Huong Le-Thi-Thu

Bio: Huong Le-Thi-Thu is an academic researcher from Vietnam National University, Hanoi. The author has contributed to research in topics: Quantitative structure–activity relationship & Linear discriminant analysis. The author has an hindex of 13, co-authored 37 publications receiving 417 citations. Previous affiliations of Huong Le-Thi-Thu include Central University, India & Vietnam National University, Ho Chi Minh City.

Organ and tissue-dependent effect of resveratrol and exercise on antioxidant defenses of old mice

Abstract: Background Oxidative stress has been considered one of the causes of aging. For this reason, treatments based on antioxidants or those capable of increasing endogenous antioxidant activity have been taken into consideration to delay aging or age-related disease progression. Aim In this paper, we determine if resveratrol and exercise have similar effect on the antioxidant capacity of different organs in old mice. Methods Resveratrol (6 months) and/or exercise (1.5 months) was administered to old mice. Markers of oxidative stress (lipid peroxidation and glutathione) and activities and levels of antioxidant enzymes (SOD, catalase, glutathione peroxidase, glutathione reductase and transferase and thioredoxin reductases, NADH cytochrome B5-reductase and NAD(P)H-quinone acceptor oxidoreductase) were determined by spectrophotometry and Western blotting in different organs: liver, kidney, skeletal muscle, heart and brain. Results Both interventions improved antioxidant activity in the major organs of the mice. This induction was accompanied by a decrease in the level of lipid peroxidation in the liver, heart and muscle of mice. Both resveratrol and exercise modulated several antioxidant activities and protein levels. However, the effect of resveratrol, exercise or their combination was organ dependent, indicating that different organs respond in different ways to the same stimulus. Conclusions Our data suggest that physical activity and resveratrol may be of great importance for the prevention of age-related diseases, but that their organ-dependent effect must be taken into consideration to design a better intervention.

The Use of Rule-Based and QSPR Approaches in ADME Profiling: A Case Study on Caco-2 Permeability.

Abstract: During the early ADME profiling the development of simple, interpretable and reliable in silico tools is very important. In this study, rule-based and QSPR approaches were investigated using a large Caco-2 permeability database. Three permeability classes were determined: high (H), moderate (M) and low (L). The main physicochemical properties related with permeability were ranked as follows: Polar Surface Area (PSA)>Lipophilicity (logP/logD)>Molecular Weight (MW)>number of Hydrogen Bond donors and acceptors>Ionization State>number of Rotatable Bonds>number of Rings. The best rule, based on the combination of PSA-MW-logD (3PRule), was able to identify the H, M and L classes with accuracy of 72.2, 72.9 and 70.6 %, respectively. Subsequently, a consensus system based on three voting binary classification trees was constructed. It accurately predicted 78.4/76.1/79.1 % of H/M/L compounds on training and 78.6/71.1/77.6 % on test set. Finally, the 3PRule and multiclassifier were validated with 23 drugs in a Caco-2 assay. The rule is very useful to improve assay design and prioritize the high absorption candidates. Meanwhile the QSPR model exhibits appropriate classification performance. Due to the simplicity, easy interpretation and accuracy, the 3PRule and consensus model developed here can be used in early ADME profiling.

Tyrosinase Enzyme: 1. An Overview on a Pharmacological Target

Abstract: The tyrosinase enzyme (EC 1.14.18.1) is an oxidoreductase inside the general enzyme classification and is involved in the oxidation and reduction process in the epidermis. These chemical reactions that the enzyme catalyzes are of principal importance in the melanogenesis process. This process of melanogenesis is related to the melanin formation, a heteropolymer of indolic nature that provides the different tonalities in the skin and helps to the protection from the ultraviolet radiation. However, a pigment overproduction, come up by the action of the tyrosinase, can cause different disorders in the skin related to the hyperpigmentation. Several studies mainly focused on the characteristics of the enzyme have been reported. In this work, an approximation to general aspects related to this enzyme is made. Besides, it is treated the researches that have been published in the part of the biochemical anatomy dealing with diseases associated with this protein (melanogenesis), its active place and its physiological states, the molecular mechanism, the methods carried out to detect the inhibitory activity, and the used substrates.

A Simple Method to Predict Blood-Brain Barrier Permeability of Drug- Like Compounds Using Classification Trees.

Abstract: Background: To know the ability of a compound to penetrate the blood-brain barrier (BBB) is a challenging task; despite the numerous efforts realized to predict/measure BBB passage, they still have several drawbacks. Methods: The prediction of the permeability through the BBB is carried out using classification trees. A large data set of 497 compounds (recently published) is selected to develop the tree model. Results: The best model shows an accuracy higher than 87.6% for training set; the model was also validated using 10-fold cross-validation procedure and through a test set achieving accuracy values of 86.1% and 87.9%, correspondingly. We give a brief explanation, in structural terms, of how our model describes the passage of molecules through the BBB. Additionally, the obtained model is compared with other approaches previously published in the international literature showing better results. Conclusion: Finally, we can say that, the present model would be a valuable tool in the early stages of drug discovery process of neuropharmaceuticals.

In Silico Assessment of ADME Properties: Advances in Caco-2 Cell Monolayer Permeability Modeling.

Abstract: One of the main goals of in silico Caco-2 cell permeability models is to identify those drug substances with high intestinal absorption in human (HIA). For more than a decade, several in silico Caco-2 models have been made, applying a wide range of modeling techniques; nevertheless, their capacity for intestinal absorption extrapolation is still doubtful. There are three main problems related to the modest capacity of obtained models, including the existence of inter- and/or intra-laboratory variability of recollected data, the influence of the metabolism mechanism, and the inconsistent in vitro-in vivo correlation (IVIVC) of Caco-2 cell permeability. This review paper intends to sum up the recent advances and limitations of current modeling approaches, and revealed some possible solutions to improve the applicability of in silico Caco-2 permeability models for absorption property profiling, taking into account the above-mentioned issues.

Learning from class-imbalanced data

Abstract: 527 articles related to imbalanced data and rare events are reviewed.Viewing reviewed papers from both technical and practical perspectives.Summarizing existing methods and corresponding statistics by a new taxonomy idea.Categorizing 162 application papers into 13 domains and giving introduction.Some opening questions are discussed at the end of this manuscript. Rare events, especially those that could potentially negatively impact society, often require humans decision-making responses. Detecting rare events can be viewed as a prediction task in data mining and machine learning communities. As these events are rarely observed in daily life, the prediction task suffers from a lack of balanced data. In this paper, we provide an in depth review of rare event detection from an imbalanced learning perspective. Five hundred and seventeen related papers that have been published in the past decade were collected for the study. The initial statistics suggested that rare events detection and imbalanced learning are concerned across a wide range of research areas from management science to engineering. We reviewed all collected papers from both a technical and a practical point of view. Modeling methods discussed include techniques such as data preprocessing, classification algorithms and model evaluation. For applications, we first provide a comprehensive taxonomy of the existing application domains of imbalanced learning, and then we detail the applications for each category. Finally, some suggestions from the reviewed papers are incorporated with our experiences and judgments to offer further research directions for the imbalanced learning and rare event detection fields.

A BOILED-Egg To Predict Gastrointestinal Absorption and Brain Penetration of Small Molecules

Abstract: Apart from efficacy and toxicity, many drug development failures are imputable to poor pharmacokinetics and bioavailability. Gastrointestinal absorption and brain access are two pharmacokinetic behaviors crucial to estimate at various stages of the drug discovery processes. To this end, the Brain Or IntestinaL EstimateD permeation method (BOILED-Egg) is proposed as an accurate predictive model that works by computing the lipophilicity and polarity of small molecules. Concomitant predictions for both brain and intestinal permeation are obtained from the same two physicochemical descriptors and straightforwardly translated into molecular design, owing to the speed, accuracy, conceptual simplicity and clear graphical output of the model. The BOILED-Egg can be applied in a variety of settings, from the filtering of chemical libraries at the early steps of drug discovery, to the evaluation of drug candidates for development.

Cross-validation pitfalls when selecting and assessing regression and classification models

Abstract: We address the problem of selecting and assessing classification and regression models using cross-validation. Current state-of-the-art methods can yield models with high variance, rendering them unsuitable for a number of practical applications including QSAR. In this paper we describe and evaluate best practices which improve reliability and increase confidence in selected models. A key operational component of the proposed methods is cloud computing which enables routine use of previously infeasible approaches. We describe in detail an algorithm for repeated grid-search V-fold cross-validation for parameter tuning in classification and regression, and we define a repeated nested cross-validation algorithm for model assessment. As regards variable selection and parameter tuning we define two algorithms (repeated grid-search cross-validation and double cross-validation), and provide arguments for using the repeated grid-search in the general case. We show results of our algorithms on seven QSAR datasets. The variation of the prediction performance, which is the result of choosing different splits of the dataset in V-fold cross-validation, needs to be taken into account when selecting and assessing classification and regression models. We demonstrate the importance of repeating cross-validation when selecting an optimal model, as well as the importance of repeating nested cross-validation when assessing a prediction error.

A comprehensive review on tyrosinase inhibitors.

Abstract: Tyrosinase is a multi-copper enzyme which is widely distributed in different organisms and plays an important role in the melanogenesis and enzymatic browning. Therefore, its inhibitors can be attractive in cosmetics and medicinal industries as depigmentation agents and also in food and agriculture industries as antibrowning compounds. For this purpose, many natural, semi-synthetic and synthetic inhibitors have been developed by different screening methods to date. This review has focused on the tyrosinase inhibitors discovered from all sources and biochemically characterised in the last four decades.