Essential oils for the inhibition of acetylcholinesterase: A critical appraisal applying multiple regression analysis and artificial intelligence tools
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- Tez No: 403284
- Danışmanlar: Dr. JOSE M. PRIETO
- Tez Türü: Yüksek Lisans
- Konular: Biyokimya, Farmasötik Toksikoloji, Biochemistry, Pharmaceutical Toxicology
- Anahtar Kelimeler: Essential oil, Acetylcholinesterase, Alzheimer's disease, Artificial neural network, Multiple regression, SPSS, Simbrain
- Yıl: 2016
- Dil: İngilizce
- Üniversite: University of London - University College London
- Enstitü: Yurtdışı Enstitü
- Ana Bilim Dalı: Belirtilmemiş.
- Bilim Dalı: Belirtilmemiş.
- Sayfa Sayısı: 89
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Özet (Çeviri)
Introduction: The potential anti-acetylcholinesterase activity of some essential oils (EOs) from aromatic plants drew research attention for Alzheimer's disease treatment in recent years. Complex chemical interactions between EO components and their effects on AChE inhibition is difficult to analyse in laboratory conditions, but appropriate computational techniques such as multiple regression analysis may help to identify predictors of antiacetylcholinesterase activity, and artificial intelligence can foretell the AChE inhibitory activity of EOs from known chemical compositions. Methods: 22 EOs with AChE inhibitory activity and known chemical composition were chosen from the literature. Of their 259 constituents, 16 were selected as independent variables to avoid excessive complexity in multiple regression analysis; the activity of EOs was used as dependent variable. A multilayer ANN with 16 input neurons, 8 hidden neurons and 1 output neuron was formed and run using back-propagation algorithm in Simbrain software. The 16 selected compounds were used as input data and the antiacetylcholinesterase activity of the EOs was used to form six different learning and validating data sets. Results and discussion: Multiple regression analysis revealed 6 predictors from among 16 compounds. ANN tended to give better results when a particular set of data providing balance in IC50 values of the EOs was formed. However, overall results indicate that ANN may not be able to accurately predict AChE inhibition of EOs based on our study. The activity of those oils might be explained by more complex pharmacological connections such as receptor occupancy. Conclusion and future work: The results do not invalidate the use of artificial intelligence to predict bioactivities; the selection of different data sets with different groups of compounds may provide better accuracy. We encourage researchers to further analyse the effect of receptor occupancy by EOs and the chemical interaction of EO constituents on AChE inhibition.
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