A new cell-based method for assessing the eye irritation potential of chemicals: An alternative to the Draize test

In silico prediction of ocular toxicity of compounds using explainable machine learning and deep learning approaches

The accurate identification of chemicals with ocular toxicity is of paramount importance in health hazard assessment. In contemporary chemical toxicology, there is a growing emphasis on refining, reducing, and replacing animal testing in safety evaluations. Therefore, the development of robust computational tools is crucial for regulatory applications. The performance of predictive models is heavily reliant on the quality and quantity of data. In this investigation, we amalgamated the most extensive dataset (4901 compounds) sourced from governmental GHS-compliant databases and literature to develop binary classification models of chemical ocular toxicity. We employed 12 molecular representations in conjunction with six machine learning algorithms and two deep learning algorithms to create a series of binary classification models. The findings indicated that the deep learning method GCN outperformed the machine learning models in cross-validation, achieving an impressive AUC of 0.915. However, the top-performing machine learning model (RF-Descriptor) demonstrated excellent performance with an AUC of 0.869 on the test set and was therefore selected as the best model. To enhance model interpretability, we conducted the SHAP method and attention weights analysis. The two approaches offered visual depictions of the relevance of key descriptors and substructures in predicting ocular toxicity of chemicals. Thus, we successfully struck a delicate balance between data quality and model interpretability, rendering our model valuable for predicting and comprehending potential ocular-toxic compounds in the early stages of drug discovery.

A microelectric cell sensing technique for in vitro assessment of ocular irritation

The animal-based Draize test remains the gold standard for assessment of ocular irritation. However, subjective scoring methods, species differences, and animal welfare concerns have spurred development of alternative test methods. In this study, a novel in vitro method for assessing ocular irritancy was developed using a microelectric cell sensing technology, real-time cell analysis (RTCA). The cytotoxicity of sixteen compounds was assessed in two cell lines: ARPE-19 (human retina) and SIRC (rabbit cornea). In vitro inhibitory (IC₅₀ and AUC₅₀) values were determined at 6, 12, 24, 48, 72, and 96 h exposure, with a subset of values confirmed with MTT testing. The values displayed comparable predictivity of in vivo ocular irritation on the basis of a linear regression between the calculated values and each compounds' corresponding Draize-determined modified maximum average score (MMAS), but the ARPE-19 derived values were more strongly correlated than those from SIRC cells. Hence, IC₅₀ values derived from ARPE-19 cells were used to predict the UN GHS/EU CLP classification of each test compound. The method was determined to have sensitivity of 90%, specificity of 50%, and overall concordance of 75%. Thus, RTCA testing may be best incorporated into a top-down tiered testing strategy for identification of ocular irritants in vitro.

A proposal for a new in vitro method for direct classification of eye irritants by cytotoxicity test - Preliminary study

None of the in vitro method are suitable for directly classifying of a substance as an eye irritant (category 2). They can classify substance as category 1 (serious eye damage) or as "no category" (not requiring classification). The aim of this study was to develop a new method for direct classification of a substance as category 2. Cytotoxicity Assay to Assess Eye Irritation (CEI) was performed on fibroblast - HDFn cell line with 36 substances. 5 concentrations of all substances and neat substances were applied directly to the cells. After 30 min, medium was added and cells were incubated at 37 °C. The next day, the cytotoxicity assay was performed (MTT assay in the first run and NRU assay in the second run). Based on viability and IC50 value (concentration with 50 % viability) a substance could be classified in category 2, category 1, and as "no category". The results obtained were referred to ECHA database. This new method had high sensitivity (53.8-88.9 %), specificity (73.9-100.0 %) and accuracy (69.4-88.9 %) in the classification to all categories. It effectively classifies not only substances in category 2 but also in category 1 and substances that do not require classification.

Discovery of Ezrin Expression as a Potential Biomarker for Chemically Induced Ocular Irritation Using Human Corneal Epithelium Cell Line and a Reconstructed Human Cornea-like Epithelium Model

Numerous studies have attempted to develop a new in vitro eye irritation test (EIT). To obtain more reliable results from EIT, potential new biomarkers that reflect eye irritation by chemicals must be identified. We investigated candidate biomarkers for eye irritation, using a proteomics approach. Sodium lauryl sulfate (SLS) or benzalkonium chloride (BAC) was applied on a reconstructed human cornea-like epithelium model, MCTT HCE, and corneal protein expression was examined by two-dimensional gel electrophoresis. We found that ezrin (EZR) was significantly upregulated by SLS or BAC. In addition, upregulation of EZR in immortalized human corneal cells treated with SLS or BAC was confirmed by quantitative reverse transcription-PCR and western blot analysis. Furthermore, other well-known eye irritants such as cetylpyridinium bromide, Triton X-100, cyclohexanol, ethanol, 2-methyl-1-pentanol, and sodium hydroxide significantly increased EZR expression in immortalized human corneal cells. Induction of EZR promoter activity in irritant-treated human corneal cells was confirmed by a luciferase gene reporter assay. In conclusion, EZR expression may be a potential biomarker for detecting eye irritation, which may substantially improve the performance of in vitro EIT.

Rapamycin Nano-Micelle Ophthalmic Solution Reduces Corneal Allograft Rejection by Potentiating Myeloid-Derived Suppressor Cells' Function

Allograft rejection is the major cause of corneal allograft failure. Rapamycin (RAPA) has been reported as an effective and novel immunosuppressive agent for patients undergoing corneal transplantation. However, its high water insolubility and low bioavailability have strongly constrained its clinical application. In this study, we successfully developed a RAPA nano-micelle ophthalmic solution and found that corneal allograft survival in recipients treated with RAPA nano-micelle ophthalmic solution was significantly prolonged for more than 2 months, with less inflammatory infiltration, decreased production of pro-inflammatory factors, and elevated recruitment of myeloid-derived suppressor cells (MDSCs). MDSCs from mice treated with RAPA nano-micelle ophthalmic solution could significantly inhibit the proliferation of CD4⁺T cells through increased expressions of inducible nitric oxidase (iNOS) and arginase-1 (Arg-1). The activity blockade of Arg-1 and iNOS pharmacologically reversed their immunosuppressive ability. Moreover, the effects of RAPA were antagonized by the administration of anti-Gr-1 antibody or by inhibiting the activity of iNOS pharmacologically. In addition, RAPA nano-micelle also effectively alleviated allograft rejection in high-risk rabbit penetrating keratoplasty (PKP) models with corneal vascularization. Collectively, our results demonstrate that RAPA nano-micelle ophthalmic solution could improve the immunosuppressive activity of MDSCs through elevated expression of Arg-1 and iNOS, which highlights the possible therapeutic applications of RAPA against corneal allograft rejection.

Eye irritation potential: palm-based methyl ester sulphonates

AIM

To evaluate eye irritation potential of palm-based methyl ester sulphonates (MES) of different chain lengths; C12, C14, C16, C16:18.

METHODS

The Bovine Corneal Opacity and Permeability test method (BCOP), OECD Test Guideline 437, was used as an initial step to study the inducing effect of palm-based MES on irreversible eye damage. The second assessment involved the use of reconstructed human corneal-like epithelium test method, OECD Test Guideline 492 using SkinEthic™ Human Corneal Epithelium to study the potential effect of palm-based MES on eye irritancy. The palm-based MES were prepared in 10% solution (w/v) in deionized water and tested as a liquid and surfactant test substances whereby both test conducted according to the liquid/surfactant treatment protocol.

RESULTS

The preliminary BCOP results showed that palm-based MES; C12, C14, C16, C16:18 were not classified as severe eye irritants test substances with in vitro irritancy score between 3 and the threshold level of 55. The second evaluation using SkinEthic™ HCE model showed that palm-based MES; C12, C14, C16, C16:18 and three commercial samples were potentially irritants to the eyes with mean tissue viability ≤ 60% and classified as Category 2 according to United Nations Globally Harmonized System of Classification and Labelling of Chemicals. However, there are some limitations of the proposed ocular irritation classification of palm-based MES due to insolubility of long chain MES in 10% solution (w/v) in deionized water.

CONCLUSION

Therefore, future studies to clarify the eye irritation potential of the palm-based MES will be needed, and could include; methods to improve the test substance solubility, use of test protocol for solids, and/or inclusion of a benchmark anionic surfactant, such as sodium dodecyl sulphate within the study design.

Analysis of Draize eye irritation testing and its prediction by mining publicly available 2008-2014 REACH data

Public data from ECHA online dossiers on 9,801 substances encompassing 326,749 experimental key studies and additional information on classification and labeling were made computable. Eye irritation hazard, for which the rabbit Draize eye test still represents the reference method, was analyzed. Dossiers contained 9,782 Draize eye studies on 3,420 unique substances, indicating frequent retesting of substances. This allowed assessment of the test's reproducibility test based on all substances tested more than once. There was a 10% chance of a non-irritant evaluation given after a prior severe-irritant result as given by UN GHS classification criteria. The most reproducible outcomes were the results negative (94% reproducible) and severe eye irritant (73% reproducible). To evaluate whether other GHS categorizations predict eye irritation we built a dataset of 5,629 substances (1,931 'irritant' and 3,698 'non-irritant'). The two best decision trees with up to three other GHS classifications resulted in balanced accuracies of 68% and 73%, i.e., in the rank order of the Draize rabbit eye test itself, but both use inhalation toxicity data ("May cause respiratory irritation"), which is not typically available. Next, a dataset of 929 substances with at least one Draize study was mapped to PubChem to compute chemical similarity using 2D conformational fingerprints and Tanimoto similarity. Using a minimum similarity of 0.7 and simple classification by the closest chemical neighbor resulted in balanced accuracy from 73% over 737 substances to 100% at a threshold of 0.975 over 41 substances. This represents a strong support of read-across and (Q)SAR approaches in this area.

Development and Effects of FTY720 Ophthalmic Solution on Corneal Allograft Survival

Fingolimod (FTY720), a novel class of sphingosine 1-phosphate receptor modulators, has received special interest among ophthalmologists, particularly given that oral administration of FTY720 has proven to effectively treat corneal graft rejection in animal models. However, no studies have examined the performance of FTY720 as an ophthalmic solution in reducing corneal rejection in high-risk corneal rejection models, and the stability and ocular irritation profile of FTY720 ophthalmic solution are also unknown. Thus, we developed 0.1%, 0.2% and 0.5% FTY720 ophthalmic solutions and evaluated their chemical stabilities under various storage conditions with high- performance liquid chromatography. To investigate the ocular irritancy of the FTY720 ophthalmic solution, New Zealand albino rabbits were subjected to the Draize test. Furthermore, classic, well-established rat allogenic penetrating keratoplasty models were used to investigate the anti-rejection efficacy of the tested FTY720 ophthalmic solutions. We found that the non-irritating 0.5% FTY720 ophthalmic solution could prolong corneal allograft survival in rats with significant efficacy for about one month. Furthermore, no significant concentration changes occurred in any of the types of FTY720 ophthalmic solutions within three months. These results revealed crucial profiles of FTY720 ophthalmic solutions and warrant further investigation and optimization of FTY720 in the anti-rejection therapy after keratoplasty.

Evaluation of Eye Irritation Potential of Solid Substance with New 3D Reconstructed Human Cornea Model, MCTT HCE(TM)

The eye irritation potential of drug candidates or pharmaceutical ingredients should be evaluated if there is a possibility of ocular exposure. Traditionally, the ocular irritation has been evaluated by the rabbit Draize test. However, rabbit eyes are more sensitive to irritants than human eyes, therefore substantial level of false positives are unavoidable. To resolve this species difference, several three-dimensional human corneal epithelial (HCE) models have been developed as alternative eye irritation test methods. Recently, we introduced a new HCE model, MCTT HCE^TM which is reconstructed with non-transformed human corneal cells from limbal tissues. Here, we examined if MCTT HCE^TM can be employed to evaluate eye irritation potential of solid substances. Through optimization of washing method and exposure time, treatment time was established as 10 min and washing procedure was set up as 4 times of washing with 10 mL of PBS and shaking in 30 mL of PBS in a beaker. With the established eye irritation test protocol, 11 solid substances (5 non-irritants, 6 irritants) were evaluated which demonstrated an excellent predictive capacity (100% accuracy, 100% specificity and 100% sensitivity). We also compared the performance of our test method with rabbit Draize test results and in vitro cytotoxicity test with 2D human corneal epithelial cell lines.

An in silico expert system for the identification of eye irritants

This report describes development of an in silico, expert rule-based method for the classification of chemicals into irritants or non-irritants to eye, as defined by the Draize test. This method was developed to screen data-poor cosmetic ingredient chemicals for eye irritancy potential, which is based upon exclusion rules of five physicochemical properties - molecular weight (MW), hydrophobicity (log P), number of hydrogen bond donors (HBD), number of hydrogen bond acceptors (HBA) and polarizability (Pol). These rules were developed using the ADMET Predictor software and a dataset of 917 eye irritant chemicals. The dataset was divided into 826 (90%) chemicals used for training set and 91 (10%) chemicals used for external validation set (every 10th chemical sorted by molecular weight). The sensitivity of these rules for the training and validation sets was 72.3% and 71.4%, respectively. These rules were also validated for their specificity using an external validation set of 2011 non-irritant chemicals to the eye. The specificity for this validation set was revealed as 77.3%. This method facilitates rapid screening and prioritization of data poor chemicals that are unlikely to be tested for eye irritancy in the Draize test.

Estimation of the chemical-induced eye injury using a Weight-of-Evidence (WoE) battery of 21 artificial neural network (ANN) c-QSAR models (QSAR-21): part II: corrosion potential

This is part II of an in silico investigation of chemical-induced eye injury that was conducted at FDA's CFSAN. Serious eye damage caused by chemical (eye corrosion) is assessed using the rabbit Draize test, and this endpoint is an essential part of hazard identification and labeling of industrial and consumer products to ensure occupational and consumer safety. There is an urgent need to develop an alternative to the Draize test because EU's 7th amendment to the Cosmetic Directive (EC, 2003; 76/768/EEC) and recast Regulation now bans animal testing on all cosmetic product ingredients and EU's REACH Program limits animal testing for chemicals in commerce. Although in silico methods have been reported for eye irritation (reversible damage), QSARs specific for eye corrosion (irreversible damage) have not been published. This report describes the development of 21 ANN c-QSAR models (QSAR-21) for assessing eye corrosion potential of chemicals using a large and diverse CFSAN data set of 504 chemicals, ADMET Predictor's three sensitivity analyses and ANNE classification functionalities with 20% test set selection from seven different methods. QSAR-21 models were internally and externally validated and exhibited high predictive performance: average statistics for the training, verification, and external test sets of these models were 96/96/94% sensitivity and 91/91/90% specificity.

Estimation of the chemical-induced eye injury using a weight-of-evidence (WoE) battery of 21 artificial neural network (ANN) c-QSAR models (QSAR-21): part I: irritation potential

Evaluation of potential chemical-induced eye injury through irritation and corrosion is required to ensure occupational and consumer safety for industrial, household and cosmetic ingredient chemicals. The historical method for evaluating eye irritant and corrosion potential of chemicals is the rabbit Draize test. However, the Draize test is controversial and its use is diminishing - the EU 7th Amendment to the Cosmetic Directive (76/768/EEC) and recast Regulation now bans marketing of new cosmetics having animal testing of their ingredients and requires non-animal alternative tests for safety assessments. Thus, in silico and/or in vitro tests are advocated. QSAR models for eye irritation have been reported for several small (congeneric) data sets; however, large global models have not been described. This report describes FDA/CFSAN's development of 21 ANN c-QSAR models (QSAR-21) to predict eye irritation using the ADMET Predictor program and a diverse training data set of 2928 chemicals. The 21 models had external (20% test set) and internal validation and average training/verification/test set statistics were: 88/88/85(%) sensitivity and 82/82/82(%) specificity, respectively. The new method utilized multiple artificial neural network (ANN) molecular descriptor selection functionalities to maximize the applicability domain of the battery. The eye irritation models will be used to provide information to fill the critical data gaps for the safety assessment of cosmetic ingredient chemicals.