Covalent organic nanospheres as a fiber coating for solid-phase microextraction of genotoxic impurities followed by analysis using GC-MS
- Author:
Zhao YANFANG
1
,
2
;
Li JINGKUN
;
Xie HANYI
;
Li HUIJUAN
;
Chen XIANGFENG
Author Information
1. School of Pharmaceutical Sciences,Qilu University of Technology(Shandong Academy of Sciences),Jinan,250014,China
2. Shandong Analysis and Test Center,Qilu University of Technology(Shandong Academy of Sciences),Jinan,250014,China
- Keywords:
Covalent organic nanospheres;
Solid-phase microextraction;
Genotoxic impurities;
Gas chromatography-mass spectrometry
- From:
Journal of Pharmaceutical Analysis
2022;12(4):583-589
- CountryChina
- Language:Chinese
-
Abstract:
Covalent organic nanospheres(CONs)were explored as a fiber coating for solid-phase microextraction of genotoxic impurities(GTIs)from active ingredients(AIs).CONs were synthesized by an easy solution-phase procedure at 25℃.The obtained nanospheres exhibited a high specific surface area,good ther-mostability,high acid and alkali resistance,and favorable crystallinity and porosity.Two types of GTIs,alkyl halides(1-iodooctane,1-chlorobenzene,1-bromododecane,1,2-dichlorobenzene,1-bromooctane,1-chlorohexane,and 1,8-dibromooctane)and sulfonate esters(methyl p-toluenesulfonate and ethyl p-toluenesulfonate),were chosen as target molecules for assessing the performance of the coating.The prepared coating achieved high enhancement factors(5097-9799)for the selected GTIs.The strong affinity between CONs and GTIs was tentatively attributed to T-T and hydrophobicity interactions,large surface area of the CONs,and size-matching of the materials.Combined with gas chromatography-mass spectrometry(GC-MS),the established analytical method detected the GTIs in capecitabine and imatinib mesylate samples over a wide linear range(0.2-200 ng/g)with a low detection limit(0.04-2.0 ng/g),satisfactory recovery(80.03％-109.5％),and high repeatability(6.20％-14.8％)and reproducibility(6.20％-14.1％).Therefore,the CON-coated fibers are promising alternatives for the sensitive detection of GTIs in AI samples.
