Meta-analysis and machine learning to explore soil-water partitioning of common pharmaceuticals

The first meta-analysis and modelling from batch-sorption literature studies of the soil/water partitioning of pharmaceuticals is presented. Analysis of the experimental conditions reported in the literature demonstrated that though batch-sorption studies have value, they are limited in evaluating partitioning under environmentally-relevant conditions. Recommendations are made to utilise environmental relevant pharmaceutical concentrations, perform batch-sorption studies at temperatures other than 4, 20 and 25 °C to better reflect climate diversity, and utilise the Guideline 106 methodology as a benchmark to enable comparison between future studies (and support modelling and prediction). The meta-dataset comprised 82 data points, which were modelled using multivariate analysis; where K_d (soil/water partitioning coefficient) was the independent variable. The dependent variables fit into three categories: 1) pharmaceutical studied (including physical-chemical properties), 2) soil characteristics and 3) experimental conditions. The pharmaceutical solubility, the soil/liquid equilibration time (prior to adding the pharmaceutical), the soil organic carbon, the soil sterilisation method and the liquid phase were found to be significantly important variables for predicting K_d.