Application of near-infrared spectroscopy combined with multivariate analysis in monitoring of crude heparin purification process

Nuclear magnetic resonance spectroscopy as an elegant tool for a complete quality control of crude heparin material

Nuclear magnetic resonance (NMR) spectrometric methods for the quantitative analysis of pure heparin in crude heparin is proposed. For quantification, a two-step routine was developed using a USP heparin reference sample for calibration and benzoic acid as an internal standard. The method was successfully validated for its accuracy, reproducibility, and precision. The methodology was used to analyze 20 authentic porcine heparinoid samples having heparin content between 4.25 w/w % and 64.4 w/w %. The characterization of crude heparin products was further extended to a simultaneous analysis of these common ions: sodium, calcium, acetate and chloride. A significant, linear dependence was found between anticoagulant activity and assayed heparin content for thirteen heparinoids samples, for which reference data were available. A Diffused-ordered NMR experiment (DOSY) can be used for qualitative analysis of specific glycosaminoglycans (GAGs) in heparinoid matrices and, potentially, for quantitative prediction of molecular weight of GAGs. NMR spectrometry therefore represents a unique analytical method suitable for the simultaneous quantitative control of organic and inorganic composition of crude heparin samples (especially heparin content) as well as an estimation of other physical and quality parameters (molecular weight, animal origin and activity).

Is infrared spectroscopy combined with multivariate analysis a promising tool for heparin authentication?

The investigation of the possibility to determine various characteristics of powder heparin (n = 115) was carried out with infrared spectroscopy. The evaluation of heparin samples included several parameters such as purity grade, distributing company, animal source as well as heparin species (i.e. Na-heparin, Ca-heparin, and heparinoids). Multivariate analysis using principal component analysis (PCA), soft independent modelling of class analogy (SIMCA), and partial least squares - discriminant analysis (PLS-DA) were applied for the modelling of spectral data. Different pre-processing methods were applied to IR spectral data; multiplicative scatter correction (MSC) was chosen as the most relevant. Obtained results were confirmed by nuclear magnetic resonance (NMR) spectroscopy. Good predictive ability of this approach demonstrates the potential of IR spectroscopy and chemometrics for screening of heparin quality. This approach, however, is designed as a screening tool and is not considered as a replacement for either of the methods required by USP and FDA.

Development of near infrared spectroscopy methodology for human albumin determination using a new calibration approach

Though near infrared spectroscopy (NIRS) has been applied widely in the field of pharmaceutical, there is still a bottleneck which limits its development. The main barrier is that conventional NIRS calibration method is based on experiences and trials, which causes the established model is not stable and difficult to explain. Therefore, a new strategy which was based on design of experiment (DoE) combined with statistical analysis was provided to solve the limitations. A pre-processing method library was set up first and orthogonal experiment design was then introduced to investigate the effects and interactions of different pre-processing methods. Paired t-test was used to select the most suitable pre-processing method. Finally, the pre-processing method selected above and three commonly used variable selection methods (CARS, UVE, VIP) were combined randomly to select the best calibration model. The results showed that the new calibration approach could provide a reasonable way for researchers to establish a more stable, objective calibration model.

Study on feasibility of determination of glucosamine content of fermentation process using a micro NIR spectrometer

N-acetyl-d-glucosamine (GlcNAc) is a microbial fermentation product, and NIR spectroscopy is an effective process analytical technology (PAT) tool in detecting the key quality attribute: the GlcNAc content. Meanwhile, the design of NIR spectrometers is under the trend of miniaturization, portability and low-cost nowadays. The aim of this study was to explore a portable micro NIR spectrometer with the fermentation process. First, FT-NIR spectrometer and Micro-NIR 1700 spectrometer were compared with simulated fermentation process solutions. The R_c², R_p², RMSECV and RMSEP of the optimal FT-NIR and Micro-NIR 1700 models were 0.999, 0.999, 3.226 g/L, 1.388 g/L and 0.999, 0.999, 1.821 g/L, 0.967 g/L. Passing-Bablok regression method and paired t-test results showed there were no significant differences between the two instruments. Then the Micro-NIR 1700 was selected for the practical fermentation process, 135 samples from 10 batches were collected. Spectral pretreatment methods and variables selection methods (BiPLS, FiPLS, MWPLS and CARS-PLS) for PLS modeling were discussed. The R_c², R_p², RMSECV and RMSEP of the optimal GlcNAc content PLS model of the practical fermentation process were 0.994, 0.995, 2.792 g/L and 1.946 g/L. The results have a positive reference for application of the Micro-NIR spectrometer. To some extent, it could provide theoretical supports in guiding the microbial fermentation or the further assessment of bioprocess.

Biomarkers for physical frailty and sarcopenia

Physical frailty (PF) and sarcopenia are major health issues in geriatric populations, given their high prevalence and association with several adverse outcomes. Nevertheless, the lack of an univocal operational definition for the two conditions has so far hampered their clinical implementation. Existing definitional ambiguities of PF and sarcopenia, together with their complex underlying pathophysiology, also account for the absence of robust biomarkers that can be used for screening, diagnostic and/or prognostication purposes. This review provides an overview of currently available biological markers for PF and sarcopenia, as well as a critical appraisal of strengths and weaknesses of traditional procedures for biomarker development in the field. A novel approach for biomarker identification and validation, based on multivariate methodologies, is also discussed. This strategy relies on the multidimensional modeling of complementary biomarkers to cope with the phenotypical and pathophysiological complexity of PF and sarcopenia. Biomarkers identified through the implementation of multivariate strategies may be used to support the detection of the two conditions, track their progression over time or in response to interventions, and reveal the onset of complications (e.g., mobility disability) at a very early stage.

Analytes related to erythrocyte metabolism are reliable biomarkers for preanalytical error due to delayed plasma processing in metabolomics studies

BACKGROUND

Delaying plasma separation after phlebotomy (processing delay) can cause perturbations of numerous small molecule analytes. This poses a major challenge to the clinical application of metabolomics analyses. In this study, we further define the analyte changes that occur during processing delays and generate a model for the post hoc detection of this preanalytical error.

METHODS

Using an untargeted metabolomics platform we analyzed EDTA-preserved plasma specimens harvested after processing delays lasting from minutes to days. Identified biomarkers were tested on (i) a test-set of samples exposed to either minimal (n=28) or long delays (n=40) and (ii) samples collected in a clinical setting for metabolomics analysis (n=141).

RESULTS

A total of 149 of 803 plasma analytes changed significantly during processing delays lasting 0-20h. Biomarkers related to erythrocyte metabolism, e.g., 5-oxoproline, lactate, and an ornithine/arginine ratio, were the strongest predictors of plasma separation delays, providing 100% diagnostic accuracy in the test set. Together these biomarkers could accurately predict processing delays >2h in a pilot study and we found evidence of sample mishandling in 4 of 141 clinically derived specimens.

CONCLUSIONS

Our study highlights the widespread effects of processing delays and proposes that erythrocyte metabolism creates a reproducible signal that can identify mishandled specimens in metabolomics studies.

On-line monitoring the extract process of Fu-fang Shuanghua oral solution using near infrared spectroscopy and different PLS algorithms

An on-line near infrared (NIR) spectroscopy monitoring method with an appropriate multivariate calibration method was developed for the extraction process of Fu-fang Shuanghua oral solution (FSOS). On-line NIR spectra were collected through two fiber optic probes, which were designed to transmit NIR radiation by a 2mm flange. Partial least squares (PLS), interval PLS (iPLS) and synergy interval PLS (siPLS) algorithms were used comparatively for building the calibration regression models. During the extraction process, the feasibility of NIR spectroscopy was employed to determine the concentrations of chlorogenic acid (CA) content, total phenolic acids contents (TPC), total flavonoids contents (TFC) and soluble solid contents (SSC). High performance liquid chromatography (HPLC), ultraviolet spectrophotometric method (UV) and loss on drying methods were employed as reference methods. Experiment results showed that the performance of siPLS model is the best compared with PLS and iPLS. The calibration models for AC, TPC, TFC and SSC had high values of determination coefficients of (R(2)) (0.9948, 0.9992, 0.9950 and 0.9832) and low root mean square error of cross validation (RMSECV) (0.0113, 0.0341, 0.1787 and 1.2158), which indicate a good correlation between reference values and NIR predicted values. The overall results show that the on line detection method could be feasible in real application and would be of great value for monitoring the mixed decoction process of FSOS and other Chinese patent medicines.

Development, validation and influence factor analysis of a near-infrared method for the molecular weight determination of xanthan gum

A practical molecular weight determination model of xanthan gum (XG), based on near-infrared (NIR) spectroscopy, was built in this study. Two sample measurement modules, integrating sphere module and fiber-optic probe module, were compared, and the best partial least square (PLS) regression model was based on fiber-optic probe module. The values of coefficient of determination in calibration (R(2)c), coefficient of determination in prediction (R(2)p), residual predictive deviation (RPD) and root mean square error of prediction (RMSEP) were 0.967, 0.975, 6.028 and 0.250×10(6)Da, respectively. The molecular weight range, linearity, accuracy and precision of the established method were also validated. Furthermore, influence factors on this method were discussed in order to establish an appropriate measurement protocol. Results showed that the proposed NIR method may be suitable for practical applications in manufacturing plants and probably be accepted as a good alternative approach for fast determination of molecular weight of XG in production process.

A tool for selective inline quantification of co-eluting proteins in chromatography using spectral analysis and partial least squares regression

Selective quantification of co-eluting proteins in chromatography is usually performed by offline analytics. This is time-consuming and can lead to late detection of irregularities in chromatography processes. To overcome this analytical bottleneck, a methodology for selective protein quantification in multicomponent mixtures by means of spectral data and partial least squares regression was presented in two previous studies. In this paper, a powerful integration of software and chromatography hardware will be introduced that enables the applicability of this methodology for a selective inline quantification of co-eluting proteins in chromatography. A specific setup consisting of a conventional liquid chromatography system, a diode array detector, and a software interface to Matlab® was developed. The established tool for selective inline quantification was successfully applied for a peak deconvolution of a co-eluting ternary protein mixture consisting of lysozyme, ribonuclease A, and cytochrome c on SP Sepharose FF. Compared to common offline analytics based on collected fractions, no loss of information regarding the retention volumes and peak flanks was observed. A comparison between the mass balances of both analytical methods showed, that the inline quantification tool can be applied for a rapid determination of pool yields. Finally, the achieved inline peak deconvolution was successfully applied to make product purity-based real-time pooling decisions. This makes the established tool for selective inline quantification a valuable approach for inline monitoring and control of chromatographic purification steps and just in time reaction on process irregularities.