First computational chemistry multi-target model for anti-Alzheimer, anti-parasitic, anti-fungi, and anti-bacterial activity of GSK-3 inhibitors in vitro, in vivo, and in different cellular lines

• Published in: Molecular diversity Vol. 15; no. 2; pp. 561 - 567
• Main Authors: García, Isela, Fall, Yagamare, Gómez, Generosa, González-Díaz, Humberto
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.05.2011; Springer Nature B.V
• Subjects: Algorithms; Alzheimer Disease - metabolism; Animals; Anti-Bacterial Agents - chemistry; Antifungal Agents - chemistry; Antiparasitic Agents - chemistry; Biochemistry; Biomedical and Life Sciences; Cell Line; Chemical synthesis; Computer Simulation; Enzyme Inhibitors - chemistry; Enzyme Inhibitors - metabolism; Full-Length Paper; Glycogen Synthase Kinase 3 - antagonists & inhibitors; Glycogen Synthase Kinase 3 - metabolism; Glycogen Synthase Kinase 3 beta; HT29 Cells; Humans; Inhibitor drugs; Kinases; Life Sciences; Models, Statistical; Molecular structure; Organic Chemistry; Pharmaceutical sciences; Pharmacy; Polymer Sciences; Quantitative Structure-Activity Relationship; Reproducibility of Results; Sensitivity and Specificity; U937 Cells; Glycogen synthase kinase-3 (GSK-3) inhibitors; Alzheimer’s disease; Malaria; QSAR; Linear discriminant analysis; Trypanosomiasis; Markov Model
• ISSN: 1381-1991; 1573-501X
• DOI: 10.1007/s11030-010-9280-3

In the work described here, we developed the first multi-target quantitative structure–activity relationship (QSAR) model able to predict the results of 42 different experimental tests for GSK-3 inhibitors with heterogeneous structural patterns. GSK-3 β inhibitors are interesting candidates for developing anti-Alzheimer compounds. GSK-3 β are also of interest as anti-parasitic compounds active against Plasmodium falciparum , Trypanosoma brucei , and Leishmania donovani ; the causative agents for Malaria, African Trypanosomiasis and Leishmaniosis. The MARCH-INSIDE technique was used to quickly calculate total and local polarizability, n -octanol/water partition coefficients, refractivity, van der Waals area and electronegativity values to 4,508 active/non-active compounds as well as the average values of these indexes for active compounds in 42 different biological assays. Both the individual molecular descriptors and the average values for each test were used as input for a linear discriminant analysis (LDA). We discovered a classification function which used in training series correctly classifies 873 out of 1,218 GSK-3 cases of inhibitors (97.4%) and 2,140 out of 2,163 cases of non-active compounds (86.1%) in the 42 different tests. In addition, the model correctly classifies 285 out of 406 GSK-3 inhibitors (96.3%) and 710 out of 721 cases of non-active compounds (85.4%) in external validation series. The result is important because, for the first time, we can use a single equation to predict the results of heterogeneous series of organic compounds in 42 different experimental tests instead of developing, validating, and using 42 different QSAR models. Lastly, a double ordinate Cartesian plot of cross-validated residuals (first ordinate), standard residuals (second ordinate), and leverages (abscissa) defined the domain of applicability of the model as a squared area within ±2 band for residuals and a leverage threshold of h = 0.0044.