Abstract

Human purine nucleoside phosphorylase (HsPNP) catalyzes reversible phosphorolysis of nucleosides and deoxynucleosides in the purine cascade. HsPNP has been a target on behalf of the development of new leads for the treatment of a variety of T-cell mediated disorders. Several studies on the HsPNP are focused on the identification of effective, safe, and selective inhibitors. Therefore, this study describes the development of direct, simple, reliable, and inexpensive enzymatic assays to screen HsPNP inhibitors. Initially, HsPNP was covalently immobilized on the surface of magnetic particles (MPs). Due to the versatility of the MPs as solid support for enzyme immobilization, two different methods to monitor the enzyme activity are presented. Firstly, the activity of HsPNP-MPs was assessed offline by HPLC-DAD quantifying the formed hypoxanthine. Then, HsPNP-MPs were trapped in a peek tube, furnishing a microreactor which was inserted on-flow in an HPLC-DAD system to monitor the enzyme activity by the hypoxanthine quantification. Kinetic assays provided KMapp values for the inosine substrate of 488.2 ± 49.1 and 1084 ± 111 µM for the offline and on-flow assays, respectively. For the first time, kinetic studies for Pi as substrate using the HsPNP-MPs exhibits a Michaelis-Menten kinetic, yielding KMapp values for offline and on-flow of 521.2 ± 62.9 µM and 601 ± 66.5 µM, respectively. Inhibition studies conducted with a fourth generation immucillin derivative (DI4G) were employed as proof of concept to validate the use of the HsPNP-MPs assays for screening purposes. Additionally, a small library containing 11 compounds was used to assess the selectivity of the developed assays. The results showed that both presented assays can be applied to selectively recognizing and characterizing HsPNP inhibitors. Particularly, the on-flow method exhibited a high throughput and performance because of its automation and represents an easy and practical approach to reuse the HsPNP-MPs. Besides, this novel enzyme activity assay model can be further applied to other biological targets.

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