Predicting the Drug Clearance Pathway with Structural Descriptors.

Autor: Kaboudi N; Student Research Committee, Tabriz University of Medical Sciences, Tabriz, Iran.; Biotechnology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran., Shayanfar A; Pharmaceutical Analysis Research Center, Tabriz University of Medical Sciences, Tabriz, Iran. shayanfara@tbzmed.ac.ir.; Editorial Office of Pharmaceutical Sciences Journal, Faculty of Pharmacy, Tabriz University of Medical Sciences, Tabriz, Iran. shayanfara@tbzmed.ac.ir.
Jazyk: angličtina
Zdroj: European journal of drug metabolism and pharmacokinetics [Eur J Drug Metab Pharmacokinet] 2022 May; Vol. 47 (3), pp. 363-369. Date of Electronic Publication: 2022 Feb 11.
DOI: 10.1007/s13318-021-00748-3
Abstrakt: Background and Objective: The clearance, by renal elimination or hepatic metabolism, is one of the most important pharmacokinetic parameters of a drug. It allows the half-life, bioavailability, and drug-drug interactions to be predicted, and it can also affect the dose regimen of a drug. Predicting the clearance pathways of new chemical candidates during drug development is vital in order to minimize the risks of possible side effects and drug interactions. Many in vivo methods have been established to predict drug clearance in humans, and these mainly rely on data from in vivo studies in preclinical species-mainly rats, dogs, and monkeys. They are also time consuming and expensive. The aim of this study was to find the relationship between structural parameters of drugs and their clearance pathways.
Methods: The clearance pathway of each drug was obtained from the literature. Various structural descriptors [Abraham solvation parameters, topological polar surface area, numbers of hydrogen-bond donors and acceptors, number of rotatable bonds, molecular weight, logarithm of the partition coefficient (logP), and logarithm of the distribution coefficient at pH 7.4 (logD 7.4 )] were applied to develop a mechanistic model for predicting clearance pathways.
Results: The results of this study indicate that compounds with logD 7.4  > 1 or with zero or one hydrogen-bond donor undergo hepatic metabolism, whereas the clearance pathway for chemicals with logD 7.4 < - 2 is renal elimination. Furthermore, models established using logistic regression based on five structural parameters for compounds with - 2 < logD 7.4 < 1 could be used in a clearance pathway prediction tool. The overall prediction accuracies of the first and second models were 84.8% and 84.4%, respectively.
Conclusion: The developed model can be used to find the clearance pathways of new drug candidates with acceptable accuracy. The main descriptors that are used to evaluate this parameter are the hydrophobicity and the number of hydrogen-bonding functional groups of the compound.
(© 2022. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)
Databáze: MEDLINE
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