In Silico Models of Human PK Parameters. Prediction of Volume of Distribution Using an Extensive Data Set and a Reduced Number of Parameters.
| Autor: | Lombardo F; Drug Metabolism and Bioanalysis Group, Alkermes Inc, Waltham, MA 02451, USA. Electronic address: franco.lombardo@alkermes.com., Bentzien J; Modeling and Informatics Group, Alkermes Inc, Waltham, MA 02451, USA., Berellini G; Drug Metabolism and Bioanalysis Group, Alkermes Inc, Waltham, MA 02451, USA., Muegge I; Modeling and Informatics Group, Alkermes Inc, Waltham, MA 02451, USA. |
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| Jazyk: | angličtina |
| Zdroj: | Journal of pharmaceutical sciences [J Pharm Sci] 2021 Jan; Vol. 110 (1), pp. 500-509. Date of Electronic Publication: 2020 Sep 04. |
| DOI: | 10.1016/j.xphs.2020.08.023 |
| Abstrakt: | A novel, descriptor-parsimonious in silico model to predict human VDss (volume of distribution at steady-state) has been derived and thoroughly tested in a quasi-prospective regimen using an independent test set of 213 compounds. The model performs on par with a former benchmark model that relied on far more descriptors. As a result, the new random forest model relying on only six descriptors allows for interpretations that help chemists to design compounds with desired human VDss values. A comparison of in silico predictions of VDss with models using in vitro derived descriptors or in vivo scaling methods supports the strength of the in-silico approach, considering its resource- and animal-sparing nature. The strong performance of the in silico VDss models on structurally novel compounds supports the high degree of confidence that can be placed in using in silico human VDss predictions for compound design and human dose predictions. (Copyright © 2020 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.) |
| Databáze: | MEDLINE |
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