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The rat acute oral toxicity of trifluoromethyl compounds (TFMs): a computational toxicology study combining the 2D-QSTR, read-across and consensus modeling methods

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Abstract

All areas of the modern society are affected by fluorine chemistry. In particular, fluorine plays an important role in medical, pharmaceutical and agrochemical sciences. Amongst various fluoro-organic compounds, trifluoromethyl (CF3) group is valuable in applications such as pharmaceuticals, agrochemicals and industrial chemicals. In the present study, following the strict OECD modelling principles, a quantitative structure–toxicity relationship (QSTR) modelling for the rat acute oral toxicity of trifluoromethyl compounds (TFMs) was established by genetic algorithm-multiple linear regression (GA-MLR) approach. All developed models were evaluated by various state-of-the-art validation metrics and the OECD principles. The best QSTR model included nine easily interpretable 2D molecular descriptors with clear physical and chemical significance. The mechanistic interpretation showed that the atom-type electro-topological state indices, molecular connectivity, ionization potential, lipophilicity and some autocorrelation coefficients are the main factors contributing to the acute oral toxicity of TFMs against rats. To validate that the selected 2D descriptors can effectively characterize the toxicity, we performed the chemical read-across analysis. We also compared the best QSTR model with public OPERA tool to demonstrate the reliability of the predictions. To further improve the prediction range of the QSTR model, we performed the consensus modelling. Finally, the optimum QSTR model was utilized to predict a true external set containing many untested/unknown TFMs for the first time. Overall, the developed model contributes to a more comprehensive safety assessment approach for novel CF3-containing pharmaceuticals or chemicals, reducing unnecessary chemical synthesis whilst saving the development cost of new drugs.

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Data availability

All data are included in this paper or from the corresponding author on reasonable request.

Abbreviations

TFMs:

Trifluoromethyl compounds

CF3 :

Trifluoromethyl

QST(A)R:

Quantitative structure–toxicity(activity) relationship

RA:

Read-across

PFAS:

Per- and polyfluoroalkyl substances

REACH:

Registration, Evaluation, Authorisation and Restriction of chemicals

GA-MLR:

Genetic algorithm-multiple linear regression

OECD:

Organization for Economic Co-Operation and Development

ATSDR:

Agency for Toxicity Substances and Disease Registry

CSTEE:

Commission’s Scientific Committee on Toxicity, Ecotoxicity and the Environment

LD50:

Median lethal dose

ORes:

Ordered by response values

PC1:

The first-axis principal component

OStr:

Ordered by structure similarity

Rnd:

Complete random division

AD:

Applicability domain

OLS:

Ordinary least squares

\({Q}_{{\text{LOO}}}^{2}\) :

Leave-one-out cross-validation coefficient

\({Q}_{{\text{LMO}}}^{2}\) :

Leave-many-out cross-validation coefficients

CCCtest :

Concordance correlation coefficient

MAEtest :

Mean absolute error

RMSEtest :

Root mean square error

MCDM:

Multi-criteria decision-making

GK:

Gaussian kernel

LK:

Laplacian kernel

ED:

Euclidean distance

SM:

Single model

CMs:

Consensus models

OPERA:

Open Structure–Activity/Property Relationship App

NTP:

National Toxicology Program

NICEATM:

NTP Interagency Center for the Evaluation of Alternative Toxicological Methods

NCCT:

National Centre for Computational Toxicology

CATMoS:

Collaborative Acute Toxicity Modeling Suite

PRI:

Prediction Reliability Indicator

TSR:

Training set range

VIP:

Variable importance plot

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Acknowledgements

This work received support from the Beijing Natural Science Foundation (No. 7242193, 7222016), the National Natural Science Foundation of China (No. 82003599) and the Project of Cultivation for young top-motch Talents of Beijing Municipal Institutions (No. BPHR202203016). The authors express their sincere gratitude to Prof. Gramatica at the University of Insubria (Varese, Italy) for granting permission to use the QSARINS 2.2.4 software and to Prof. Roy at Jadavpur University (Kolkata, India) for permitting the use of the ‘PRI’ tools.

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  1. Xinyi Lu and Xin Wang contributed equally to this work.

Authors and Affiliations

  1. Beijing Key Laboratory of Environmental and Viral Oncology, College of Chemistry and Life Science, Beijing University of Technology, Beijing, 100124, People’s Republic of China

    Xinyi Lu, Shuo Chen, Tengjiao Fan, Lijiao Zhao, Rugang Zhong & Guohui Sun

  2. Department of Clinical Trials Center, Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, 030013, People’s Republic of China

    Xin Wang

  3. Department of Medical Technology, Beijing Pharmaceutical University of Staff and Workers, Beijing, 100079, China

    Tengjiao Fan

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Contributions

Xinyi Lu: methodology, formal analysis and investigation and writing—original draft; Xin Wang: formal analysis and investigation and writing—review and editing; Shuo Chen: methodology and supervision; Tengjiao Fan: resources; Lijiao Zhao: writing—review and editing; Rugang Zhong: supervision; Guohui Sun: conceptualization, funding acquisition and writing—review and editing.

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Lu, X., Wang, X., Chen, S. et al. The rat acute oral toxicity of trifluoromethyl compounds (TFMs): a computational toxicology study combining the 2D-QSTR, read-across and consensus modeling methods. Arch Toxicol (2024). https://doi.org/10.1007/s00204-024-03739-w

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