SSTR2 receptor states and cellular metabolism modulate ¹⁷⁷Lu-DOTATATE binding affinity.

Abstract

G protein-coupled receptors (GPCRs) are the target for approximately 36% of approved drugs and control many cell signaling pathways. Among these is the somatostatin receptor 2 (SSTR2), which can be targeted by the radiolabeled somatostatin analog [¹⁷⁷Lu]Lu-DOTA-Tyr³-octreotate (¹⁷⁷Lu-DOTATATE) for treatment of neuroendocrine cancers. Here, we provide a detailed kinetic characterization of the uptake and retention dynamics for ¹⁷⁷Lu-DOTATATE binding to SSTR2 on live tumor cells to gain a deeper understanding on how observed binding affinities relate to receptor biology and subsequent treatment outcome. Real-time cell binding assays have revealed two main interaction populations distinguished primarily by their retention times. High-affinity binding in the sub-nanomolar range was associated with ¹⁷⁷Lu-DOTATATE interacting with G protein-coupled receptors, whereas low-affinity binding in the nanomolar range corresponded to interactions with uncoupled receptors. Cells fixed prior to ligand exposure also exhibited high-affinity binding, although to a lesser degree, implying the presence of pre-coupled SSTR2. Cellular retention decreased with ligand exposure, suggesting a shift in receptor coupling state after prolonged stimulation. Intracellular accumulation upon ligand stimulation was confirmed for both ¹⁷⁷Lu-DOTATATE and SSTR2. Unlabeled DOTATATE was able to induce excretion of internalized ¹⁷⁷Lu-DOTATATE, revealing a highly dynamic interaction with constant turnover of ligands. This study highlights the dynamic nature of agonists binding to GPCRs in a live cell environment. By employing cell-based real-time binding assays, we were able de-convolute the contribution of distinct receptor states to ¹⁷⁷Lu-DOTATATE affinity and retention and study the impact of internalization and recycling dynamics. Improved understanding of ligand/drug-GPCR interactions and how these are impacted by the cellular environment and metabolism can be used to optimize existing and new therapeutic approaches.