Predicting sorption of pharmaceuticals and personal care products onto soil and digested sludge using artificial neural networks

Abstract

A comprehensive analytical investigation of the sorption behaviour of a large selection of over-the-counter, prescribed pharmaceuticals and illicit drugs to agricultural soils and freeze-dried digested sludges is presented. Batch sorption experiments were carried out to identify which compounds could potentially concentrate in soils as a result of biosolid enrichment. Analysis of aqueous samples was carried out directly using liquid chromatography-tandem mass spectrometry (LC-MS/MS). For solids analysis, combined pressurised liquid extraction and solid phase extraction methods were used prior to LC-MS/MS. Solid–water distribution coefficients (K_d) were calculated based on slopes of sorption isotherms over a defined concentration range. Molecular descriptors such as log P, pK_a, molar refractivity, aromatic ratio, hydrophilic factor and topological surface area were collected for all solutes and, along with generated K_d data, were incorporated as a training set within a developed artificial neural network to predict K_d for all solutes within both sample types. Therefore, this work represents a novel approach using combined and cross-validated analytical and computational techniques to confidently study sorption modes within the environment. The logarithm plots of predicted versus experimentally determined K_d are presented which showed excellent correlation (R² > 0.88), highlighting that artificial neural networks could be used as a predictive tool for this application. To evaluate the developed model, it was used to predict K_d for meclofenamic acid, mefenamic acid, ibuprofen and furosemide and subsequently compared to experimentally determined values in soil. Ratios of experimental/predicted K_d values were found to be 1.00, 1.00, 1.75 and 1.65, respectively.