Literature DB >> 16863474

High-throughput pKa screening and prediction amenable for ADME profiling.

Hong Wan1, Johan Ulander.   

Abstract

Recent technological advances have made it possible for several new pK(a) assays to be used in drug screening. In this review, a critical overview is provided of current new methodologies for high-throughput screening and prediction of pK(a). Typical applications of using pK(a )constants and charge state for absorption, distribution, metabolism and excretion (ADME) profiling and quantitative structure-activity relationship modelling complements the methodological comparisons and discussions. The experimental methods discussed include high-throughput screening of pK(a) by multiplexed capillary with ultraviolet absorbance detection on a 96-capillary format instrument, capillary electrophoresis and mass spectrometry (CEMS) based on sample pooling, determination of pK(a) by pH gradient high-performance liquid chromatography, and measurement of pK(a) by a mixed-buffer liner pH gradient system. Comparisons of the different experimental assays are made with emphasis on the newly developed CEMS method. The current status and recent progress in computational approaches to pK(a) prediction are also discussed. In particular, the accuracy limits of simple fragment-based approaches as well as quantum mechanical methods are addressed. Examples of pK(a) prediction from in-house drug candidates as well as commercially available drug molecules are shown and an outline is provided for how drug discovery companies can integrate experiments with computational approaches for increased applications for ADME profiling.

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Year:  2006        PMID: 16863474     DOI: 10.1517/17425255.2.1.139

Source DB:  PubMed          Journal:  Expert Opin Drug Metab Toxicol        ISSN: 1742-5255            Impact factor:   4.481


  12 in total

1.  Multi-task learning for pKa prediction.

Authors:  Grigorios Skolidis; Katja Hansen; Guido Sanguinetti; Matthias Rupp
Journal:  J Comput Aided Mol Des       Date:  2012-06-20       Impact factor: 3.686

Review 2.  Modeling kinetics of subcellular disposition of chemicals.

Authors:  Stefan Balaz
Journal:  Chem Rev       Date:  2009-05       Impact factor: 60.622

3.  Understanding Mechanisms of Food Effect and Developing Reliable PBPK Models Using a Middle-out Approach.

Authors:  Xavier J H Pepin; James E Huckle; Ravindra V Alluri; Sumit Basu; Stephanie Dodd; Neil Parrott; Arian Emami Riedmaier
Journal:  AAPS J       Date:  2021-01-04       Impact factor: 4.009

4.  Facile and rapid route for the synthesis of novel conformationally constrained norstatine analogs via PADAM-cyclization methodology.

Authors:  Arthur Y Shaw; Federico Medda; Christopher Hulme
Journal:  Tetrahedron Lett       Date:  2012-03-14       Impact factor: 2.415

5.  SAMPL6 challenge results from [Formula: see text] predictions based on a general Gaussian process model.

Authors:  Caitlin C Bannan; David L Mobley; A Geoffrey Skillman
Journal:  J Comput Aided Mol Des       Date:  2018-10-15       Impact factor: 3.686

6.  Modeling and Simulation of Intracellular Drug Transport and Disposition Pathways with Virtual Cell.

Authors:  Jason Baik; Gus R Rosania
Journal:  J Pharm Pharmacol (Los Angel)       Date:  2013-09-13

Review 7.  A Systematic Analysis of Physicochemical and ADME Properties of All Small Molecule Kinase Inhibitors Approved by US FDA from January 2001 to October 2015.

Authors:  Zhihong O Brien; Mehran F Moghaddam
Journal:  Curr Med Chem       Date:  2017       Impact factor: 4.530

8.  An Experimental Approach to the Synthesis and Optimisation of a 'Green' Nanofibre.

Authors:  Md Nahid Pervez; George K Stylios
Journal:  Nanomaterials (Basel)       Date:  2018-05-30       Impact factor: 5.076

9.  The pK(a) Distribution of Drugs: Application to Drug Discovery.

Authors:  David T Manallack
Journal:  Perspect Medicin Chem       Date:  2007-09-17

10.  Predicting p Ka values from EEM atomic charges.

Authors:  Radka Svobodová Vařeková; Stanislav Geidl; Crina-Maria Ionescu; Ondřej Skřehota; Tomáš Bouchal; David Sehnal; Ruben Abagyan; Jaroslav Koča
Journal:  J Cheminform       Date:  2013-04-10       Impact factor: 5.514

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