q-RASAR modeling of antibiotics-microplastics mixtures: Towards predictive aquatic toxicology and resistance risk assessment.

作者：Shubha Das, S. Mukhopadhyay, M. Chatterjee, Supratik Kar, P. K. Ojha · 发表于：Aquatic Toxicology · 年份：2026 · DOI：10.1016/j.aquatox.2026.107819 · 被引用次数：2 · 研究领域：Medicine

Microplastics (MPs) and antibiotics are emerging pollutants that frequently co-occur in aquatic environments, where their interactions intensify ecotoxicological risks and may accelerate the spread of antibiotics resistance. Experimental assessment of such sorption-driven environmental behavior of mixture is costly, time-intensive, and ethically constrained, underscoring the need for predictive computational approaches. We have reported a Partial Least Squares (PLS)-based quantitative Read-Across Structure-Activity Relationship (q-RASAR) framework to evaluate the sorption-driven toxicity of antibiotics-microplastics mixtures. The distribution coefficient (log Kd) was selected as the endpoint, reflecting partitioning between aqueous and plastic phases, a key determinant of environmental persistence and bioavailability. A curated dataset of antibiotics-microplastics mixtures was used to generate mixture descriptors based on additivity, squared, and norm-based rules. Descriptors reduction and q-RASAR integration yielded a final model with five hybrid descriptors (structural + similarity-based). The model exhibited strong internal robustness (Q2LOO = 0.761) and high external predictivity (Q2F1 = 0.832; MAEtest = 0.152). Applicability domain and Y-randomization confirmed statistical soundness. Mechanistic analysis indicated that hydrogen-bond donors, oxygen-based polar functionalities, and terminal unsaturation enhance adsorption, while steric hindrance and charge asymmetry reduce...