Literature DB >> 18991580

High throughput ADME screening: practical considerations, impact on the portfolio and enabler of in silico ADME models.

Cornelis E C A Hop1, Mark J Cole, Ralph E Davidson, David B Duignan, James Federico, John S Janiszewski, Kelly Jenkins, Suzanne Krueger, Rebecca Lebowitz, Theodore E Liston, Walter Mitchell, Mark Snyder, Stefan J Steyn, John R Soglia, Christine Taylor, Matt D Troutman, John Umland, Michael West, Kevin M Whalen, Veronica Zelesky, Sabrina X Zhao.   

Abstract

Evaluation and optimization of drug metabolism and pharmacokinetic data plays an important role in drug discovery and development and several reliable in vitro ADME models are available. Recently higher throughput in vitro ADME screening facilities have been established in order to be able to evaluate an appreciable fraction of synthesized compounds. The ADME screening process can be dissected in five distinct steps: (1) plate management of compounds in need of in vitro ADME data, (2) optimization of the MS/MS method for the compounds, (3) in vitro ADME experiments and sample clean up, (4) collection and reduction of the raw LC-MS/MS data and (5) archival of the processed ADME data. All steps will be described in detail and the value of the data on drug discovery projects will be discussed as well. Finally, in vitro ADME screening can generate large quantities of data obtained under identical conditions to allow building of reliable in silico models.

Mesh:

Substances:

Year:  2008        PMID: 18991580     DOI: 10.2174/138920008786485092

Source DB:  PubMed          Journal:  Curr Drug Metab        ISSN: 1389-2002            Impact factor:   3.731


  13 in total

1.  Defining desirable central nervous system drug space through the alignment of molecular properties, in vitro ADME, and safety attributes.

Authors:  Travis T Wager; Ramalakshmi Y Chandrasekaran; Xinjun Hou; Matthew D Troutman; Patrick R Verhoest; Anabella Villalobos; Yvonne Will
Journal:  ACS Chem Neurosci       Date:  2010-03-25       Impact factor: 4.418

2.  Quantitative-qualitative data acquisition using a benchtop Orbitrap mass spectrometer.

Authors:  Kevin P Bateman; Markus Kellmann; Helmut Muenster; Robert Papp; Lester Taylor
Journal:  J Am Soc Mass Spectrom       Date:  2009-03-06       Impact factor: 3.109

3.  Prediction of Metabolic Clearance for Low-Turnover Compounds Using Plated Hepatocytes with Enzyme Activity Correction.

Authors:  Bennett Ma; Roy Eisenhandler; Yuhsin Kuo; Paul Rearden; Ying Li; Peter J Manley; Sheri Smith; Karsten Menzel
Journal:  Eur J Drug Metab Pharmacokinet       Date:  2017-04       Impact factor: 2.441

Review 4.  Addressing the challenges of low clearance in drug research.

Authors:  Li Di; R Scott Obach
Journal:  AAPS J       Date:  2015-01-08       Impact factor: 4.009

5.  A probabilistic method to report predictions from a human liver microsomes stability QSAR model: a practical tool for drug discovery.

Authors:  Ignacio Aliagas; Alberto Gobbi; Timothy Heffron; Man-Ling Lee; Daniel F Ortwine; Mark Zak; S Cyrus Khojasteh
Journal:  J Comput Aided Mol Des       Date:  2015-02-24       Impact factor: 3.686

6.  Optimizing PK properties of cyclic peptides: the effect of side chain substitutions on permeability and clearance().

Authors:  Arthur C Rand; Siegfried S F Leung; Heather Eng; Charles J Rotter; Raman Sharma; Amit S Kalgutkar; Yizhong Zhang; Manthena V Varma; Kathleen A Farley; Bhagyashree Khunte; Chris Limberakis; David A Price; Spiros Liras; Alan M Mathiowetz; Matthew P Jacobson; R Scott Lokey
Journal:  Medchemcomm       Date:  2012-10       Impact factor: 3.597

7.  Screening candidate anticancer drugs for brain tumor chemotherapy: pharmacokinetic-driven approach for a series of (E)-N-(substituted aryl)-3-(substituted phenyl)propenamide analogues.

Authors:  Hua Lv; Fan Wang; M V Ramana Reddy; Qingyu Zhou; Xiaoping Zhang; E Premkumar Reddy; James M Gallo
Journal:  Invest New Drugs       Date:  2012-03-01       Impact factor: 3.850

8.  Strategic use of plasma and microsome binding to exploit in vitro clearance in early drug discovery.

Authors:  George Chang; Stefanus J Steyn; John P Umland; Dennis O Scott
Journal:  ACS Med Chem Lett       Date:  2010-02-03       Impact factor: 4.345

Review 9.  Successful and Unsuccessful Prediction of Human Hepatic Clearance for Lead Optimization.

Authors:  Jasleen K Sodhi; Leslie Z Benet
Journal:  J Med Chem       Date:  2021-03-25       Impact factor: 7.446

10.  A systems biology approach to dynamic modeling and inter-subject variability of statin pharmacokinetics in human hepatocytes.

Authors:  Joachim Bucher; Stephan Riedmaier; Anke Schnabel; Katrin Marcus; Gabriele Vacun; Thomas S Weiss; Wolfgang E Thasler; Andreas K Nüssler; Ulrich M Zanger; Matthias Reuss
Journal:  BMC Syst Biol       Date:  2011-05-06
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.