Sensitive and rapid spectrophotometric methods for sertraline monitoring in pharmaceutical formulations

Purpose: To develop simple, rapid, and selective spectrophotometric methods for the assay of sertraline in a pharmaceutical formulation. Method: These methods depend on the formation of colored ion-pair complexes between the drug and five different reagents; methyl blue (MB), bromophenol red (BPR), methyl green (MG), phenol red (PR), and methyl orange (MO) in B-R buffer solutions of pH ranging from 2.0 – 8.0. The colored products were measured at 668, 747, 647, 717, and 553 nm, respectively. Results: The calibration graphs were linear over the concentration range of 2 – 18 μg/mL for MB and BPR, and 2 – 16 μg/mL for MG, PR, and MO. In all cases, the reaction stoichiometry was 1:1. The proposed methods were successfully applied to solid-dose pharmaceutical preparations (tablets). Excipients in the commercial formulation did not interfere with the analysis. Conclusion: The investigated methods can be recommended for routine analysis and quality control where cost-effectiveness, high specificity of the analytical technique, and time are of great importance

Multiple metabolomics of uropathogenic E. coli reveal different information content in terms of metabolic potential compared to virulence factors.

No single analytical method can cover the whole metabolome and the choice of which platform to use may inadvertently introduce chemical selectivity. In order to investigate this we analysed a collection of uropathogenic Escherichia coli. The selected strains had previously undergone extensive characterisation using classical microbiological methods for a variety of metabolic tests and virulence factors. These bacteria were analysed using Fourier transform infrared (FT-IR) spectroscopy; gas chromatography mass spectrometry (GC-MS) after derivatisation of polar non-volatile analytes; as well as reversed-phase liquid chromatography mass spectrometry in both positive (LC-MS(+ve)) and negative (LC-MS(-ve)) electrospray ionisation modes. A comparison of the discriminatory ability of these four methods with the metabolic test and virulence factors was made using Procrustes transformations to ascertain which methods produce congruent results. We found that FT-IR and LC-MS(-ve), but not LC-MS(+ve), were comparable with each other and gave highly similar clustering compared with the virulence factors tests. By contrast, FT-IR and LC-MS(-ve) were not comparable to the metabolic tests, and we found that the GC-MS profiles were significantly more congruent with the metabolic tests than the virulence determinants. We conclude that metabolomics investigations may be biased to the analytical platform that is used and reflects the chemistry employed by the methods. We therefore consider that multiple platforms should be employed where possible and that the analyst should consider that there is a danger of false correlations between the analytical data and the biological characteristics of interest if the full metabolome has not been measured

Partial least squares with structured output for modelling the metabolomics data obtained from complex experimental designs::A study into the Y-block coding

Partial least squares (PLS) is one of the most commonly used supervised modelling approaches for analysing multivariate metabolomics data. PLS is typically employed as either a regression model (PLS-R) or a classification model (PLS-DA). However, in metabolomics studies it is common to investigate multiple, potentially interacting, factors simultaneously following a specific experimental design. Such data often cannot be considered as a “pure” regression or a classification problem. Nevertheless, these data have often still been treated as a regression or classification problem and this could lead to ambiguous results. In this study, we investigated the feasibility of designing a hybrid target matrix Y that better reflects the experimental design than simple regression or binary class membership coding commonly used in PLS modelling. The new design of Y coding was based on the same principle used by structural modelling in machine learning techniques. Two real metabolomics datasets were used as examples to illustrate how the new Y coding can improve the interpretability of the PLS model compared to classic regression/classification coding

Metabolic fingerprinting of Pseudomonas putida DOT-T1E strains: understanding the influence of divalent cations in adaptation mechanisms following exposure to toluene

Pseudomonas putida strains can adapt and overcome the activity of toxic organic solvents by the employment of several resistant mechanisms including efflux pumps and modification to lipopolysaccharides (LPS) in their membranes. Divalent cations such as magnesium and calcium play a crucial role in the development of solvent tolerance in bacterial cells. Here, we have used Fourier transform infrared (FT-IR) spectroscopy directly on cells (metabolic fingerprinting) to monitor bacterial response to the absence and presence of toluene, along with the influence of divalent cations present in the growth media. Multivariate analysis of the data using principal component-discriminant function analysis (PC-DFA) showed trends in scores plots, illustrating phenotypic alterations related to the effect of Mg2+, Ca2+ and toluene on cultures. Inspection of PC-DFA loadings plots revealed that several IR spectral regions including lipids, proteins and polysaccharides contribute to the separation in PC-DFA space, thereby indicating large phenotypic response to toluene and these cations. Finally, the saturated fatty acid ratio from the FT-IR spectra showed that upon toluene exposure, the saturated fatty acid ratio was reduced, while it increased in the presence of divalent cations. This study clearly demonstrates that the combination of metabolic fingerprinting with appropriate chemometric analysis can result in practicable knowledge on the responses of important environmental bacteria to external stress from pollutants such as highly toxic organic solvents, and indicates that these changes are manifest in the bacterial cell membrane. Finally, we demonstrate that divalent cations improve solvent tolerance in P. putida DOT‑T1E strains

Metabolomics analysis reveals the participation of efflux pumps and ornithine in the response of pseudomonas putida DOT-T1E cells to challenge with propranolol

Efflux pumps are critically important membrane components that play a crucial role in strain tolerance in Pseudomonas putida to antibiotics and aromatic hydrocarbons that result in these toxicants being expelled from the bacteria. Here, the effect of propranolol on P. putida was examined by sudden addition of 0.2, 0.4 and 0.6 mg mL-1 of this β-blocker to several strains of P. putida, including the wild type DOT-T1E and the efflux pump knockout mutants DOT-T1E-PS28 and DOT-T1E-18. Bacterial viability measurements reveal that the efflux pump TtgABC plays a more important role than the TtgGHI pump in strain tolerance to propranolol. Mid-infrared (MIR) spectroscopy was then used as a rapid, high-throughput screening tool to investigate any phenotypic changes resulting from exposure to varying levels of propranolol. Multivariate statistical analysis of these MIR data revealed gradient trends in resultant ordination scores plots, which were related to the concentration of propranolol. MIR illustrated phenotypic changes associated with the presence of this drug within the cell that could be assigned to significant changes that occurred within the bacterial protein components. To complement this phenotypic fingerprinting approach metabolic profiling was performed using gas chromatography mass spectrometry (GC-MS) to identify metabolites of interest during the growth of bacteria following toxic perturbation with the same concentration levels of propranolol. Metabolic profiling revealed that ornithine, which was only produced by P. putida cells in the presence of propranolol, presents itself as a major metabolic feature that has important functions in propranolol stress tolerance mechanisms within this highly significant and environmentally relevant species of bacteria

Carbon foam composites containing carbon nanotubes and graphene oxide as additives for enhanced mechanical, thermal, electrical and catalytic properties

Based on the versatile nature and applications of Carbon foam (CF), up to now many attempts were performed to improve the structure and properties of CF by incorporating various additives in the CF matrix. But these additives improved one property on the cost of another ones. Herein, we have synergistically incorporated multi-walled carbon nanotubes (MWCNTs) and graphene oxide (GO) with varying loadings as additives in the CF matrix via direct pyrolysis to achieve all the desired properties. Seven different types of CF composites including pure CF were prepared and their effect on the structure, mechanical, thermal, electrical and catalytic characteristics has been reported. The results revealed that after the inclusion of MWCNTs and GO contents, the microstructural performance of CF samples was amazingly improved. Additionally, it was observed that mechanical, thermal, electrical and catalytic behaviors of the CF samples were significantly enhances by the increase of nanohybrids. The compressive strength and Young's modulus reveals their optimum limits up to 19.3 and 57.4 MPa respectively on 2 wt.% MWCNTs-GO additive loadings. Similarly, the greatest thermal and electrical conductivities of 30.92 W/m. K and 27.4 × 103 S/m were showed by CF samples having 2 wt. % MWCNTs-GO loadings. Whereas, the decolorization activity of the CF and their nanocomposites were tested against methyl orange dye and it was observed that the sample with enhanced MWCNTs and GO have good decolorization activity and much sustainable than other samples. The 4% CF/MWCNTs-GO decolorized about 76% MO dye under exposure to UV light within 60 min. The decolorization of MO dye increases with increasing nanocomposite dosage and decreasing initial dye concentration

Metabolic analysis of the response of Pseudomonas putida DOT-T1E strains to toluene using Fourier transform infrared spectroscopy and gas chromatography mass spectrometry

Introduction: An exceptionally interesting stress response of Pseudomonas putida strains to toxic substances is the induction of efflux pumps that remove toxic chemical substances from the bacterial cell out to the external environment. To exploit these microorganisms to their full potential a deeper understanding of the interactions between the bacteria and organic solvents is required. Thus, this study focuses on investigation of metabolic changes in P. putida upon exposure to toluene. Objective: Investigate observable metabolic alterations during interactions of three strains of P. putida (DOT-T1E, and its mutants DOT-T1E-PS28 and DOT-T1E-18) with the aromatic hydrocarbon toluene. Methods: The growth profiles were measured by taking optical density (OD) measurement at 660 nm (OD660) at various time points during incubation. For fingerprinting analysis, Fourier-transform infrared (FT-IR) spectroscopy was used to investigate any phenotypic changes resulting from exposure to toluene. Metabolic profiling analysis was performed using gas chromatography-mass spectrometry (GC–MS). Principal component—discriminant function analysis (PC-DFA) was applied to the FT-IR data while multiblock principal component analysis (MB-PCA) and N-way analysis of variance (N-way ANOVA) were applied to the GC–MS data. Results: The growth profiles demonstrated the effect of toluene on bacterial cultures and the results suggest that the mutant P. putida DOT-T1E−18 was more sensitive (significantly affected) to toluene compared to the other two strains. PC-DFA on FT-IR data demonstrated the differentiation between different conditions of toluene on bacterial cells, which indicated phenotypic changes associated with the presence of the solvent within the cell. Fifteen metabolites associated with this phenotypic change, in P. putida due to exposure to solvent, were from central metabolic pathways. Investigation of MB-PCA loading plots and N-way ANOVA for condition | strain × time blocking (dosage of toluene) suggested ornithine as the most significant compound that increased upon solvent exposure. Conclusion: The combination of metabolic fingerprinting and profiling with suitable multivariate analysis revealed some interesting leads for understanding the mechanism of Pseudomonas strains response to organic solvent exposure