Structure Driven Prediction of Chromatographic Retention Times: Applications to Pharmaceutical Analysis

Autor: Claudio Brunelli, James C. Heaton, Roman Szucs, Roland Brown, Jasna Hradski
Jazyk: angličtina
Rok vydání: 2021
Předmět:
0301 basic medicine
Pharmaceutical drug
Data Analysis
Computer science
Process (engineering)
medicine.medical_treatment
Quantitative Structure-Activity Relationship
Validation Studies as Topic
01 natural sciences
Catalysis
Article
Inorganic Chemistry
lcsh:Chemistry
03 medical and health sciences
Molecular descriptor
Component (UML)
medicine
quantitative structure retention relationships
Pharmacokinetics
Physical and Theoretical Chemistry
Related impurities
Molecular Biology
lcsh:QH301-705.5
Spectroscopy
Structure (mathematical logic)
Active ingredient
Chromatography
010401 analytical chemistry
Organic Chemistry
chromatographic method development
Quantitative structure
General Medicine
Models
Theoretical
pharmaceutical analysis
0104 chemical sciences
Computer Science Applications
Kinetics
030104 developmental biology
Pharmaceutical Preparations
lcsh:Biology (General)
lcsh:QD1-999
Algorithms
Zdroj: International Journal of Molecular Sciences
Volume 22
Issue 8
International Journal of Molecular Sciences, Vol 22, Iss 3848, p 3848 (2021)
ISSN: 1422-0067
DOI: 10.3390/ijms22083848
Popis: Pharmaceutical drug development relies heavily on the use of Reversed-Phase Liquid Chromatography methods. These methods are used to characterize active pharmaceutical ingredients and drug products by separating the main component from related substances such as process related impurities or main component degradation products. The results presented here indicate that retention models based on Quantitative Structure Retention Relationships can be used for de-risking methods used in pharmaceutical analysis and for the identification of optimal conditions for separation of known sample constituents from postulated/hypothetical components. The prediction of retention times for hypothetical components in established methods is highly valuable as these compounds are not usually readily available for analysis. Here we discuss the development and optimization of retention models, selection of the most relevant structural molecular descriptors, regression model building and validation. We also present a practical example applied to chromatographic method development and discuss the accuracy of these models on selection of optimal separation parameters.
Databáze: OpenAIRE
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