Neurotensin (NT) is a potent, non-opioid analgesic which exerts its antinociceptive effects through the activation of two neurotensin receptor subtypes (NTS1 and NTS2). Whilst NTS1 activation is known to induce undesirable physiological effects including hypotension and hypothermia, NTS2-selective compounds show good analgesic effects with none of the adverse effects. However, the therapeutic use of NT is limited by its low oral bioavailability, poor resistance to proteolytic degradation and poor brain-blood barrier (BBB) crossing. To create a molecule that could become a primary pharmacological choice for pain treatment, PK-favorable parameters should be implemented.

We devised a two-front approach at the interface of computational and medicinal chemistry, with the objective to create PK-favorable small molecules crossing the BBB and selectively activating NTS2, starting from a peptide sequence. In-silico simulations of previously found macrocycles led to the discovery of the minimal fragment needed for selective NTS2 binding. This dipeptide respected Lipinski's rule-of-5 parameters. However it still possessed a peptidic backbone, which was detrimental to BBB penetration. The removal of the amide bond and the terminal amino was therefore necessary to obtain PK-favorable parameters. A pharmacophoric model of the lead was performed using MOE (Molecular Operating Environment) and was subsequently submitted to ZincPharmer, an online software which screens ZINC12, a database of 22 million compounds. This experiment yielded several small molecule hits in the micromolar range with molecular weights around 400 g/mol.

Our novel peptide-to-small molecule methodology is suitable for hit discovery as peptides are rapidly synthesized but are usually not bioavailable. We managed to create a model which accurately predicted minimal fragments necessary for NTS2-selective binding. Pharmacophore extraction and screening of the model on ZINC12 yielded several small molecule hits in the micromolar range. These novel molecules will contribute to the optimization of compounds to treat pain-related behaviour in preclinical pain models.