Ethanol Precipitation Process Research Articles

Application of continuous mixing technology in ethanol precipitation process of Salvia miltiorrhiza by using micromixer

Continuous pharmaceutical manufacturing is one of the development directions in international pharmaceutical technology. In this study, a continuous mixing technology of ethanol and concentrated extract in the ethanol precipitation of Salvia miltiorrhiza was realized by using a membrane dispersion method. The effects of ethanol flowrate, concentrated extract flowrate, and flowrate ratio on ethanol precipitation results were investigated. With the increase of the flowrates of ethanol and concentrated extract, retention rate of active phenolic acids components was increased, and the total solid removal rate was decreased. The purity of active components in supernatants was mainly affected by the ratio of ethanol flowrate and concentrated extract flowrate. The mixing efficiency of adding ethanol under continuous flow mixing mode in this study was comparable to that of industrial ethanol precipitation. Continuous adding ethanol by using a membrane dispersion mixer is a promising technology with many advantages such as easy enlargement, large production per unit volume, and easy control.

Rapid monitoring five components of ethanol precipitation process of Shenzhiling oral solution using near infrared spectroscopy

To develop a method for the rapid monitoring of five components during the alcohol precipitation process of Shenzhiling oral solution using near infrared spectroscopy(NIRS).The contents of five components detemined by high performance liquid chromatography(HPLC) were used as the reference values, and the NIRS based partial least square regression(PLSR) models were used to monitor the concentrations of paeoniflorin, albiflorin, liquiritin, cinnamic acid and glycyrrhizic acid during the alcohol precipitation process of Shenzhiling oral solution, which were optimized and verified through comparing of different spectral pre-processing and variables selection methods. Determination coefficients(Rcal2 and Rpred2), root mean squares error of prediction (RMSEP), root mean squares error of calibration(RMSEC) and ratiao of performance to deviation(RPD) were applied to evaluate the performance of the models, and the corresponding values were 0.993 3 and 0.997 6, 0.084 9 g•L⁻¹, 0.073 3 g•L⁻¹ and 14.7 for paeoniforin; 0.991 4, 0.992 7, 0.028 1 g•L⁻¹, 0.030 5 g•L⁻¹ and 10.2 for albiforin; 0.955 3, 0.976 1, 0.012 0 g•L⁻¹, 0.012 3 g•L⁻¹ and 5.1 for liquiritin; 0.958 8, 0.990 3, 0.003 89 g•L⁻¹, 0.002 89 g•L⁻¹ and 7.1 for cinnamic acid; 0.982 0, 0.986 3, 0.053 8 g•L⁻¹, 0.059 0 g•L⁻¹, 7.2 for glycyrrhizic acid, respectively. The results indicated that the presented approach was effectively for the quantitative monitoring of the alcohol precipitation process of Shenzhiling oral solution.

Impact of parameter fluctuations on the performance of ethanol precipitation in production of Re Du Ning Injections, based on HPLC fingerprints and principal component analysis

The present study was designed to determine the relationships between the performance of ethanol precipitation and seven process parameters in the ethanol precipitation process of Re Du Ning Injections, including concentrate density, concentrate temperature, ethanol content, flow rate and stir rate in the addition of ethanol, precipitation time, and precipitation temperature. Under the experimental and simulated production conditions, a series of precipitated resultants were prepared by changing these variables one by one, and then examined by HPLC fingerprint analyses. Different from the traditional evaluation model based on single or a few constituents, the fingerprint data of every parameter fluctuation test was processed with Principal Component Analysis (PCA) to comprehensively assess the performance of ethanol precipitation. Our results showed that concentrate density, ethanol content, and precipitation time were the most important parameters that influence the recovery of active compounds in precipitation resultants. The present study would provide some reference for pharmaceutical scientists engaged in research on pharmaceutical process optimization and help pharmaceutical enterprises adapt a scientific and reasonable cost-effective approach to ensure the batch-to-batch quality consistency of the final products.

Design space approach to optimize first ethanol precipitation process of Dangshen

Design space approach is applied in this study to enhance the robustness of first ethanol precipitation process of Codonopsis Radix (Dangshen) by optimizing parameters. Total flavonoid recovery, dry matter removal, and pigment removal were defined as the process critical quality attributes (CQAs). Plackett-Burman designed experiments were carried out to find the critical process parameters (CPPs). Dry matter content of concentrated extract (DMCE), mass ratio of ethanol to concentrated extract (E/C ratio) and concentration of ethanol (CEA) were identified as the CPPs. Box-Behnken designed experiments were performed to establish the quantitative models between CPPs and CQAs. Probability based design space was obtained and verified using Monte-Carlo simulation method. According to the verification results, the robustness of first ethanol precipitation process of Dangshen can be guaranteed by operating within the design space parameters. Recommended normal operation space are as follows: dry matter content of concentrated extract of 45.0% - 48.0%, E/C ratio of 2.48-2.80 g x g(-1), and the concentration of ethanol of 92.0% - 92.7%.

Rapid screening of critical process parameters based on near infrared spectroscopy: a case study of the ethanol precipitation process

Rapid screening of critical process parameters based on NIRS.

Removing tannins from medicinal plant extracts using an alkaline ethanol precipitation process: a case study of Danshen injection.

The alkaline ethanol precipitation process is investigated as an example of a technique for the removal of tannins extracted from Salviae miltiorrhizae Radix et Rhizoma for the manufacture of Danshen injection. More than 90% of the tannins can be removed. However, the recoveries of danshensu, rosmarinic acid, and salvianolic acid B were less than 60%. Total tannin removal increased as the refrigeration temperature decreased or the amount of NaOH solution added increased. Phenolic compound recoveries increased as refrigeration temperature increased or the amount of NaOH solution added decreased. When operated at a low refrigeration temperature, a relative high separation selectivity can be realized. Phenolic compound losses and tannin removal were mainly caused by precipitation. The formation of phenol salts, whose solubility is small in the mixture of ethanol and water used, is probably the reason for the precipitation. A model considering dissociation equilibrium and dissolution equilibrium was established. Satisfactory correlation results were obtained for phenolic compound recoveries and total tannin removal. Two important parameters in the model, which are the water content and pH value of alkaline supernatant, are suggested to be monitored and controlled to obtain high batch-to-batch consistency.

The relevance study of effective information between near infrared spectroscopy and chondroitin sulfate in ethanol precipitation process

Near infrared spectroscopy (NIRS) is based on molecular overtone and combination vibrations. It is difficult to assign specific features under complicated system. So it is necessary to find the relevance between NIRS and target compound. For this purpose, the chondroitin sulfate (CS) ethanol precipitation process was selected as the research model, and 90 samples of 5 different batches were collected and the content of CS was determined by modified carbazole method. The relevance between NIRS and CS was studied throughout optical pathlength, pretreatment methods and variables selection methods. In conclusion, the first derivative with Savitzky–Golay (SG) smoothing was selected as the best pretreatment, and the best spectral region was selected using interval partial least squares (iPLS) method under 1 mm optical cell. A multivariate calibration model was established using PLS algorithm for determining the content of CS, and the root mean square error of prediction (RMSEP) is 3.934 g⋅L-1. This method will have great potential in process analytical technology in the future.

Network analysis of ethanol precipitation process for Schisandrae Chinensis Fructus

A set of central composite design experiments were designed by using four factors which were ethanol amount, ethanol concentration, refrigeration temperature and refrigeration time. The relation between these factors with the target variables of the retention rate of schizandrol A, the soluble solids content, the removal rate of fructose and the removal rate of glucose were analyzed with Bayesian networks, and ethanol amount and ethanol concentration were found as the critical process parameters. Then a network model was built with 2 inputs and 4 outputs using back propagation artificial neural networks which was optimized by genetic algorithms. The R2 and MSE from the training set were 0.983 8 and 0.001 1. The R2 and MSE from the test set were 0.975 9 and 0.001 8. The results showed that network analysis method could be used for modeling of Schisandrae Chinensis Fructus ethanol precipitation process and identify critical operating parameters.

Control the effects caused by noise parameter fluctuations to improve pharmaceutical process robustness: A case study of design space development for an ethanol precipitation process

To provide a new method to develop a design space to improve the robustness of drug manufacturing processes based on the control of noise parameter influences, the ethanol precipitation process of Danhong injection was investigated as a sample. Water content in the concentrated extract (WCCE), the concentration of ethanol (CEA), and the amount of ethanol added (AEA) are three adjustable parameters. The effects of refrigeration temperature (RT) was investigated on three levels for simulating its fluctuations. The models between parameters and process critical quality attributes (CQAs) were obtained using a simplified central composite design with determination coefficients more than 0.84. The decrease of RT led to lower active ingredient recoveries and higher DMR. The increase of CEA and the decrease of WCCE caused more precipitation. The decrease of CEA or AEA resulted in higher active ingredient recoveries. The design space was calculated using an exhaustive search-Monte Carlo method and normal operation ranges were also obtained. The results of verification experiments agreed well with prediction results. The proposed method can be used to develop a design space applicable in a larger scale manufacturing process with the negative effects caused by the fluctuations of noise parameters controlled.

Optimization for the Ethanol Precipitation Process of Botanical Injection: Indicator Selection and Factor Influences

Ethanol precipitation is an important purification process affecting the safety and efficacy of botanical injections. In this paper, the second ethanol precipitation of Guanxinning injection was optimized with response surface methodology as a sample to represent the principles of indicator selection. The influences of three factors, including dry matter content of concentrated supernatant (DMCCS), refrigeration temperature, and the mass ratio of ethanol and concentrated supernatant (E/CS ratio) were explained based on the precipitation mechanism. To characterize the effects of ethanol precipitation on drug safety, drug efficacy, and drug quality control, phenolic compound recovery and dry matter removal were selected as optimization criteria. In the optimization, E/CS ratio was set to 3.60. The optimum operating conditions were found at refrigeration temperature of 18.66°C and DMCCS of 0.58. Most of the experimental results at optimum operating conditions agreed well with the predicted results.

Quality by Design for Herbal Drugs: a Feedforward Control Strategy and an Approach to Define the Acceptable Ranges of Critical Quality Attributes

The concept of quality by design (QbD) has been widely accepted and applied in the pharmaceutical manufacturing industry. There are still two key issues to be addressed in the implementation of QbD for herbal drugs. The first issue is the quality variation of herbal raw materials and the second issue is the difficulty in defining the acceptable ranges of critical quality attributes (CQAs). To propose a feedforward control strategy and a method for defining the acceptable ranges of CQAs for the two issues. In the case study of the ethanol precipitation process of Danshen (Radix Salvia miltiorrhiza) injection, regression models linking input material attributes and process parameters to CQAs were built first and an optimisation model for calculating the best process parameters according to the input materials was established. Then, the feasible material space was defined and the acceptable ranges of CQAs for the previous process were determined. In the case study, satisfactory regression models were built with cross-validated regression coefficients (Q(2) ) all above 91 %. The feedforward control strategy was applied successfully to compensate the quality variation of the input materials, which was able to control the CQAs in the 90-110 % ranges of the desired values. In addition, the feasible material space for the ethanol precipitation process was built successfully, which showed the acceptable ranges of the CQAs for the concentration process. The proposed methodology can help to promote the implementation of QbD for herbal drugs.

Rapid determination of immunoglobulin G concentration in cold ethanol precipitation process of raw plasma with near-infrared spectroscopy

Near-infrared spectroscopy (NIRS) is known to be a powerful analytical tool in process monitoring. The feasibility of NIRS was investigated for determination of immunoglobulin G (IgG) in raw plasma cold ethanol precipitation process. Partial least squares (PLS) was used to develop regression model for 63 samples between spectra and reference data measured with a UV spectrophotometer. Three different variable selection methods, including correlation coefficient method, interval partial least squares (iPLS) and successive projection algorithm (SPA), were performed and compared with models based on all the variables. The values of Rc and root mean square error of cross validation (RMSECV) produced by the best model for the calibration set were 0.9599 and 0.6135g/L, respectively. While for the validation set, the values of Rp and root mean square error of prediction (RMSEP) were 0.9577 and 0.4913g/L, respectively. The results of this paper demonstrated that NIRS could be a feasible alternative approach for rapid determination of IgG in the cold ethanol precipitation process and can be used as a PAT tool in the future.

An approach to determine critical process parameters for ethanol precipitation process of Danhong injection

To establish a method integrating multi-targets for determining critical process parameters of the manufacturing process of traditional Chinese medicine. The ethanol precipitation process of Danhong injection was taken as an application case of the method established. Fractional factorial design of experiments were conducted. Mathematical models relating seven process parameters to ten targets in the ethanol precipitation process were established. Then the sums of the absolute values of the regression coefficients in the models were used to evaluate the criticality of process parameters. Water content in the concentrate, ethanol concentration and ethanol consumption were identified as the critical process parameters. The method established can integrate multi-targets effectively for the evaluation of critical process parameters.

Feedforward control strategy and its application in quality improvement of ethanol precipitation process of Danhong injection

In this work, a feedforward control strategy basing on the concept of quality by design was established for the manufacturing process of traditional Chinese medicine to reduce the impact of the quality variation of raw materials on drug. In the research, the ethanol precipitation process of Danhong injection was taken as an application case of the method established. Box-Behnken design of experiments was conducted. Mathematical models relating the attributes of the concentrate, the process parameters and the quality of the supernatants produced were established. Then an optimization model for calculating the best process parameters basing on the attributes of the concentrate was built. The quality of the supernatants produced by ethanol precipitation with optimized and non-optimized process parameters were compared. The results showed that using the feedforward control strategy for process parameters optimization can control the quality of the supernatants effectively. The feedforward control strategy proposed can enhance the batch-to-batch consistency of the supernatants produced by ethanol precipitation.

NIR analysis for batch process of ethanol precipitation coupled with a new calibration model updating strategy

Ethanol precipitation plays a major role in the pretreatment of Flos Lonicerae Japonicae of Qingkailing injection, and is also one of the most popular purification techniques in Chinese herbal medicines. In order to monitor and have a better understanding of the ethanol precipitation process, a PLS model was built based on NIR spectroscopy and HPLC analysis of chlorogenic acid content within the framework of FDA's PAT initiative. Nevertheless, due to the complex mechanism of and the raw materials’ natural variability introduced into the ethanol precipitation process, it was unable to foresee the variations in new batches which may jeopardize the robustness of the established model. Therefore, based on the simple interval calculation (SIC) theory, a new model expansion updating strategy which could continuously expand the variation coverage of the calibration model along with the batch proceeding of ethanol precipitation process was proposed. Effects of model updating were validated by an individual batch with 60 samples. After two times of updating, the root mean squared error of prediction (RMSEP) decreased from 0.268mgmL−1 to 0.199mgmL−1, while the insiders in the object status plot (OSP) increased from 44 to 58, demonstrating the good performance of the proposed approach.

Development and validation of NIR model using low-concentration calibration range: rapid analysis of Lonicera japonica solution in ethanol precipitation process

A strategy for both low-concentration calibration range and large number of sample sets selection in the development of a PLS model is studied. A novel approach based on accuracy profile validated the accuracy and precision of the PLS model. The strategy was applied to the determination of chlorogenic acid content in Lonicera japonica using ethanol precipitation by near-infrared (NIR) transmission spectroscopy. The results found the determination coefficient (R2), standard errors of calibration and prediction (SEC and SEP) were 0.9648, 71.2 ppm and 74.9 ppm, respectively. The further study showed that PLS model could be used to determine chlorogenic acid content based on accuracy profile, which has a lower limit of quantification (LLOQ) ∼1700 ppm. Analytical properties such as accuracy, precision, range and linearity from validation criteria also demonstrated the feasibility of the strategy using a low-concentration calibration set in the PLS model, paving the way for analyses in Chinese Herbal Medicine (CHM) applications.

Validation of a NIR quantification method for the determination of chlorogenic acid in Lonicera japonica solution in ethanol precipitation process

The feasibility of near-infrared spectroscopy (NIRS) for chlorogenic acid content analysis in ethanol precipitation process of water extract of Lonicera japonica was verified in this work. A calibration and validation set was designed for the conception and evaluation of the method adequacy. An experimental protocol was then followed, involving two different NIR instruments for data acquisition. On the basis of this protocol, the model was developed based on partial least squares regression (PLS) and the determination coefficient (R2cal and R2val), standard error of calibration and prediction (SEC and SEP) were 0.9962, 0.9955, 111.1μg/mL and 107.1μg/mL for Holographic Grating NIR instrument, and 0.9984, 0.9971, 53.6μg/mL and 83.3μg/mL for Fourier Transform NIR instrument. However, such above criteria did not clearly demonstrate the model's prediction error over each analyzed content range. Consequently, a novel approach based on accuracy profile which allowed the acquisition of the lower limit of quantification (LLOQ) was used to validate the robustness and accuracy of PLS model. The resulting accuracy profile showed that PLS model was able to determine chlorogenic acid content by two NIR systems, whose LLOQ was about 1550μg/mL. It was concluded that the two NIR systems were suitable for use as Process Analytical Technology (PAT) to understand ethanol precipitation process of water extract of Lonicera japonica.