Improved Antibacterial Activity of Water-Soluble Nanoformulated Kaempferol and Combretastatin Polyphenolic Compounds

Kaempferol and combretastatin are polyphenolic compounds derived from plant sources which are known for their antibacterial activity. However, owing to their large size and water insolubility, their antibacterial activity is limited. In this context, the present study focused on the nanoformulation of kaempferol (NF-k) and combretastatin (NF-c) and their influence on water solubility and antibacterial properties. The NF-k and NF-c were prepared using the solvent evaporation method and were thoroughly characterized for evaluating the morphology, molecular vibrations, size, etc. Based on the results, it is observed that the pristine forms of kaempferol and combretastatin drugs get nanoformulated and completely soluble in water. Using particle size analyzer, the particle sizes of NF-k and NF-c were estimated as 334 nm and 260 nm, respectively, which are very fine compared to pristine kaempferol and combretastatin (5193 nm and 1217 nm, respectively). The molecular vibrations that exist in NF-k and NF-c were confirmed by the Fourier transform infrared spectra, where the nanoformulated drug showed lower intensities than the pristine form of kaempferol and combretastatin. The drug release kinetics of the nanoformulated drugs were carried out using the dialysis membrane method and were compared with their pristine forms. Owing to the size effect, the NF-k and NF-c release up to 50% of the drug in a sustained manner till 50 h showing twofold higher concentration than the control where it released 25%. The antibacterial activity was assessed by measuring the optical density at 600 nm using UV-vis spectrophotometer and displayed significant activity against gram-positive Staphylococcus aureus strain. The mechanisms behind the antibacterial activity of NF-k and NF-c were discussed in detail. The activation of ATP-dependent efflux pump system and the blockage of porin channels could be the cause for the bactericidal activity. Our understanding of efflux pumps and their role in antibacterial activity is still in its early stages. No studies have been performed to date using nanoformulations of kaempferol and combretastatin to investigate their roles. This complicates the determination of the exact mechanisms acting against bacterial growth when using nanoformulation drugs. Our increasing knowledge of water-soluble nanoformulation drugs and their roles in reduced bacterial activity will pave the way to developing effective treatments in the future.

Quantitative prediction model for affinity of drug–target interactions based on molecular vibrations and overall system of ligand-receptor

Background The study of drug–target interactions (DTIs) affinity plays an important role in safety assessment and pharmacology. Currently, quantitative structure–activity relationship (QSAR) and molecular docking (MD) are most common methods in research of DTIs affinity. However, they often built for a specific target or several targets, and most QSAR and MD methods were based either on structure of drug molecules or on structure of receptors with low accuracy and small scope of application. How to construct quantitative prediction models with high accuracy and wide applicability remains a challenge. To this end, this paper screened molecular descriptors based on molecular vibrations and took molecule-target as a whole system to construct prediction models with high accuracy-wide applicability based on dissociation constant (Kd) and concentration for 50% of maximal effect (EC50), and to provide reference for quantifying affinity of DTIs. Results After comprehensive comparison, the results showed that RF models are optimal models to analyze and predict DTIs affinity with coefficients of determination (R2) are all greater than 0.94. Compared to the quantitative models reported in literatures, the RF models developed in this paper have higher accuracy and wide applicability. In addition, E-state molecular descriptors associated with molecular vibrations and normalized Moreau-Broto autocorrelation (G3), Moran autocorrelation (G4), transition-distribution (G7) protein descriptors are of higher importance in the quantification of DTIs. Conclusion Through screening molecular descriptors based on molecular vibrations and taking molecule-target as whole system, we obtained optimal models based on RF with more accurate-widely applicable, which indicated that selection of molecular descriptors associated with molecular vibrations and the use of molecular-target as whole system are reliable methods for improving performance of models. It can provide reference for quantifying affinity of DTIs.

Molecular Vibrations Are Not Asymmetric

There is considerable confusion when naming vibrations in infrared and Raman spectra. One of the most common errors is the identification of some stretching and bending vibrations as “asymmetric”. There are no asymmetric vibrations as such vibrations incur rotations and translations. The correct term is antisymmetric and it is demonstrated, through molecular symmetry operations, why this is the correct term.

Quantitative Prediction Model for Affinity of Drug-Target Interactions Based on Molecular Vibration and Overall System of Ligand-Receptor

Background: the study of drug-target interactions (DTIs) affinity plays an important role in safety assessment and pharmacology. Currently, quantitative structure-activity relationship (QSAR) and molecular docking (MD) are most common methods in research of DTIs affinity. However, they often built for a specific target or several targets and most QSAR and MD were based either only on structure of drug molecules or on structure of targets with low accuracy and small scope of application. How to construct quantitative prediction models with high accuracy with wide applicability remains a challenge. To this end, this paper screened molecular descriptors based on molecular vibrations and took molecule-target as a whole system to construct prediction model with high accuracy-wide applicability based on Kd and EC50, and to provide reference for quantifying affinity of DTIs.Methods: Through parametric characterization based on molecular vibrations and protein sequences, taking molecule-target as whole system and feature selection of drug molecule-target, we constructed feature datasets of DTIs quantified by Kd and EC50, respectively. Then, prediction models were constructed using above datasets and SVM, RF and ANN. In addition, optimal models were selected for application evaluation and comprehensive comparison.Results: Under ten-fold cross-validation, evaluation parameters based on RF for EC50 dataset are as follows: R2 (RF) of training and test sets are 0.9611, 0.9641; MSE (RF) of training and test sets are 0.0891, 0.0817. Evaluation parameters based on RF for Kd dataset are as follows: R2 (RF) of training and test sets are 0.9425, 0.9485; MSE (RF) of training and test sets are 0.1208, 0.1191. After comprehensive comparison, the results showed that RF model in this paper is optimal model. In application evaluation of RF model, the errors of most prediction results were in range of 1.5-2.0.Conclusion: Through screening molecular descriptors based on molecular vibrations and taking molecule-target as whole system, we obtained optimal model based on RF with more accurate-widely applicable, which indicated that selection of molecular descriptors associated with molecular vibrations and the use of molecular-target as whole system are reliable methods for improving performance of model. It can provide reference for quantifying affinity of DTIs.