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Supramolecular carriers as a targeted drug delivery system have a significant role in managing the pharmacokinetics and pharmacodynamics of drugs. Therefore, in this project, we used cyclodextrin (CD) as a host molecule for tamoxifen (TMX) and methotrexate (MTX) to evaluate apoptosis mechanism and statistical patterns of release. The flow cytometry analysis showed that combination therapy by anticancer drugs was successful in apoptosis induction compared to free TMX and MTX. Also, real-time PCR and western blot analyses were performed to measure gene expression of Bak1, Bclx, and caspase-3 in estrogen and folate receptor-positive MCF-7 cell lines. All results confirmed that MNPs containing TMX and MTX can significantly increase Bak1/Bclx ratios plus the activity of caspase-3, while free MTX and free TMX showed no effects on triggering apoptosis after 24 h of exposure. According to results of pharmacokinetic studies onto TMX and MTX release, it is revealed that both models that consist of Sahlin-Peppas and Ritger-Peppas create the good fitting curves more than other statistical patterns at both low and high temperatures (37°C and 42°C). For this reason, activation parameters (ΔH‡, ΔS‡, and ΔG‡) were calculated to estimate the favorable model. The lower energy barrier indicates that the Sahlin-Peppas is the favorable model with both diffusion and relaxation process. Therefore, combination therapy by two anticancer drugs, TMX and MTX, utilizing cyclodextrin-based host–guest supramolecular magnetic nanoparticles (CD-MNPs) provided new results for induction of apoptosis and pharmacokinetic model.

We propose a methodology to select essential spectral pixels (ESPs) of chemical images. These pixels are on the outer envelope of the principal component scores of the data and can be identified by convex-hull computation. As ESPs carry all the linearly mixed spectral information, large hyperspectral images can be dramatically reduced before multivariate curve resolution (MCR) analysis. We investigated chemical images of different spectroscopies, sizes, and complexities and show that the analysis of full data sets of hundreds of thousands of spectral pixels only require a few tenths of them.

G-quadruplexes are formed by association of DNA strands containing multiple contiguous guanines. The capability of drugs to induce formation or stabilize G-quadruplexes is an active area of cancer therapy investigation. We evaluated interaction between two cationic tetrapyridinoporphyrazines with Na+and K+forms of human telomeric G-quadruplex DNA by chemometrics method. An antiparallel quadruplex structure was found to be stabilized more greatly by these two isomers in the presence of K+and Na+ions. Equilibrium model of a ligand binding with DNA oligomer has been considered as a process of small molecule adsorption on to a lattice of multiple binding sites. In multivariate analysis methods, it is accounted this assertion that during saturation of the macromolecule by a ligand should expect effect of cooperativity due to changes in DNA conformation or the mutual influence between bound ligands. Such phenomenon cannot be entirely described by the classical stepwise complex formation model. From the results of absorption and circular dichroism measurements, the unique site for the ligand binding is suggested to be the intercalating in guanine tetrad plane quadruplex. We found a 2:1 binding stoichiometry for both ligands and Tel22.

Aims & Scope: In this research, 8 variable selection approaches were used to investigate the effect of variable selection on the predictive power and stability of CoMFA models. Materials & Methods: Three data sets including 36 EPAC antagonists, 79 CD38 inhibitors and 57 ATAD2 bromodomain inhibitors were modelled by CoMFA. First of all, for all three data sets, CoMFA models with all CoMFA descriptors were created then by applying each variable selection method a new CoMFA model was developed so for each data set, 9 CoMFA models were built. Obtained results show noisy and uninformative variables affect CoMFA results. Based on created models, applying 5 variable selection approaches including FFD, SRD-FFD, IVE-PLS, SRD-UVEPLS and SPA-jackknife increases the predictive power and stability of CoMFA models significantly. Result & Conclusion: Among them, SPA-jackknife removes most of the variables while FFD retains most of them. FFD and IVE-PLS are time consuming process while SRD-FFD and SRD-UVE-PLS run need to few seconds. Also applying FFD, SRD-FFD, IVE-PLS, SRD-UVE-PLS protect CoMFA countor maps information for both fields.

Distinguishing isomeric saccharides poses a major challenge for analytical workflows based on (liquid chromatography) mass spectrometry (LC–MS). In recent years, many studies have proposed infrared ion spectroscopy as a possible solution as the orthogonal, spectroscopic characterization of mass-selected ions can often distinguish isomeric species that remain unresolved using conventional MS. However, the high conformational flexibility and extensive hydrogen bonding in saccharides cause their room-temperature fingerprint infrared spectra to have broad features that often lack diagnostic value. Here, we show that room-temperature infrared spectra of ion-complexed saccharides recorded in the previously unexplored far-infrared wavelength range (300–1000 cm–1) provide well-resolved and highly diagnostic features. We show that this enables distinction of isomeric saccharides that differ either by their composition of monosaccharide units and/or the orientation of their glycosidic linkages. We demonstrate the utility of this approach from single monosaccharides up to isomeric tetrasaccharides differing only by the configuration of a single glycosidic linkage. Furthermore, through hyphenation with hydrophilic interaction liquid chromatography, we identify oligosaccharide biomarkers in patient body fluid samples, demonstrating a generalized and highly sensitive MS-based method for the identification of saccharides found in complex sample matrices.