<i>In silico</i> prediction of volume of distribution of drugs in man using conformal prediction performs on par with animal data-based models
Creators: name: Fagerholm, Urban
name: Hellberg, Sven
name: Alvarsson, Jonathan
name: McShane, Staffan Arvidsson
name: Spjuth, Ola
Publisher: name: Taylor & Francis
Year: 2021
Subjects: subject: Biophysics
subject: Biochemistry
subject: Pharmacology
subject: Biotechnology
subject: 69999 Biological Sciences not elsewhere classified
subject: FOS: Biological sciences
subject: 80699 Information Systems not elsewhere classified
subject: FOS: Computer and information sciences
subject: 19999 Mathematical Sciences not elsewhere classified
subject: FOS: Mathematics
subject: 110309 Infectious Diseases
subject: FOS: Health sciences
subject: Plant Biology

description: Volume of distribution at steady state (V<sub>ss</sub>) is an important pharmacokinetic endpoint. In this study we apply machine learning and conformal prediction for human V<sub>ss</sub> prediction, and make a head-to-head comparison with rat-to-man scaling, allometric scaling and the Rodgers-Lukova method on combined <i>in silico</i> and <i>in vitro</i> data, using a test set of 105 compounds with experimentally observed V<sub>ss</sub>.The mean prediction error and % with &lt;2-fold prediction error for our method were 2.4-fold and 64%, respectively. 69% of test compounds had an observed V<sub>ss</sub> within the prediction interval at a 70% confidence level. In comparison, 2.2-, 2.9- and 3.1-fold mean errors and 69, 64 and 61% of predictions with &lt;2-fold error was reached with rat-to-man and allometric scaling and Rodgers-Lukova method, respectively.We conclude that our method has theoretically proven validity that was empirically confirmed, and showing predictive accuracy on par with animal models and superior to an alternative widely used <i>in silico</i>-based method. The option for the user to select the level of confidence in predictions offers better guidance on how to optimise V<sub>ss</sub> in drug discovery applications. Volume of distribution at steady state (V<sub>ss</sub>) is an important pharmacokinetic endpoint. In this study we apply machine learning and conformal prediction for human V<sub>ss</sub> prediction, and make a head-to-head comparison with rat-to-man scaling, allometric scaling and the Rodgers-Lukova method on combined <i>in silico</i> and <i>in vitro</i> data, using a test set of 105 compounds with experimentally observed V<sub>ss</sub>. The mean prediction error and % with &lt;2-fold prediction error for our method were 2.4-fold and 64%, respectively. 69% of test compounds had an observed V<sub>ss</sub> within the prediction interval at a 70% confidence level. In comparison, 2.2-, 2.9- and 3.1-fold mean errors and 69, 64 and 61% of predictions with &lt;2-fold error was reached with rat-to-man and allometric scaling and Rodgers-Lukova method, respectively. We conclude that our method has theoretically proven validity that was empirically confirmed, and showing predictive accuracy on par with animal models and superior to an alternative widely used <i>in silico</i>-based method. The option for the user to select the level of confidence in predictions offers better guidance on how to optimise V<sub>ss</sub> in drug discovery applications.