Multivariate Analysis and Fuzzy Analysis of Acetylcholinesterase Inhibitors: Correlating Inhibition Mechanism and Ligands Properties with Pharmacophoric Profile

Alzheimer’s disease (AD) is the most common kind of dementia. This neurodegenerative disease is characterized by the drastic reduction of the neurotransmitters (NTs) acetylcholine (ACh), serotonin and dopamine in the synapses. The use of the acetylcholinesterase (AChE) inhibitors (AChEIs) is the main strategy used in the treatment of Alzheimer’s disease [1]. During the molecular recognition of ACh by AChE, some important interactions are observed between the ligand and the active site of the enzyme. The quaternary nitrogen atom of the AChE substrate forms a strong electrostatic interaction with the carboxyl group of the Asp72 residue (3.67 ± 0.13 Å) [2]. In addition, strong hydrogen bound interactions are observed between the oxygen atom of the ester group of ACh and the residues Gly118, Ala201 and Tyr130 [2,3]. In this way, the intramolecular distance between the nitrogen and the oxygen atoms in a molecule which will be recognized by AChE should be at least similar as the ACh molecule (3.5 6.5Å), Figure 1. In order to elucidate the correlation between the principal properties of 42 molecules and their inhibitory activity against AChE, quantum theoretical studies associated with principal component analysis [4], clusters analysis [5], classification and missing values methods [6] were carried out. Figure 1. PC1 vs. PC1 and ACh [d(O=N)] and [d(O-N)] distances. 12 a 17/Nov, 2017, Águas de Lindóia/SP, Brasil We investigated exhaustively 26 determinant properties that correlate these 42 molecules and their performance in the central nervous system. Among these, 25 AChEIs, 10 monoamine oxidase inhibitors (MAOIs) and the 7 NTs involved in the development of dementias as AD, Parkinson's disease and depression. Due the diversity of the molecular structure of some ligands, the properties: distance of nearest N and O atoms [d(O-N)] atoms, and the distance of nearest N and O atom of the carbonyl group [d(O=N)] are discarded, i.e., the absence of oxygen and/or nitrogen atom in the molecule is observed in 32 of the 42 inhibitors studied. The PCA method is not possible to be performed using as variable a set of values with some missed data. For this reason, we performed the PCA analysis without consider the properties d(O-N) and d(O=N) distances. On the other hand, considering the importance of the properties d(O-N) and d(O=N) distances in the molecular recognition of the AChE, and in attempt to mark the load of these two properties in this biochemical process we took on board potent pattern recognition and data mining tools to treating all data set including the missed data. In order to enrich and validate PCA results, clustering studies were performed with pattern recognition algorithms. Also, missing values problem was treated to avoid missing data may to affect the clustering analyses. In this way, missing values were obtained with K-Nearest Neighbor Imputation and K-Means Imputation methods. PCA studies revealed that properties as molar mass, number of chiral carbon atom, energy of the molecular orbital LUMO, molecular volume, molecular size, polarizability and number of aromatic rings are the most relevant properties in the study of AChE inhibitors. The missing data values, i.e., the distance of the nitrogen atom and the oxygen atoms in the molecules, are important variable in the study of recognition pattern and correlation in our data set. Hierarchical clustering method was the best predictor with cross-validation coefficient equal to 0.931. Key-words: Acetylcholinesterase, Inhibitors, PCA, Fuzzy Analysis, Recognition Pattern, Missing Values Methods. Support: This work has been supported by Generalitat Valenciana (Prometeo II/2014/02) and Ministerio de Economia y Competitividad of Spain (project CTQ2015-65207-P).