RNSMOKE
Computational Identification of Natural Products for Smoking Reduction through Inhibition of CYP2A6
Computational Identification of Natural Products for Smoking Reduction through Inhibition of CYP2A6
RNSmoke utilises advanced computational techniques to identify plant-derived natural products that inhibit the cytochrome P450 2A6 enzyme, which is implicated in nicotine metabolism and nicotine addiction. P450 2A6 inhibitors can potentially enhance the effectiveness of nicotine replacement therapies. The goal is to identify natural products that can be employed as dietary or over-the-counter supplements to aid heavy smokers in quitting or reducing smoking.
As computational capabilities advance and the value of data is increasingly recognised, AI approaches are being employed to identify patterns in large datasets predictive of future outcomes. A combined ligand and structure-based framework utilises existing knowledge to develop predictive models based on known 2A6 inhibitors, identifying potent compounds among thousands of natural products using the Enalos Cheminformatics Tools developed by NovaMechanics. These in silico models will guide the identification of supplements to be tested in vitro and in vivo at the University of Nicosia Research Foundation (UNRF). The best molecules, whether isolated as pure compounds or in supplements, will be validated in vivo for efficacy, thus meeting TRL4 and TRL5 requirements.
The RNSmoke project is being driven by a consortium of diverse and competent entities within the Cyprus R&I ecosystem:
- SMEs: NovaMechanics Ltd
- University of Nicosia Foundation (UNRF) – Research organisation
