The first report on chronic toxicity assessment of metals towards Ceriodaphnia dubia using QSTR technique: A step towards healthier and safer human health and eco-system

Abstract

Exposure of humans and other living organisms to metals (including heavy metals) can lead to serious chronic and acute health effects, which may sometimes be life-threatening. As a result, assessing the toxicity of heavy metals is essential. However, experimental toxicity data for heavy metals is limited, and their toxicity estimation can be highly costly, lengthy analysis durations, and may require animal testing. Therefore, in-silico approaches such as quantitative structure–activity relationship (QSAR) are a suitable alternative. In this work, we have developed multi-endpoints MLR-QSAR models to assess the chronic toxicity of heavy metal towards Ceriodaphnia dubia using 48 data points and obeying the Organization for Economic Cooperation and Development (OECD) guidelines. Intra-endpoint uni-variate models were developed to fill the toxicity data gaps between the endpoints (acute to chronic). The statistical results of the developed models (individual models M1-M4; R² = 0.691–0.738, Q²_LOO = 0.542–0.578, Q²_F1 = 0.673–0.732, Q²_F2 = 0.552–0.580, MAE_95%data = 0.437–0.753; intra-endpoints models IEM1-IEM9; R² = 0.952–0.988, Q²_LOO = 0.907–0.987, Q²_F1 = 0.885–0.991, Q²_F2 = 0.979–0.991, MAE_95%data = 0.120–0.436) infer that the models are robust, reliable, reproducible, and predictive. The descriptors contributing to the development of the model imply that the release of electrons, formation of cations, higher electronegativity, and the presence of neutrons in the heavy metals significantly influence the toxicity caused by the metals. Thus, this study presents in silico models aimed at controlling the exposure of living organisms to toxic heavy metals. It assesses both acute and chronic toxicity, addresses gaps in toxicity data, and strives to create healthier and safer ecosystems by strictly following the principles of reduction, replacement, and refinement (the RRR framework).