Development of a decision flow-based read-across approach for assessing skin sensitization using publicly available tools.

Animal tests like the guinea pig maximization test (GPMT) and murine local lymph node assay (LLNA) were historically used to assess skin sensitization in cosmetics. The EU banned animal testing for cosmetics in 2013, prompting the development of alternative in vitro methods and new approach methodologies (NAMs) based on adverse outcome pathways (AOPs). These NAMs are integrated into Defined Approaches (DAs) to evaluate skin sensitization hazards and potency, which challenge is incorporating them into next-generation risk assessment (NGRA) frameworks. To address this challenge, we aim to achieve quantitative risk assessment in humans. The NGRA framework proposed by Cosmetics Europe emphasizes read-across methods, which predict toxicity using data from similar substances. This approach, which utilizes data from analogues, is described as an effective way to reduce uncertainty in risk assessment. This paper proposes a transparent read-across method using the OECD QSAR Toolbox, ranked by reliability based on structural similarity, protein binding, sensitization alerts, and percutaneous absorption. The sensitization data of the extracted analogues prioritize the LLNA EC3 values, followed by predicted EC3 values from Derek nexus or GPMT data. A case study using p-isobutyl-α-methyl hydrocinnamaldehyde confirmed the target's classification as a moderate sensitizer. The proposed method ensures transparency by relying on publicly available tools, reducing uncertainty in NGRA.