Dataset Information

Semi-Correlations for Building Up a Simulation of Eye Irritation.

ABSTRACT: The OECD recognizes that data on a compound's ability to treat eye irritation are essential for the assessment of new compounds on the market. In silico models are frequently used to provide information when experimental data are lacking. Semi-correlations, as they are called, can be useful to build up categorical models for eye irritation. Semi-correlations are latent regressions that can be used when the endpoint is expressed by two values: 1 for an active molecule and 0 for an inactive molecule. The regression line is based on the descriptor values which serve to distribute the data into four classes: true positive, true negative, false positive, and false negative. These values are applied to calculate the corresponding statistical criterion for assessing the predictive potential of the categorical model. In our model, the descriptor is the sum of what are termed correlation weights. These are defined by optimization using the Monte Carlo method. The target function of the optimization is related to the determination coefficient and the mean absolute error for the training set. Our model gives results that are better than those previously reported for the same endpoint.

SUBMITTER: Toropov AA

PROVIDER: S-EPMC10747944 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Semi-Correlations for Building Up a Simulation of Eye Irritation.

Toropov Andrey A AA Toropova Alla P AP Roncaglioni Alessandra A Benfenati Emilio E

Toxics 20231206 12

The OECD recognizes that data on a compound's ability to treat eye irritation are essential for the assessment of new compounds on the market. In silico models are frequently used to provide information when experimental data are lacking. Semi-correlations, as they are called, can be useful to build up categorical models for eye irritation. Semi-correlations are latent regressions that can be used when the endpoint is expressed by two values: 1 for an active molecule and 0 for an inactive molecu ...[more]

PMID: 38133394

Similar Datasets

Project description:PurposeOptiSafe is an in chemico test method that identifies potential eye irritants based on macromolecular damage following test chemical exposure. The OptiSafe protocol includes a prescreen assessment that identifies test chemicals that are outside the applicability domain of the test method and thus determines the optimal procedure. We assessed the usefulness and limitations of the OptiSafe test method for identifying chemicals not requiring classification for ocular irritation (i.e. bottom-up testing strategy).Materials and methodsSeventeen chemicals were selected by the lead laboratory and tested as an independent study. Ninety-five unique coded chemicals were selected by a validation management team to assess the intra- and interlaboratory reproducibility and accuracy of OptiSafe in a multilaboratory, three-phased validation study. Three laboratories (lead laboratory and two naïve laboratories) evaluated 35 chemicals, with the remaining 60 chemicals evaluated by the lead laboratory only. Test method performance was assessed by comparing classifications based on OptiSafe results to classifications based on available retrospective in vivo data, using both the EPA and GHS eye irritation hazard classification systems. No prospective in vivo testing was conducted.ResultsPhase I testing of five chemicals showed that the method could be transferred to naïve laboratories; within-lab reproducibility ranged from 93% to 100% for both classification systems. Thirty coded chemicals were evaluated in Phase II of the validation study to demonstrate both intra- and interlaboratory reproducibility. Intralaboratory reproducibility for both EPA and GHS classification systems for Phase II of the validation study ranged from 93% to 99%, while interlaboratory reproducibility was 91% for both systems. Test method accuracy for the EPA and GHS classification systems based on results from individual laboratories ranged from 82% to 88% and from 78% to 88%, respectively, among the three laboratories; false negative rates ranged from 0% to 7% (EPA) and 0% to 15% (GHS). When results across all three laboratories were combined based on the majority classification, test method accuracy and false negative rates were 89% and 0%, respectively, for both classification systems, while false positive rates were 25% and 23% for the EPA and GHS classification systems, respectively. Validation study Phase III evaluation of an additional 60 chemicals by the lead laboratory provided a comprehensive assessment of test method accuracy and defined the applicability domain of the method. Based on chemicals tested in Phases II and III by the lead laboratory, test method accuracy was 83% and 79% for the EPA and GHS classification systems, respectively; false negative rates were 4% (EPA) and 0% (GHS); and false positive rates were 40% (EPA) and 42% (GHS). Potential causes of false positives in certain chemical (e.g. ethers and alcohols) or hazard classes are being further investigated.ConclusionThe OptiSafe test method is useful for identifying nonsurfactant substances not requiring classification for ocular irritancy. OptiSafe represents a new tool for the in vitro assessment of ocular toxicity in a tiered-testing strategy where chemicals can be initially tested and identified as not requiring hazard classification.

Project description:Assessment of eye irritation hazard has long been a core requirement in any chemical legislation. Nevertheless, publications focussing on the eye damaging potential of nanomaterials are scarce. Traditionally, eye irritation testing was performed using rabbits. The OECD Test Guideline 437 Bovine Corneal Opacity and Permeability (BCOP) test method allows determining severely irritating substances without animals, and the recently adopted OECD Test Guideline 492 Reconstructed human cornea-like epithelium test method allows identifying chemicals that neither induce eye irritation nor serious eye damage. For substances applicable to these tests, huge progress has been made in replacing animal testing.The in vitro eye irritation potential of 20 nanosized and 3 non-nanosized materials was investigated in a 2-tier EpiOcular™ Eye Irritation Test (EpiOcular™-EIT) and BCOP testing strategy including histopathology of the bovine corneas. Furthermore, applicability of the testing strategy for nanomaterials was assessed. Test materials encompassed OECD representative nanomaterials (metals (Ag), metal oxides (ZnO, TiO2, CeO2), amorphous SiO2 and MWCNTs), three organic pigments, quartz, and talc.None of the dry-powder nanomaterials elicited eye irritation in either the EpiOcular™-EIT or the BCOP assay. Likewise, an amorphous SiO2 nanomaterial that was supplied as suspension was tested negative in both assays. By contrast, in the EpiOcular™-EIT, the silver nanomaterial that was supplied as dispersion was tested positive, whereas its surfactant-containing dispersant was borderline to negative. In the BCOP assay, the silver nanomaterial elicited highly variable results and dark-brown patches remained on the corneal surface, whereas the results for its dispersant alone were borderline to positive, which was assessed as inconclusive due to high inter-assay variability.The present study points to the low eye irritation potential of a spectrum of nanomaterials, which is consistent with available in vivo data for the same test materials or for nanosized or bulk materials of the same composition.

Dataset Information

Semi-Correlations for Building Up a Simulation of Eye Irritation.

Publications

Semi-Correlations for Building Up a Simulation of Eye Irritation.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets