Andrew G Mercader

Publications (11) View all

• Article: (Iso)Flav(an)ones, Chalcones, Catechins, and Theaflavins as Anticarcinogens: Mechanisms, Anti-Multidrug Resistance and QSAR Studies.

  Andrew G Mercader, Alicia B Pomilio
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  ABSTRACT: 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), 17β-hydroxysteroid dehydrogenase (17β-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.
  Current Medicinal Chemistry 07/2012; 19(25):4324-47. · 4.86 Impact Factor
• Article: QSAR applications during last decade on inhibitors of acetylcholinesterase in Alzheimer's disease.

  K Y Wong, P R Duchowicz, A G Mercader, E A Castro
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  ABSTRACT: This article reviews multi-criteria QSAR applications on Acetylcholinesterase inhibitors as palliative drugs for Alzheimer's Disease, published in the period 2001-2011. It includes QSAR models for different series of compounds, comparative studies, and advances in methodologies. This period is marked by a shift in focus from palliative treatment to pathogenesis. However, we believe that research into palliative treatment should continue. More comparative studies are desirable. In order to facilitate comparative and general studies on Acetylcholinesterase inhibitors, a standard experimental protocol for measuring an inhibitor's potency is needed. Finally, we recommend chemists to work closely with system and molecular biologists.
  Mini Reviews in Medicinal Chemistry 02/2012; 12(10):936-46. · 2.53 Impact Factor
• Article: Advances in the replacement and enhanced replacement method in QSAR and QSPR theories.

  Andrew G Mercader, Pablo R Duchowicz, Francisco M Fernández, Eduardo A Castro
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  ABSTRACT: 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.
  Journal of Chemical Information and Modeling 06/2011; 51(7):1575-81. · 4.68 Impact Factor
• Article: Predictive QSPR study of the dissociation constants of diverse pharmaceutical compounds.

  Andrew G Mercader, Mohammad Goodarzi, Pablo R Duchowicz, Francisco M Fernández, Eduardo A Castro
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  ABSTRACT: 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.
  Chemical Biology &amp Drug Design 11/2010; 76(5):433-40. · 2.28 Impact Factor
• Article: Replacement method and enhanced replacement method versus the genetic algorithm approach for the selection of molecular descriptors in QSPR/QSAR theories.

  Andrew G Mercader, Pablo R Duchowicz, Francisco M Fernández, Eduardo A Castro
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  ABSTRACT: 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.
  Journal of Chemical Information and Modeling 09/2010; 50(9):1542-8. · 4.68 Impact Factor