Predict of metabolic stability of xenobiotics by the PASS and GUSAR programs

   
Korotkevich E.I.1 , Rudik A.V.2, Dmitriev A.V.2, Lagunin A.A.1, Filimonov D.A.2

1. Institute of Biomedical Chemistry, Moscow, Russia; Medico-biological Faculty, Pirogov Russian National Research Medical University, Moscow, Russia
2. Institute of Biomedical Chemistry, Moscow, Russia
Section: Experimental Study
DOI: 10.18097/PBMC20216703295      PubMed Id: 34142537
Year: 2021  Volume: 67  Issue: 3  Pages: 295-299
Metabolic stability refers to the susceptibility of compounds to the biotransformation; it is characterized by such pharmacokinetic parameters as half-life (T1/2) and clearance (CL). Generally, these parameters are estimated by in vitro assays, which are based on cells or subcellular fractions (mainly liver microsomal enzymes) and serve as models of the processes occurring in living organisms. Data obtained from the experiments are used to build QSAR (Quantitative Structure-Activity Relationship) models. More than 8000 compounds with known CL and/or T1/2 values obtained in vitro using human liver microsomes were selected from the freely available ChEMBL v.27 database. GUSAR (General Unrestricted Structure-Activity Relationships) and PASS (Prediction of Activity Spectra for Substances) softwares were used to make quantitative and classification models. The quality of the models was evaluated using 5-fold cross-validation. Compounds were subdivided into “stable” and “unstable” by means of the following threshold parameters: T1/2 = 30 minutes, CL = 20 ml/min/kg. The accuracy of the models ranged from 0.5 (calculated in 5-fold CV on the test set for the half-life prediction quantitative model) to 0.96 (calculated in 5-fold CV on the test set for the clearance prediction classification model).
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Keywords: metabolic stability, half-life, clearance, QSAR, PASS, GUSAR
Citation:

Korotkevich, E. I., Rudik, A. V., Dmitriev, A. V., Lagunin, A. A., Filimonov, D. A. (2021). Predict of metabolic stability of xenobiotics by the PASS and GUSAR programs. Biomeditsinskaya Khimiya, 67(3), 295-299.
This paper is also available as the English translation: 10.1134/S1990750821040089
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