Evaluation of Existing Models to Estimate Sorption Coefficients for Ionisable Pharmaceuticals in Soils and Sludge

Abstract

In order to assess the environmental risk of a pharmaceutical, information is needed on the sorption of the compound to solids. Here we use a high-quality database of measured sorption coefficients, all determined following internationally recognised protocols, to evaluate models that have been proposed for estimating sorption of pharmaceuticals from chemical structure, some of which are already being used for environmental risk assessment and prioritization purposes. Our analyses demonstrate that octanol-water partition coefficient (K ow ) alone is not an effective predictor of ionisable pharmaceutical sorption in soils. Polyparameter models based on pharmaceutical characteristics in combination with key soil properties, such as cation exchange capacity, increase model complexity but yield an improvement in the predictive capability of soil sorption models. Nevertheless, as the models included in this analysis were only able to predict a maximum of 71% and 67% of the sorption coefficients for the compounds to within one log unit of the corresponding measured value in soils and sludge, respectively, there is a need for new models to be developed to better predict the sorption of ionisable pharmaceuticals in soil and sludge systems. The variation in sorption coefficients, even for a single pharmaceutical across different solid types, makes this an inherently difficult task, and therefore requires a broad understanding of both chemical and sorbent properties driving the sorption process.

How microbial community composition, sorption and simultaneous application of six pharmaceuticals affect their dissipation in soils

“…Whereas there have been developed several models for estimating sorption coefficients of pharmaceuticals in soils and sediments from sorbent and compound properties (e.g., Carter et al, 2020;Klement et al, 2018;Kodešová et al, 2015;Li et al, 2020), models for estimating dissipation half-lives has not been proposed. Previous studies only evaluated dissipation half-lives in few soils or sediments.…”

New Models for Estimating the Sorption of Sulfonamide and Tetracycline Antibiotics in Soils

“…The different effects of inclusion of non-edaphic variable(s) on model performance among antibiotics and affinity coefficients indicated that more than one sorption mechanism might dominate and the relative importance of one mechanism over another depended on, in addition to soil properties, antibiotic species and environmental conditions (e.g., pollution level and soil to water ratio). Given the complex relationships of affinity coefficients with varying properties/parameters, some previous studies employed machine learning approaches (artificial neural network, random forest, and support vector machine) to develop nonlinear models for antibiotics (together with non-antibiotic pharmaceuticals), and the best performance was achieved by a random forest-based model using antibiotic and soil properties as the independent variable(s) [ 25 , 73 , 74 ]. Notably, the random forest model can be utilized to reveal the relative importance order of variables and thus may help select the top contributing variables for the development of new models [ 74 ].…”

“…Statistical regression analyses are traditional approaches to establishing linear or nonlinear quantitative models relating to the sorption parameters of antibiotics with soil properties [ 23 , 24 ]. Regression-based models using antibiotic physico-chemical properties alone as inputs were also developed, and the performance of such models can be improved by also including soil properties as inputs [ 25 , 26 , 27 ]. Moreover, satisfactory estimation of sorption parameters was obtained using machine learning approaches (e.g., artificial neural network, and random forest), which can involve many more inputs than regression-based approaches can do [ 28 , 29 ].…”

A framework to assess the terrestrial risk of antibiotic resistance from antibiotics in slurry or manure amended soils

“…Approaches to predict soil sorption coefficients have been recently reviewed elsewhere and compared to an independent experimentally-derived data set of pharmaceutical sorption coefficients. 48 The model developed by Droge and Goss 2013, 36 which assumes that sorption of cations is driven by cation exchange processes, was deemed the most appropriate model to estimate the sorption coefficients for pharmaceuticals in their cationic state ( r 2 of 0.29 for 66 compounds). 48 K d (L kg −1 ) = K CEC CLAY (CEC CLAY ) + f oc DOC ie…”

“…37 This model was also assessed using an independent data set to evaluate its suitability to predict K OC for 68 compounds and it was deemed the most acceptable of models assessed for acids with an r 2 of 0.17. 48 K oc (L kg −1 ) = F n (10 0.54logPn+1.11 ) + F ion (10 0.11logPn+1.54 ) (3) Soil organic carbon partitioning coefficient normalised to organic matter or antibiotics in anionic state. Where F n is neutral fraction and…”