Application of Biomimetic Chromatography and QSRR Approach for Characterizing Organophosphate Pesticides

Biomimetic chromatography is a powerful tool used in the pharmaceutical industry to characterize the physicochemical properties of molecules during early drug discovery. Some studies have indicated that biomimetic chromatography may also be useful for the evaluation of toxicologically relevant molecules. In this study, we evaluated the usefulness of the biomimetic chromatography approach for determining the lipophilicity, affinity to phospholipids, and bind to plasma proteins of selected organophosphate pesticides. Quantitative structure-retention relationship (QSRR) models were proposed to understand the structural features that influence the experimentally determined properties. ACD/labs, Chemicalize, and alvaDesc software were used to calculate theoretical descriptors. Multilinear regression was used as the regression type, and feature selection was supported by a genetic algorithm. The obtained QSRR models were validated internally and externally, and they demonstrated satisfactory performance with key statistical parameters ranged from 0.844 to 0.914 for R<sup>2</sup> and 0.696-0.898 for R<sup>2</sup><sub>ext</sub>, respectively, indicating good predictive ability.

Authors

Institutions

Publication Details

Journal
International Journal of Molecular Sciences
Published
2025-02-21
DOI
https://doi.org/10.3390/ijms26051855
Citations
4
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
6.02

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Application of Biomimetic Chromatography and QSRR Approach for Characterizing Organophosphate Pesticides

Katarzyna Ewa Greber, Karolina Jagiełło, Tomasz Puzyn, Krzesimir Ciura et al.
4 citations
International Journal of Molecular Sciences
Computational Drug Discovery Methods
6.02
article

Application of Biomimetic Chromatography and QSRR Approach for Characterizing Organophosphate Pesticides

Katarzyna Ewa Greber, Karolina Jagiełło, Tomasz Puzyn, Krzesimir Ciura, Wiesław Sawicki, Beata Judzińska, Kamila Jarzyńska, Yash Raj Singh, Karol Topka Kłończyński, Julia Nicman
article en
4 citations

Abstract

Biomimetic chromatography is a powerful tool used in the pharmaceutical industry to characterize the physicochemical properties of molecules during early drug discovery. Some studies have indicated that biomimetic chromatography may also be useful for the evaluation of toxicologically relevant molecules. In this study, we evaluated the usefulness of the biomimetic chromatography approach for determining the lipophilicity, affinity to phospholipids, and bind to plasma proteins of selected organophosphate pesticides. Quantitative structure-retention relationship (QSRR) models were proposed to understand the structural features that influence the experimentally determined properties. ACD/labs, Chemicalize, and alvaDesc software were used to calculate theoretical descriptors. Multilinear regression was used as the regression type, and feature selection was supported by a genetic algorithm. The obtained QSRR models were validated internally and externally, and they demonstrated satisfactory performance with key statistical parameters ranged from 0.844 to 0.914 for R<sup>2</sup> and 0.696-0.898 for R<sup>2</sup><sub>ext</sub>, respectively, indicating good predictive ability.

International Journal of Molecular SciencesVol. 26(5)
University of Gdańsk (PL), Gdańsk Medical University (PL)
European Commission, HORIZON EUROPE Framework Programme
Openalex Percentile: Top 4%
Computational Drug Discovery Methods
6.02
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.