Literature DB >> 26855285

A high throughput solubility assay for drug discovery using microscale shake-flask and rapid UHPLC-UV-CLND quantification.

Baiwei Lin1, Joseph H Pease2.   

Abstract

The rapid determination of key physical properties of lead compounds is essential to the drug discovery process. Solubility is one of the most important properties since good solubility is needed not only for obtaining reliable in vitro and in vivo assay results in early discovery but also to ensure sufficient concentration of the drug being in circulation to get the desired therapeutic exposure at the target of interest. In order for medicinal chemists to tune solubility of lead compounds, a rapid assay is needed to provide solubility data that is accurate and predictive so that it can be reliably used for designing the next generation of compounds with improved properties. To ensure speed and data quality, we developed a high throughput solubility assay that utilizes a single calibration UHPLC-UV-CLND method and a 24h shake-flask format for rapid quantification. A set of 46 model compounds was used to demonstrate that the method is accurate, reproducible and predictive. Here we present development of the assay, including evaluation of quantification method, filtration membranes, equilibrium times, DMSO concentrations, and buffer conditions. A comparison of thermodynamic solubility results to our high throughput 24h shake-flask solubility assay results is also discussed.
Copyright © 2016 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  CLND—chemiluminescence nitrogen detector; HPLC hyphenated technique; Physicochemical properties; Rapid quantitative analysis; Shake-flask solubility assay

Mesh:

Substances:

Year:  2016        PMID: 26855285     DOI: 10.1016/j.jpba.2016.01.022

Source DB:  PubMed          Journal:  J Pharm Biomed Anal        ISSN: 0731-7085            Impact factor:   3.935


  2 in total

1.  Discovery of Potent and Selective Tricyclic Inhibitors of Bruton's Tyrosine Kinase with Improved Druglike Properties.

Authors:  Xiaojing Wang; James Barbosa; Peter Blomgren; Meire C Bremer; Jacob Chen; James J Crawford; Wei Deng; Liming Dong; Charles Eigenbrot; Steve Gallion; Jonathon Hau; Huiyong Hu; Adam R Johnson; Arna Katewa; Jeffrey E Kropf; Seung H Lee; Lichuan Liu; Joseph W Lubach; Jen Macaluso; Pat Maciejewski; Scott A Mitchell; Daniel F Ortwine; Julie DiPaolo; Karin Reif; Heleen Scheerens; Aaron Schmitt; Harvey Wong; Jin-Ming Xiong; Jianjun Xu; Zhongdong Zhao; Fusheng Zhou; Kevin S Currie; Wendy B Young
Journal:  ACS Med Chem Lett       Date:  2017-05-03       Impact factor: 4.345

2.  Prediction of aqueous intrinsic solubility of druglike molecules using Random Forest regression trained with Wiki-pS0 database.

Authors:  Alex Avdeef
Journal:  ADMET DMPK       Date:  2020-03-04
  2 in total

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