Literature DB >> 29378316

Fragment screening for drug leads by weak affinity chromatography (WAC-MS).

Sten Ohlson1, Minh-Dao Duong-Thi2.   

Abstract

Fragment-based drug discovery is an important tool for design of small molecule hit-to-lead compounds against various biological targets. Several approved drugs have been derived from an initial fragment screen and many such candidates are in various stages of clinical trials. Finding fragment hits, that are suitable for optimisation by medicinal chemists, is still a challenge as the binding between the small fragment and its target is weak in the range of mM to µM of Kd and irrelevant non-specific interactions are abundant in this area of transient interactions. Fortunately, there are methods that can study weak interactions quite efficiently of which NMR, surface plasmon resonance (SPR) and X-ray crystallography are the most prominent. Now, a new technology based on zonal affinity chromatography, weak affinity chromatography (WAC), has been introduced which has remedied many of the problems with other technologies. By combining WAC with mass spectrometry (WAC-MS), it is a powerful tool to identify binders quantitatively in terms of affinity and kinetics either from fragment libraries or from complex mixtures of biological extracts. As WAC-MS can be multiplexed by analysing mixtures of fragments (20-100 fragments) in one sample, this approach yields high throughput, where a whole library of e.g. >2000 fragments can be analysed quantitatively within a day. WAC-MS is easy to perform, where the robustness and quality of HPLC is fully utilized. This review will highlight the rationale behind the application of WAC-MS for fragment screening in drug discovery.
Copyright © 2018 Elsevier Inc. All rights reserved.

Keywords:  Drug discovery; Fragment screening; Fragment-based drug discovery; Fragment-based lead discovery; WAC-MS; Weak affinity chromatography

Mesh:

Substances:

Year:  2018        PMID: 29378316     DOI: 10.1016/j.ymeth.2018.01.011

Source DB:  PubMed          Journal:  Methods        ISSN: 1046-2023            Impact factor:   3.608


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