Quantum Mechanical (QM) Calculations Applied to ADMET Drug Prediction: A Review

被引:8
|
作者
Silva-Junior, E. F. [1 ,2 ]
Aquino, T. M. [2 ]
Araujo-Junior, J. X. [1 ,2 ]
机构
[1] Univ Fed Alagoas, Nursing & Pharm Sch, Lab Med Chem, POB 57072-900, Maceio, Brazil
[2] Univ Fed Alagoas, Chem & Biotechnol Inst, Maceio, Brazil
关键词
Hartree-Fock; ab initio; DFT; semiempirical; metabolism; ADMET; DENSITY-FUNCTIONAL THEORY; COMPOSITION-BASED MODEL; BLOOD-BRAIN-BARRIER; IN-VITRO METABOLISM; CYTOCHROME-P450; 2D6; MOLECULAR DOCKING; N-DEMETHYLATION; SILICO TOOL; ETHANOL; DFT;
D O I
10.2174/1389200218666170316094514
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
The discovery of new drugs is generally considered a long and expensive process, which often leads to molecules with low efficacy and high toxicity, which in many cases can be related to metabolism. In an attempt to reduce these failures and the production costs of a new drug, in silico studies have been used to obtain important information about the behavior of these compounds in the metabolism phases: absorption, distribution, metabolism (or biotransformation) and elimination (or excretion). Quantum Mechanical (QM) calculations are based on Schrdinger's equation that can be used to develop models and theoretical parameters able to explain properties observed experimentally. In recent years, there has been an increase in the development of studies involving the application of QM methods to describe properties related to ADMET profile of new compounds. Amongst these, the most commonly used methods are ab initio (Hartree-Fock), Semiempirical (AM1 and PM3) and Density Functional Theory (DFT). The application of these methods allows the modeling of the predicted profile of absorption and elimination of chalcone-chloroquinoline hybrids; the ability of drugs to cross the blood-brain barrier (distribution); proposal of the route for oxidation of several compounds, via CYP450; and to predict the toxicity of pyrethroid analogs. Finally, QM methods can be considered as a valuable tool in the prediction of metabolism when applied to drug discovery.
引用
收藏
页码:511 / 526
页数:16
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