QSAR analysis on tacrine-related acetylcholinesterase inhibitors.

BACKGROUND: The evaluation of the clinical effects of Tacrine has shown efficacy in delaying the deterioration of the symptoms of Alzheimer's disease, while confirming the adverse events consisting mainly in the elevated liver transaminase levels. The study of tacrine analogs presents a continuous interest, and for this reason we establish Quantitative Structure-Activity Relationships on their Acetylcholinesterase inhibitory activity. RESULTS: Ten groups of new developed Tacrine-related inhibitors are explored, which have been experimentally measured in different biochemical conditions and AChE sources. The number of included descriptors in the structure-activity relationship is characterized by 'Rule of Thumb'. The 1502 applied molecular descriptors could provide the best linear models for the selected Alzheimer's data base and the best QSAR model is reported for the considered data sets. CONCLUSION: The QSAR models developed in this work have a satisfactory predictive ability, and are obtained by selecting the most representative molecular descriptors of the chemical structure, represented through more than a thousand of constitutional, topological, geometrical, quantum-mechanical and electronic descriptor types.

Naturally-occurring dimers of flavonoids as anticarcinogens.

Biflavonoids are dimers of flavonoid moieties linked by a C-C or C-O-C bond. Simple, complex, rearranged, natural and ketalized Diels-Alder adducts, benzofuran derivatives, and spirobiflavonoids are some of the structural groups of biflavonoids. These compounds are mainly distributed in the Gymnosperms, Angiosperms (monocots and dicots), ferns (Pteridophyta), and mosses (Bryophyta). Biflavonoids have shown a variety of biological activities, including anticancer, antibacterial, antifungal, antiviral, antiinflammatory, analgesic, antioxidant, vasorelaxant, anticlotting, among others. This work is focused on probably the most potentially relevant biological activity of biflavonoids, the anticancer activity and the involved mechanisms of action, such as induction of apoptosis [inhibition of cyclic nucleotide phosphodiesterases; effects on NF-κB family of transcription factors; activation of caspase(s); inhibition effects on bcl-2 expression, and upregulation of p53 and caspase-3 gene expression]; inhibition of angiogenesis [anti-proliferative effects; activation of Rho-GTPases and ERK signaling pathways; inhibition of FASN activity]; inhibition of pre-mRNA splicing; inhibition of human DNA topoisomerases I and II-α; anti-inflammatory/ immunoregulatory effects [inhibition of XO; inhibition of proinflammatory enzymes, such as PLA2 and COX; effects on cytokines mediated COX-2 and iNOS expression]; modulation of immune response; inhibition of protein tyrosine phosphorylation; antioxidant and analgesic activities in relation to the anticarcinogen behavior. For that reason the structures and anticarcinogenic activities of 83 biflavonoids are thoroughly discussed. The results of this work indicate that biflavonoids strongly affect the cancer cells with little effect on normal cell proliferation, suggesting a therapeutic potential against cancer.

(Iso)flav(an)ones, chalcones, catechins, and theaflavins as anticarcinogens: mechanisms, anti-multidrug resistance and QSAR studies.

Flavonoids have shown anticarcinogenic activity in cancer cell lines, animal models, and some human studies. Quantitative structure-activity relationship (QSAR) models have become useful tools for identification of promising lead compounds in anticancer drug development. However, epidemiological and clinical studies are still scarce. Compounds with flavonoid scaffold have been the subject of many mechanistic studies in cells, but information on human chemopreventive properties is still missing. The knowledge of the mechanisms of action, anti-multidrug resistance, and QSAR studies on flavonoids and related compounds may help to enhance research on these compounds and their bioactivity. Therefore, once the issue is introduced, the mechanisms involved, and QSAR studies developed to predict the activity and toxicity of these chemicals to biological systems are discussed. QSAR studies on flavonoids as inhibitors of breast cancer resistance protein (BCRP/ABCG2), 17ß-hydroxysteroid dehydrogenase (17ß-HSD), PIM-1 kinase and cyclin-dependent kinases (CDKs) are analyzed. Combined treatment of flavonoids with TRAIL and current chemotherapy agents is also discussed as a promising cancer chemoprevention and/or therapy.

Advances in the replacement and enhanced replacement method in QSAR and QSPR theories.

The selection of an optimal set of molecular descriptors from a much greater pool of such regression variables is a crucial step in the development of QSAR and QSPR models. The aim of this work is to further improve this important selection process. For this reason three different alternatives for the initial steps of our recently developed enhanced replacement method (ERM) and replacement method (RM) are proposed. These approaches had previously proven to yield near optimal results with a much smaller number of linear regressions than the full search. The algorithms were tested on four different experimental data sets, formed by collections of 116, 200, 78, and 100 experimental records from different compounds and 1268, 1338, 1187, and 1306 molecular descriptors, respectively. The comparisons showed that one of the new alternatives further improves the ERM, which has shown to be superior to genetic algorithms for the selection of an optimal set of molecular descriptors from a much greater pool. The new proposed alternative also improves the simpler and the lower computational demand algorithm RM.

Predictive QSPR study of the dissociation constants of diverse pharmaceutical compounds.

The objective of the article was to perform a predictive analysis, based on quantitative structure-property relationships, of the dissociation constants (pK(a)) of different medicinal compounds (e.g., salicylic acid, salbutamol, lidocaine). Given the importance of this property in medicinal chemistry, it is of interest to develop theoretical methods for its prediction. The descriptors selection from a pool containing more than a thousand geometrical, topological, quantum-mechanical, and electronic types of descriptors was performed using the enhanced replacement method. Genetic algorithm and the replacement method (RM) techniques were used as reference points. A new methodology for the selection of the optimal number of descriptors to include in a model was presented and successfully used, showing that the best model should contain four descriptors. The best quantitative structure-property relationships linear model constructed using 62 molecular structures not previously used in this type of quantitative structure-property study showed good predictive attributes. The root mean squared error of the 26 molecules test set was 0.5600. The analysis of the quantitative structure-property relationships model suggests that the dissociation constants depend significantly on the number of acceptor atoms for H-bonds and on the number of carboxylic acids present in the molecules.

Replacement method and enhanced replacement method versus the genetic algorithm approach for the selection of molecular descriptors in QSPR/QSAR theories.

We compare three methods for the selection of optimal subsets of molecular descriptors from a much greater pool of such regression variables. On the one hand is our enhanced replacement method (ERM) and on the other is the simpler replacement method (RM) and the genetic algorithm (GA). These methods avoid the impracticable full search for optimal variables in large sets of molecular descriptors. Present results for 10 different experimental databases suggest that the ERM is clearly preferable to the GA that is slightly better than the RM. However, the latter approach requires the smallest amount of linear regressions and, consequently, the lowest computation time.

QSAR study of flavonoids and biflavonoids as influenza H1N1 virus neuraminidase inhibitors.

We performed a predictive analysis based on Quantitative Structure-Activity Relationships (QSAR) of a very important property of flavonoids which is the inhibition (IC50) of influenza H1N1 virus neuraminidase. The best linear model constructed from 20 molecular structures incorporated four molecular descriptors, selected from more than a thousand geometrical, topological, quantum-mechanical and electronic types of descriptors. The obtained model suggests that the activity depends on the electric charges, masses and polarizabilities of the atoms present in the molecule as well as its conformation. The model showed good predictive ability established by the theoretical and external test set validations.

Predictive modeling of the total deactivation rate constant of singlet oxygen by heterocyclic compounds.

We constructed a predictive model of the total deactivation rate constant (k(t)) of singlet oxygen by heterocyclic compounds that are widespread in biological systems and participate in highly relevant biologic functions related with photochemical processes, by means of quantitative structure-property relationships (QSPR). The study of the reactivity of singlet oxygen with biomolecules provides their antioxidant capability, and the determination of the rate constants allows evaluation of the efficiencies of these processes. Our optimal linear model based on 41 molecular structures, which have not been used previously in a QSPR study, consists of six variables, selected from more than thousand geometrical, topological, quantum-mechanical and electronic types of molecular descriptors. Our recently developed strategy to determine the optimal number of descriptors in model is successfully applied. As a practical application of our QSPR model we estimated the unknown k(t) of several heterocyclic compounds that are of particular interest for further experimental studies in our research group.

QSAR prediction of inhibition of aldose reductase for flavonoids.

We performed a predictive analysis based on quantitative structure-activity relationships (QSAR) of an important property of flavonoids, which is the inhibition (IC(50)) of aldose reductase (AR). The importance of AR inhibition is that it prevents cataract formation in diabetic patients. The best linear model constructed from 55 molecular structures incorporated six molecular descriptors, selected from more than a thousand geometrical, topological, quantum-mechanical, and electronic types of descriptors. As a practical application, we used the obtained QSAR model to predict the AR inhibitory effect of newly synthesized flavonoids that present 2-, 7-substitutions in the benzopyrane backbone.