Research Output

De Novo Structural Elucidation Principle via In Silico Chromatographic Retention Index Prediction for Micropollutants in Wastewater

  Micropollutants, such as pharmaceuticals, industrial chemicals, steroid hormones, etc. are defined as anthropogenic chemicals and can be found in water. It is seen as a serious threat, not just to aquatic life but also to humans, which requires the availability of tools allowing structural elucidation and ideally, fast identification of unknowns. Over the past decade, high-performance liquid chromatography, coupled with high resolution mass spectrometry (HPLCHRMS), has been increasingly used in the analysis of environmental and treated wastewater samples. However, HRMS prediction software cannot always reliably predict the elemental composition of (larger) molecules while structural information obtained by MS remains limited. This hinders the identification and structural characterization of unknowns in wastewater.
In this research, a Quantitative Structure- Retention Relationship (QSRR) approach is used to build predictive retention index (RI) models to assist in the identification of unknowns. Development of algorithms based on LC retentive data allows confirmation or invalidation of the ensuing hypothesized structural formulas. The novelty of this work is that for the first time a complete workflow is provided allowing narrowing down the possibilities in de novo structural elucidation of in principle any carbon, hydrogen or oxygen containing organic solute (< 500 Da) purely based on chromatographic (RPLC) retention.

  • Type:

    Poster

  • Date:

    18 May 2022

  • Publication Status:

    Unpublished

  • Funders:

    Italian Ministry of Health

Citation

Kajtazi, G., Wicht, K., Russo, G., Eghbali, H., & Lynen, F. (2022, May). De Novo Structural Elucidation Principle via In Silico Chromatographic Retention Index Prediction for Micropollutants in Wastewater. Poster presented at 17th International Symposium on Hyphenated Techniques in Chromatography and Separation Technology, Ghent, Belgium

Authors

Keywords

In silico prediction, QSRR, Micropollutants

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