In Vitro Toxicity Screening of Fifty Complex Mixtures in HepG2 Cells.

To develop the risk prediction technology for mixture toxicity, a reliable and extensive dataset of experimental results is required. However, most published literature only provides data on combinations containing two or three substances, resulting in a limited dataset for predicting the toxicity of complex mixtures. Complex mixtures may have different mode of actions (MoAs) due to their varied composition, posing difficulty in the prediction using conventional toxicity prediction models, such as the concentration addition (CA) and independent action (IA) models. The aim of this study was to generate an experimental dataset comprising complex mixtures. To identify the target complex mixtures, we referred to the findings of the HBM4EU project. We identified three groups of seven to ten components that were commonly detected together in human bodies, namely environmental phenols, perfluorinated compounds, and heavy metal compounds, assuming these chemicals to have different MoAs. In addition, a separate mixture was added consisting of seven organophosphate flame retardants (OPFRs), which may have similar chemical structures. All target substances were tested for cytotoxicity using HepG2 cell lines, and subsequently 50 different complex mixtures were randomly generated with equitoxic mixtures of EC10 levels. To determine the interaction effect, we calculated the model deviation ratio (MDR) by comparing the observed EC10 with the predicted EC10 from the CA model, then categorized three types of interactions: antagonism, additivity, and synergism. Dose-response curves and EC values were calculated for all complex mixtures. Out of 50 mixtures, none demonstrated synergism, while six mixtures exhibited an antagonistic effect. The remaining mixtures exhibited additivity with MDRs ranging from 0.50 to 1.34. Our experimental data have been formatted to and constructed for the database. They will be utilized for further research aimed at developing the combined CA/IA approaches to support mixture risk assessment.

Computational modeling approaches for developing a synergistic effect prediction model of estrogen agonistic activity.

The concerns regarding the potential health threats caused by estrogenic endocrine-disrupting chemicals (EDCs) and their mixtures manufactured by the chemical industry are increasing worldwide. Conventional experimental tests for understanding the estrogenic activity of mixtures are expensive and time-consuming. Although non-testing methods using computational modeling approaches have been developed to reduce the number of traditional tests, they are unsuitable for predicting synergistic effects because current prediction models consider only a single chemical. Thus, the development of predictive models is essential for predicting the mixture toxicity, including chemical interactions. However, selecting suitable computational modeling approaches to develop a high-performance prediction model requires considerable time and effort. In this study, we provide a suitable computational approach to develop a predictive model for the synergistic effects of estrogenic activity. We collected datasets on mixture toxicity based on the synergistic effect of estrogen agonistic activity in binary mixtures. Using the model deviation ratio approach, we classified the labels of the binary mixtures as synergistic or non-synergistic effects. We assessed five molecular descriptors, four machine learning-based algorithms, and a deep learning-based algorithm to provide a suitable computational modeling approach. Compared with other modeling approaches, the prediction model using the deep learning-based algorithm and chemical-protein network descriptors exhibited the best performance in predicting the synergistic effects. In conclusion, we developed a new high-performance binary classification model using a deep neural network and chemical-protein network-based descriptors. The developed model will be helpful for the preliminary screening of the synergistic effects of binary mixtures during the development process of chemical products.

MRA Toolbox v. 1.0: a web-based toolbox for predicting mixture toxicity of chemical substances in chemical products.

The chemical risk assessment paradigm is shifting from "substance-based" to "product/mixture-based" and from "animal testing" to "alternative testing" under chemical regulations. Organisms and the environment may be exposed to mixtures rather than a single substance. Conducting toxicity tests for all possible combinations is impractical due to the enormous combinatorial complexity. This study highlights the development and application case studies of Mixture Risk Assessment Toolbox, a novel web-based platform that supports mixture risk assessment through the use of different prediction models and public databases. This integrated framework provides new functional values for assessors to easily screen and compare the toxicity of mixture products using different computational techniques and find strategic solutions to reduce the mixture toxicity in the product development process. The toolbox ( https://www.mratoolbox.org ) includes four additive toxicity models: two conventional (Concentration Addition; and Independent Action) and two advanced (Generalized Concentration Addition; and Quantitative Structure-Activity Relationship-based Two-Stage Prediction) models. We demonstrated the multiple functions of the toolbox using three cases: (i) how it can be used to calculate the mixture toxicity, (ii) those for which safety data sheet (SDS) only indicating representative toxicity values (EC50; and LC50), and (iii) those comprising chemicals with low toxic effects.

Developing random forest based QSAR models for predicting the mixture toxicity of TiO2 based nano-mixtures to Daphnia magna.

During emission, TiO2 nanoparticles (NPs) might meet various chemicals, including metal ions and organic compounds in aquatic environments (e.g., surface water, sediments). At environmentally safe concentrations, combinations of both TiO2 NPs and those chemicals might cause cocktail effects (i.e., mixture toxicity) to aquatic organisms. Previous models such as concentration addition and independent action require dose-response curves of single components in the mixtures to predict the mixture toxicity. Structure-activity relationship (QSAR) models might predict the toxicity of nano-mixtures without dose-response curves of single components in the mixtures. However, current quantitative structure-activity relationship (QSAR) models are mainly focused on predicting cytotoxicity (i.e., cell viability) of heterogeneous metallic TiO2 nanoparticles (NPs) or mixtures of TiO2 NPs and four metal ions (Cu2+, Cd2+, Ni2+, and Zn2+). To minimize the experimental cost of nano-mixture risk assessment, in this study, we developed novel nano-mixture QSAR models to predict i) EC50 of 76 nano-mixtures containing TiO2 NPs and one of eight inorganic/organic compounds (i.e., AgNO3, Cd(NO3)2, Cu(NO3)2, CuSO4, Na2HAsO4, NaAsO2, Benzylparaben and Benzophenone-3), to Daphnia magna(D. magna), and ii) immobilization of D. magna exposed to one of 98 mixtures containing TiO2 NPs and one of eleven inorganic/organic compounds (i.e., AgNO3, Cd(NO3)2, Cu(NO3)2, CuSO4, Na2HAsO4, NaAsO2, Benzylparaben Benzophenone-3, Pirimicarb, Pentabromodiphenyl Ether and Triton X-100). The nano-mixture QSAR models were developed with mixture descriptors (Dmix) combing quantum descriptors of mixture components (e.g., TiO2 NPs and its partners) by using different machine learning techniques (i.e., random forest, neural network, support vector machine, and multiple linear regression). Nano-mixture QSAR models built with the random forest algorithm and proposed mixture descriptors exhibited good performance for predicting logEC50 (Adj.R2test = 0.955 ± 0.003, RMSEtest = 0.016 ± 0.002, and MAEtest = 0.008 ± 0.001) and immobilization (Adj.R2test = 0.888 ± 0.011, RMSEtest = 11.327 ± 0.730, and MAEtest = 5.933 ± 0.442). The models developed in this study were implemented in a user-friendly application for assessing the aquatic toxicity of TiO2 based nano-mixtures.

Novel QSAR Models for Molecular Initiating Event Modeling in Two Intersecting Adverse Outcome Pathways Based Pulmonary Fibrosis Prediction for Biocidal Mixtures.

The adverse outcome pathway (AOP) was introduced as an alternative method to avoid unnecessary animal tests. Under the AOP framework, an in silico methods, molecular initiating event (MIE) modeling is used based on the ligand-receptor interaction. Recently, the intersecting AOPs (AOP 347), including two MIEs, namely peroxisome proliferator-activated receptor-gamma (PPAR-γ) and toll-like receptor 4 (TLR4), associated with pulmonary fibrosis was proposed. Based on the AOP 347, this study developed two novel quantitative structure-activity relationship (QSAR) models for the two MIEs. The prediction performances of different MIE modeling methods (e.g., molecular dynamics, pharmacophore model, and QSAR) were compared and validated with in vitro test data. Results showed that the QSAR method had high accuracy compared with other modeling methods, and the QSAR method is suitable for the MIE modeling in the AOP 347. Therefore, the two QSAR models based on the AOP 347 can be powerful models to screen biocidal mixture related to pulmonary fibrosis.

Investigation on Combined Inhalation Exposure Scenarios to Biocidal Mixtures: Biocidal and Household Chemical Products in South Korea.

Global regulations of biocides have been continuously enhanced for protecting human health and the environment from potentially harmful biocidal products. Such regulations consider the combined toxicity caused by mixture components in a biocidal product of which approval and authorization are to be enhanced. Although the combined exposure scenarios of components in mixtures are firstly needed to conduct the mixture risk assessment, systematic combined exposure scenarios are still lacking. In this study, combined inhalation exposure scenarios of biocides in household chemical and biocidal products marketed in South Korea were investigated based on the European Union (EU) and Korean chemical product databases and various data sources integration. The information of 1058 biocidal products and 675 household chemical products that are likely to cause inhalation exposure with two or more biocides was collected, and mixture combination patterns were investigated. Binary mixtures occupied 72% in biocidal products. The most frequently appearing binary mixture was phthalthrin and d-phenothrin. Based on the frequency of use, we suggested a priority list of biocide mixture combinations which need to be firstly evaluated for identifying their combined toxicity for the mixture risk assessment. This study highlights that the derived combined inhalation exposure scenarios can support and facilitate further studies on priority settings for mixture risk assessment and management of potentially inhalable biocides.

PreMetabo: An in silico phase I and II drug metabolism prediction platform.

This study aimed to develop a drug metabolism prediction platform using knowledge-based prediction models. Site of Metabolism (SOM) prediction models for four cytochrome P450 (CYP) subtypes were developed along with uridine 5'-diphosphoglucuronosyltransferase (UGT) and sulfotransferase (SULT) substrate classification models. The SOM substrate for a certain CYP was determined using the sum of the activation energy required for the reaction at the reaction site of the substrate and the binding energy of the substrate to the CYP enzyme. Activation energy was calculated using the EaMEAD model and binding energy was calculated by docking simulation. Phase II prediction models were developed to predict whether a molecule is the substrate of a certain phase II conjugate protein, i.e., UGT or SULT. Using SOM prediction models, the predictability of the major metabolite in the top-3 was obtained as 72.5-84.5% for four CYPs, respectively. For internal validation, the accuracy of the UGT and SULT substrate classification model was obtained as 93.94% and 80.68%, respectively. Additionally, for external validation, the accuracy of the UGT substrate classification model was obtained as 81% in the case of 11 FDA-approved drugs. PreMetabo is implemented in a web environment and is available at https://premetabo.bmdrc.kr/.

BMDMS-NP: A comprehensive ESI-MS/MS spectral library of natural compounds.

The Bioinformatics & Molecular Design Research Center Mass Spectral Library - Natural Products (BMDMS-NP) is a library containing the mass spectra of natural compounds, especially plant specialized metabolites. At present, the library contains the electrospray ionization tandem mass spectrometry (ESI-MS/MS) spectra of 2739 plant metabolites that are commercially available. The contents of the library were made comprehensive by incorporating data generated under various experimental conditions for compounds with diverse molecular structures. The structural diversity of the BMDMS-NP data was evaluated using molecular fingerprints, and it was sufficiently exhaustive enough to represent the structures of the natural products commercially available. The MS/MS spectra of each metabolite were obtained with different types/brands of ion traps (tandem-in-time) or combinations of mass analyzers (tandem-in-space) at multiple collision energies. All spectra were measured repeatedly in each environment because variations can occur in spectra, even under the same conditions. Moreover, the probability, separability of searching, and transferability of this spectral library were evaluated against those of MS/MS libraries, namely: NIST17 and MoNA.

Development of Natural Compound Molecular Fingerprint (NC-MFP) with the Dictionary of Natural Products (DNP) for natural product-based drug development.

Computer-aided research on the relationship between molecular structures of natural compounds (NC) and their biological activities have been carried out extensively because the molecular structures of new drug candidates are usually analogous to or derived from the molecular structures of NC. In order to express the relationship physically realistically using a computer, it is essential to have a molecular descriptor set that can adequately represent the characteristics of the molecular structures belonging to the NC's chemical space. Although several topological descriptors have been developed to describe the physical, chemical, and biological properties of organic molecules, especially synthetic compounds, and have been widely used for drug discovery researches, these descriptors have limitations in expressing NC-specific molecular structures. To overcome this, we developed a novel molecular fingerprint, called Natural Compound Molecular Fingerprints (NC-MFP), for explaining NC structures related to biological activities and for applying the same for the natural product (NP)-based drug development. NC-MFP was developed to reflect the structural characteristics of NCs and the commonly used NP classification system. NC-MFP is a scaffold-based molecular fingerprint method comprising scaffolds, scaffold-fragment connection points (SFCP), and fragments. The scaffolds of the NC-MFP have a hierarchical structure. In this study, we introduce 16 structural classes of NPs in the Dictionary of Natural Product database (DNP), and the hierarchical scaffolds of each class were calculated using the Bemis and Murko (BM) method. The scaffold library in NC-MFP comprises 676 scaffolds. To compare how well the NC-MFP represents the structural features of NCs compared to the molecular fingerprints that have been widely used for organic molecular representation, two kinds of binary classification tasks were performed. Task I is a binary classification of the NCs in commercially available library DB into a NC or synthetic compound. Task II is classifying whether NCs with inhibitory activity in seven biological target proteins are active or inactive. Two tasks were developed with some molecular fingerprints, including NC-MFP, using the 1-nearest neighbor (1-NN) method. The performance of task I showed that NC-MFP is a practical molecular fingerprint to classify NC structures from the data set compared with other molecular fingerprints. Performance of task II with NC-MFP outperformed compared with other molecular fingerprints, suggesting that the NC-MFP is useful to explain NC structures related to biological activities. In conclusion, NC-MFP is a robust molecular fingerprint in classifying NC structures and explaining the biological activities of NC structures. Therefore, we suggest NC-MFP as a potent molecular descriptor of the virtual screening of NC for natural product-based drug development.