Evaluation of radio-thin-layer chromatography as an alternative to radio-HPLC for [F]SynVesT-1 metabolism analysis in rats.

Purpose: Alterations in synaptic vesicle glycoprotein 2A (SV2A) are linked to various neurodegenerative and neuropsychiatric disorders. Positron emission tomography (PET) imaging with radiotracers targeting SV2A, such as [F]SynVesT-1, has proven effective for monitoring these changes. However, SV2A PET quantification using kinetic modeling requires radiometabolite analysis, which presents challenges, particularly in preclinical longitudinal studies due to the relatively large sample volume required by the standard radio-high-performance liquid chromatography (radio-HPLC) method. This study aimed to evaluate radio-thin layer chromatography combined with autoradiography (radio-TLC/AR) as an alternative to radio-HPLC in rat plasma radiometabolite analysis.

Methods: All rats received intravenous infusions of [F]SynVesT-1. Arterial blood samples were collected at predetermined time points for up to 60 min post injection. [F]SynVesT-1 radiometabolites in plasma and brain were assessed using both radio-HPLC and radio-TLC/AR.

Results: We observed a decline in [F]SynVesT-1 plasma concentrations within the first 5 min post-injection. The parent fractions obtained by the radio-HPLC method significantly correlated with those obtained using radio-TLC/AR (R = 0.99, p < 0.0001). While radio-HPLC detected minimal radiometabolites in the brain (1.34 % ± 0.83 %, n = 4), these radiometabolites were not identifiable in selected brain regions using the radio-TLC/AR method (n = 1).

Conclusion: We were able to reliably evaluate the parent fractions of [F]SynVesT-1 in plasma over a 60-min period using normal-phase radio-TLC/AR as an alternative to radio-HPLC. This approach requires less plasma and is less time-consuming with high reproducibility. Future studies will focus on applying this radio-TLC/AR method for metabolism correction of input functions, in the quantitative analysis of PET imaging data using kinetic modeling.