In silico Predictive Phytotoxicity Modeling of lactuca sativa of Personal Care Product Ingredients
Aadarsh Vishvkarma, Purusottam Banjare, Jagadish Singh, Partha Pratim Roy · International Journal of Quantitative Structure-Property Relationships · 2021
Detection and evaluation of environmental toxicity of emerging contaminants like personal care product (PCP) additives are the thrust area in the recent day risk assessment of chemical hazards. The phytotoxicity assay is usually performed to identify and quantify the environmental impact of pollutants. In this background, the authors have developed in silico predictive phytotoxicity models for 36 PCP ingredients using 2D molecular descriptors using multiple linear regression as a chemometric tool. The statistical validation parameters assured the robustness of the developed models according to OECD guidelines. The mechanistic output of the models indicated the importance of the partition coefficient (CrippenLogP) and molecular hydrophilicity. The applicability domain explicitly defines the reliability of the application of the developed models for the unknown PCP ingredients in a consensus manner. The first reported predictive phytotoxicity models for PCP ingredients can help depict the environmental impacts of these classes of emerging pollutants.