ArticleJournal of pharmaceutical analysis2013
Reversed-phase fused-core HPLC modeling of peptides.
Article in Journal of pharmaceutical analysis, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Two Antimicrobial Peptides Derived fromMolecules (Basel, Switzerland) · 2023Article
- Development and Validation of an LC-MS/MS Method for Simultaneous Determination of Short Peptide-Based Drugs in Human Blood Plasma.Molecules (Basel, Switzerland) · 2022Article
- A comparative study on properties of fish meat hydrolysates produced by an enzymatic process at high pressure.Food science and biotechnology · 2020Article
- Derringer desirability and kinetic plot LC-column comparison approach for MS-compatible lipopeptide analysis.Journal of pharmaceutical analysis · 2014Article
- A critical quality parameter in quantitative fused-core chromatography: The injection volume.Journal of pharmaceutical analysis · 2013Article
- Chemical-functional diversity in cell-penetrating peptides.PloS one · 2013Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Different fused-core stationary phase chemistries (C18, Amide, Phenyl-hexyl and Peptide ES-C18) were used for the analysis of 21 structurally representative model peptides. In addition, the effects of the mobile phase composition (ACN or MeOH as organic modifier; formic acid or acetic acid, as acidifying component) on the column selectivity, peak shape and overall chromatographic performance were evaluated. The RP-amide column, combined with a formic acid-acetonitrile based gradient system, performed as best. A peptide reversed-phase retention model is proposed, consisting of 5 variables: log SumAA, log Sv, clog P, log nHDon and log nHAcc. Quantitative structure-retention relationship (QSRR) models were constructed for 16 different chromatographic systems. The accuracy of this peptide retention model was demonstrated by the comparison between predicted and experimentally obtained retention times, explaining on average 86% of the variability. Moreover, using an external set of 5 validation peptides, the predictive power of the model was also demonstrated. This peptide retention model includes the novel
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