» Articles » PMID: 35192406

STopTox: An Alternative to Animal Testing for Acute Systemic and Topical Toxicity

Abstract

Background: Modern chemical toxicology is facing a growing need to Reduce, Refine, and Replace animal tests (Russell 1959) for hazard identification. The most common type of animal assays for acute toxicity assessment of chemicals used as pesticides, pharmaceuticals, or in cosmetic products is known as a "6-pack" battery of tests, including three topical (skin sensitization, skin irritation and corrosion, and eye irritation and corrosion) and three systemic (acute oral toxicity, acute inhalation toxicity, and acute dermal toxicity) end points.

Methods: We compiled, curated, and integrated, to the best of our knowledge, the largest publicly available data sets and developed an ensemble of quantitative structure-activity relationship (QSAR) models for all six end points. All models were validated according to the Organisation for Economic Co-operation and Development (OECD) QSAR principles, using data on compounds not included in the training sets.

Results: In addition to high internal accuracy assessed by cross-validation, all models demonstrated an external correct classification rate ranging from 70% to 77%. We established a publicly accessible Systemic and Topical chemical Toxicity (STopTox) web portal (https://stoptox.mml.unc.edu/) integrating all developed models for 6-pack assays.

Conclusions: We developed STopTox, a comprehensive collection of computational models that can be used as an alternative to 6-pack tests for predicting the toxicity hazard of small organic molecules. Models were established following the best practices for the development and validation of QSAR models. Scientists and regulators can use the STopTox portal to identify putative toxicants or nontoxicants in chemical libraries of interest. https://doi.org/10.1289/EHP9341.

Citing Articles

Toxicity of ACP-105: a substance used as doping in sports: application of in silico methods for prediction of selected toxicological endpoints.

Fijalkowska O, Jurowski K Arch Toxicol. 2025; .

PMID: 40064700 DOI: 10.1007/s00204-025-03962-z.


Application of in silico methods to predict the acute toxicity of bicyclic organophosphorus compounds as potential chemical weapon.

Noga M, Jurowski K Arch Toxicol. 2025; .

PMID: 40050428 DOI: 10.1007/s00204-025-04000-8.


Investigating new drugs from marine seaweed metabolites for cervical cancer therapy by molecular dynamic modeling approach.

Islam S, Ahmed S, Mahfuj S, Das G, Tareq M, Almehmadi M Sci Rep. 2025; 15(1):3866.

PMID: 39890793 PMC: 11785738. DOI: 10.1038/s41598-024-82043-0.


Asoprisnil as a Novel Ligand Interacting with Stress-Associated Glucocorticoid Receptor.

Ejiohuo O, Bajia D, Pawlak J, Szczepankiewicz A Biomedicines. 2025; 12(12.

PMID: 39767652 PMC: 11726916. DOI: 10.3390/biomedicines12122745.


QSAR Classification Modeling Using Machine Learning with a Consensus-Based Approach for Multivariate Chemical Hazard End Points.

Fuadah Y, Pramudito M, Firdaus L, Vanheusden F, Lim K ACS Omega. 2025; 9(51):50796-50808.

PMID: 39741811 PMC: 11683616. DOI: 10.1021/acsomega.4c09356.


References
1.
Alves V, Borba J, Capuzzi S, Muratov E, Andrade C, Rusyn I . Oy Vey! A Comment on "Machine Learning of Toxicological Big Data Enables Read-Across Structure Activity Relationships Outperforming Animal Test Reproducibility". Toxicol Sci. 2018; 167(1):3-4. PMC: 6317419. DOI: 10.1093/toxsci/kfy286. View

2.
Alves V, Muratov E, Zakharov A, Muratov N, Andrade C, Tropsha A . Chemical toxicity prediction for major classes of industrial chemicals: Is it possible to develop universal models covering cosmetics, drugs, and pesticides?. Food Chem Toxicol. 2017; 112:526-534. PMC: 5638676. DOI: 10.1016/j.fct.2017.04.008. View

3.
Cruz-Monteagudo M, Gonzalez-Diaz H, Borges F, Gonzalez-Diaz Y . Simple stochastic fingerprints towards mathematical modeling in biology and medicine. 3. Ocular irritability classification model. Bull Math Biol. 2006; 68(7):1555-72. DOI: 10.1007/s11538-006-9083-y. View

4.
Alves V, Muratov E, Fourches D, Strickland J, Kleinstreuer N, Andrade C . Predicting chemically-induced skin reactions. Part II: QSAR models of skin permeability and the relationships between skin permeability and skin sensitization. Toxicol Appl Pharmacol. 2015; 284(2):273-80. PMC: 4408226. DOI: 10.1016/j.taap.2014.12.013. View

5.
Barroso J, Pfannenbecker U, Adriaens E, Alepee N, Cluzel M, De Smedt A . Cosmetics Europe compilation of historical serious eye damage/eye irritation in vivo data analysed by drivers of classification to support the selection of chemicals for development and evaluation of alternative methods/strategies: the Draize eye.... Arch Toxicol. 2016; 91(2):521-547. PMC: 5306081. DOI: 10.1007/s00204-016-1679-x. View