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

For the first time in sigma-2 (σ 2 ) receptor field, a quantitative structure–activity relationship (QSAR) model has been built using p K i values of the whole set of known selective σ 2 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 (σ 2 receptor p K i ). The statistical quality reached, suggested that model for p K 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 σ 2 receptor ligands (predicted p K i ≥ 8). A literature check showed that six of these compounds have already been tested for affinity at σ 2 receptor and, of these, two (Flunarizine and Terbinafine) have shown an experimental σ 2 receptor p K 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 σ 2 receptor, and overall allowing for an enhanced hit rate respect to a random screening. [ABSTRACT FROM AUTHOR]