Core, Coating, or Corona? The Importance of Considering Protein Coronas in nano-QSPR Modeling of Zeta Potential

To control stability in a biological medium, several factors affecting the zeta potential (ζ) of nanoparticles (NPs) must be considered, including complex interactions between the nanostructure and the composition of the protein corona (PC). Effective in silico methods (based on machine learning and quantitative structure-property relationship (QSPR) models) could help predict and characterize the relationship between the physicochemical properties of NP and the formation of PC and biological outcomes in the medium at an early stage of the experiment. However, the models currently developed are limited to simple descriptors that do not represent the complex interactions between the core, the coating, and their PC fingerprints. To be useful, the models developed should be described as a function of both the structural properties determined by the core and coating of the NPs and the biological medium determined by the formation of the protein corona. We have developed a set of complex descriptors that describe the quantitative relationship between the value of the zeta potential (ζ), core, the coating of NPs, and their PC fingerprints (the so-called nano-QSPR model). The nano-QSPR model was developed based on a genetic algorithm using a partial least-squares regression method (GA-PLS), which is characterized by high external predictive power (Q²_EXT = 0.89). The GA-PLS model was developed using descriptors that describe (i) the core structure (determined by 7 different types of polymer-based NMs in the range of 20 different sizes), (ii) the coating structure with 7 different functional groups, and (iii) 80 different types of protein compositions adsorbed on the surface of the NPs. The presented study answers the question of how complex interactions between the corona and NP determine the zeta potential (ζ) of NP in a given medium. Moreover, our current study is a proof-of-concept that the zeta potential of NPs modeled on the original structure depends not only on the NPs themselves but also on the structure and properties determined by the NP core and coating, as well as the biological medium determined by the formation of the protein corona. On the basis of these results, our studies will be useful in determining the stability and mechanism of cell uptake, toxicity, and ability to predict the zeta potential of compounds not yet tested.

Bilinear and trilinear algorithms utilizing full and selected variables for resolution and quantitation of four components with overlapped spectral signals in bulk and syrup dosage form

Spectrophotometric-assisted chemometric techniques are beneficial for resolving spectral overlapping and are considered comparable to traditional chromatographic methods. In this work, different chemometric approaches were applied for simultaneous determination of Bromhexine HCl (BRHX), Guaifenesin (GUA) and Salbutamol sulphate (SALB) in the presence of Guaiacol (GUAIA), without any prior separation. Two-way and three-way techniques were applied. The resolving power of genetic algorithm (GA-PLS), trilinear partial least square (N-PLS) and multivariate curve resolution (MCR-ALS) were investigated. A set of 17 synthetic samples in the concentration range 10.0-30.0 μg/mL of BRHX, GUA and SALB and 6.0-10.0 μg/mL of GUAIA were used in the construction of the calibration models. Commercially available syrup dosage form was successfully analyzed by the developed methods without interference from formulation additives. The developed models were evaluated through calculation of root mean squared error of prediction (RMSEP), the obtained values were 0.263, 0.419 and 0.342 for BRHX, 0.254, 0.318 and 0.503 for GUA and 0.298, 0.268 and 0.302 for SALB using N-PLS, MCR-ALS and GA-PLS, respectively. The resolving power of the developed models was emphasized through comparison with a reported HPLC method, where no significant difference was found regarding both accuracy and precision.

Optimizing genetic algorithm-partial least squares model of soluble solids content in Fukumoto navel orange based on visible-near-infrared transmittance spectroscopy using discrete wavelet transform

BACKGROUND

The thick rind of Fukumoto navel orange is a great barrier to light penetration, which makes it difficult to evaluate the internal quality of Fukumoto navel orange accurately by visible-near-infrared (Vis-NIR) transmittance spectroscopy. The information carried by the transmission spectrum is limited. Thus, the application of genetic algorithm (GA) for variable selection may not reach the expected results, and selected variables may contain redundancy. In this paper, we present the use of discrete wavelet transforms for optimizing a GA-partial least squares (PLS) model based on Vis-NIR transmission spectra of Fukumoto navel orange. Haar, Db, Sym, Coif and Bior wavelets were used to compress the spectral data selected by GA. Then a PLS model was established based on the variables compressed by each wavelet function.

RESULTS

The use of Db4, Sym4, Coif2 and Bior3.5 succeeded in further simplification of the GA-PLS model by reducing the number of variables by 40-44% without decreasing the prediction accuracy. The application of Bior3.5 not only could reduce the number of variables in the GA-PLS model by 40%, but also increase the value of correlation coefficient of prediction by 1% and decrease the value of root mean square error of prediction by 3%.

CONCLUSIONS

The results indicated that the combination of GA and discrete wavelet transforms for variable selection in the internal quality assessment of Fukumoto navel orange by Vis-NIR transmittance spectroscopy was feasible. © 2019 Society of Chemical Industry.

Docking and QSAR analysis of tetracyclic oxindole derivatives as α-glucosidase inhibitors

The α-glucosidase inhibitors are considered as important agents in drug discovery against diabetes mellitus. Molecular docking and quantitative structure-activity relationship (QSAR) were performed based on a series of tetracyclic oxindole derivatives to elucidate key structural properties affecting inhibitory activity and support the design of new α-glucosidase inhibitors. The molecular docking results demonstrate that at least two hydrogen bonds between Thr681 and Arg676 residues and the oxygen atoms in amid groups have an important role in the optimum binding of inhibitors. In addition, the sum of polar contacts of Arg699, Arg670, Glu792 and Glu301 residues with the α-glucosidase inhibitors have more than one third of total binding free energy. The docked conformations of the inhibitors with the best binding free energy were used to construct QSAR models. As a primary survey and a graphical comparing tool, the partial least squares-discriminant analysis (PLS-DA) technique was successfully employed to classify active and inactive inhibitors. The validated QSAR analysis were performed through genetic algorithm-partial least squares (GA-PLS) and support vector machine (SVM) techniques. The QSAR model reveals that important features of J3D, Mor26 u and HOMA have a high predictive capability (R²_p = 0.837, Q²_LOO = 0.871, R²_LSO = 0.790 and r²_m  = 0.758) using GA-PLS/SVM strategy. Generally, the suggested QSAR analysis based on classification, docking and GA-PLS/SVM strategy may help suggest chemical scaffold to design novel oxindole derivatives as α-glucosidase inhibitors.

Chemometric-assisted spectrophotometric methods and high performance liquid chromatography for simultaneous determination of seven β-blockers in their pharmaceutical products: a comparative study

Chemometric-assisted spectrophotometric methods and high performance liquid chromatography (HPLC) were developed for the simultaneous determination of the seven most commonly prescribed β-blockers (atenolol, sotalol, metoprolol, bisoprolol, propranolol, carvedilol and nebivolol). Principal component regression PCR, partial least square PLS and PLS with previous wavelength selection by genetic algorithm (GA-PLS) were used for chemometric analysis of spectral data of these drugs. The compositions of the mixtures used in the calibration set were varied to cover the linearity ranges 0.7-10 μg ml(-1) for AT, 1-15 μg ml(-1) for ST, 1-15 μg ml(-1) for MT, 0.3-5 μg ml(-1) for BS, 0.1-3 μg ml(-1) for PR, 0.1-3 μg ml(-1) for CV and 0.7-5 μg ml(-1) for NB. The analytical performances of these chemometric methods were characterized by relative prediction errors and were compared with each other. GA-PLS showed superiority over the other applied multivariate methods due to the wavelength selection. A new gradient HPLC method had been developed using statistical experimental design. Optimum conditions of separation were determined with the aid of central composite design. The developed HPLC method was found to be linear in the range of 0.2-20 μg ml(-1) for AT, 0.2-20 μg ml(-1) for ST, 0.1-15 μg ml(-1) for MT, 0.1-15 μg ml(-1) for BS, 0.1-13 μg ml(-1) for PR, 0.1-13 μg ml(-1) for CV and 0.4-20 μg ml(-1) for NB. No significant difference between the results of the proposed GA-PLS and HPLC methods with respect to accuracy and precision. The proposed analytical methods did not show any interference of the excipients when applied to pharmaceutical products.

Application of different chemometric tools in QSAR study of azolo-adamantanes against influenza A virus

Quantitative relationships between molecular structure and azolo-adamantanes derivatives were discovered by different chemometric tools including factor analysis based multiple linear regressions (FA-MLR), principle component regression analysis (PCRA), and genetic algorithm-partial least squares GA-PLS. The FA-MLR describes the effect of geometrical and quantum indices on enzyme inhibition activity of the studied molecules. The quality of PCRA equation was found to be better than those derived from FA-MLR. GA-PLS analysis indicated that the topological (IC4 and MPC06), constitutional (nf) and geometrical (G (N..S] parameters were the most significant ones on influenza A virus activity. Comparison of the different statistical methods employed revealed that GA-PLS represented superior results and it could explain and predict 85% and 77% of variances in the pIC(50) data, respectively.