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Computational Identification of Natural Products for Smoking Reduction

How NovaMechanics identified novel natural CYP2A6 inhibitors using virtual screening, machine learning, and experimental validation — achieving results comparable to the gold standard.

Funded by the Research and Innovation Foundation (RIF) of Cyprus

The Challenge

The Problem with Smoking Cessation

Tobacco consumption is the single largest avoidable health risk in the EU. Around 700,000 deaths occur every year, with 26% of the overall population classified as smokers. Nicotine Replacement Therapy (NRT) is the primary approach for smoking cessation, yet it exhibits low success and adherence rates due to individual variations in nicotine metabolism.

The enzyme CYP2A6 plays a key role in nicotine metabolism. Inhibiting this enzyme can slow nicotine breakdown, enhancing the effectiveness of NRT protocols. NovaMechanics developed a strategy focusing on natural products as potential CYP2A6 inhibitors.

700K
Deaths per year in EU from smoking
26%
EU population classified as smokers
29%
Young Europeans (15–24) who smoke

Our Methodology

A multi-stage computational pipeline validated through experimental testing

Database Curation

Curated three major natural product databases: Analyticon (6,500 compounds), EthnoHERBS (23,000 compounds), and COCONUT (682,114 compounds). Isolated unique compounds and filtered by molecular weight (<300 Da).

Virtual Screening with Enalos Asclepios

Used the Enalos Asclepios KNIME nodes to set up structure-based virtual screening workflows. Performed molecular docking simulations against the CYP2A6 crystal structure (PDB ID: 4EJJ) using AutoDock Vina. Zero-code, reproducible, and automated.

Machine Learning with Isalos

Built predictive ML models using the Isalos Analytics Platform. Used 77 molecular descriptors, 80/20 training/test split (3,600/790 compounds). Validated with Random Forest and XGBoost — XGBoost selected as the final model for CYP2A6 inhibition prediction.

In Vitro Evaluation

Top 30 compounds from Analyticon tested in vitro for CYP2A6 inhibitory activity using mouse microsomes. Multiple rounds of optimization identified 5 potent inhibitors with IC50 values close to the gold standard 8-methoxypsoralen (MOPS).

Toxicity Assessment

Best compounds (MO9, MO22) tested in HepG2 human hepatocyte cells using MTT assay. No toxicity observed at 24 or 48 hours at concentrations up to 100 µM.

In Vivo Validation

Compounds tested in mice for inhibition of nicotine metabolism. Both MO9 and MO22 exhibited significant, concentration-dependent inhibition of nicotine metabolism in vivo. Results confirmed against human recombinant CYP2A6 enzyme.

Results at a Glance

0.64 µM
IC50 of Best Compound
MO9 compound comparable to gold standard MOPS (0.95 µM)
Zero Toxicity
No Cytotoxicity
No toxicity in human hepatocyte cells at therapeutic concentrations
In Vivo Confirmed
Validated in Models
Concentration-dependent inhibition of nicotine metabolism in mice
700K+
Compounds Screened
Natural products virtually screened across three major databases
Human CYP2A6
Cross-Species Activity
Compounds retain inhibitory activity against human recombinant enzyme
ML Validated
XGBoost Model
Predictive model built for identifying novel CYP2A6 inhibitors

Powered by NovaMechanics Software

Enalos Asclepios KNIME Nodes

Used for structure-based virtual screening workflows including molecular docking with AutoDock Vina. Zero-code, reproducible, and fully automated pipeline for hit discovery.

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Isalos Analytics Platform

Used for building the XGBoost machine learning model for CYP2A6 inhibition prediction with 77 molecular descriptors, featuring comprehensive model validation and interpretability.

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Natural products present a cost-efficient and rich source of potential enzyme inhibitors. This project demonstrates that NovaMechanics' computational pipeline — from virtual screening through machine learning to experimental validation — can successfully identify novel drug candidates with real therapeutic potential.