Development of a room temperature phosphorescence methodology to evaluate the interaction of AI-designed peptides with the tumor biomarker 5-hydroxyindoleacetic acid.

Early detection of neuroendocrine tumors (NETs) is crucial for early and effective intervention, thus reducing the likelihood of tumor progression and metastasis. These tumors have been thoroughly investigated and associated with an excessive secretion of the biomarker 5-hydroxyindoleacetic acid (5HIAA). Although, HPLC is the most commonly used method for 5HIAA detection, spectroscopic alternatives show interesting advantages such as much shorter analysis times, inexpensiveness, as well as the lack of necessity of environmentally harmful solvents. In this work, we propose a double approach to 5HIAA identification: from one side, we used Artificial Intelligence-assisted design to synthesize pentapetides with high affinity toward 5HIAA. On the other hand, we propose a room temperature phosphorescence methodology (RTP) as a detection technique, to take advantage of its delayed light emission, which effectively suppresses interference from the intrinsic fluorescence present in biological samples. Following optimization, it was determined that the concentrations of Na₂SO₃ and KI are critical parameters for the phosphorescent emission of 5HIAA, as well as maintaining a pH value above the pKa of the biomarker. Additionally, this novel methodology was used to evaluate the interaction of the AI-assisted peptides with 5-HIAA. These studies revealed the formation of 1:1 and 1:2 stoichiometric complexes, with these results being consistent with the computational predictions. The developed AI-assisted peptides, combined with RTP detection, demonstrate promising potential as selective sensing phases for 5HIAA, allowing for sensitive and interference-minimized detection, which may support earlier diagnosis of neuroendocrine tumors.