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Correlating GC/MS Relative Response Factors to Analyte’s Physicochemical and Chromatographic Properties to Facilitate the Quantitation of Organic Extractables and Leachables in Non-Targeted Analysis (NTA). Concepts and Empirical Considerations

分析物 色谱法 人口 化学 鉴定(生物学) 生物系统 植物 人口学 社会学 生物
作者
Dennis Jenke,Piet Christiaens,P. Verlinde,Jan Baeten,Jean-Marie Beusen
出处
期刊:Pda Journal of Pharmaceutical Science and Technology [Parenteral Drug Association]
卷期号:78 (6): 625-642 被引量:4
标识
DOI:10.5731/pdajpst.2023.012884
摘要

Leachables in drug products and from medical devices can adversely affect patient health and thus must be identified and quantified. Accurate and protective quantitation in target analysis for leachables (and extractables as potential leachables) is accomplished via compound-specific calibration curves. Quantification in non-targeted analysis (NTA) is complicated by the variable relative response factors (RRF) among and between individual leachables and the circumstance that the leachables are not known until the NTA is completed. Protective quantitation in NTA is accomplished in various ways, depending on the identification status of the analyte. When an analyte′s identity is confirmed, it is quantified using the compound′s own RRF, obtained by analysis of a reference standard. In other identification circumstances, the concentration is calculated using a surrogate response, either linked to a surrogate compound or representative of a domain of leachables. Given the difficulty in matching an analyte with a proper surrogate, this manuscript addresses quantitation via the latter approach. This manuscript uses a database of more than 3000 GC/MS response factors to empirically divide the population of leachables into response factor domains, differentiated by either the analyte′s polarity (log Po/w) or retention time. Using the database, mean RRF values and uncertainty factors (UFs) are established for each domain and are used for quantitation. Protective quantitation is accomplished for nearly 84% of all leachables in the database (and presumably the entire population) by placing an analyte into its proper domain and then using the mean RRF divided by the UF for that domain as a universal response factor for all compounds in the domain.
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