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Pattern Recognition Analysis of Endogenous Cell Metabolites for High Throughput Mode of Action Identification: Removing the Postscreening Dilemma Associated with Whole-Organism High Throughput Screening

Zeneca Agrochemicals, Jealott's Hill Research Station, Bracknell, Berkshire, United Kingdom

Peter W.A. Howe

Zeneca Agrochemicals, Jealott's Hill Research Station, Bracknell, Berkshire, United Kingdom

Paul D. Stanley

Zeneca Agrochemicals, Jealott's Hill Research Station, Bracknell, Berkshire, United Kingdom

Stephen T. Hadfield

Zeneca Agrochemicals, Jealott's Hill Research Station, Bracknell, Berkshire, United Kingdom

Although whole-organism HTS can give clear indications of in vivo activity, typically few clues are given as to the mechanism of action (MOA), and determining the MOA for large numbers of active compounds can be costly and complex—an alternative approach is required. This report demonstrates that it is possible to conduct relatively high throughput MOA characterization of HTS hits utilizing a single sample preparation and analytical method. By monitoring a wide range of endogenous cellular metabolites via ¹H nuclear magnetic resonance spectroscopy, the MOA of herbicides can be predicted using computational methods to compare the metabolite perturbation patterns. Herbicides that induce a characteristic pattern of metabolic perturbation in maize include inhibitors of acetolactate synthase, acetyl co-enzyme A carboxylase, protoporphyrinogen oxidase, 5-enolpyruvylshikimate-3-phosphate synthase, and phytoene desaturase. In soya, photosystem II inhibitors can also be detected, further demonstrating that this method is not limited to inhibitors of enzymes that directly act upon endogenous metabolites, or a single species. The methods, including data analysis, can be readily automated, enabling relatively high throughput MOA elucidation of whole-organism screen hits. Additionally, for compounds with a novel MOA, this approach may lead to MOA identification faster than traditional metods. It is envisaged that application of these data analysis methods to other data types—for example, transcription (mRNA) or translation (protein) profilesis likely to permit higher throughput with smaller sample requirements, along with ability to discriminate MOAs that are not adequately discriminated based upon endogenous metabolite profiles.

Journal of Biomolecular Screening, Vol. 5, No. 5, 335-342 (2000)
DOI: 10.1177/108705710000500505


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