Screening benzimidazole derivatives for atypical antipsychotic activity

The development of innovative antipsychotic drugs is one of the key tasks of modern pharmacology. Due to their unique chemical properties, benzimidazole derivatives demonstrate diverse neuropsychotropic effects, highlighting their high potential as antipsychotic agents. Bioinformatics methods enable optimization of the process of identifying compounds with high affinity for target receptors.

The aim. To identify and evaluate benzimidazole derivatives with atypical antipsychotic activity using QSAR analysis and pharmacophore modeling, followed by in vivo experimental testing in preclinical models of psychotic disorders.

Materials and methods. QSAR models were constructed based on data from 2615 compounds from the ChEMBL database. Pharmacophore modeling was performed based on the structure of the 5-HT2A receptor (PDB ID: 6A94). The antipsychotic activity of the most promising compound was assessed in vivo using tests with apomorphine in rats and mice.

Results. Machine learning models were developed and tested to predict the antipsychotic activity of benzimidazole derivatives. The Neural Networks (MAE=0.019) and Random Forest (MAE=0.020) algorithms demonstrated the highest prediction performance. Pharmacophore modeling of interaction with the 5-HT2A receptor identified a promising compound for further testing. Compound RU-31 demonstrated significant reduction (p <0.05) in climbing behavior in mice (ED₈₀=10.16 mg/kg intraperitoneally) and high efficacy when administered with low presynaptic doses of apomorphine (yawning frequency decreased by 49,3% compared to control, p  <0.05).

Conclusions. Compound RU-31 showed activity in the climbing test and in the test with low presynaptic doses of apomorphine, suggesting potential atypical antipsychotic effects. Benzimidazole derivative RU-31 is a promising candidate for further investigation in the development of novel atypical antipsychotics.

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