In silico prediction of volume of distribution of drugs in man using conformal prediction performs on par with animal data-based models.
Xenobiotica
; 51(12): 1366-1371, 2021 Dec.
Article
en En
| MEDLINE
| ID: mdl-34845977
ABSTRACT
Volume of distribution at steady state (Vss) is an important pharmacokinetic endpoint. In this study we apply machine learning and conformal prediction for human Vss prediction, and make a head-to-head comparison with rat-to-man scaling, allometric scaling and the Rodgers-Lukova method on combined in silico and in vitro data, using a test set of 105 compounds with experimentally observed Vss.The mean prediction error and % with <2-fold prediction error for our method were 2.4-fold and 64%, respectively. 69% of test compounds had an observed Vss 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 <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 in silico-based method. The option for the user to select the level of confidence in predictions offers better guidance on how to optimise Vss in drug discovery applications.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Preparaciones Farmacéuticas
/
Modelos Biológicos
Tipo de estudio:
Prognostic_studies
/
Risk_factors_studies
Límite:
Animals
Idioma:
En
Revista:
Xenobiotica
Año:
2021
Tipo del documento:
Article
País de afiliación:
Suecia