Acute Toxicity-Supported Chronic Toxicity Prediction: A k-Nearest Neighbor Coupled Read-Across Strategy

International Journal of Molecular Sciences

Swapnil Chavan¹  Ran Friedman²  Ian A. Nicholls¹ 

[1] Bioorganic and Biophysical Chemistry Laboratory, Department of Chemistry and Biomedical Sciences and Linnaeus University Centre for Biomaterials Chemistry, Linnaeus University, SE-391 82 Kalmar, Sweden;Computational Chemistry and Biochemistry Group, Department of Chemistry and Biomedical Sciences and Linnaeus University Centre for Biomaterials Chemistry, Linnaeus University, SE-391 82 Kalmar, Sweden; E-Mail:

关键词: k-nearest neighbor;    classification model;    Estate fingerprint;    LD₅₀;    LOEL;    read-across;    category formation;   

DOI  :  10.3390/ijms160511659

来源: mdpi

【 摘 要 】

A k-nearest neighbor (k-NN) classification model was constructed for 118 RDT NEDO (Repeated Dose Toxicity New Energy and industrial technology Development Organization; currently known as the Hazard Evaluation Support System (HESS)) database chemicals, employing two acute toxicity (LD₅₀)-based classes as a response and using a series of eight PaDEL software-derived fingerprints as predictor variables. A model developed using Estate type fingerprints correctly predicted the LD₅₀ classes for 70 of 94 training set chemicals and 19 of 24 test set chemicals. An individual category was formed for each of the chemicals by extracting its corresponding k-analogs that were identified by k-NN classification. These categories were used to perform the read-across study for prediction of the chronic toxicity, i.e., Lowest Observed Effect Levels (LOEL). We have successfully predicted the LOELs of 54 of 70 training set chemicals (77%) and 14 of 19 test set chemicals (74%) to within an order of magnitude from their experimental LOEL values. Given the success thus far, we conclude that if the k-NN model predicts LD₅₀ classes correctly for a certain chemical, then the k-analogs of such a chemical can be successfully used for data gap filling for the LOEL. This model should support the in silico prediction of repeated dose toxicity.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.