A comprehensive evaluation method for predicting toxicity of environmental pollutants

Abstract

This invention provides a comprehensive evaluation method for predicting the toxicity of environmental pollutants, belonging to the technical field of bioanalytical chemistry. This invention constructs a multi-module integrated evaluation system that integrates toxicity assessment software, toxicological priority indices, probabilistic risk entropy, and transformation product prediction technology to achieve efficient and accurate assessment of the toxicity of environmental pollutants.

Description

Technical Field

[0001] This invention relates to the technical field of bioanalytical chemistry, and more particularly to a comprehensive evaluation method for predicting the toxicity of environmental pollutants. Background Technology

[0002] Analysis of global chemical inventories suggests that the number of man-made chemicals may currently exceed 350,000, including 27,000 registered under the European REACH regulation and over 100,000 pre-registered. With continuous industrialization, the types of environmental pollutants in the human body have increased dramatically, reaching tens of thousands, while their metabolites number in the hundreds of thousands. The accumulation and interaction of these pollutants and their metabolites in the body can have profound impacts on human health. While traditional chemical detection methods can identify some pollutants, the sheer number of pollutants and the complexity of their metabolic pathways mean that relying solely on chemical detection for toxicity assessment is far from sufficient to meet practical needs. Therefore, a more efficient and accurate method for toxicity assessment is urgently required.

[0003] Machine learning technology, with its big data analysis and model prediction capabilities, can quickly identify the toxic characteristics of pollutants and the potential risks of their metabolites, compensating for the shortcomings of traditional detection methods and providing a more comprehensive and scientific basis for the toxicity assessment of environmental pollutants. Although some studies have attempted to screen based solely on exposure or toxicity data, their coverage and depth remain limited. For example, the U.S. Environmental Protection Agency's (EPA) ToxCast program established an assessment system containing over 700 high-throughput endpoints and ranked the bioactivity of approximately 2,000 chemicals, but its scale still falls short of practical needs. Furthermore, risk-based chemical prioritization methods have only assessed 180 chemicals and primarily rely on the National Institutes of Health's (NIH) Tox21 program and ToxCast high-throughput screening data. Another limitation of existing research in chemical prioritization is its overemphasis on parent chemicals while neglecting their biotransformation processes. These processes may detoxify chemicals or activate their toxic potential, thus significantly impacting toxicity assessments. Summary of the Invention

[0004] To address the aforementioned technical problems of low efficiency and accuracy in environmental chemical substance risk assessment, this invention provides a comprehensive evaluation method for predicting the toxicity of environmental pollutants. This invention primarily constructs a multi-module integrated assessment system. This system integrates toxicity assessment software, toxicological priority indices, probabilistic risk entropy, and transformation product prediction technology to achieve efficient and accurate assessment of the toxicity of environmental pollutants.

[0005] The technical means employed in this invention are as follows:

[0006] A comprehensive evaluation method for predicting the toxicity of environmental pollutants includes the following steps:

[0007] Step 1: Use bioinformatics tools to predict the biotransformation products of target chemicals in the environment, generate the names and structural information of possible metabolites, and construct a dataset to be predicted by combining the parent compound and possible metabolites.

[0008] Step 2: Toxicity assessment method based on QSAR model to determine the median lethal dose (LD50) of the target chemical in the dataset to be predicted. 50 To make predictions in order to assess the acute toxicity of the target chemical;

[0009] Step 3: Generate the comprehensive toxicity score of the target chemical in the dataset to be predicted using the ToxPi model (Toxics Priority Index). Sort the comprehensive toxicity scores from high to low to obtain the comprehensive toxicity score dataset. Simultaneously, predict the median lethal dose (LD50) of the target chemical. 50 The non-carcinogenic risk score of each target chemical in the target chemical is generated after combining the actual exposure concentration, and then sorted according to the comprehensive toxicity score from high to low to obtain the comprehensive toxicity score dataset.

[0010] Step 4: Intersect the non-carcinogenic risk assessment dataset and the comprehensive toxicity score dataset obtained in Step 3 to obtain the intersection dataset;

[0011] Step 5: Sort the data in the intersection dataset from highest to lowest to obtain the comprehensive evaluation result.

[0012] Furthermore, the probabilistic risk entropy method quantifies the human exposure dose (EHE) and the baseline human dose. The ratio of [value] is used to assess non-carcinogenic risk.

[0013] Furthermore, in step 3, the probabilistic risk entropy method includes the following steps:

[0014] Step 31: Obtain the Baseline Response Value (BMD) for an additional 10% risk. 10 The reference response value BMD 10 Based on the calculated oral LD50 in rats 50 Modeling is performed, and an uncertainty factor UF is used for adjustment. The calculation formula is as follows:

[0015]

[0016] Among them, UF animal-human Factors representing cross-species interactions; UF ED-BMD Indicates LD 50 Convert to BMD 10The conversion factor;

[0017] Step 32: Obtain baseline human dose values Based on the baseline response value BMD 10 Calculate the baseline human dose value And adjust using the uncertainty factor UF, calculated as follows:

[0018]

[0019] Among them, UF A,BW UF represents the cross-species body weight scaling factor. A,TKTD UF represents the scaling factor for differences in cross-species toxicokinetics and toxicokinetics. H,I Statistical factors representing changes in human sensitivity;

[0020] Step 33: Obtain the probabilistic risk entropy value PrHQ, based on the human exposure level EHE and the baseline human dose. The ratio is used to calculate the probability risk entropy value PrHQ:

[0021]

[0022] Furthermore, in step 1, bioinformatics tools are used to predict the biotransformation metabolites of target chemicals in the environment. The prediction results include metabolites / transformation products produced by the parent compound in human tissues, the gastrointestinal tract, and the environment.

[0023] Furthermore, the bioinformatics tool is one of CyProduct, Meteor Nexus, and BioTransformer.

[0024] Furthermore, the cross-species weight scaling factor UF A,BW =4.5; the scaling factor UF for the difference in cross-species toxicokinetics and toxicodynamics. A,TKTD =1; the statistical factor UF for the change in human sensitivity H,I =9.7.

[0025] Furthermore, the cross-species factor UF animal-human =10; from LD 50 To BMD 10 conversion factor UF ED-BMD =10.

[0026] Compared with the prior art, the present invention has the following advantages:

[0027] (1) By integrating human blood exposure group data and combining it with existing toxicity databases, this invention can comprehensively obtain information on chemical substances and their concentrations, thus making up for the shortcomings of existing technologies in terms of single data sources and limited coverage.

[0028] (2) This invention innovatively combines the Toxicology Priority Index (ToxPi) model and the Probability Risk Entropy (PrHQ) index with toxicity assessment software to achieve a systematic evaluation of chemical substances from multiple dimensions such as molecular structure, bioactivity data, and toxicological endpoint information, ensuring the comprehensiveness and scientific nature of the evaluation results.

[0029] (3) This invention uses bioinformatics tools to predict the metabolites of chemical substances, which further expands the scope of toxicity assessment and improves the systematicness and comprehensiveness of the assessment. Attached Figure Description

[0030] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0031] Figure 1 This is a schematic diagram of the method flow of the present invention.

[0032] Figure 2 This is a schematic diagram of ToxPi for some environmental pollutants in the biological sample of this invention.

[0033] Figure 3 This is a schematic diagram of PrHQ of some environmental pollutants in the biological sample of this invention.

[0034] Figure 4 This is a schematic diagram illustrating the toxicity prediction results of some environmental pollutant metabolites in biological samples according to the present invention. Detailed Implementation

[0035] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0036] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0037] like Figure 1 As shown, this invention provides a comprehensive evaluation method for predicting the toxicity of environmental pollutants, comprising the following steps:

[0038] Step 1: Use a bioinformatics tool (one of CyProduct, Meteor Nexus, or BioTransformer) to predict the biotransformation products of target chemicals in the environment. The prediction results include metabolites / transformation products generated by the parent compound in human tissues, the gastrointestinal tract, and the environment. Construct a dataset to be predicted, consisting of the parent compound and possible metabolites.

[0039] Step 2: Toxicity assessment method based on QSAR model to determine the median lethal dose (LD50) of the target chemical in the dataset to be predicted. 50 To make predictions in order to assess the acute toxicity of the target chemical;

[0040] Step 3: Generate the comprehensive toxicity score of the target chemical in the dataset to be predicted using the ToxPi model (Toxics Priority Index). Sort the comprehensive toxicity scores from high to low to obtain the comprehensive toxicity score dataset. Simultaneously, predict the median lethal dose (LD50) of the target chemical. 50 The probabilistic risk entropy (PrHQ) is used to calculate the non-carcinogenic risk score for each target chemical based on its actual exposure concentration. These scores are then sorted from highest to lowest according to the comprehensive toxicity score, resulting in a comprehensive toxicity score dataset. The probabilistic risk entropy method quantifies the human exposure level (EHE) and the baseline human dose. The ratio of [value] is used to assess non-carcinogenic risk.

[0041] Step 3 of the probabilistic risk entropy method includes the following steps:

[0042] Step 31: Obtain the Baseline Response Value (BMD) for an additional 10% risk. 10 The reference response value BMD 10Based on the calculated oral LD50 in rats 50 Modeling is performed, and an uncertainty factor UF is used for adjustment. The calculation formula is as follows:

[0043]

[0044] Among them, UF animal-human Factors representing cross-species interactions; UF ED-BMD Indicates LD 50 Convert to BMD 10 The conversion factor;

[0045] Step 32: Obtain baseline human dose values Based on the baseline response value BMD 10 Calculate the baseline human dose value And adjust using the uncertainty factor UF, calculated as follows:

[0046]

[0047] Among them, UF A,BW UF represents the cross-species body weight scaling factor. A,TKTD UF represents the scaling factor for differences in cross-species toxicokinetics and toxicokinetics. H,I Statistical factors representing changes in human sensitivity;

[0048] Step 33: Obtain the probabilistic risk entropy value PrHQ, based on the human exposure level EHE and the baseline human dose. The ratio is used to calculate the probability risk entropy value PrHQ:

[0049]

[0050] Step 4: Intersect the non-carcinogenic risk assessment dataset and the comprehensive toxicity score dataset obtained in Step 3 to obtain the intersection dataset;

[0051] Step 5: Sort the data in the intersection dataset from highest to lowest to obtain the comprehensive evaluation result.

[0052] like Figure 2-4As shown, this invention, through a comprehensive assessment of the potential health risks of environmental pollutants, reveals significant differences in risk rankings obtained using different assessment methods. Specifically: 1) Toxicity potential assessment based on the ToxPi model shows that the five compounds with the highest risk are, in order: permethrin, fenpropathrin, chlorpyrifos-methyl, chlorpyrifos, and dibutyl phthalate; 2) However, when calculating PrHQ (potential risk entropy) by incorporating actual human exposure levels, the top five compounds in the risk ranking become phenanthrene, fluorene, dimethyl phthalate, naphthalene, and dichlorvos. This difference indicates that traditional single toxicity assessment methods have limitations. The comprehensive assessment method proposed in this invention, by integrating toxicity potential and exposure level data, can more accurately reflect the actual health risks of pollutants.

[0053] Further research revealed that the metabolites of the parent compound had even stronger toxic effects, with a median lethal dose (LD50) of [missing information]. 50 The levels of pollutants were generally lower than those of the parent compound. This finding provides new scientific evidence for environmental health risk assessment, suggesting that the following must be considered simultaneously in risk assessment: 1) the inherent toxicity of pollutants; 2) the actual human exposure level; and 3) the potential toxicity of metabolites. The comprehensive assessment system established in this invention provides an effective tool for more comprehensive and accurate environmental health risk assessment, and has important guiding significance for environmental pollution prevention and control and public health management.

[0054] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0055] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0056] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0057] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0058] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0059] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0060] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A comprehensive evaluation method for predicting the toxicity of environmental pollutants, characterized in that, Includes the following steps: Step 1: Use bioinformatics tools to predict the biotransformation products of target chemicals in the environment, generate the names and structural information of possible metabolites, and construct a dataset to be predicted by combining the parent compound and possible metabolites. Step 2: Toxicity assessment method based on QSAR model to determine the median lethal dose (LD50) of the target chemical in the dataset to be predicted. 50 To make predictions in order to assess the acute toxicity of the target chemical; Step 3: Generate the comprehensive toxicity score of the target chemical in the dataset to be predicted using the ToxPi model (Toxics Priority Index). Sort the comprehensive toxicity scores from high to low to obtain the comprehensive toxicity score dataset. Simultaneously, predict the median lethal dose (LD50) of the target chemical. 50 The non-carcinogenic risk score of each target chemical is generated by inputting it into the calculation of the probabilistic risk entropy PrHQ, and then sorted according to the comprehensive toxicity score from high to low to obtain the comprehensive toxicity score dataset. Step 4: Intersect the non-carcinogenic risk assessment dataset and the comprehensive toxicity score dataset obtained in Step 3 to obtain the intersection dataset; Step 5: Sort the data in the intersection dataset from highest to lowest to obtain the comprehensive evaluation result.

2. The comprehensive evaluation method for predicting the toxicity of environmental pollutants according to claim 1, characterized in that, The probabilistic risk entropy method quantifies the human exposure dose (EHE) and the baseline human dose. The ratio of [value] is used to assess non-carcinogenic risk.

3. The comprehensive evaluation method for predicting the toxicity of environmental pollutants according to claim 1, characterized in that, Step 3 of the probabilistic risk entropy method includes the following steps: Step 31: Obtain the Baseline Response Value (BMD) for an additional 10% risk. 10 The reference response value BMD 10 Based on the calculated oral LD50 in rats 50 Modeling is performed, and an uncertainty factor UF is used for adjustment. The calculation formula is as follows: Among them, UF animal-human Factors representing cross-species interactions; UF ED-BMD Indicates LD 50 Convert to BMD 10 The conversion factor; Step 32: Obtain baseline human dose values Based on the baseline response value BMD 10 Calculate the baseline human dose value And adjust using the uncertainty factor UF, calculated as follows: Among them, UF A,BW UF represents the cross-species weight scaling factor. A,TKTD UF represents the scaling factor for differences in cross-species toxicokinetics and toxicokinetics. H,I Statistical factors representing changes in human sensitivity; Step 33: Obtain the probabilistic risk entropy value PrHQ, based on the human exposure level EHE and the baseline human dose. The ratio is used to calculate the probability risk entropy value PrHQ:

4. The comprehensive evaluation method for predicting the toxicity of environmental pollutants according to claim 1, characterized in that, In step 1, bioinformatics tools are used to predict the biotransformation metabolites of target chemicals in the environment. The prediction results include metabolites / transformation products produced by the parent compound in human tissues, gastrointestinal tract and environment.

5. The comprehensive evaluation method for predicting the toxicity of environmental pollutants according to claim 1, characterized in that, The bioinformatics tool is one of CyProduct, Meteor Nexus, and BioTransformer.

6. The comprehensive evaluation method for predicting the toxicity of environmental pollutants according to claim 3, characterized in that, The cross-species weight scaling factor UF A,BW =4.5; the scaling factor UF for the difference in cross-species toxicokinetics and toxicodynamics. A,TKTD =1; the statistical factor UF for the change in human sensitivity H,I =9.

7.

7. The comprehensive evaluation method for predicting the toxicity of environmental pollutants according to claim 3, characterized in that, The cross-species factor UF animal-human =10; From LD 50 To BMD 10 conversion factor UF ED-BMD =10.

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