Karolinska Radiopharmacy, Karolinska University Hospital, Akademiska stråket 1, S-171 64 Stockholm, Sweden; Department of Oncology-Pathology, Karolinska Institutet, Akademiska stråket 1, S-171 77 Stockholm, Sweden.
Karolinska Radiopharmacy, Karolinska University Hospital, Akademiska stråket 1, S-171 64 Stockholm, Sweden; Department of Oncology-Pathology, Karolinska Institutet, Akademiska stråket 1, S-171 77 Stockholm, Sweden.
J Chromatogr A. 2020 Nov 22;1632:461611. doi: 10.1016/j.chroma.2020.461611. Epub 2020 Oct 10.
Chiral column chromatography (CCC) is a revolutionary analytical methodology for the enantioseparation of novel positron emission tomography (PET) tracers in the primary stages of drug development. Due to the different behaviors of tracer enantiomers (e.g. toxicity, metabolism and side effects) in administrated subjects, their separation and purification is a challenging endeavor. Over the last three decades, different commercial chiral columns have been applied for the enantioseparation of PET-radioligand (PET-RL) or radiotracers (PET-RT), using high-performance liquid chromatography (HPLC). The categorization and reviewing of them is a vital topic. This review presents a brief overview of advances, applications, and future prospectives of CCC in radiopharmaceutical approaches. In addition, the effective chromatographic parameters and degravitation trends to enhance enantioseparation resolution are addressed. Moreover, the application and potential of chiral super fluidical chromatography (CSFC) as an alternative for enantioseparation in the field of radiopharmaceutical is discussed. Finally, the crucial application challenges of CCC are explained and imminent tasks are suggested.
手性柱色谱(CCC)是一种革命性的分析方法,用于在药物开发的早期阶段对新型正电子发射断层扫描(PET)示踪剂进行对映体分离。由于在给予的受试者中示踪剂对映体的行为不同(例如毒性、代谢和副作用),因此它们的分离和纯化是一项具有挑战性的工作。在过去的三十年中,不同的商业手性柱已被应用于使用高效液相色谱(HPLC)对 PET 放射性配体(PET-RL)或放射性示踪剂(PET-RT)进行对映体分离。对它们进行分类和审查是一个重要的主题。本综述简要概述了 CCC 在放射性药物方法中的进展、应用和未来前景。此外,还讨论了增强对映体分离分辨率的有效色谱参数和去重趋势。此外,还讨论了手性超临界流体色谱(CSFC)作为放射性药物领域对映体分离替代方法的应用和潜力。最后,解释了 CCC 的关键应用挑战,并提出了迫在眉睫的任务。