Key result
QSAR models of benzimidazole derivatives predict potency and selectivity of anti-RSV analogues before synthesis.
Why the study?
Current therapeutic options for RSV infection are limited to ribavirin and palivizumab with variable efficacy, necessitating better understanding of chemical features for potency and safety of new agents.
Computational QSAR models were successfully developed to predict the potency and safety profiles of benzimidazole-based anti-RSV agents, aiding future drug design.
Respiratory syncytial virus (RSV) has been identified as a main cause of hospitalisation in infants and children. To date, the current therapeutic arsenal is limited to ribavirin and palivizumab with variable efficacy. In this work, starting from a number of in-house series of previously described anti-RSV agents based on the benzimidazole scaffold, with the aim at gaining a better understanding of the related chemical features involved in potency and safety profiles, we applied a computational study including two focussed comparative molecular fields analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). The results allowed us to derive useful suggestions for the design of derivatives and also to set up statistical models predicting the potency and selectivity index (SI = CC50/EC50) of any new analogue prior to synthesis. Accordingly, here, we discuss preliminary results obtained through the applied exhaustive QSAR analyses, leading to design and synthesise more effective anti-RSV agents.
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Cichero et al. (2017) studied Respiratory syncytial virus (RSV) infection. Benzimidazole-based derivatives was evaluated on Potency and selectivity index (SI = CC50/EC50). Computational QSAR models (CoMFA and CoMSIA) of benzimidazole-based derivatives were developed to predict the potency and selectivity index of new anti-RSV analogues prior to synthesis.
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