In farm animals, kinetic data of exogenous chemicals, such as pharmaceuticals, environmental pollutants or feed contaminants, are scarce. To allow extrapolation across chemicals and species this study developed a machine learning approach that integrated allometric scaling and quantitative structure–activity relationships to predict total body clearance in farm animals. Using body weight and molecular descriptors of chemicals, the models applied both linear and non-linear machine learning methods such as random forest to predict clearance. Data for intravenously administered chemicals were collected from literature from a variety of species. Molecular descriptors of these chemicals were computed. Log-transformed clearances were predicted for five farm animal species—cattle, sheep, goat, swine, horse—as well as dogs and cats for comparative analysis. Two models using machine learning methods were developed: a purely extrapolative machine learning model, and an approach titled “augmented allometric scaling” which, similarly to simple allometric scaling, used pre-existing data in other species to predict a chemicals’ clearance in a target species. The extrapolative approach had large differences in training and test set metrics, while the latter approach demonstrated modestly improved predictive accuracy over simple allometric scaling in farm animals with up to 60.8% of predictions below a fold error of 2, compared to 51% given by allometry, with a difference of up to 0.5 fold errors. In dogs, the new approach performed comparably and worse in cats. This study highlights potentials and limits of machine learning in refining kinetic predictions in farm animals.
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