The process of silver immobilization onto and/or into bovine lactoferrin (LTF), the physicochemical properties of bovine lactoferrin and obtained silver-lactoferrin complexes, as well as antibacterial activity of silver-lactoferrin complexes were investigated in this work. Kinetic study of the silver immobilization into lactoferrin was carried out using batch sorption techniques. Spectrometric (MALDI-TOF/TOF-MS, ICP-MS), spectroscopic (FTIR, SERS), electron microscopic (TEM) and electrophoretic (I-DE) techniques, as well as zeta potential measurements, were applied for characterization of LTF and binding nature of silver in Ag-LTF complexes. On the basis of the results of the kinetics study, it was established that the silver binding to LTF is a heterogeneous process involving two main stages: (i) internal diffusion and sorption onto external surface of lactoferrin globules; and (ii) internal diffusion and binding into lactoferrin globule structure. Spectroscopic techniques combined with TEM analysis confirmed the binding process. Molecular dynamics (MD) analysis was carried out in order to simulate the mechanism of the binding process, and locate potential binding sites, as well as complement the experimental findings. Quantum mechanics (QM) simulations were performed utilizing density functional theory (DFT) in order to support the reduction mechanism of silver ions to elemental silver. Antimicrobial activity of synthesized lactoferrin complexes against selected clinical bacteria was confirmed using flow cytometry and antibiograms.

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In this work, performance of five nature-inspired optimization algorithms, genetic algorithm (GA), particle swarm optimization (PSO), artificial bee colony (ABC), firefly algorithm (FA), and flower pollination algorithm (FPA), was compared in molecular descriptor selection for development of quantitative structure-retention relationship (QSRR) models for 83 peptides that originate from eight model proteins. The matrix with 423 descriptors was used as input, and QSRR models based on selected descriptors were built using partial least squares (PLS), whereas root mean square error of prediction (RMSEP) was used as a fitness function for their selection. Three performance criteria, prediction accuracy, computational cost, and the number of selected descriptors, were used to evaluate the developed QSRR models. The results show that all five variable selection methods outperform interval PLS (iPLS), sparse PLS (sPLS), and the full PLS model, whereas GA is superior because of its lowest computational cost and higher accuracy (RMSEP of 5.534%) with a smaller number of variables (nine descriptors). The GA-QSRR model was validated initially through Y-randomization. In addition, it was successfully validated with an external testing set out of 102 peptides originating from Bacillus subtilis proteomes (RMSEP of 22.030%). Its applicability domain was defined, from which it was evident that the developed GA-QSRR exhibited strong robustness. All the sources of the model's error were identified, thus allowing for further application of the developed methodology in proteomics.

Pharmaceutical cocrystals are a novel drug form with the potential to enhance pharmaceutical properties, including the solubility and dissolution behavior for BCS class II drug substances such as indomethacin (IMC). Recently, we reported that pure indomethacin–saccharin (IMC–SAC) cocrystals were prepared via anti-solvent crystallization. In this study, we investigated the solubility behavior of IMC–SAC cocrystals and individual components in methanol–water cosolvent. Also, the phase solubility diagram (PSD) of the cocrystal was determined to increase our understanding of cocrystallization. The criterion for pure IMC–SAC cocrystal formation was proposed and verified through supporting experiments performed with different concentrations. We also found that S cocrystal and S cocrystal/S drug are critical factors for the design of the cocrystallization process via anti-solvent addition. Real-time monitoring of the cocrystallization process was performed using an in-line near-infrared (NIR) system. Principal component analysis (PCA) was applied to NIR spectral analysis. Based on the PCA results, distinct differences were observed in the pathways of IMC–SAC cocrystal formation depending on the initial concentrations.

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