Jenke Dennis, Christiaens Piet, Verlinde Philippe, Baeten Jan, Beusen Jean-Marie
Nelson Labs, Romeinsestraat 12, 3001 Heverlee, Belgium
Nelson Labs, Romeinsestraat 12, 3001 Heverlee, Belgium.
PDA J Pharm Sci Technol. 2024 Dec 26;78(6):625-642. doi: 10.5731/pdajpst.2023.012884.
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 (RRFs) 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 article addresses quantitation via the latter approach. This article uses a database of >3000 GC/MS response factors to empirically divide the population of leachables into response factor domains, differentiated by either the analyte's polarity (log P) 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.
药品和医疗器械中的可浸出物会对患者健康产生不利影响,因此必须对其进行识别和定量。通过化合物特异性校准曲线可实现对可浸出物(以及作为潜在可浸出物的提取物)目标分析中的准确和保护性定量。非靶向分析(NTA)中的定量则较为复杂,因为各个可浸出物之间以及不同可浸出物之间的相对响应因子(RRF)存在差异,而且在NTA完成之前可浸出物是未知的。NTA中的保护性定量可通过多种方式实现,具体取决于分析物的识别状态。当分析物的身份得到确认时,使用通过参考标准品分析获得的该化合物自身的RRF对其进行定量。在其他识别情况下,浓度通过替代响应来计算,该替代响应要么与替代化合物相关,要么代表可浸出物的一个域。鉴于将分析物与合适的替代物进行匹配存在困难,本文通过后一种方法探讨定量问题。本文使用一个包含>3000个气相色谱/质谱响应因子的数据库,根据分析物的极性(log P)或保留时间,凭经验将可浸出物群体划分为响应因子域。利用该数据库,为每个域建立平均RRF值和不确定因子(UFs),并将其用于定量。通过将分析物置于其合适的域中,然后使用该域的平均RRF除以UF作为该域中所有化合物的通用响应因子,可对数据库中近84%的所有可浸出物(大概也是整个群体)实现保护性定量。