| Autor: |
Van Rompaey D; Janssen Pharmaceutica NV, 2340 Beerse, Belgium., Morrison D; Janssen Pharmaceutica NV, 2340 Beerse, Belgium., Van Den Bergh A; Janssen Pharmaceutica NV, 2340 Beerse, Belgium., Wegner JK; Janssen Pharmaceutica NV, 2340 Beerse, Belgium. |
| Jazyk: |
angličtina |
| Zdroj: |
Molecular pharmaceutics [Mol Pharm] 2023 May 01; Vol. 20 (5), pp. 2436-2442. Date of Electronic Publication: 2023 Mar 31. |
| DOI: |
10.1021/acs.molpharmaceut.2c01048 |
| Abstrakt: |
It is common practice in the early drug discovery process to conduct in vitro screening experiments using liver microsomes in order to obtain an initial assessment of test compound metabolic stability. Compounds which bind to liver microsomes are unavailable for interaction with the drug metabolizing enzymes. As such, assessment of the unbound fraction of compound available for biotransformation is an important factor for interpretation of in vitro experimental results and to improve prediction of the in vivo metabolic clearance. Various in silico methods have been proposed for the prediction of test compound binding to microsomes, from various simple lipophilicity-based models with moderate performance to sophisticated machine learning models which demonstrate superior performance at the cost of increased complexity and higher data requirements. In this work, we attempt to strike a middle ground by developing easily implementable equations with improved predictive performance. We employ a symbolic regression approach based on a medium-size in-house data set of fraction unbound in human liver microsomes measurements allowing the identification of novel equations with improved performance. We validate the model performance on an in-house held-out test set and an external validation set. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
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