Development of a Sigma-2 Receptor affinity filter through a Monte Carlo based QSAR analysis

For the first time in sigma-2 (σ₂) receptor field, a quantitative structure-activity relationship (QSAR) model has been built using pK_i values of the whole set of known selective σ₂ receptor ligands (548 compounds), taken from the Sigma-2 Receptor Selective Ligands Database (S2RSLDB) (http://www.researchdsf.unict.it/S2RSLDB/), through the Monte Carlo technique and employing the software CORAL. The model has been developed by using a large and structurally diverse set of compounds, allowing for a prediction of different populations of chemical compounds endpoint (σ₂ receptor pK_i). The statistical quality reached, suggested that model for pK_i determination is robust and possesses a satisfactory predictive potential. The statistical quality is high for both visible and invisible sets. The screening of the FDA approved drugs, external to our dataset, suggested that sixteen compounds might be repositioned as σ₂ receptor ligands (predicted pK_i≥8). A literature check showed that six of these compounds have already been tested for affinity at σ₂ receptor and, of these, two (Flunarizine and Terbinafine) have shown an experimental σ₂ receptor pK_i>7. This suggests that this QSAR model may be used as focusing screening filter in order to prospectively find or repurpose new drugs with high affinity for the σ₂ receptor, and overall allowing for an enhanced hit rate respect to a random screening.