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Xenobiotica
the fate of foreign compounds in biological systems
Volume 49, 2019 - Issue 9
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General Xenobiochemistry

QSPR modelling of in vitro degradation half-life of acyl glucuronides

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Pages 1007-1014 | Received 02 Aug 2018, Accepted 18 Sep 2018, Published online: 29 Oct 2018

References

  • Baba A, Yoshioka T. (2009). Structure − activity relationships for degradation reaction of 1-β-O-acyl glucuronides: kinetic description and prediction of intrinsic electrophilic reactivity under physiological conditions. Chem Res Toxicol 22:158–72.
  • Boelsterli UA. 2013. Mechanisms underlying the hepatotoxicity of nonsteroidal antiinflammatory drugs. In: Kaplowitz N, DeLeve LD, eds. Drug-Induced Liver Disease (3rd Edition). London: Elsevier, 343–367.
  • Cactus. (2018). https://cactus.nci.nih.gov/translate/ (last accessed March, 2018).
  • Camilleri P, Buch A, Soldo B, Hutt AJ. (2017). The influence of physicochemical properties on the reactivity and stability of acyl glucuronides. Xenobiotica 1:15.
  • Chang C, Duignan DB, Johnson KD, et al. (2009). The development and validation of a computational model to predict rat liver microsomal clearance. J Pharm Sci 98:2857–67.
  • Consonni V, Todeschini R, Pavan M. (2002). Structure/response correlations and similarity/diversity analysis by GETAWAY descriptors. 1. Theory of the novel 3D molecular descriptors. J Chem Inf Comput Sci 42:682–92.
  • Gasteiger J, Jochum C. (1979). An algorithm for the perception of synthetically important rings. J Chem Inf Comput Sci 19:43–8.
  • Ghafourian T, Amin Z. (2013). QSAR models for the prediction of plasma protein binding. BioImpacts: BI 3:21.
  • Golbraikh A, Tropsha A. (2002). Beware of q2! J Mol Graph Model 20:269–76.
  • Gramatica P. (2007). Principles of QSAR models validation: internal and external. Mol Inform 26:694–701.
  • Gramatica P, Cassani S, Chirico N. (2014). QSARINS‐chem: insubria datasets and new QSAR/QSPR models for environmental pollutants in QSARINS. J Comput Chem 35:1036–44.
  • Gramatica P, Chirico N, Papa E, et al. (2013). QSARINS: a new software for the development, analysis, and validation of QSAR MLR models. J Comput Chem 34:2121–32.
  • Gramatica P, Sangion A. (2016). A historical excursus on the statistical validation parameters for QSAR models: a clarification concerning metrics and terminology. J Chem Inf Model 56:1127–31.
  • Grover M, Singh B, Bakshi M, Singh S. (2000a). Quantitative structure–property relationships in pharmaceutical research–Part 1. Pharm Sci Technol Today 3:28–35.
  • Grover M, Singh B, Bakshi M, Singh S. (2000b). Quantitative structure–property relationships in pharmaceutical research–Part 2. Pharm Sci Technol Today 3:50–7.
  • Hammond TG, Meng X, Jenkins RE, et al. (2014). Mass spectrometric characterization of circulating covalent protein adducts derived from a drug acyl glucuronide metabolite: multiple albumin adductions in diclofenac patients. J Pharmacol Exp Ther 350:387–402.
  • Hong H, Xie Q, Ge W, et al. (2008). Mold2, molecular descriptors from 2D structures for chemoinformatics and toxicoinformatics. J Chem Inf Model 48:1337–44.
  • Hu Y, Unwalla R, Denny RA, et al. (2010). Development of QSAR models for microsomal stability: identification of good and bad structural features for rat, human and mouse microsomal stability. J Comput Aid Mol Des 24:23–35.
  • Iwamura A, Nakajima M, Oda S, Yokoi T. (2017). Toxicological potential of acyl glucuronides and its assessment. Drug Metab Pharmacokinet 32:2–11.
  • Jinno N, Ohashi S, Tagashira M, et al. (2013). A simple method to evaluate reactivity of acylglucuronides optimized for early stage drug discovery. Biol Pharm Bull 36:1509–13.
  • Karlsson ES, Johnson CH, Sarda S, et al. (2010). High‐performance liquid chromatography/mass spectrometric and proton nuclear magnetic resonance spectroscopic studies of the transacylation and hydrolysis of the acyl glucuronides of a series of phenylacetic acids in buffer and human plasma. Rapid Commun Mass Spectrom 24:3043–51.
  • Katritzky AR, Dobchev DA, Slavov S, Karelson M. (2008). Legitimate utilization of large descriptor pools for QSPR/QSAR models. J Chem Inf Model 48:2207–13.
  • Lassila T, Hokkanen J, Aatsinki S-M, et al. (2015). Toxicity of carboxylic acid-containing drugs: the role of acyl migration and CoA conjugation investigated. Chem Res Toxicol 28:2292–303.
  • Lee PH, Cucurull-Sanchez L, Lu J, Du YJ. (2007). Development of in silico models for human liver microsomal stability. J Comput Aid Mol Des 21:665–73.
  • Leonard JT, Roy K. (2006). On selection of training and test sets for the development of predictive QSAR models. QSAR Comb Sci 25:235–51.
  • Lindgren F, Hansen B, Karcher W, et al. (1996). Model validation by permutation tests: applications to variable selection. J Chemomet 10:521–32.
  • Monrad RN, Errey JC, Barry CS, et al. (2014). Dissecting the reaction of phase II metabolites of ibuprofen and other NSAIDS with human plasma protein. Chem Sci 5:3789–94.
  • OECD. (2007). Guidance document on the validation of (Quantitative) structure–activity relationship [(Q) SAR] models, series on testing and assessment (ENV/JM/MONO(2007)2). Paris: OECD.
  • Perez Gonzalez M, Terán C, Teijeira M, Morales Helguera A. (2006). Quantitative structure activity relationships as useful tools for the design of new adenosine receptor ligands. 1. Agonist. Curr Med Chem 13:2253–66.
  • Potter T, Lewis R, Luker T, et al. (2011). In silico prediction of acyl glucuronide reactivity. J Comput Aid Mol Des 25:997–1005.
  • Pourbasheer E, Riahi S, Ganjali MR, Norouzi P. (2010). QSAR study on melanocortin-4 receptors by support vector machine. Eur J Med Chem 45:1087–93.
  • Regan SL, Maggs JL, Hammond TG, et al. (2010). Acyl glucuronides: the good, the bad and the ugly. Biopharm Drug Dispos 31:367–95.
  • Roy K, Kar S, Das RN. (2015). QSAR/QSPR modeling: introduction. A Primer on QSAR/QSPR Modeling. Heidelberg: Springer.
  • Sakiyama Y, Yuki H, Moriya T, et al. (2008). Predicting human liver microsomal stability with machine learning techniques. J Mol Graph Model 26:907–15.
  • Sawamura R, Okudaira N, Watanabe K, et al. (2010). Predictability of idiosyncratic drug toxicity risk for carboxylic acid-containing drugs based on the chemical stability of acyl glucuronide. Drug Metab Dispos 38:1857–64.
  • Shen M, Xiao Y, Golbraikh A, et al. (2003). Development and validation of k-nearest-neighbor QSPR models of metabolic stability of drug candidates. J Med Chem 46:3013–20.
  • Smith DA, Hammond T, Baillie TA. (2018). Safety assessment of acyl glucuronides-A simplified paradigm. Drug Metab Dispos 46:908–12.
  • Stachulski AV, Harding JR, Lindon JC, et al. (2006). Acyl glucuronides: biological activity, chemical reactivity, and chemical synthesis. J Med Chem 49:6931–45.
  • Stepensky D. (2013). Prediction of drug disposition on the basis of its chemical structure. Clin Pharmacokinet 52:415–31.
  • Tetko IV, Gasteiger J, Todeschini R, et al. (2005). Virtual computational chemistry laboratory–design and description. J Comput Aid Mol Des 19:453–63.
  • Todeschini R, Consonni V. (2009). Molecular descriptors for chemoinformatics: volume I: alphabetical listing/volume II: appendices, references. Weinheim: John Wiley & Sons.
  • Van Vleet TR, Liu H, Lee A, Blomme EA. (2017). Acyl glucuronide metabolites: implications for drug safety assessment. Toxicol Lett 272:1–7.
  • Vanderhoeven S, Troke J, Tranter G, et al. (2004). Nuclear magnetic resonance (NMR) and quantitative structure–activity relationship (QSAR) studies on the transacylation reactivity of model 1β-O-acyl glucuronides. II: QSAR modelling of the reaction using both computational and experimental NMR parameters. Xenobiotica 34:889–900.
  • Yap CW. (2011). PaDEL‐descriptor: an open source software to calculate molecular descriptors and fingerprints. J Comput Chem 32:1466–74.
  • Yin C, Yang X, Wei M, Liu H. (2017). Predictive models for identifying the binding activity of structurally diverse chemicals to human pregnane X receptor. Env Sci Pollut Res 24:20063–71.
  • Zakharov AV, Peach ML, Sitzmann M, et al. (2012). Computational tools and resources for metabolism-related property predictions. 2. Application to prediction of half-life time in human liver microsomes. Future Med Chem 4:1933–44.
  • Zhong S, Jones R, Lu W, et al. (2015). A new rapid in vitro assay for assessing reactivity of acyl glucuronides. Drug Metab Dispos 43:1711–17.

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