Literature DB >> 19108654

Metabolic soft spot identification and compound optimization in early discovery phases using MetaSite and LC-MS/MS validation.

Markus Trunzer1, Bernard Faller, Alfred Zimmerlin.   

Abstract

Metabolic stability is a key property to enable drugs to reach therapeutic concentrations. Microsomal clearance assays are used to dial out labile compounds in early discovery phases. However, because they do not provide any information on soft spots, the rational design of more stable compounds remains challenging. A robust soft spot identification procedure combining in silico prediction ranking using MetaSite and mass-spectrometric confirmation is described. MetaSite's first rank order predictions were experimentally confirmed for only about 55% of the compounds. For another 29% of the compounds, the second (20%) or the third (9%) rank order predictions were detected. This automatic and high-throughput reprioritization of a likely soft-spot increases the likelihood of working on the right soft spot from about 50% to more than 80%. With this information, the structure-metabolism relationships are likely to be understood faster and earlier in drug discovery.

Mesh:

Year:  2009        PMID: 19108654     DOI: 10.1021/jm8008663

Source DB:  PubMed          Journal:  J Med Chem        ISSN: 0022-2623            Impact factor:   7.446


  18 in total

1.  Exploration of 3,6-dihydroimidazo(4,5-d)pyrrolo(2,3-b)pyridin-2(1H)-one derivatives as JAK inhibitors using various in silico techniques.

Authors:  Radhakrishnan S Jisha; Lilly Aswathy; Vijay H Masand; Jayant M Gajbhiye; Indira G Shibi
Journal:  In Silico Pharmacol       Date:  2017-10-12

2.  Development of QSAR models for microsomal stability: identification of good and bad structural features for rat, human and mouse microsomal stability.

Authors:  Yongbo Hu; Ray Unwalla; R Aldrin Denny; Jack Bikker; Li Di; Christine Humblet
Journal:  J Comput Aided Mol Des       Date:  2009-11-24       Impact factor: 3.686

3.  Site of metabolism prediction on cytochrome P450 2C9: a knowledge-based docking approach.

Authors:  Akos Tarcsay; Róbert Kiss; György M Keseru
Journal:  J Comput Aided Mol Des       Date:  2010-04-02       Impact factor: 3.686

4.  Using a homology model of cytochrome P450 2D6 to predict substrate site of metabolism.

Authors:  Rayomand J Unwalla; Jason B Cross; Sumeet Salaniwal; Adam D Shilling; Louis Leung; John Kao; Christine Humblet
Journal:  J Comput Aided Mol Des       Date:  2010-04-02       Impact factor: 3.686

5.  The N,N,O-Trisubstituted Hydroxylamine Isostere and Its Influence on Lipophilicity and Related Parameters.

Authors:  Jarvis Hill; David Crich
Journal:  ACS Med Chem Lett       Date:  2022-04-20       Impact factor: 4.632

6.  Impact of Established and Emerging Software Tools on the Metabolite Identification Landscape.

Authors:  Anne Marie E Smith; Kiril Lanevskij; Andrius Sazonovas; Jesse Harris
Journal:  Front Toxicol       Date:  2022-06-21

7.  Rapid LC-MS drug metabolite profiling using microsomal enzyme bioreactors in a parallel processing format.

Authors:  Besnik Bajrami; Linlin Zhao; John B Schenkman; James F Rusling
Journal:  Anal Chem       Date:  2009-12-15       Impact factor: 6.986

8.  Discovery and Pharmacological Characterization of Novel Quinazoline-Based PI3K Delta-Selective Inhibitors.

Authors:  Klemens Hoegenauer; Nicolas Soldermann; Frédéric Stauffer; Pascal Furet; Nadege Graveleau; Alexander B Smith; Christina Hebach; Gregory J Hollingworth; Ian Lewis; Sascha Gutmann; Gabriele Rummel; Mark Knapp; Romain M Wolf; Joachim Blanz; Roland Feifel; Christoph Burkhart; Frédéric Zécri
Journal:  ACS Med Chem Lett       Date:  2016-06-02       Impact factor: 4.345

Review 9.  Computational prediction of metabolism: sites, products, SAR, P450 enzyme dynamics, and mechanisms.

Authors:  Johannes Kirchmair; Mark J Williamson; Jonathan D Tyzack; Lu Tan; Peter J Bond; Andreas Bender; Robert C Glen
Journal:  J Chem Inf Model       Date:  2012-02-17       Impact factor: 4.956

10.  Combining structure- and ligand-based approaches to improve site of metabolism prediction in CYP2C9 substrates.

Authors:  Laura J Kingsley; Gregory L Wilson; Morgan E Essex; Markus A Lill
Journal:  Pharm Res       Date:  2014-09-11       Impact factor: 4.200

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.