Optimization of a chromatographic process for the purification of saponins in Panax notoginseng extract using a design space approach

Self Cite

A novel analytical quality by design approach for developing a chromatographic fingerprint was established for analyzing complex traditional Chinese medicine, using a licorice standard decoction as an example. Considering the characteristics of integrity and ambiguity, the resolution of eight common peaks, total peak number, capacity factor distributions, and peak purity were selected as potential critical method attributes for assessing the quality of the chromatographic fingerprint. A central composite design was used to evaluate the relationship between critical method attributes and critical method parameters, including column temperature, wavelength, flow rate, formic‐acid concentration, and gradient parameters. A standard probability method was employed to calculate the design space of the fingerprint analysis parameters and evaluate the robustness of the methodology. The optimized high‐performance liquid chromatography fingerprint conditions were acetonitrile and 0.1% formic acid water gradient elution (0‐5 min, 5–19% A; 5–10 min, 19% A; 10–50 min, 19–42% A; 50–54 min, 42–100% A; 54–60 min, 100% A), column temperature 25±5°C, detection wavelength 265 nm. The design space of fingerprint analytical method based on the analytical quality by design approach not only met the requirements of the fingerprint analysis, but also improved the robustness and applicability of the fingerprint method.

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Establishing the chromatographic fingerprint of traditional Chinese medicine standard decoction based on quality by design approach: A case study of Licorice

Zhang

Wang

Chen

et al. 2019

Self Cite

“…F. H. Chen and has been used as a main raw material of many botanical drug products (such as Xuesaitong Injection) in China improving product purity [2]. F. H. Chen and has been used as a main raw material of many botanical drug products (such as Xuesaitong Injection) in China improving product purity [2].…”

Section: Introductionmentioning

confidence: 99%

“…

The chromatographic elution process is a key step in the production of notoginseng total saponins. Notoginseng total saponins (NTS) are the main active ingredients of Sanqi and have pharmacological activities such as anti-atherosclerotic, haemostatic, neuroprotective, anti-inflammatory, antiaging, and antioxidant effects [1,2]. In this study, convolutional neural networks, one of the most popular deep learning methods, were used to develop quantitative calibration models based on in-line near-infrared spectroscopy for notoginsenoside R 1 , ginsenosides Rg 1 , Re, Rb 1 and Rd, and their sum concentration, with root mean square error of prediction values of 0.87, 2.76, 0.60, 1.57, 0.28, and 4.99 mg/mL, respectively.

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mentioning

confidence: 99%

Combining convolutional neural networks and in‐line near‐infrared spectroscopy for real‐time monitoring of the chromatographic elution process in commercial production of notoginseng total saponins

Yan

Zhang

et al. 2019

Self Cite

The chromatographic elution process is a key step in the production of notoginseng total saponins. Due to quality variability of loading samples and resin capacity decreasing over cycle time, saponins, especially the five main saponins of notoginseng total saponins, need to be monitored in real time during the elution process. In this study, convolutional neural networks, one of the most popular deep learning methods, were used to develop quantitative calibration models based on in-line near-infrared spectroscopy for notoginsenoside R 1 , ginsenosides Rg 1 , Re, Rb 1 and Rd, and their sum concentration, with root mean square error of prediction values of 0.87, 2.76, 0.60, 1.57, 0.28, and 4.99 mg/mL, respectively. Partial least squares calibration models were also developed for model performance comparison. Results show predicted concentration profiles outputted by both the convolutional neural network models and partial least squares models show agreements with the real trends defined by reference measurements, and can be used for elution process monitoring and endpoint determination. To the best of our knowledge, this is the first reported case study of combining convolutional neural networks and in-line near-infrared spectroscopy for monitoring of the chromatographic elution process in commercial production of botanical drug products. K E Y W O R D Sbotanical drugs, chromatographic elution process, convolutional neural network, deep learning, nearinfrared spectroscopy Article Related Abbreviations: BN, batch normalization; CNN, convolutional neural network; FC, fully connected; NIR, near-infrared; NTS, notoginseng total saponins; PLS, partial least squares; ReLU, rectified linear unit; RMSEP, root mean square error of prediction.[1-3] and a dietary supplement in USA [4]. Notoginseng total saponins (NTS) are the main active ingredients of Sanqi and have pharmacological activities such as anti-atherosclerotic, haemostatic, neuroprotective, anti-inflammatory, antiaging, and antioxidant effects [1,2]. Notoginsenoside R 1 , ginsenosides Rg 1 , Re, Rb 1 , and Rd are the five main saponins of NTS. These five saponins are chosen as index components for quality evaluation of NTS [5] and have been taken as qualitymarkers (Q-markers) of NTS [6].As a key step in the production of NTS, the column chromatographic process is used for removing impurities and

“…DS can be generated by the simultaneous study of all chromatographic critical process parameters such as gradient time, column oven temperature, flow rate, pH of mobile phase, etc. by using Designs of Experiments (DoE) [26][27][28][29][30][31][32][33][34]. Finally, the in silico toxicity of the characterized impurities was predicted using TOPKAT (toxicity prediction by computer-assisted technology) and DEREK (deductive estimate of risk from existing knowledge) software [35][36][37].…”

Section: Introductionmentioning

confidence: 99%

Automated statistical experimental design approach for rapid separation of coenzyme Q10 and identification of its biotechnological process related impurities using UHPLC and UHPLC–APCI‐MS

Talluri¹,

Kalariya²,

Dharavath³

et al. 2016

A novel ultra high performance liquid chromatography method development strategy was ameliorated by applying quality by design approach. The developed systematic approach was divided into five steps (i) Analytical Target Profile, (ii) Critical Quality Attributes, (iii) Risk Assessments of Critical parameters using design of experiments (screening and optimization phases), (iv) Generation of design space, and (v) Process Capability Analysis (Cp) for robustness study using Monte Carlo simulation. The complete quality-by-design-based method development was made automated and expedited by employing sub-2 μm particles column with an ultra high performance liquid chromatography system. Successful chromatographic separation of the Coenzyme Q10 from its biotechnological process related impurities was achieved on a Waters Acquity phenyl hexyl (100 mm × 2.1 mm, 1.7 μm) column with gradient elution of 10 mM ammonium acetate buffer (pH 4.0) and a mixture of acetonitrile/2-propanol (1:1) as the mobile phase. Through this study, fast and organized method development workflow was developed and robustness of the method was also demonstrated. The method was validated for specificity, linearity, accuracy, precision, and robustness in compliance to the International Conference on Harmonization, Q2 (R1) guidelines. The impurities were identified by atmospheric pressure chemical ionization-mass spectrometry technique. Further, the in silico toxicity of impurities was analyzed using TOPKAT and DEREK software.