• Bulletin of the Korean Chemical Society
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• v.33 no.2
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• pp.613-619
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• 2012

We applied several machine learning methods for developing QSAR models for prediction of acute toxicity to fathead minnow. The multiple linear regression (MLR) and artificial neural network (ANN) method were applied to predict 96 h $LC_{50}$ (median lethal concentration) of 555 chemical compounds. Molecular descriptors based on 2D chemical structure were calculated by PreADMET program. The recursive partitioning (RP) model was used for grouping of mode of actions as reactive or narcosis, followed by MLR method of chemicals within the same mode of action. The MLR, ANN, and two RP-MLR models possessed correlation coefficients ($R^2$) as 0.553, 0.618, 0.632, and 0.605 on test set, respectively. The consensus model of ANN and two RP-MLR models was used as the best model on training set and showed good predictivity ($R^2$=0.663) on the test set.

• Proceedings of the Korea Society of Environmental Toocicology Conference
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• 2001.11a
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• pp.89-89
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• 2001

• Journal of Korean Society of Environmental Engineers
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• v.27 no.1
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• pp.67-74
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• 2005

Toxicity tests were performed to evaluate the feasibility of application with prediction models to 10 mixture chemicals (chloroneb, butylbenzylphthalate, pendimethaline, di-n-butylphthalate, di-iso-butylphthalate, diazinon, isofenphos, 2-chlorophenol, 2,4,6-trichlorophenol and p-octylphenol) detected in effluents from wastewater treatment plants (WWTPs). Ten chemicals were selected in the basis of their toxicities to Daphnia magna and the concentrations in effluents measured by GC/MS. Three models including concentration addition (CA), independent action (IA) and effect summation (ES) were employed for the comparison of the predicted and the observed mortality of D. magna exposed to 10 mixture chemicals for 48 hours. With a comparative study it was ineffective to predict the mortality through the CA and the ES prediction model, while the IA prediction model showed a high correlation($r^2\;=\;0.85$). Moreover, the ES model over-estimated the toxicity observed by bioassay experiments about five-fold. Consequently, IA model is a reasonable tool to predict the mixture toxicity of the discharging water from WWTPs.

• Environmental Analysis Health and Toxicology
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• v.30 no.sup
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• pp.7.1-7.10
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• 2015

Objectives For successful adoption of legislation controlling registration and assessment of chemical substances, it is important to obtain sufficient toxicological experimental evidence and other related information. It is also essential to obtain a sufficient number of predicted risk and toxicity results. Particularly, methods used in predicting toxicities of chemical substances during acquisition of required data, ultimately become an economic method for future dealings with new substances. Although the need for such methods is gradually increasing, the-required information about reliability and applicability range has not been systematically provided. Methods There are various representative environmental and human toxicity models based on quantitative structure-activity relationships (QSAR). Here, we secured the 10 representative QSAR-based prediction models and its information that can make predictions about substances that are expected to be regulated. We used models that predict and confirm usability of the information expected to be collected and submitted according to the legislation. After collecting and evaluating each predictive model and relevant data, we prepared methods quantifying the scientific validity and reliability, which are essential conditions for using predictive models. Results We calculated predicted values for the models. Furthermore, we deduced and compared adequacies of the models using the Alternative non-testing method assessed for Registration, Evaluation, Authorization, and Restriction of Chemicals Substances scoring system, and deduced the applicability domains for each model. Additionally, we calculated and compared inclusion rates of substances expected to be regulated, to confirm the applicability. Conclusions We evaluated and compared the data, adequacy, and applicability of our selected QSAR-based toxicity prediction models, and included them in a database. Based on this data, we aimed to construct a system that can be used with predicted toxicity results. Furthermore, by presenting the suitability of individual predicted results, we aimed to provide a foundation that could be used in actual assessments and regulations.

• Journal of Environmental Health Sciences
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• v.48 no.3
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• pp.159-166
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• 2022

Background: A quantitative structure-activity relationship (QSAR) model was adopted in the Registration, Evaluation, Authorization, and Restriction of Chemicals (REACH, EU) regulations as well as the Act on Registration, Evaluation, etc. of Chemicals (AREC, Republic of Korea). It has been previously used in the registration of chemicals. Objectives: In this study, we investigated the correlation between the predicted data provided by three prediction programs using a QSAR model and actual experimental results (acute fish, daphnia magna toxicity). Through this approach, we aimed to effectively conjecture on the performance and determine the most applicable programs when designating toxic substances through the AREC. Methods: Chemicals that had been registered and evaluated in the Toxic Chemicals Control Act (TCCA, Republic of Korea) were selected for this study. Two prediction programs developed and operated by the U.S. EPA - the Ecological Structure-Activity Relationship (ECOSAR) and Toxicity Estimation Software Tool (T.E.S.T.) models - were utilized along with the TOPKAT (Toxicity Prediction by Komputer Assisted Technology) commercial program. The applicability of these three programs was evaluated according to three parameters: accuracy, sensitivity, and specificity. Results: The prediction analysis on fish and daphnia magna in the three programs showed that the TOPKAT program had better sensitivity than the others. Conclusions: Although the predictive performance of the TOPKAT program when using a single predictive program was found to perform well in toxic substance designation, using a single program involves many restrictions. It is necessary to validate the reliability of predictions by utilizing multiple methods when applying the prediction program to the regulation of chemicals.

• Journal of Environmental Health Sciences
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• v.49 no.2
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• pp.89-98
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• 2023

Background: Organophosphorus flame retardants (OPFRs) are a group of chemical substances used in building materials and plastic products to suppress or mitigate the combustion of materials. Although OPFRs are generally used in mixed form, information on their mixture toxicity is quite scarce. Objectives: This study aims to elucidate the toxicity and determine the types of interaction (e.g., synergistic, additive, and antagonistic effect) of OPFRs mixtures. Methods: Nine organophosphorus flame retardants, including TEHP (tris(2-ethylhexyl) phosphate) and TDCPP (tris(1,3-dichloro-2-propyl) phosphate), were selected based on indoor dust measurement data in South Korea. Nine OPFRs were exposed to the luminescent bacteria Aliivibrio fischeri for 30 minutes and the human hepatocyte cell line HepG2 for 48 hours. Chemicals with significant toxicity were only used for mixture toxicity tests in HepG2. In addition, the observed EC_x values were compared with the predicted toxicity values in the CA (concentration addition) prediction model, and the MDR (model deviation ratio) was calculated to determine the type of interaction. Results: Only four chemicals showed significant toxicity in the luminescent bacteria assays. However, EC₅₀ values were derived for seven out of nine OPFRs in the HepG2 assays. In the HepG2 assays, the highest to lowest EC₅₀ were in the order of the molecular weight of the target chemicals. In the further mixture tests, most binary mixtures show additive interactions except for the two combinations that have TPhP (triphenyl phosphate), i.e., TPhP and TDCPP, and TPhP and TBOEP (tris(2-butoxyethyl) phosphate). Conclusions: Our data shows OPFR mixtures usually have additivity; however, more research is needed to find out the reason for the synergistic effect of TPhP. Also, the mixture experimental dataset can be used as a training and validation set for developing the mixture toxicity prediction model as a further step.

• Korean Chemical Engineering Research
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• v.61 no.4
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• pp.523-541
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• 2023

As accessibility to 3D printers increases, there is a growing frequency of exposure to chemicals associated with 3D printing. However, research on the toxicity and harmfulness of chemicals generated by 3D printing is insufficient, and the performance of toxicity prediction using in silico techniques is limited due to missing molecular structure data. In this study, quantitative structure-activity relationship (QSAR) model based on data-centric AI approach was developed to predict the toxicity of new 3D printing materials by imputing missing values in molecular descriptors. First, MissForest algorithm was utilized to impute missing values in molecular descriptors of hazardous 3D printing materials. Then, based on four different machine learning models (decision tree, random forest, XGBoost, SVM), a machine learning (ML)-based QSAR model was developed to predict the bioconcentration factor (Log BCF), octanol-air partition coefficient (Log Koa), and partition coefficient (Log P). Furthermore, the reliability of the data-centric QSAR model was validated through the Tree-SHAP (SHapley Additive exPlanations) method, which is one of explainable artificial intelligence (XAI) techniques. The proposed imputation method based on the MissForest enlarged approximately 2.5 times more molecular structure data compared to the existing data. Based on the imputed dataset of molecular descriptor, the developed data-centric QSAR model achieved approximately 73%, 76% and 92% of prediction performance for Log BCF, Log Koa, and Log P, respectively. Lastly, Tree-SHAP analysis demonstrated that the data-centric-based QSAR model achieved high prediction performance for toxicity information by identifying key molecular descriptors highly correlated with toxicity indices. Therefore, the proposed QSAR model based on the data-centric XAI approach can be extended to predict the toxicity of potential pollutants in emerging printing chemicals, chemical process, semiconductor or display process.

• Electronics and Telecommunications Trends
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• v.38 no.1
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• pp.46-54
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• 2023

The announcement of the US Environmental Protection Agency that it will stop conducting or funding experimental studies on mammals by 2035 should prioritize ongoing efforts to develop and use alternative toxicity screening methods to animal testing. Toxicity screening is likely to be further developed considering the combination of human-induced pluripotent-stem-cell-derived organ-on-a-chip and multielectrode array (MEA) technologies. We briefly review the current status of MEA technology and MEA-based neuropharmacological drug screening using various cellular model systems. Highlighting the coronavirus disease pandemic, we shortly comment on the importance of early prediction of toxicity by applying artificial intelligence to the development of rapid screening methods.

• The Korean Journal of Applied Statistics
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• v.37 no.3
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• pp.355-380
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• 2024

In this paper, we compare the performance of graph-based deep learning models using OECD test guideline (TG) data. OECD TG are a unique tool for assessing the potential effects of chemicals on health and environment. but many guidelines include animal testing. Animal testing is time-consuming and expensive, and has ethical issues, so methods to find or minimize alternatives are being studied. Deep learning is used in various fields using chemicals including toxicity prediciton, and research on graph-based models is particularly active. Our goal is to compare the performance of graph-based deep learning models on OECD TG data to find the best performance model on there. We collected the results of OECD TG from the website eChemportal.org operated by the OECD, and chemicals that were impossible or inappropriate to learn were removed through pre-processing. The toxicity prediction performance of five graph-based models was compared using the collected OECD TG data and MoleculeNet data, a benchmark dataset for predicting chemical properties.

• Proceedings of the Korea Society of Environmental Toocicology Conference
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• 2001.11a
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• pp.91-91
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• 2001