Datasets Construction and Development of QSAR Models for Predicting Micronucleus In Vitro and In Vivo Assay Outcomes

Overview

Journal Toxics

Date 2023 Sep 27

PMID 37755795

Authors

Lusine Khondkaryan

Ani Tevosyan

Hayk Navasardyan

Hrant Khachatrian

Gohar Tadevosyan

Lilit Apresyan

Gayane Chilingaryan

Zaven Navoyan

Helga Stopper

Nelly Babayan

Affiliations

Soon will be listed here.

Abstract

In silico (quantitative) structure-activity relationship modeling is an approach that provides a fast and cost-effective alternative to assess the genotoxic potential of chemicals. However, one of the limiting factors for model development is the availability of consolidated experimental datasets. In the present study, we collected experimental data on micronuclei in vitro and in vivo, utilizing databases and conducting a PubMed search, aided by text mining using the BioBERT large language model. Chemotype enrichment analysis on the updated datasets was performed to identify enriched substructures. Additionally, chemotypes common for both endpoints were found. Five machine learning models in combination with molecular descriptors, twelve fingerprints and two data balancing techniques were applied to construct individual models. The best-performing individual models were selected for the ensemble construction. The curated final dataset consists of 981 chemicals for micronuclei in vitro and 1309 for mouse micronuclei in vivo, respectively. Out of 18 chemotypes enriched in micronuclei in vitro, only 7 were found to be relevant for in vivo prediction. The ensemble model exhibited high accuracy and sensitivity when applied to an external test set of in vitro data. A good balanced predictive performance was also achieved for the micronucleus in vivo endpoint.

References

Tetko I, Gasteiger J, Todeschini R, Mauri A, Livingstone D, Ertl P . Virtual computational chemistry laboratory--design and description. J Comput Aided Mol Des. 2005; 19(6):453-63. DOI: 10.1007/s10822-005-8694-y. View

Ashby J, Tennant R . Chemical structure, Salmonella mutagenicity and extent of carcinogenicity as indicators of genotoxic carcinogenesis among 222 chemicals tested in rodents by the U.S. NCI/NTP. Mutat Res. 1988; 204(1):17-115. DOI: 10.1016/0165-1218(88)90114-0. View

Hsieh J, Smith-Roe S, Huang R, Sedykh A, Shockley K, Auerbach S . Identifying Compounds with Genotoxicity Potential Using Tox21 High-Throughput Screening Assays. Chem Res Toxicol. 2019; 32(7):1384-1401. PMC: 6740247. DOI: 10.1021/acs.chemrestox.9b00053. View

Yang C, Tarkhov A, Marusczyk J, Bienfait B, Gasteiger J, Kleinoeder T . New publicly available chemical query language, CSRML, to support chemotype representations for application to data mining and modeling. J Chem Inf Model. 2015; 55(3):510-28. DOI: 10.1021/ci500667v. View

Van Bossuyt M, Raitano G, Honma M, Van Hoeck E, Vanhaecke T, Rogiers V . New QSAR models to predict chromosome damaging potential based on the in vivo micronucleus test. Toxicol Lett. 2020; 329:80-84. DOI: 10.1016/j.toxlet.2020.04.016. View

Yoo J, Kruhlak N, Landry C, Cross K, Sedykh A, Stavitskaya L . Development of improved QSAR models for predicting the outcome of the in vivo micronucleus genetic toxicity assay. Regul Toxicol Pharmacol. 2020; 113:104620. DOI: 10.1016/j.yrtph.2020.104620. View

Muller L, Mauthe R, Riley C, Andino M, Antonis D, Beels C . A rationale for determining, testing, and controlling specific impurities in pharmaceuticals that possess potential for genotoxicity. Regul Toxicol Pharmacol. 2006; 44(3):198-211. DOI: 10.1016/j.yrtph.2005.12.001. View

Fan D, Yang H, Li F, Sun L, Di P, Li W . prediction of chemical genotoxicity using machine learning methods and structural alerts. Toxicol Res (Camb). 2018; 7(2):211-220. PMC: 6062245. DOI: 10.1039/c7tx00259a. View

Elder D, Snodin D . Drug substances presented as sulfonic acid salts: overview of utility, safety and regulation. J Pharm Pharmacol. 2009; 61(3):269-78. DOI: 10.1211/jpp/61.03.0001. View

10.

Wu Z, Ramsundar B, Feinberg E, Gomes J, Geniesse C, Pappu A . MoleculeNet: a benchmark for molecular machine learning. Chem Sci. 2018; 9(2):513-530. PMC: 5868307. DOI: 10.1039/c7sc02664a. View

11.

Chilingaryan G, Tamoyan H, Tevosyan A, Babayan N, Hambardzumyan K, Navoyan Z . BartSmiles: Generative Masked Language Models for Molecular Representations. J Chem Inf Model. 2024; 64(15):5832-5843. DOI: 10.1021/acs.jcim.4c00512. View

12.

Lee J, Yoon W, Kim S, Kim D, Kim S, So C . BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics. 2019; 36(4):1234-1240. PMC: 7703786. DOI: 10.1093/bioinformatics/btz682. View

13.

Morita T, Shigeta Y, Kawamura T, Fujita Y, Honda H, Honma M . In silico prediction of chromosome damage: comparison of three (Q)SAR models. Mutagenesis. 2018; 34(1):91-100. DOI: 10.1093/mutage/gey017. View

14.

Baderna D, Gadaleta D, Lostaglio E, Selvestrel G, Raitano G, Golbamaki A . New in silico models to predict in vitro micronucleus induction as marker of genotoxicity. J Hazard Mater. 2019; 385:121638. DOI: 10.1016/j.jhazmat.2019.121638. View

15.

Tweats D, Johnson G, Scandale I, Whitwell J, Evans D . Genotoxicity of flubendazole and its metabolites in vitro and the impact of a new formulation on in vivo aneugenicity. Mutagenesis. 2015; 31(3):309-21. PMC: 4840262. DOI: 10.1093/mutage/gev070. View

16.

Blagus R, Lusa L . SMOTE for high-dimensional class-imbalanced data. BMC Bioinformatics. 2013; 14:106. PMC: 3648438. DOI: 10.1186/1471-2105-14-106. View

17.

Mitchell J . Machine learning methods in chemoinformatics. Wiley Interdiscip Rev Comput Mol Sci. 2014; 4(5):468-481. PMC: 4180928. DOI: 10.1002/wcms.1183. View

18.

Canipa S, Cayley A, Drewe W, Williams R, Hamada S, Hirose A . Using in vitro structural alerts for chromosome damage to predict in vivo activity and direct future testing. Mutagenesis. 2015; 31(1):17-25. DOI: 10.1093/mutage/gev047. View

19.

Coley C, Jin W, Rogers L, Jamison T, Jaakkola T, Green W . A graph-convolutional neural network model for the prediction of chemical reactivity. Chem Sci. 2019; 10(2):370-377. PMC: 6335848. DOI: 10.1039/c8sc04228d. View

20.

Corvi R, Madia F . In vitro genotoxicity testing-Can the performance be enhanced?. Food Chem Toxicol. 2016; 106(Pt B):600-608. DOI: 10.1016/j.fct.2016.08.024. View