Ligand‐based design,virtual screening,and ADME/T‐based profiling on a dataset of 1,3,5‐triazine‐substituted benzene sulfonamides as carbonic anhydrase inhibitors

A quantitative structure–activity relationship (QSAR) analysis was performed on a dataset of 62 (1,3,5‐triazine‐substituted) benzene sulfonamides as carbonic anhydrase II and IX inhibitors using simulated annealing‐based multiple linear regression analysis. The selected QSAR model for carbonic anhydrase II inhibition (cross‐validated Q² = 0.689, , ) showed that aromaticity, lipophilicity, electronegativity, and molecular projection in the XZ plane influence the activity, whereas that for carbonic anhydrase IX inhibition (cross‐validated Q² = 0.767, , ) showed that activity was influenced by hydrophilicity, linker between the aromatic rings, electronegativity, and molecular weight. The QSAR model selected was internally and externally validated to define its predictability. Activity prediction of an external dataset containing nine compounds (within the same sphere of applicability) was performed to prove the models' specificity, selectivity, and sensitivity. The hypothesis in the form of the QSAR model was used for ligand‐based virtual screening on the ZINC database to obtain some potential hits. Similarly, docking studies on screened hits showed that the molecules interact and orient at the catalytic site in a way similar to acetazolamide. Additionally, an absorption, distribution, metabolism, excretion, and toxicity screening was also performed, and results showed that most of the compounds that can be possible drug candidates obey the Lipinski rule of five and Jorgensen rule of three. Copyright © 2013 John Wiley & Sons, Ltd.     

Integrated pharmacophore and docking‐based designing of dual inhibitors of aldose reductase (ALR2) and protein tyrosine phosphatase 1B (PTP1B) as novel therapeutics for insulin‐resistant diabetes and its complications

The dual inhibitors against aldose reductase (ALR2) and protein tyrosine phosphatase 1B (PTP1B) may present an anti‐diabetic potency in insulin resistance without risks of serious diabetic complications. Therefore, in the present study, we constructed two separate pharmacophore mapping‐based 3D quantitative structure–activity relationship models for ALR2 (AADRR.1109₃ with standard deviation 0.663, 0.719, F 22.3, root‐mean‐square error 0.705, 0.647, Pearson‐r 0.802) and PTP1B (AARR.15₅ with standard deviation 0.146, 0.945, F 82.70, root‐mean‐square error 0.351, 0.621, Pearson‐r 0.831) employing the dataset of 54 flavonoids as ALR2 inhibitors and 46 naphthoquinones as PTP1B inhibitors to identify structural features necessary for the inhibition of both enzymes. These models were subsequently used as 3D query search for hierarchical virtual screening‐based designing using the PHASE database of 1.5 million compounds. Designed dual inhibitors were further subjected to GLIDE XP docking analysis using high‐resolution 3D structures of ALR2 (1US0, at resolution of 0.66 Å) and PTP1B (2F71 at resolution of 1.55 Å) available in the Protein Data Bank to authenticate identified structural features with important binding interactions necessary for dual inhibition. Copyright © 2014 John Wiley & Sons, Ltd.     

Resolution of Differential Pulse Voltammetric Peaks Using Genetic Algorithm Based Variable Selection‐Partial Least Squares and Principal Component‐Artificial Neural Networks

Differential Pulse Voltammetry has been used for the simultaneous determination of cysteine, tyrosine and trptophan on the unmodified glassy carbon electrode. In the analysis of these analytes in the same samples, the main difficulty is the high degree of overlapping of voltammograms. The relationships between the currents and the concentrations are complex and highly nonlinear. The predictive ability of principal component regression (PCR), partial least squares regression (PLS), genetic algorithm‐partial least squares regression (GA‐PLS) and principal component‐artificial neural networks (PC‐ANNs) were examined for simultaneous determination of three amino acids. For a regression model, everything that could not help in constructing the model may be considered as noise without further specification. PC‐ANN and GA‐PLS use significant data and show superiority over other applied multivariate methods. The proposed method was also applied satisfactorily to determination of analytes in some synthetic samples.     

Multivariate calibration leverages and spectral F‐ratios via the filter factor representation

Diagnostics are fundamental to multivariate calibration (MC). Two common diagnostics are leverages and spectral F‐ratios and these have been formulated for many MC methods such as partial least square (PLS), principal component regression (PCR) and classical least squares (CLS). While these are some of the most common methods of calibration in analytical chemistry, ridge regression is also common place and yet spectral F‐ratios have not been developed for it. Noting that ridge regression is a form of Tikhonov regularization (TR) and using the unifying filter factor representation for MC, this paper develops the filter factor form of leverages and spectral F‐ratios. The approach is applied to a spectral data set to demonstrate computational speed‐up advantages and ease of implementation for the filter factor representation. Copyright © 2010 John Wiley & Sons, Ltd.     

A Quantitative Structure–Activity Relationship Study of Calpeptin (Calpain Inhibitor) as an Anticancer Agent

Calpeptin analogs show anticancer properties with inhibition of calpain. In this work, we applied a quantitative structure–activity relationship (QSAR) model on 34 calpeptin derivatives to select the most appropriate compound. QSAR was employed to generate the models and predict the more significant compounds through a series of calpeptin derivatives. The HyperChem, Gaussian 09, and Dragon software programs were used for geometry optimization of the molecules. The 2D and 3D molecular structures were drawn by ChemDraw (Ultra 16.0) and Chem3D (Pro16.0) software. The Unscrambler program was used for the analysis of data. Multiple linear regression (MLR‐MLR), partial least‐squares (MLR‐PLS1), principal component regression (MLR‐PCR), a genetic algorithm‐artificial neural networks (GA‐ANN), and a novel similarity analysis‐artificial neural network (SA‐ANN) method were used to create QSAR models. Among the three MLR models, MLR‐MLR provided better statistical parameters. The R² and RMSE of the prediction were estimated as 0.8248 and 0.26, respectively. Nevertheless, the constructed model using GA‐ANN revealed the best statistical parameters among the studied methods (R² test = 0.9643, RMSE test = 0.0155, R² train = 0.9644, RMSE train = 0.0139). The GA‐ANN model is found to be the most favorable method among the statistical methods and can be employed for designing new calpeptin analogs as potent calpain inhibitors in cancer treatment.     

Orthogonal signal correction‐based prediction of total antioxidant activity using partial least squares regression from chromatograms

The multivariate calibration methods—partial least squares (PLS), orthogonal signal correction and partial least squares (OSC‐PLS)—were employed for the prediction of total antioxidant activities of four Prunella L. species. High‐performance liquid chromatography (HPLC) and spectrophotometric approaches were used to determine the total antioxidant activity of the Prunella L. samples. Several preprocessing techniques such as smoothing and normalization were employed to extract the chemically relevant information from the data after alignment with correlation optimized warping. The importance of the preprocessing was investigated by calculating the root mean square error for the calibration set for the total antioxidant activity of Prunella L. samples. The models developed on the basis of the preprocessed data were able to predict the total antioxidant activity with a precision comparable to that of the reference 2,2‐azino‐di‐(3‐ethylbenzothialozine‐sulfonic acid) and 2,2‐diphenyl‐1‐picrylhydrazyl methods. The OSC‐PLS model seems preferable because of its predictive and describing abilities and good interpretability of the contribution of compounds to the total antioxidant activity. The contribution of individual phenolic compounds to the total antioxidant activity was identified by HPLC. Copyright © 2012 John Wiley & Sons, Ltd.     

Near Infrared Spectral Similarity Combined with Variable Selection Method in the Quality Control of Flos Lonicerae: A Preliminary Study

This paper indicates the possibility to use near infrared (NIR) spectral similarity as a rapid method to estimate the quality of Flos Lonicerae. Variable selection together with modelling techniques is utilized to select representative variables that are used to calculate the similarity. NIR is used to build calibration models to predict the bacteriostatic activity of Flos Lonicerae. For the determination of the bacteriostatic activity, the in vitro experiment is used. Models are built for the Gram‐positive bacteria and also for the Gram‐negative bacteria. A genetic algorithm combined with partial least squares regression (GA‐PLS) is used to perform the calibration. The results of GA‐PLS models are compared to interval partial least squares (iPLS) models, full‐spectrum PLS and full‐spectrum principal component regression (PCR) models. Then, the variables in the two GA‐PLS models are combined and then used to calculate the NIR spectral similarity of samples. The similarity based on the characteristic variables and full spectrum is used for evaluating the fingerprints of Flos Lonicerae, respectively. The results show that the combination of variable selection method, modelling techniques and similarity analysis might be a powerful tool for quality control of traditional Chinese medicine (TCM).     

Genetic algorithm-based method for selecting conditions in multivariate determination of povidone-iodine using hand scanner

Application of hand scanner in multivariate quantification of povidone-iodine (PVI), as a popular antiseptic agent, in some of pharmaceutical products is presented. Brightness, contrast, and mixed gamma were the adjustable scanner parameters. For selection of optimum values of the scanner parameters, partial least squares (PLS) and multiple linear regression (MLR), coupled with genetic algorithm, were performed. For the selected variables, both MLR and PLS performances were similar and appropriate. From the results obtained, it was concluded that the simpler method of MLR could be successfully applied instead of PLS, which requires more statistical experience. The considered concentration range for PVI in the calibration and prediction samples was 0.0-10.0% (w/v). For the analysis of pharmaceutical samples, generalized standard addition method (GSAM) was applied (on the variables selected by GA) and desirable results were obtained. Relative standard error (RSE) of less than 8% was obtained for the majority of samples analyzed.     

Rapid Determination of Metabolites in Bio‐fluid Samples by Raman Spectroscopy and Optimum Combinations of Chemometric Methods

The application of Raman spectroscopic techniques combined with multivariate chemometrics signal processing promise new means for the rapid multidimensional analysis of metabolites non‐destructively, with little or no sample preparation and little sensitivity to water. However, Rayleigh scattering, fluorescence and uncontrolled variance present substantial challenges for the accurate quantitative analysis of metabolites at physiological levels in biologically varying samples. Effective strategies include the application of chemometrics pretreatments for reducing Raman spectral interference. However, the arbitrary application of individual or combined pretreatment procedures can significantly alter the outcome of a measurement, thereby complicating spectral analysis. This paper evaluates and compares six signal pretreatment methods for correcting the baseline variances, together with three variable selection methods for eliminating uninformative variables, all within the context of multivariate calibration models based on partial least squares (PLS) regression. Raman spectra of 90 artificial bio‐fluid samples with eight urine metabolites at near‐physiological concentrations were used to test these models. The combination of multiplicative scatter correction (MSC), continuous wavelet transform (CWT), randomization test (RT) and PLS modeling presented the best performance for all the metabolites. The correlation coefficient (R) between predicted and prepared concentration reached as high as 0.96.     

Application of multivariate calibration to the simultaneous routine determination of ethanol, glycerol, fructose, glucose and total residual sugars in botrytized-grape sweet wines by means of near-infrared reflectance spectroscopy

The simultaneous determination of ethanol, glycerol, fructose, glucose and residual sugars in botrytized-grape sweet white wines was performed by means of near-infrared reflectance using 19 interference filters and a partial least squares (PLS) model in latent variables as a multivariate calibration technique. The results were compared with those obtained using other multivariate calibration techniques, such as MLR, SWR and PCR, by means of a validation set of samples with known compositions. Ethanol, fructose and residual sugars were well predicted by all the multivariate techniques. Glycerol and glucose showed the highest prediction residuals. The technique may be of practical interest in the routine analysis of these types of wines with low cost in terms of samples, time and personnel. Received: 12 June 1995 / Revised: 5 February 1996 / Accepted: 7 February 1996     

Multivariate quality control solved by one‐class partial least squares regression: identification of adulterated peanut oils by mid‐infrared spectroscopy

The Partial least squares class model (PLSCM) was recently proposed for multivariate quality control based on a partial least squares (PLS) regression procedure. This paper presents a case study of quality control of peanut oils based on mid‐infrared (MIR) spectroscopy and class models, focusing mainly on the following aspects: (i) to explain the meanings of PLSCM components and make comparisons between PLSCM and soft independent modeling of class analogy (SIMCA); (ii) to correct the estimation of the original PLSCM confidence interval by considering a nonzero intercept term for center estimation; (iii) to investigate the potential of MIR spectroscopy combined with class models for identifying peanut oils with low doping concentrations of other edible oils. It is demonstrated that PLSCM is actually different from the ordinary PLS procedure, but it estimates the class center and class dispersion in the framework of a latent variable projection model. While SIMCA projects the original variables onto a few dimensions explaining most of the data variances, PLSCM components consider simultaneously the explained variances and the compactness of samples belonging to the same class. The analysis results indicate PLSCM is an intuitive and easy‐to‐use tool to tackle one‐class problems and has comparable performance with SIMCA. The advantages of PLSCM might be attributed to the great success and well‐established foundations of PLS. For PLSCM, the optimization of model complexity and estimation of decision region can be performed as in multivariate calibration routines. Copyright © 2011 John Wiley & Sons, Ltd.     

Boosting partial least‐squares discriminant analysis with application to near infrared spectroscopic tea variety discrimination

In the present study, boosting has been combined with partial least‐squares discriminant analysis (PLS‐DA) to develop a new pattern recognition method called boosting partial least‐squares discriminant analysis (BPLS‐DA). BPLS‐DA is implemented by firstly constructing a series of PLS‐DA models on the various weighted versions of the original calibration set and then combining the predictions from the constructed PLS‐DA models to obtain the integrative results by weighted majority vote. Coupled with near infrared (NIR) spectroscopy, BPLS‐DA has been applied to discriminate different kinds of tea varieties. As comparisons to BPLS‐DA, the conventional principal component analysis, linear discriminant analysis (LDA), and PLS‐DA have also been investigated. Experimental results have shown that the inter‐variety difference can be accurately and rapidly distinguished via NIR spectroscopy coupled with BPLS‐DA. Moreover, the introduction of boosting drastically enhances the performance of an individual PLS‐DA, and BPLS‐DA is a well‐performed pattern recognition technique superior to LDA. Copyright © 2012 John Wiley & Sons, Ltd.     

Selection of individual variables versus intervals of variables in PLSR

The selection abilities of the two well‐known techniques of variable selection, synergy interval‐partial least‐squares (SiPLS) and genetic algorithm‐partial least‐squares (GA‐PLS), have been examined and compared. By using different simulated and real (corn and metabolite) datasets, keeping in view the spectral overlapping of the components, the influence of the selection of either intervals of variables or individual variables on the prediction performances was examined. In the simulated datasets, with decrease in the overlapping of the spectra of components and cases with components of narrow bands, GA‐PLS results were better. In contrast, the performance of SiPLS was higher for data of intermediate overlapping. For mixtures of high overlapping analytes, GA‐PLS showed slightly better performance. However, significant differences between the results of the two selection methods were not observed in most of the cases. Although SiPLS resulted in slightly better performance of prediction in the case of corn dataset except for the prediction of the moisture content, the improvement obtained by SiPLS compared with that by GA‐PLS was not significant. For real data of less overlapped components (metabolite dataset), GA‐PLS that tends to select far fewer variables did not give significantly better root mean square error of cross‐validation (RMSECV), cross‐validated R² (Q²), and root mean square error of prediction (RMSEP) compared with SiPLS. Irrespective of the type of dataset, GA‐PLS resulted in models with fewer latent variables (LVs). When comparing the computational time of the methods, GA‐PLS is considered superior to SiPLS. Copyright © 2010 John Wiley & Sons, Ltd.     

Slice transform‐based weight updating strategy for PLS

A modified partial least squares (PLS) algorithm is presented on the basis of a novel weight updating strategy. The new weight can handle situations with directions in X space having large variance unrelated to Y , whereas the linear PLS may not work well. In the proposed algorithm, the slice transform technique is introduced to provide a piecewise linear representation of the weight vectors. Then, the corresponding mapping functions are estimated by a least square criterion of the inner relation between the observed variables and the score of response variables. At last, weight vectors are updated by the obtained mapping functions, and the corresponding scores and loadings are calculated with the new weights. An optimal piecewise linear replacements of the PLS weights are achieved by the proposed method. The predictive performances of the new approach and other methods are compared statistically using the Wilcoxon signed rank test. Experimental results show that the proposed method can achieve simpler models, whereas the model performances are at least comparable with PLS and other methods. Copyright © 2012 John Wiley & Sons, Ltd.