Quantitative Prediction of Thiazole Derivatives as Potent Xanthine Oxidase Inhibitors. Issue 37 (5th October 2018)

• Record Type: Journal Article
• Authors: Sun, Jiaying; Mei, Hu
• Is Part Of: ChemistrySelect. Volume 3:Issue 37(2018)
• Page Start: 10402
• Page End: 10407
• Publication Date: 2018-10-05
• Subjects: Inhibitors -- QSAR -- Thiazole derivatives -- Xanthine oxidase; Chemistry -- Periodicals; 540.5
• DOI: 10.1002/slct.201801977
• Languages: English
• ISSNs: 2365-6549

Abstract

Abstract: In humans, xanthine oxidase (XO) is a critical rate‐limiting enzyme in biosynthesis of uric acid. Moreover, XO is the most promising target to control uric acid levels and the treat of hyperuricemia and gout. However, XO inhibitors can effectively treat gout through the inhibition of urate synthesis, and by impairing the conversion of hypoxanthine and xanthine to uric acid. In this paper, four different quantitative structure‐activity relationship (QSAR) models of 80 thiazole derivatives were constructed to comprehend chemical‐biological interactions and predict their bioactivities using GA‐MLR (Genetic Algorithm‐Multiple Linear Regression), HQSAR (Hologram Quantitative Structure Activity Relationship), Topomer CoMFA (Comparative Molecular Field Analysis) and CoMSIA (Comparative Molecular Similarity Indices Analysis) methods. Furthermore, R 2 and Q 2 of the obtained GA‐MLR, HQSAR, Topomer CoMFA and CoMSIA models are 0.956 and 0.911, 0.932 and 0.876, 0.946 and 0.917, 0.964 and 0.877, respectively. In addition, the external predictive Q F 3 2 values of the GA‐MLR, HQSAR, Topomer CoMFA and CoMSIA models are 0.963, 0.936, 0.973 and 0.934, respectively. Therefore, all QSAR models exhibited good prediction performances, which can be used to quantitatively evaluate bioactivity and screen new XO inhibitors. Moreover, topological charge index, atomic electronegativity and polarizability, atomic van der Waals volumes and hydrophobic van der Waals surface area are very relevant to bioactivity of thiazole derivatives. Abstract : In order to screen and quantitatively predict novel XO inhibitors, 80 thiazole derivatives were collected and constructed different QSAR models using GA‐MLR, HQSAR, Topomer CoMFA and CoMSIA methods. Four QSAR models exhibited good prediction performances. Moreover, Topomer CoMFA has the best predictive abilities.