Facilitating the performance of qNMR analysis using automated quantification and results verification

Self Cite

In our recent paper, a new technique for automated spectra integration and quality control of the acquired results in qNMR was developed and validated (Monakhova & Diehl, Magn. Res. Chem. 2017, doi: 10.1002/mrc.4591). The present study is focused on the influence of acquisition and postacquisition parameters on the developed automated routine in particular, and on the quantitative NMR (qNMR) results in general, which has not been undertaken previously in a systematic and automated manner. Results are presented for a number of model mixtures and authentic pharmaceutical products measured on 500- and 600-MHz NMR spectrometers. The influence of the most important acquisition (spectral width, transmitter [frequency] offset, number of scans, and time domain) and processing (size of real spectrum, deconvolution, Gaussian window multiplication, and line broadening) parameters for qNMR was automatically investigated. Moderate modification of the majority of the investigated parameters from default instrument settings within evaluated ranges does not significantly affect the trueness and precision of the qNMR. Lite Gaussian window multiplication resulted in accuracy improvement of the qNMR output and is recommended for routine measurements. In general, given that the acquisition and processing parameters were selected based on the presented guidelines, automated qNMR analysis can be employed for reproducible high-precision concentration measurements in practice.

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Practical guide for selection of ¹H qNMR acquisition and processing parameters confirmed by automated spectra evaluation

Monakhova

Diehl²

2017

Self Cite

“…The fully automated manner needs simple manual improvement or quick check for plausibility only, which is of benefit to the uniform standard on data processing. It has been tested on binary mixtures of internal standards as well as pharmaceutical products …”

Section: Discussionmentioning

confidence: 99%

Quantitative determination and validation of octreotide acetate using ¹H‐NMR spectroscopy with internal standard method

Zhang

Bai

et al. 2017

Quantitative nuclear magnetic resonance (qNMR) is a well-established technique in quantitative analysis. We presented a validated H-qNMR method for assay of octreotide acetate, a kind of cyclic octopeptide. Deuterium oxide was used to remove the undesired exchangeable peaks, which was referred to as proton exchange, in order to make the quantitative signals isolated in the crowded spectrum of the peptide and ensure precise quantitative analysis. Gemcitabine hydrochloride was chosen as the suitable internal standard. Experimental conditions, including relaxation delay time, the numbers of scans, and pulse angle, were optimized first. Then method validation was carried out in terms of selectivity, stability, linearity, precision, and robustness. The assay result was compared with that by means of high performance liquid chromatography, which is provided by Chinese Pharmacopoeia. The statistical F test, Student's t test, and nonparametric test at 95% confidence level indicate that there was no significant difference between these two methods. qNMR is a simple and accurate quantitative tool with no need for specific corresponding reference standards. It has the potential of the quantitative analysis of other peptide drugs and standardization of the corresponding reference standards.

Monitoring daily routine performance in quantitative NMR (qNMR) spectroscopy: Is the system suitability test necessary?

Monakhova

Diehl²

2018

Self Cite

Nuclear magnetic resonance spectrometry (NMR) finds numerous applications in pharmacy, cosmetic, and food control as well as in developing tools for “big data” analysis. However, there remains a need for automated tools to assess instrument system suitability in real time for each particular routine sample. An automated procedure has been introduced to monitor a number of characteristics (resolution, symmetry, and half width) in real time after the measurement of two samples distributed by the vendor (0.3% CHCl3 in acetone‐d6 with tetramethylsilane and 2 mM sucrose in H2O‐D2O). The results over 11 months were discussed in terms of average values, standard deviations, and spectrometer variability. Moreover, multivariate statistical procedure was implemented to evaluate metrics generated from three NMR spectrometers. Performance of three NMR spectrometers (500 MHz with BBO Prodigy Cryoprobe, 500 MHz with BBFOPLUS SmartProbe, and 600 MHz with BBO Cryoprobe) differed significantly. The developed routine was also applied to calculate the performance characteristics during routine quantitative NMR experiments. The procedure was evaluated for NMR spectra of 659 active pharmaceutical ingredients dissolved in CDCl3, DMSO, and CH3OD. This test is more preferable than the routine procedure using standard solutions because the performance is estimated separately for each matrix at the specific time point of measurements. Our automated routine is the ideal tool for any NMR laboratory. In full automation, the NMR data are validated directly for each sample, making unnecessary daily measurements of standard solutions and manual evaluation to their NMR spectra.