Simultaneous targeting of the prostate-specific membrane antigen (PSMA) and gastrin-releasing peptide receptor (GRPR) could improve the diagnostic accuracy in prostate cancer (PCa). The aim of this study was to develop a PSMA/GRPR-targeting bispecific heterodimer for SPECT and positron emission tomography (PET) diagnostic imaging of PCa. The heterodimer NOTA-DUPA-RM26 was produced by manual solid-phase peptide synthesis. NOTA-DUPA-RM26 was labeled with ¹¹¹In and ⁶⁸Ga, with yields >98%, and demonstrated a high stability and binding specificity to PSMA and GRPR. IC₅₀ values for ^natIn-NOTA-DUPA-RM26 were 4 ± 1 nM towards GRPR and 824 ± 230 nM towards PSMA. An in vivo binding specificity 1 h pi of ¹¹¹In-NOTA-DUPA-RM26 in PC3-PIP-xenografted mice demonstrated partially blockable tumor uptake when co-injected with an excess of PSMA- or GRPR-targeting agents. Simultaneous co-injection of both agents induced pronounced blocking. The biodistribution of ¹¹¹In-NOTA-DUPA-RM26 and ⁶⁸Ga-NOTA-DUPA-RM26 revealed fast activity clearance from the blood and normal organs via the kidneys. Tumor uptake exceeded normal organ uptake for both analogs 1 h pi. ⁶⁸Ga-NOTA-DUPA-RM26 had a significantly lower tumor uptake (8 ± 2%ID/g) compared to ¹¹¹In-NOTA-DUPA-RM26 (12 ± 2%ID/g) 1 h pi. Tumor-to-organ ratios increased 3 h pi, but decreased 24 h pi, for ¹¹¹In-NOTA-DUPA-RM26. MicroPET/CT and microSPECT/CT scans confirmed biodistribution data, suggesting that ⁶⁸Ga-NOTA-DUPA-RM26 and ¹¹¹In-NOTA-DUPA-RM26 are suitable candidates for the imaging of GRPR and PSMA expression in PCa shortly after administration.

NLRP3 (NOD-like receptor family, pyrin domain-containing protein 3) activation has been linked to several chronic pathologies, including atherosclerosis, type-II diabetes, fibrosis, rheumatoid arthritis, and Alzheimer’s disease. Therefore, NLRP3 represents an appealing target for the development of innovative therapeutic approaches. A few companies are currently working on the discovery of selective modulators of NLRP3 inflammasome. Unfortunately, limited structural data are available for this target. To date, MCC950 represents one of the most promising noncovalent NLRP3 inhibitors. Recently, a possible region for the binding of MCC950 to the NLRP3 protein was described but no details were disclosed regarding the key interactions. In this communication, we present an in silico multiple approach as an insight useful for the design of novel NLRP3 inhibitors. In detail, combining different computational techniques, we propose consensus-retrieved protein residues that seem to be essential for the binding process and for the stabilization of the protein–ligand complex.

In the last year, the COVID-19 pandemic has highly affected the lifestyle of the world population, encouraging the scientific community towards a great effort on studying the infection molecular mechanisms. Several vaccine formulations are nowadays available and helping to reach immunity. Nevertheless, there is a growing interest towards the development of novel anti-covid drugs. In this scenario, the main protease (Mpro) represents an appealing target, being the enzyme responsible for the cleavage of polypeptides during the viral genome transcription. With the aim of sharing new insights for the design of novel Mpro inhibitors, our research group developed a machine learning approach using the support vector machine (SVM) classification. Starting from a dataset of two million commercially available compounds, the model was able to classify two hundred novel chemo-types as potentially active against the viral protease. The compounds labelled as actives by SVM were next evaluated through consensus docking studies on two PDB structures and their binding mode was compared to well-known protease inhibitors. The best five compounds selected by consensus docking were then submitted to molecular dynamics to deepen binding interactions stability. Of note, the compounds selected via SVM retrieved all the most important interactions known in the literature.