Novel computational models offer alternatives to animal testing for assessing eye irritation and corrosion potential of chemicals

Abstract

Eye irritation and corrosion are fundamental considerations in developing chemicals to be used in or near the eye, from cleaning products to ophthalmic solutions. Unfortunately, animal testing is currently the standard method to identify compounds that cause eye irritation or corrosion. Yet, there is growing pressure on the part of regulatory agencies both in the USA and abroad to develop New Approach Methodologies (NAMs) that help reduce the need for animal testing and address unmet need to modernize safety evaluation of chemical hazards. In furthering the development and applications of computational NAMs in chemical safety assessment, in this study we have collected the largest expertly curated dataset of compounds tested for eye irritation and corrosion, and employed this data to build and validate binary and multi-classification Quantitative Structure-Activity Relationships (QSAR) models that can reliably assess eye irritation/corrosion potential of novel untested compounds. QSAR models were generated with Random Forest (RF) and Multi-Descriptor Read Across (MuDRA) machine learning (ML) methods, and validated using a 5-fold external cross-validation protocol. These models demonstrated high balanced accuracy (CCR of 0.68-0.88), sensitivity (SE of 0.61-0.84), positive predictive value (PPV of 0.65-0.90), specificity (SP of 0.56-0.91), and negative predictive value (NPV of 0.68-0.85). Overall, MuDRA models outperformed RF models and were applied to predict compounds' irritation/corrosion potential from the Inactive Ingredient Database, which contains components present in FDA-approved drug products, and from the Cosmetic Ingredient Database, the European Commission source of information on cosmetic substances. All models built and validated in this study are publicly available at the STopTox web portal (https://stoptox.mml.unc.edu/). These models can be employed as reliable tools for identifying potential eye irritant/corrosive compounds.

Fig. 1.

Data compilation and curation workflow.

Fig. 2.

Graphical representation of a self-organized map for the chemical space covered by modeling set chemicals. Red circles represent corrosives, yellow circles represent irritants, and green circles represent NC class. Blue-green regions show compounds that share structural similarities compared to their neighbors, and yellow-orange-red regions represent an abrupt change in the chemical structure of the compounds compared to their neighbors. The dataset is notably complex; there are similar compounds belonging to different classes, which makes the construction of multiclassification models a challenge.

Fig. 3.

Venn diagram showing the overlap between correct predictions done by all models for the eye irritation dataset.

Fig. 4.

Example of compounds correctly predicted only by MuDRA.