of pollutants, and D mix,i This represents a pollutant mixture molecular descriptor obtained through different calculation formulas, x i p represents the molecular descriptor remaining after step 3 screening for a single pollutant. i TEF represents the concentration percentage of neonicotinoids and their metabolites calculated using a Monte Carlo model. i The TEF represents a single neonicotinoid or its metabolite in step 2.

[0025] 5. Screen the neonicotinoids and their metabolites mixed molecular descriptors obtained in step 4. The screening principles are as follows: (1) Remove descriptors with a standard deviation of less than 0.0001; (2) Remove any descriptors with missing values; (3) Remove descriptors with a correlation greater than or equal to 0.8; (4) Remove descriptors with an absolute value of Pearson correlation coefficient with the mixed toxicity equivalent factor of less than 0.3.

[0026] 6. Incorporate the screened mixed molecular descriptors and the mixed TEF calculated in step 4 into the multiple linear regression model, select the optimal descriptor using the least squares method, and establish the final model.

[0027] 7. After the model is built, perform internal and external validation on the model's statistical parameters. Specific parameters include: coefficient of determination R0. 2 >0.6, internal validation uses leave-one-out (LOO) method, with the corresponding parameter being the LOO cross-validation correlation coefficient. This indicates that the model possesses good predictive power and robustness internally. For external testing, the predictive power of the model is evaluated using statistical parameters, specifically the correlation coefficient difference. and the average correlation coefficient

[0028] This invention provides a method for assessing the risk of mixed reproductive exposure to neonicotinoids and their metabolites in the environment. This method differs from general risk assessment methods in that:

[0029] 1. The reproductive toxicity of neonicotinoid metabolites was taken into consideration;

[0030] 2. The interaction between neonicotinoids and their metabolites in terms of toxic effects;

[0031] 3. Replace commonly used toxicity endpoints with toxicity endpoints related to reproductive toxicity;

[0032] 4. Use the Monte Carlo model to simulate all possible concentrations of neonicotinoids and their metabolites in the real environment.

[0033] This invention calculates the toxic equivalents of neonicotinoids and their metabolites, screens out mixed molecular descriptors, and uses multiple linear regression to give the mixed TEQ of neonicotinoids and their metabolites. Finally, based on the actual concentrations of neonicotinoids and their metabolites in the environment, it assesses the reproductive exposure risk. This provides more accurate data support for evaluating the toxic hazards of neonicotinoids. Attached Figure Description

[0034] To more clearly illustrate the technical solution of the present invention, the accompanying drawings will be briefly described below. Figure 1 This is a flowchart illustrating the reproductive exposure risk of neonicotinoids and their metabolites as described in this invention. Detailed Implementation

[0035] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be described in detail below. Obviously, the described embodiments are merely some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the protection scope of this invention.

[0036] The flowchart of the method for predicting reproductive exposure risk of neonicotinoids and their metabolites in this invention is as follows: Figure 1 As shown, the process includes the collection of concentration data of neonicotinoids and their metabolites in drinking water and the prediction of mixed toxicity equivalent factors. This implementation case assesses the reproductive exposure risk of neonicotinoids and their metabolites detected in drinking water based on literature collected from the literature. The contaminant concentrations are average concentrations, and specific information is shown in Table 1.

[0037] Data on the toxicity equivalent factor (TEF) of neonicotinoids and its metabolites related to reproductive toxicity were obtained from admetSAR 2.0 and ADMETlab 2.0. The parameters predicted by admetSAR 2.0 included reproductive toxicity; the parameters predicted by ADMETlab 2.0 were androgen receptor (NR-AR) and estrogen receptor (NR-ER).

[0038] The process of establishing a reproductive exposure risk assessment model for neonicotinoids and their metabolites and screening mixed molecular descriptors in this invention is as follows: Figure 1 As shown, it mainly includes the following steps:

[0039] 1. The chemical formulas, CAS numbers, SMILE structures, and molecular structures of neonicotinoids and their metabolites in drinking water were collected, and this information was input into PaDEL software to obtain molecular descriptors for the pollutants. A total of 1444 molecular descriptors were collected.

[0040] 2. The individual neonicotinoid and metabolite molecular descriptors collected in step 1 were screened according to the following principles: (1) Descriptors with a standard deviation of less than 0.0001 were removed; (2) Any descriptor with missing values ​​was removed; (3) Descriptors with a correlation of greater than or equal to 0.8 were removed; (4) Descriptors with an absolute value of Pearson correlation coefficient with androgen receptor toxicity equivalent factor (TEF) of less than 0.3 were removed. After preliminary screening, 12 molecular descriptors remained.

[0041] 3. Simulate the concentration ratio of neonicotinoids and their metabolites in the actual environment using a Monte Carlo model (10,000 times), and calculate the mixed molecular descriptor and mixed TEF of neonicotinoids and their metabolites using formulas (1)-(9):

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[0051] Where i represents the number of a single neonicotinoid and its metabolites, n represents the total number of neonicotinoids and their metabolites (n = 8), D mix,i This represents a mixed molecular descriptor for neonicotinoids and their metabolites obtained through different calculation formulas, x i p represents the molecular descriptor remaining after step 2 screening for a single neonicotinoid or its metabolite. i TEF represents the percentage of pollutant concentrations calculated using the Monte Carlo model. i TEF represents a single neonicotinoid or its metabolite.

[0052] 4. Screen the mixed molecular descriptors of neonicotinoid and its metabolites obtained in step 3. The screening principles are as follows: (1) Remove descriptors with a standard deviation of less than 0.0001; (2) Remove any descriptors with missing values; (3) Remove descriptors with a correlation greater than or equal to 0.8; (4) Remove descriptors with an absolute value of Pearson correlation coefficient with androgen receptor toxicity equivalent factor (TEF) of less than 0.3. After screening, 6 descriptors remain.

[0053] 5. The screened mixed molecular descriptors and the mixed androgen receptor TEF calculated in step 3 were incorporated into a multiple linear regression model. The optimal descriptor was selected using the least squares method, and the final model was established as shown below:

[0054] Y=0.0155-0.0021GATS8s-0.001MDEO_12-0.0143nHBint5-0.0054GATS4s+0.7445SC_5

[0055] Wherein GATS8s is the Geary autocorrelation weighted by I state with lag of 8, MDEO_12 is the molecular distance edge between primary and secondary oxygen atoms, nHBint5 is the E state strength of a potential hydrogen bond with a path length of 5, GATS4s is the Geary autocorrelation weighted by I state with lag of 4, and SC_5 is a molecular connectivity index that is related to the number and position of substituents and branches in the compound.

[0056] 6. After the model is built, perform internal and external validation on the model's statistical parameters. Specific parameters include: coefficient of determination R0. 2 >0.6, internal validation uses leave-one-out (LOO) method, with the corresponding parameter being the LOO cross-validation correlation coefficient. This indicates that the model has good internal predictive power and robustness. For external testing, statistical measures can be used to evaluate the model's predictive power. These measures include the correlation coefficient difference and the correlation coefficient mean, with the corresponding parameter being the correlation coefficient difference. and the average correlation coefficient

[0057] This model R 2 =0.9102, R 2 adj =0.9101; Internal validation result is External validation results are The model used in this invention has been verified to be statistically significant.

[0058] 7. Calculate EDI and Toxicity Equivalent Factor (TEQ)

[0059] EDI = C × D f / BW (1)

[0060] TEQ = EDI × TEF (2)

[0061] Where EDI represents the estimated daily intake of neonicotinoids and their metabolites through drinking water, C is the concentration of neonicotinoids and their metabolites measured in drinking water (ng / L), and D... f The daily intake of drinking water is 2.4 L / day, and BW is the body weight of an adult (60 kg). TEF is the corresponding toxicity equivalent factor.

[0062] We calculated the EDI (Exposure Distress Index) using the 50th percentile (P50) as the typical exposure concentration and the 95th percentile (P95) as the high exposure concentration, and the mixed TEF (Transmission Emission Factor) with androgen receptor as the health endpoint (0.05), respectively. The corresponding mixed TEQs were 0.33 and 0.60, respectively. These were compared with traditional exposure risk assessment methods (see Table 3). We found that traditional methods overestimated the reproductive exposure risk of neonicotinoids and their metabolites in drinking water related to androgen receptors, indicating a possible antagonistic effect between neonicotinoids and their metabolites and androgen receptors. Traditional assessment models cannot predict the interactions between pollutants, thus failing to reflect the true exposure situation and environment.

[0063] 8. Assess health using estrogen receptors as the endpoint.

[0064] The basic steps for assessing health endpoints using estrogen receptors are the same as those for androgen receptors. The main differences are: after the initial screening in step 2, 19 molecular descriptors related to estrogen receptor TEF remain; and in step 4, 12 molecular descriptors related to mixed estrogen receptor TEF remain.

[0065] The final model is as follows:

[0066] Y=0.2522-0.0076SdsN-493.8922JGI8+0.041GATS8s-0.4985SpMin8_Bhp+0.0525MA TS5i+0.0002MPC10_6-0.037ATSC5e+3.6513C2Sp3+0.4723MLFER_A-0.0167MPC10_8

[0067] Where SdsN is the E state describing the nitrogen atom type, JGI8 is the 8th order average topological charge index, GATS8s is the Geary autocorrelation weighted by I state with lag of 8, SpMin8_Bhp is the minimum absolute eigenvalue of the Burden correction matrix, weighted by -n8 relative polarizability, MATS5i is Moran autocorrelation with lag of 5 / weighted by first ionization potential, MPC10_6 is the 10th order molecular path count, ATSC5e is the central Broto-Moreau autocorrelation with lag of 5 / Sanderson electronegativity weighted, C2Sp3 is the ability of a single-bonded carbon to bond with two other carbons, and MLFER_A is the total or summative solute hydrogen bond acidity.

[0068] This model R 2 =0.9058, R 2 adj =0.9057; Internal validation result is External validation results are The model used in this invention has been verified to be statistically significant.

[0069] We calculated the EDI using P50 as the typical exposure concentration and P95 as the high exposure concentration, and the mixed TEF (0.15) with estrogen receptor as the health endpoint, yielding mixed TEQs of 0.99 and 1.81, respectively. These results were compared with traditional exposure risk assessment methods (see Table 3). The results showed that traditional methods underestimated the reproductive exposure risk of neonicotinoids and their metabolites in drinking water related to estrogen receptors, indicating a possible synergistic effect between neonicotinoids and their metabolites and estrogen receptors. Traditional assessment models cannot predict interactions between pollutants, thus failing to reflect the true exposure situation and environment.

[0070] 9. Assess using reproductive toxicity as a health endpoint.

[0071] After the first screening, 20 molecular descriptors related to reproductive toxicity equivalent factors remained. After the second screening, 9 molecular descriptors remained for model building.

[0072] Y=0.8644-0.0994SdsN_2-0.1061SHBa-0.0248SdsN_4+0.0027GATS8s-0.3116MDEO_12-0.4503SHCsats+0.0317MLFER_A

[0073] SdsN represents the E-state describing the type of nitrogen atom, SHBa represents the sum of the E-states of (strong) hydrogen bond acceptors, GATS8s represents the Geary autocorrelation weighted by I-state with lag of 8, MDEO_12 represents the molecular distance edge between all primary and secondary oxygen atoms, SHCsats represents the sum of hydrogen bond E-states: sp3 bonding of hydrogen bonds with saturated carbon bonds, and MLFER_A represents the total or summative solute hydrogen bond acidity.

[0074] This model R 2 =0.9438, R 2 adj =0.9437; Internal validation result is External validation results are The model used in this invention has been verified to be statistically significant.

[0075] We calculated the EDI using P50 as the typical exposure concentration and P95 as the high exposure concentration, and the mixed TEF (0.65) with reproductive toxicity as the health endpoint. The corresponding mixed TEQs were 4.29 and 7.84 ng / kg BW / day, respectively. These results were compared with traditional exposure risk assessment methods (see Table 3). Traditional methods overestimated the reproductive exposure risk of neonicotinoids and their metabolites in drinking water regarding reproductive toxicity, indicating a possible antagonistic effect between neonicotinoids and their metabolites and reproductive toxicity. Traditional assessment models cannot predict the interactions between pollutants, thus failing to reflect the true exposure situation and environment.

[0076] Table 1. Concentrations of neonicotinoids and their metabolites in drinking water

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[0078] Table 2 Reproductive toxicity parameters of neonicotinoids and their metabolites

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[0081] Table 3 Results of Exposure Risk Assessment Using Traditional Methods

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Claims

1. A method for assessing the reproductive exposure risk of neonicotinoids and their metabolites, characterized in that: By collecting information on neonicotinoids and their metabolites in the environment, toxicity equivalent factors related to reproductive health were obtained using ADMETlab2.0 and the admetSAR database. Molecular descriptors of neonicotinoids and their metabolites were obtained using PaDEL software. A multiple linear regression model was established to assess the mixed toxicity equivalent factor (TEF) of neonicotinoids and their metabolites. Based on the actual concentrations of neonicotinoids and their metabolites in the environment, reproductive exposure risk was assessed. Information obtained about neonicotinoids and their metabolites includes their CAS number, SMILE code, and molecular structure. The molecular structure is imported into PaDEL software to obtain molecular descriptors of neonicotinoids and their metabolites. The SMILE code is input into ADMETlab2.0 and the admetSAR database to obtain the toxicity equivalent factor (TEF) related to reproductive toxicity. Molecular descriptors of individual neonicotinoids or their metabolites were screened, and the concentration ratio of pollutants in the real environment was simulated more than 10,000 times using a Monte Carlo model. Mixed molecular descriptors and mixed TEFs of neonicotinoids and their metabolites were calculated. Mixed molecular descriptors of neonicotinoids and their metabolites were screened, and the screened mixed molecular descriptors and the calculated mixed TEF were incorporated into a multiple linear regression model. The optimal descriptor was selected using a genetic algorithm, and the final model was established. 1) Collect information on neonicotinoids and their metabolites present in the environment; 2) Obtain molecular descriptors of neonicotinoids and their metabolites using PaDEL software; 3) Obtain toxicity equivalent factors (TEFs) related to reproductive toxicity using ADMETlab2.0 and admetSAR database; 4) Screen the individual molecular descriptors collected in step 1 using QSARINS software, with the following screening principles: (1) Remove descriptors with a standard deviation less than 0.0001; (2) Remove any descriptors with missing values; (3) Remove descriptors with a correlation greater than or equal to 0.8; (4) Remove descriptors with an absolute value of the Pearson correlation coefficient with the toxicity equivalent factor (TEF) less than 0.

3. 5). Monte Carlo simulation was performed using the Crystal Ball software nested within EXCEL, simulating the concentration ratio of neonicotinoids and their metabolites in the actual environment more than 10,000 times, and the mixed molecular descriptors and mixed TEFs of neonicotinoids and their metabolites were calculated using formulas (1)-(9): ； （1）； ；（2）； ；（3）； ；（4）； ；（5）； ； （6）； ；（7）； ；（8）； ； （9）； Where i represents the ID of a single pollutant, and n represents the total number of pollutants. This represents a pollutant mixture molecular descriptor obtained through different calculation formulas. This represents the molecular descriptors remaining after screening a single pollutant in step 3). This indicates the concentration percentage of neonicotinoids and their metabolites calculated using the Monte Carlo model. This refers to the TEF of a single neonicotinoid or its metabolite in step 2); 6) Screen the mixed molecular descriptors of neonicotinoids and their metabolites obtained in step 5); 7) Incorporate the screened mixed molecular descriptors and the mixed TEF calculated in step 5) into the multiple linear regression model, use a genetic algorithm to select the optimal descriptor, and establish the final model; 8) After the model is established, the environmental concentration is substituted into the model to calculate the daily intake of neonicotinoids and their metabolites, and to assess the reproductive exposure risk of neonicotinoids and their metabolites in the actual environment.

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