In silico predictions of hERG channel blockers in drug discovery: from ligand-based and target-based approaches to systems chemical biology

The risk for cardiotoxic side effects represents a major problem in clinical studies of drug candidates and regulatory agencies have explicitly recommended that all new drug candidates should be tested for blockage of the human Ether-a-go-go Related-Gene (hERG) potassium channel. Indeed, several drugs with different therapeutic indications and recognized as hERG blockers were recently withdrawn due to the risk of QT prolongation, arrhythmia and Torsade de Pointes. In silico techniques can provide a priori knowledge of hERG blockers, thus reducing the costs associated with screening assays. Significant progress has been made in structure-based and ligand-based drug design and a number of models have been developed to predict hERG blockage. Although approaches such as homology modeling in combination with docking and molecular dynamics bring us closer to understand the drug-channel interactions whereas QSAR and classification models provide a faster assessment and detection of hERG-related drug toxicity, limitation on the applicability domain of the current models and integration of data from diverse in vitro approaches are still issues to challenge. Therefore, this review will emphasize on current methods to predict hERG blockers and the need of consistent data to improve the quality and performance of the in silico models. Finally, integration of network-based analysis on drugs inducing potentially long-QT syndrome and arrhythmia will be discussed as a new perspective for a better understanding of the drug responses in systems chemical biology.     

Generalized proteochemometric model of multiple cytochrome p450 enzymes and their inhibitors

Cytochrome P450 enzymes are a superfamily of heme-containing enzymes responsible for the oxidation of structurally diverse chemical compounds. Inhibition of CYP enzymes is probably the most common mechanism underlying acute drug toxicity, loss of therapeutic drug efficacy, and drug-drug interactions. The presence of polymorphic genetic variants of CYPs among the population makes it difficult to foresee undesired effects of drugs and is a common cause of drug candidate failure. Computational models that can predict early drug failures due to the inhibition of CYP isoforms can substantially reduce the cost of drug development. Although several computational models for CYP inhibition have been developed recently, all were constructed for one CYP isoform at a time, thus limiting their use for comprehensive analysis and generalizations to other CYP isoforms and polymorphisms. Here we report a novel approach based on the principles of proteochemometrics for the generalized concomitant modeling of multiple CYP isoforms and their inhibitors. We created a predictive and statistically valid proteochemometric model for CYP enzymes by combining data from a large number of publicly available reports that describe the interactions of 14 CYP enzyme subtypes and 375 structurally diverse inhibitors. Our results demonstrate that our model is capable of predicting the potential of new drug candidates to inhibit multiple CYP enzymes. Analysis of the CYP model also revealed molecular properties of CYP enzymes and xenobiotics that are important for CYP inhibition. This approach may aid in the selection of novel drug candidates that are unlikely to inhibit multiple CYP subtypes.     

Boosted regression trees, multivariate adaptive regression splines and their two-step combinations with multiple linear regression or partial least squares to predict blood-brain barrier passage: a case study

The use of some unconventional non-linear modeling techniques, i.e. classification and regression trees and multivariate adaptive regression splines-based methods, was explored to model the blood-brain barrier (BBB) passage of drugs and drug-like molecules. The data set contains BBB passage values for 299 structural and pharmacological diverse drugs, originating from a structured knowledge-based database. Models were built using boosted regression trees (BRT) and multivariate adaptive regression splines (MARS), as well as their respective combinations with stepwise multiple linear regression (MLR) and partial least squares (PLS) regression in two-step approaches. The best models were obtained using combinations of MARS with either stepwise MLR or PLS. It could be concluded that the use of combinations of a linear with a non-linear modeling technique results in some improved properties compared to the individual linear and non-linear models and that, when the use of such a combination is appropriate, combinations using MARS as non-linear technique should be preferred over those with BRT, due to some serious drawbacks of the BRT approaches.     

Heterocycles in drugs and drug discovery

Heterocycles are common fragments of the vast majority of marketed drugs. This is a reflection of the central role that heterocycles play in modern drug design. They can serve as useful tools to manipulate lipophilicity, polarity, and hydrogen bonding capacity of molecules, which may lead to improved pharmacological, pharmacokinetic, toxicological, and physicochemical properties of drug candidates and ultimately drugs.     

Development of Phenothiazine Hybrids with Potential Medicinal Interest: A Review

The molecular hybridization approach has been used to develop compounds with improved efficacy by combining two or more pharmacophores of bioactive scaffolds. In this context, hybridization of various relevant pharmacophores with phenothiazine derivatives has resulted in pertinent compounds with diverse biological activities, interacting with specific or multiple targets. In fact, the development of new drugs or drug candidates based on phenothiazine system has been a promising approach due to the diverse activities associated with this tricyclic system, traditionally present in compounds with antipsychotic, antihistaminic and antimuscarinic effects. Actually, the pharmacological actions of phenothiazine hybrids include promising antibacterial, antifungal, anticancer, anti-inflammatory, antimalarial, analgesic and multi-drug resistance reversal properties. The present review summarizes the progress in the development of phenothiazine hybrids and their biological activity.     

Computational chemistry approach for the early detection of drug-induced idiosyncratic liver toxicity

Idiosyncratic drug toxicity (IDT), considered as a toxic host-dependent event, with an apparent lack of dose response relationship, is usually not predictable from early phases of clinical trials, representing a particularly confounding complication in drug development. Albeit a rare event (usually <1/5000), IDT is often life threatening and is one of the major reasons new drugs never reach the market or are withdrawn post marketing. Computational methodologies, like the computer-based approach proposed in the present study, can play an important role in addressing IDT in early drug discovery. We report for the first time a systematic evaluation of classification models to predict idiosyncratic hepatotoxicity based on linear discriminant analysis (LDA), artificial neural networks (ANN), and machine learning algorithms (OneR) in conjunction with a 3D molecular structure representation and feature selection methods. These modeling techniques (LDA, feature selection to prevent over-fitting and multicollinearity, ANN to capture nonlinear relationships in the data, as well as the simple OneR classifier) were found to produce QSTR models with satisfactory internal cross-validation statistics and predictivity on an external subset of chemicals. More specifically, the models reached values of accuracy/sensitivity/specificity over 84%/78%/90%, respectively in the training series along with predictivity values ranging from ca. 78 to 86% of correctly classified drugs. An LDA-based desirability analysis was carried out in order to select the levels of the predictor variables needed to trigger the more desirable drug, i.e. the drug with lower potential for idiosyncratic hepatotoxicity. Finally, two external test sets were used to evaluate the ability of the models in discriminating toxic from nontoxic structurally and pharmacologically related drugs and the ability of the best model (LDA) in detecting potential idiosyncratic hepatotoxic drugs, respectively. The computational approach proposed here can be considered as a useful tool in early IDT prognosis.     

Comparative study of artificial neural network and multivariate methods to classify Spanish DO rose wines

Classical multivariate analysis techniques such as factor analysis and stepwise linear discriminant analysis and artificial neural networks method (ANN) have been applied to the classification of Spanish denomination of origin (DO) rose wines according to their geographical origin. Seventy commercial rose wines from four different Spanish DO (Ribera del Duero, Rioja, Valdepeñas and La Mancha) and two successive vintages were studied. Nineteen different variables were measured in these wines. The stepwise linear discriminant analyses (SLDA) model selected 10 variables obtaining a global percentage of correct classification of 98.8% and of global prediction of 97.3%. The ANN model selected seven variables, five of which were also selected by the SLDA model, and it gave a 100% of correct classification for training and prediction. So, both models can be considered satisfactory and acceptable, being the selected variables useful to classify and differentiate these wines by their origin. Furthermore, the casual index analysis gave information that can be easily explained from an enological point of view.     

从药物的三维分子结构预测人体小肠吸收

在口服药物的发展过程中，人体小肠吸收的预测是候选药物设计、优化和选择的一个主要目标.VolSurf/GRID计算方法作为一个新的工具被用来预测被测化合物的人体小肠吸收，以及测定人体小肠吸收所必需的重要的分子特征.被测化合物包括112个结构不同的类似药物的化合物.使用偏最小二乘判别分析方法在实验数据和人体小肠吸收的理论分子特征之间建立相关性.建立的两个模型之间具有较高的一致性.小肠吸收实验数据与分子特征之间好的相关性（r2=0.82, q2=0.67）表明,从化合物的三维分子结构能够预测人体小肠吸收.有利于人体小肠吸收的药物分子特征包括,分子量中心与亲水区重心的不平衡性，较大的疏水区域以及分子内较少的氢键给体.     

Phosphate-Based Self-Immolative Linkers for the Delivery of Amine-Containing Drugs

Amine-containing drugs often show poor pharmacological properties, but these disadvantages can be overcome by using a prodrug approach involving self-immolative linkers. Accordingly, we designed l-lactate linkers as ideal candidates for amine delivery. Furthermore, we designed linkers bearing two different cargos (aniline and phenol) for preferential amine cargo release within 15 min. Since the linkers carrying secondary amine cargo showed high stability at physiological pH, we used our strategy to prepare phosphate-based prodrugs of the antibiotic Ciprofloxacin. Therefore, our study will facilitate the rational design of new and more effective drug delivery systems for amine-containing drugs.     

Metabolic stability studies of lead compounds supported by separation techniques and chemometrics analysis

With metabolism being one of the main routes of drug elimination from the body (accounting for removal of around 75% of known drugs), it is crucial to understand and study metabolic stability of drug candidates. Metabolically unstable compounds are uncomfortable to administer (requiring repetitive dosage during therapy), while overly stable drugs increase risk of adverse drug reactions. Additionally, biotransformation reactions can lead to formation of toxic or pharmacologically active metabolites (either less‐active than parent drug, or even with different action). There were numerous approaches in estimating metabolic stability, including in vitro, in vivo, in silico, and high‐throughput screening to name a few. This review aims at describing separation techniques used in in vitro metabolic stability estimation, as well as chemometric techniques allowing for creation of predictive models which enable high‐throughput screening approach for estimation of metabolic stability. With a very low rate of drug approval, it is important to understand in silico methods that aim at supporting classical in vitro approach. Predictive models that allow assessment of certain biological properties of drug candidates allow for cutting not only cost, but also time required to synthesize compounds predicted to be unstable or inactive by in silico models.     

Rapid honey characterization and botanical classification by an electronic tongue

In this paper a commercial electronic tongue (αAstree, Alpha M.O.S.) was applied for botanical classification and physicochemical characterization of honey samples. The electronic tongue was comprised of seven potentiometric sensors coupled with an Ag/AgCl reference electrode. Botanical classification was performed by PCA, CCA and ANN modeling on 12 samples of acacia, chestnut and honeydew honey. The physicochemical characterization of honey was obtained by ANN modeling and the parameters included were electrical conductivity, acidity, water content, invert sugar and total sugar. The initial reference values for the physicochemical parameters observed were determined by traditional methods. Botanical classification of honey samples obtained by ANN was 100% accurate while the highest correlation between observed and predicted values was obtained for electrical conductivity (0.999), followed by acidity (0.997), water content (0.994), invert sugar content (0.988) and total sugar content (0.979).All developed ANN models for rapid honey characterization and botanical classification performed excellently showing the potential of the electronic tongue as a tool in rapid honey analysis and characterization. The advantage of using such a technique is a simple sample preparation procedure, there are no chemicals involved and there are no additional costs except the initial measurements required for ANN model development.     

Application of artificial neural network in food classification

Artificial neural network (ANN) classifiers have been successfully implemented for various quality inspection and grading tasks of diverse food products. ANN are very good pattern classifiers because of their ability to learn patterns that are not linearly separable and concepts dealing with uncertainty, noise and random events. In this research, the ANN was used to build the classification model based on the relevant features of beer. Samples of the same brand of beer but with varying manufacturing dates, originating from miscellaneous manufacturing lots, have been represented in the multidimensional space by data vectors, which was an assembly of 12 features (% of alcohol, pH, % of CO(2) etc.). The classification has been performed for two subsets, the first that included samples of good quality beer and the other containing samples of unsatisfactory quality. ANN techniques allowed the discrimination between qualities of beer samples with up to 100% of correct classifications.