Ng Hui Wen, Leggett Carmine, Sakkiah Sugunadevi, Pan Bohu, Ye Hao, Wu Leihong, Selvaraj Chandrabose, Tong Weida, Hong Huixiao
Division of Bioinformatics and Biostatistics, Office of Research, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA.
Division of Non-clinical Science, Office of Science, Center for Tobacco Products, U.S. Food and Drug Administration, Silver Spring, MD 20993, USA.
Oncotarget. 2018 Feb 8;9(24):16899-16916. doi: 10.18632/oncotarget.24458. eCollection 2018 Mar 30.
The detrimental health effects associated with tobacco use constitute a major public health concern. The addiction associated with nicotine found in tobacco products has led to difficulty in quitting among users. Nicotinic acetylcholine receptors (nAChRs) are the targets of nicotine and are responsible for addiction to tobacco products. However, it is unknown if the other >8000 tobacco constituents are addictive. Since it is time-consuming and costly to experimentally assess addictive potential of such larger number of chemicals, computationally predicting human nAChRs binding is important for in silico evaluation of addiction potential of tobacco constituents and needs structures of human nAChRs. Therefore, we constructed three-dimensional structures of the ligand binding domain of human nAChR α7 subtype and then developed a predictive model based on the constructed structures to predict human nAChR α7 binding activity of tobacco constituents. The predictive model correctly predicted 11 out of 12 test compounds to be binders of nAChR α7. The model is a useful tool for high-throughput screening of potential addictive tobacco constituents. These results could inform regulatory science research by providing a new validated predictive tool using cutting-edge computational methodology to high-throughput screen tobacco additives and constituents for their binding interaction with the human α7 nicotinic receptor. The tool represents a prediction model capable of screening thousands of chemicals found in tobacco products for addiction potential, which improves the understanding of the potential effects of additives.
与烟草使用相关的有害健康影响是一个重大的公共卫生问题。烟草制品中发现的尼古丁所致成瘾导致使用者难以戒烟。烟碱型乙酰胆碱受体（nAChRs）是尼古丁的作用靶点，与烟草制品成瘾有关。然而，尚不清楚烟草中其他8000多种成分是否具有成瘾性。由于通过实验评估如此大量化学物质的成瘾潜力既耗时又昂贵，因此计算预测人类nAChRs结合对于在计算机上评估烟草成分的成瘾潜力很重要，且需要人类nAChRs的结构。因此，我们构建了人类nAChR α7亚型配体结合域的三维结构，然后基于所构建的结构开发了一个预测模型，以预测烟草成分与人类nAChR α7的结合活性。该预测模型正确预测了12种测试化合物中的11种为nAChR α7的结合剂。该模型是高通量筛选潜在成瘾性烟草成分的有用工具。这些结果可为监管科学研究提供信息，通过使用前沿计算方法提供一种经过验证的新预测工具，以高通量筛选烟草添加剂和成分与人类α7烟碱受体的结合相互作用。该工具代表了一种能够筛选烟草制品中数千种化学物质成瘾潜力的预测模型，这有助于增进对添加剂潜在影响的理解。
