Identification of new positron emission tomography (PET) imaging agents for the ghrelin receptor in disease through molecular modeling

Radiopharmaceuticals, which are drugs containing radioactive isotopes, have become increasingly important for disease diagnosis and treatment. However, the development of new radiopharmaceuticals is costly and time-consuming, and only a limited number of them have been approved for clinical use. The development of a PET imaging agent targeting the ghrelin receptor (GHS-R1a) could enable the detection and assessment of elevated GHS-R1a expression in diseases like prostate, breast, and ovarian cancer. Additionally, GHS-R1a has been recognized as a biomarker associated with cardiovascular disease. Computational modeling techniques have the potential to expedite and streamline the discovery of new radiopharmaceuticals. In this study, we used three-dimensional quantitative structure–activity relationship (3D-QSAR) analysis to investigate 20 PET imaging agents targeted toward the ghrelin receptor. We developed CoMFA and CoMSIA models to predict the activity of structurally diverse chemicals. The best predictions were obtained with a CoMFA model (q² = 0.65, R²_pred = 0.78), and CoMSIA (electrostatic, hydrophobic, and hydrogen bond donor) model (q² = 61, R²_pred = 0.74). A structure-based pharmacophore model was used to screen 123,611,544 drug-like molecules from various databases. The resulting molecules were filtered by drug-like molecule properties and subjected to molecular docking analysis, which identified eight potential inhibitory molecules with strong binding interactions. In silico ADMET studies were conducted to assess the safety and pharmacokinetics of these hit molecules. The hydrogen bond (HB) interactions between most active compound and amino acids at active site of receptor were surveyed using quantum mechanical computations. Our findings suggest that these eight molecules can be used to design novel ghrelin receptor-PET imaging agents with improved efficacy. Overall, this study demonstrates the potential of computational modeling techniques to accelerate the discovery of new radiopharmaceuticals.