For the first time in sigma-2 (σ2) receptor field, a quantitative structure–activity relationship (QSAR) model has been built using pKi 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 pKi). The statistical quality reached, suggested that model for pKi 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 pKi ≥ 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 pKi > 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.

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

Rescifina Antonio;Floresta Giuseppe;Marrazzo Agostino;Parenti Carmela;Prezzavento Orazio;Nastasi Giovanni;Dichiara Maria;Amata Emanuele
2017

Abstract

For the first time in sigma-2 (σ2) receptor field, a quantitative structure–activity relationship (QSAR) model has been built using pKi 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 pKi). The statistical quality reached, suggested that model for pKi 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 pKi ≥ 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 pKi > 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.
CORAL software; Monte Carlo method; QSAR; Repurposing; Sigma receptor; Sigma-2 Receptor; Virtual screening; 3003
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11769/298153
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