Aline Thaís Bruni

Bio: Aline Thaís Bruni is an academic researcher from Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto. The author has contributed to research in topics: Humanities & Philosophy. The author has an hindex of 10, co-authored 32 publications receiving 274 citations. Previous affiliations of Aline Thaís Bruni include Sao Paulo State University & State University of Campinas.

A partial least squares and principal component regression study of quinone compounds with trypanocidal activity

Abstract: A quantitative structure–activity relationship (QSAR) study of 19 quinone compounds with trypanocidal activity was performed by Partial Least Squares (PLS) and Principal Component Regression (PCR) methods with the use of leave-one-out crossvalidation procedure to build the regression models. The trypanocidal activity of the compounds is related to their first cathodic potential (Epc1). The regression PLS and PCR models built in this study were also used to predict the Epc1 of six new quinone compounds. The PLS model was built with three principal components that described 96.50% of the total variance and present Q 2= 0.83 and R 2 = 0.90. The results obtained with the PCR model were similar to those obtained with the PLS model. The PCR model was also built with three principal components that described 96.67% of the total variance with Q 2 = 0.83 and R 2 = 0.90. The most important descriptors for our PLS and PCR models were HOMO-1 (energy of the molecular orbital below HOMO), Q4 (atomic charge at position 4), MAXDN (maximal electrotopological negative difference), and HYF (hydrophilicity index).

The use of classification methods for modeling the antioxidant activity of flavonoid compounds.

Abstract: A study using two classification methods (SDA and SIMCA) was carried out in this work with the aim of investigating the relationship between the structure of flavonoid compounds and their free-radical-scavenging ability. In this work, we report the use of chemometric methods (SDA and SIMCA) able to select the most relevant variables (steric, electronic, and topological) responsible for this ability. The results obtained with the SDA and SIMCA methods agree perfectly with our previous model, in which we used other chemometric methods (PCA, HCA and KNN) and are also corroborated with experimental results from the literature. This is a strong indication of how reliable the selection of variables is.

Conformational analysis: a new approach by means of chemometrics.

Abstract: In conformational analysis, the systematic search method completely maps the space but suffers from the combinatorial explosion problem because the number of conformations increases exponentially with the number of free rotation angles. This study introduces a new methodology of conformational analysis that controls the combinatorial explosion. It is based on a dimensional reduction of the system through the use of principal component analysis. The results are exactly the same as those obtained for the complete search but, in this case, the number of conformations increases only quadratically with the number of free rotation angles. The method is applied to a series of three drugs: omeprazole, pantoprazole, lansoprazole—benzimidazoles that suppress gastric-acid secretion by means of H + , K + -ATPase enzyme inhibition. © 2002 John Wiley & Sons, Inc. J Comput Chem 23: 222-236, 2002

Theoretical study of omeprazole behavior: Racemization barrier and decomposition reaction

Abstract: Omeprazole is a substituted benzimidazole which suppresses gastric-acid secretion by means of H+, K+-ATPase inhibition. It is an optically active drug with the sulfur of the sulfoxide being the chiral center. This pro-drug can be easily converted into its respective sulfenamide at low pH. In this work, omeprazole has been studied in relation to racemization barrier and decomposition reaction. Quantum chemistry coupled to PCA chemometric method were used to find all minimum energy structures. Conformational analysis and calculation of racemization barriers were carried out by PM3 semiempirical method (Gaussian 98). The average racemization energy barrier for all minimum energy structures (43.56 kcal mol−1) can be related to the velocity constant in Eyring's equation. The enormous half-life time at 100°C (9.04 × 104 years) indicates that the process cannot be observed in human time scale. On the other hand, the difference of free energy change (Δ(ΔG) = −266.78 kcal mol−1) for the decomposition reaction shows that the process is favorable to the sulfenamide formation. The highly negative Δ(ΔG) obtained for the decomposition reaction shows that this process is extremely exothermic. This result explains why omeprazole decomposes and does not racemize. © 2008 Wiley Periodicals, Inc. Int J Quantum Chem, 2008

Funnels, Pathways and the Energy Landscape of Protein Folding: A Synthesis

Abstract: The understanding, and even the description of protein folding is impeded by the complexity of the process. Much of this complexity can be described and understood by taking a statistical approach to the energetics of protein conformation, that is, to the energy landscape. The statistical energy landscape approach explains when and why unique behaviors, such as specific folding pathways, occur in some proteins and more generally explains the distinction between folding processes common to all sequences and those peculiar to individual sequences. This approach also gives new, quantitative insights into the interpretation of experiments and simulations of protein folding thermodynamics and kinetics. Specifically, the picture provides simple explanations for folding as a two-state first-order phase transition, for the origin of metastable collapsed unfolded states and for the curved Arrhenius plots observed in both laboratory experiments and discrete lattice simulations. The relation of these quantitative ideas to folding pathways, to uni-exponential {\em vs.} multi-exponential behavior in protein folding experiments and to the effect of mutations on folding is also discussed. The success of energy landscape ideas in protein structure prediction is also described. The use of the energy landscape approach for analyzing data is illustrated with a quantitative analysis of some recent simulations, and a qualitative analysis of experiments on the folding of three proteins. The work unifies several previously proposed ideas concerning the mechanism protein folding and delimits the regions of validity of these ideas under different thermodynamic conditions.

Silibinin prevents amyloid β peptide-induced memory impairment and oxidative stress in mice

Abstract: Background and purpose: Accumulated evidence suggests that oxidative stress is involved in amyloid β (Aβ)-induced cognitive dysfunction. Silibinin (silybin), a flavonoid derived from the herb milk thistle (Silybum marianum), has been shown to have antioxidative properties; however, it remains unclear whether silibinin improves Aβ-induced neurotoxicity. In the present study, we examined the effect of silibinin on the memory impairment and accumulation of oxidative stress induced by Aβ25–35 in mice.

Machine learning techniques and drug design.

Abstract: The interest in the application of machine learning techniques (MLT) as drug design tools is growing in the last decades. The reason for this is related to the fact that the drug design is very complex and requires the use of hybrid techniques. A brief review of some MLT such as self-organizing maps, multilayer perceptron, bayesian neural networks, counter-propagation neural network and support vector machines is described in this paper. A comparison between the performance of the described methods and some classical statistical methods (such as partial least squares and multiple linear regression) shows that MLT have significant advantages. Nowadays, the number of studies in medicinal chemistry that employ these techniques has considerably increased, in particular the use of support vector machines. The state of the art and the future trends of MLT applications encompass the use of these techniques to construct more reliable QSAR models. The models obtained from MLT can be used in virtual screening studies as well as filters to develop/discovery new chemicals. An important challenge in the drug design field is the prediction of pharmacokinetic and toxicity properties, which can avoid failures in the clinical phases. Therefore, this review provides a critical point of view on the main MLT and shows their potential ability as a valuable tool in drug design.

A review of biogas production from palm oil mill effluents using different configurations of bioreactors

Abstract: Palm oil mill effluent (POME) is generated from the sterilization, condensation and hydrocycloning of palm oil in mills. If the effluent is discharged into the aquatic and terrestrial ecosystem without treatment, it could lead to high biological oxygen demand (BOD), chemical oxygen demand (COD) and acidic pH of the receiving waters. Biogas consisting mostly of methane, carbon dioxide, and to a lesser hydrogen has been produced through anaerobic treatment of this toxic effluent. The process of biogas production involves microbial synthesis involving hydrolysis, acidogenesis, acetogenesis and methanogenesis. Biogas is formed during anaerobic degradation of POME by indigenous microbial communities. This review updates the current state of art of biogas production through anaerobic digestion of POME using different configurations of reactors such as fluidized bed reactor, anaerobic filtration, up-flow anaerobic sludge blanket (UASB) reactor, anaerobic contact digestion, up-flow anaerobic sludge fixed-film (UASFF) reactor, modified anaerobic baffled bioreactor (MABB), anaerobic baffled bioreactor (ABR), continuous stirred tank reactor (CSTR), expanded granular sludge bed (EGSB) reactor, Ultrasonicated membrane anaerobic system (UMAS), Ultrasonic-assisted Membrane Anaerobic System (UAMAS), membrane anaerobic system (MAS)and upflow anaerobic sludge blanket reactor (UASBR). The factors that influences biogas yield during treatment include pH, temperature (environmental factors), organic loading rate (OLR), hydraulic retention time (HRT), mixing rate, pressure, equilibrium, nutrient and microbial activities (Internal factors). Based on this study, UAMAS is the best configuration for methane production from POME during anaerobic treatment. Biogas from POME could contribute to energy sources of oil palm producing nations, while preventing the attendant environmental impacts associated with its disposal.

Zebrafish Models in NeuroPsychopharmacology and CNS Drug Discovery

Abstract: Despite the high prevalence of neuropsychiatric disorders, their aetiology and molecular mechanisms remain poorly understood. The zebrafish (Danio rerio) is increasingly utilized as a powerful animal model in neuropharmacology research and in vivo drug screening. Collectively, this makes zebrafish a useful tool for drug discovery and the identification of disordered molecular pathways. Here, we discuss zebrafish models of selected human neuropsychiatric disorders and drug-induced phenotypes. As well as covering a broad range of brain disorders (from anxiety and psychoses to neurodegeneration), we also summarize recent developments in zebrafish genetics and small molecule screening, which markedly enhance the disease modelling and the discovery of novel drug targets.