Optimization of membrane dispersion ethanol precipitation process with a set of temperature control improved equipment

doi:10.21203/rs.3.rs-42371/v1

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Optimization of membrane dispersion ethanol precipitation process with a set of temperature control improved equipment

https://doi.org/10.21203/rs.3.rs-42371/v1

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published 03 Nov, 2020

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posted 21 Jul, 2020

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Background

Ethanol precipitation is an important separation and purification process in the traditional Chinese medicine (TCM) industry. However, ethanol precipitation process suffers from loss of active ingredients, low batch-to-batch consistency of supernatant composition, and long standing time. These problems are mainly caused by low efficient equipment and imperfect control strategy.

Methods

In the present study, a membrane dispersion ethanol precipitation device which can achieve rapid cooling was developed for Astragali radix ethanol precipitation. Ethanol precipitation process was optimized according to Quality by design concept. To identify critical material attributes (CMAs), ten batches of Astragali radix was used to prepare concentrates. Definitive screening design was used to investigate the relationships among critical process parameters, CMAs, and process critical quality attributes (CQAs).

Results

Calycosin-7-O-β-D-glucoside content, the sucrose content, and the electrical conductivity were found to be CMAs after the correlation analysis and stepwise regression modelling. Quadratic models were developed and design space was calculated according to the probability of attaining process CQA standards. A material quality control strategy was proposed. High quality and low quality Astragali radix concentrates can be discriminated by the inequalities. Low quality Astragali radix concentrates should not be released for ethanol precipitation process directly. Verification experiment results indicated accurate models and reliable design space.

Conclusions

The temperature control method and control strategy in this study are effective and promising for ethanol precipitation process of other TCMs or foods.

Biotechnology and Bioengineering

Clinical Pharmacology

Ethanol precipitation

Micromixer

Astragali radix

Quality control strategy

Ethanol precipitation is a widely applied separation technology in the production of traditional Chinese medicines (TCMs) [1]. Ethanol precipitation has many advantages, including low cost, convenient operation, and safe solvent [2–4]. Polar impurities, such as proteins and sugars, can be at least partly removed in ethanol precipitation [5–10]. However, ethanol precipitation process suffers from loss of active ingredients, low batch-to-batch consistency of supernatant composition, and long standing time [11–13]. These problems are mainly caused by low efficient equipment and imperfect control strategy.

In industry, ethanol precipitation process is usually performed in a stirring tank by adding an ethanol solution to a TCMs concentrate [3]. However, large density difference between ethanol solution and concentrate, high viscosity of concentrate, and rapid formation of precipitate all result in poor mixing of the two phases [14, 15]. Because precipitate easily encapsulates some concentrate, the encapsulation loss of active ingredient is commonly observed in industry. After adding ethanol solution, a long standing time is usually required to let the active ingredients encapsulated dissolve to ethanol phase slowly. The low heat exchange efficiency of the stirring tank is another reason for long standing time because it is time consuming to cool the precipitation system. Recently, the mixing of ethanol and concentrates was intensified by using a membrane dispersion micromixer, and the encapsulation loss of active components was effectively reduced [14]. However, there is little research on the temperature control of ethanol precipitation process.

According to the concept of Quality by design (QbD), operating a pharmaceutical process with the design space containing the acceptable ranges of material attributes and process parameters is an effective way to improve the batch-to-batch consistency [16–22]. There are some published works on the development of design space for ethanol precipitation process control [23–26]. However, only the concentrate attributes that are easily controlled in upstream concentration process were concerned, such as water content, dry matter content, and concentrate density [2, 27]. These concentrate attributes are usually considered as controllable material attributes.

According to Yan et al and Zhang et al, active ingredient content, and other physical or chemical properties may also be critical material attributes (CMAs) of an ethanol precipitation process[28, 29]. These properties were mainly affected by medicinal material quality and changes among different batches. These concentrate attributes are considered as uncontrollable material attributes in this work. In the published works[24, 27], ethanol precipitation design space was developed without the consideration of uncontrollable material attributes, which lead to unsatisfactory control of batch-to-batch consistency. Therefore, design space considering controllable and uncontrollable material attributes, critical process parameters (CPPs), and process critical quality attributes (CQAs) is required for the control of ethanol precipitation process.

In this work, a membrane dispersion micromixer was used for continuously adding ethanol solution to Astragali radix concentrates. Temperature control was enhanced by on-line cooling. Ethanol precipitation process was optimized according to a design space approach. CMAs of Astragali radix extracts were identified. Definitive screening design was used to investigate the relationships among CPPs, CMAs and CQAs. The models were then developed and the design space was calculated. A material quality control strategy considering the requirements of manufacturing processes was proposed. Verification experiments were carried out for high quality Astragali radix concentrates. Figure 1 is a schematic diagram of this work.

Materials and chemicals

Ten batches of Astragali radix were collected from Yunnan province and Gansu province of China. Reference substances of Astragaloside IV, Astragaloside Ⅱ, Calycosin-7-O-β-D-glucoside (CG), 9,10-dimethoxypterocarpan-3-O-β-D-glucoside (PG), 2-hydroxy-3',4'-dimethoxyisoflavane-7-O-β-D-glucoside (IFG) were purchased from Shanghai Winherb Medical Technology Co., Ltd. (Shanghai, China). The standard substances of D-fructose (99.5%) and sucrose (99%) were supplied by Aladdin Chemistry Co., Ltd. (Shanghai, China) and Sigma-Aldrich Co., Ltd. (Shanghai, China), respectively. Acetonitrile (HPLC grade) and methanol (HPLC grade) were purchased from Merck (Darmstadt, Germany). Formic acid (HPLC grade) was purchased from ROE scientific Inc. (Newark, America). Na₂CO₃ (analytical grade) and ethylene glycol (analytical grade) were obtained from Sinopharm Chemical Reagent Co., Ltd. (Shanghai, China). Triethylamine (HPLC grade) was purchased from Aladdin Chemistry Co., Ltd. (Shanghai, China). Ethanol (analytical grade) was purchased from Shanghai Lingfeng Chemical Reagent Co., Ltd. (Shanghai, China). Deionized water was prepared by an academic water purification system (Milli-Q, Milford, MA, USA).

Preparation of Astragali radix concentrate

The Astragali radix was reflux-extracted with water of 6 mL/g decoction piece water twice. Extraction time of each extraction was 0.5 h. The two extracts were obtained by filtration and then combined. The combined extracts were concentrated under reduced pressure to obtain the concentrate. The temperature was controlled at about 70 ^oC and the pressure was about 50 mbar during concentration, and the concentrate density at the end point was about 1.2 g/mL.

Apparatus and ethanol precipitation process

Figure 2 is the schematic diagram of the experimental setup. The membrane dispersion micromixer employed in this experiment was detailly described in previous work[14]. The sizes of mixing chamber (8 × 1 × 0.5 mm) were controlled by PTFE gaskets. The average pore size of stainless-steel membrane was 18 µm.

Ethanol solution at room temperature was cooled with a heat exchanger (K030-20M-NB4, Ningbo Gaori Technology Co., Ltd.). The heat exchanger was connected to the refrigeration circulation device (CA-1116A, Tokyo Rikakikai, Co. Ltd.). The heat transfer medium in the heat exchanger was 60% ethylene glycol-water (v/v). The low-temperature ethanol solution was served as the dispersed phase pumped into the micromixer by a gear pump (CT3001F, Baoding Reef Fluid Technology Co., Ltd.). A concentrate was pumped into the micromixer by an advection pump (2 PB-20005II, Beijing Xingda Technology Development Co., Ltd.) as the continuous phase. The outlet mixture was collected in a jacketed beaker and magnetically stirred for 5 minutes. The temperature of the jacketed beaker was controlled by a thermostatic bath (THYD-1030W, Ningbo Tianheng instrument factory). The medium in the thermostatic bath connected to the jacket beaker is 30% glycerol-water (v/v). The supernatant was collected after filtration. After an experiment, the apparatus was washed with 0.05% (wt.) Na₂CO₃ solution and ethanol sequentially.

The low-temperature ethanol solution and a temperature-controlled jacket beaker were used to control the temperature of the mixture together. The use of low temperature ethanol solution could reduce the temperature of the mixture in a short time. The temperature controlled jacketed beaker was used to control the temperature precisely. The temperature ranges are shown in Table S1.

Experimental design

2.4.1 The evaluation of ethanol precipitation process

The main active components of Astragali radix were flavonoids and saponins. In Chinese Pharmacopeia, CG and Astragaloside IV are chosen as the representatives for flavonoids and saponins, respectively. In this study, three flavonoids of CG, PG, IFG, and two saponins of Astragaloside IV and Astragaloside Ⅱ were measured as the representative active components of Astragali radix. In this work, the purity of five active components and dry matter removal were selected as the process CQAs.

2.4.2 CMAs identification

To identify the CMAs, the experiments were carried out with different concentrates under fixed process conditions. The mass ratio of ethanol solution to concentrate (ECR) was 1.5 g/g, the dry matter content of concentrates was 45%, the ethanol solution concentration was 92% (v/v), the refrigeration temperature was 15 ^oC, and the flow rate of concentrate was 60 mL/min. The electrical conductivity, flavonoid contents, saponin contents, and sugar contents of different concentrates were measured.

2.4.3 Definitive screening design

After identifying the CMAs, the definitive screening design was used to study the quantitative relationships among the potential CPPs, CMAs, and process CQAs. Many parameters can be studied in a very small number of experiments with the definitive screening experiment design [30]. Dry matter content (X₁), ECR (X₂), the ethanol solution concentration (X₃), and the temperature (X₄) were selected as potential CPPs because they were found to be CPPs in published works[27, 31]. The experimental conditions are listed in Table 1. To study the effects of materials, different concentrates of Astragali radix were used in these experiments, as shown in Table 1.

Table 1

Conditions and results of definitive screening designed experiments
Experimental No.	Astragali radix concentrate	Process parameters (coded variable)				Purity of flavonoids and saponins in the supernatant (µg/g dry matter)					Dry matter removal (Y₆)
Experimental No.	Astragali radix concentrate	Dry matter content (X₁, %)	ECR (X₂, g/g)	The ethanol solution concentration (X₃, %)	Temperature (X₄,℃)	Astragaloside IV (Y₁)	Astragaloside Ⅱ (Y₂)	CG (Y₃)	PG (Y₄)	IFG (Y₅)	Dry matter removal (Y₆)
11	N1	40(-1)	2.0(+ 1)	92(0)	25.0(+ 1)	1582	1544	1734	865.3	826.2	0.395
12	N2	40(-1)	2.0(+ 1)	89(-1)	5.0(-1)	1488	1003	2217	1088	1120	0.459
13	N3	45(0)	1.5(0)	92(0)	15.0(0)	2596	2447	2199	1476	1356	0.384
14	N7	50(+ 1)	1.0(-1)	95(+ 1)	25.0(+ 1)	692.4	763.6	1279	348.3	359.3	0.461
15	N10	40(-1)	1.0(-1)	95(+ 1)	15.0(0)	235.6	162.7	1534	332.2	208.5	0.331
16	N6	50(+ 1)	1.0(-1)	92(0)	5.0(-1)	244.9	346.7	1761	367.0	237.3	0.373
17	N2	40(-1)	1.5(0)	95(+ 1)	5.0(-1)	1831	1294	2643	1317	1316	0.448
18	N8	45(0)	2.0(+ 1)	95(+ 1)	25.0(+ 1)	496.7	527.2	1907	388.8	303.3	0.547
19	N9	50(+ 1)	1.5(0)	89(-1)	25.0(+ 1)	368.7	758.3	2133	531.3	306.0	0.447
20	N5	50(+ 1)	2.0(+ 1)	95(+ 1)	5.0(-1)	852.1	867.6	2350	479.9	486.6	0.561
21	N4	45(0)	1.0(-1)	89(-1)	5.0(-1)	1465	1540	2317	1168	897.4	0.378
22	N8	40(-1)	1.0(-1)	89(-1)	25.0(+ 1)	373.6	292.3	1223	195.5	327.4	0.285
23	N10	50(+ 1)	2.0(+ 1)	89(-1)	15.0(0)	505.6	265.1	1613	258.6	430.2	0.385
24	N3	45(0)	1.5(0)	92(0)	15.0(0)	2548	2527	2205	1418	1214	0.365
25	N3	45(0)	1.5(0)	92(0)	15.0(0)	2409	2368	2112	1357	1172	0.356
26	N3	45(0)	1.5(0)	92(0)	15.0(0)	2554	2514	2171	1390	1197	0.371

Analytical methods

The contents of Astragaloside IV, Astragaloside Ⅱ, CG, PG, and IFG were determined using a HPLC-ELSD method developed by Luo et al [32]. A HPLC (1260, Agilent Technologies, USA) system with a UV detector and ELSD detector was used. Samples were diluted with 50% (v/v) methanol solution. Analyses were conducted on a Zorbax SB-C18 column (4.6 mm × 250 mm, 5 µm) with the column temperature controlled at 30 ^oC. The flow rate of solvent was maintained at 0.8 mL/min, while the injection volume of sample was set at 10 µL. The detection wavelength was fixed at 270 nm. The ELSD operation parameters were as follows: the evaporator temperature was fixed at 30 ^oC, nebulizer temperature was fixed at 80 ^oC, and gas flow rate was fixed at 1.6 L/min. The mobile phase was consisted of solvent A (0.2% (v/v) formic acid in water) and solvent B (acetonitrile). The solvent gradients were as follows: 0–16 min, 15%-23% B; 16–20 min, 23%-28% B; 20–25 min, 28%-30% B; 25–30 min, 30% B; 30–40 min, 30%-55%B; 40–50 min, 55%-95% B. The signals of CG, PG, and IFG were detected by the UV detector. The signals of Astragaloside IV and Astragaloside Ⅱ were detected by the ELSD detector. A typical chromatogram of Astragali radix supernatant was shown in Figure S1.

The HPLC method developed by Shao et al was used to determine the contents of D-fructose and sucrose [33]. The HPLC system (1260, Agilent Technologies, USA) was equipped with an Alltech 2000ES ELSD detector. All the samples were diluted with 85% (v/v) acetonitrile-water mixture and carried out on a XBridge Amide column (5 µm, 4.6 × 250 mm; Waters, Milford, MA, USA). The column temperature was fixed at 34 ^oC. The solvent flow rate was fixed at 0.9 mL/min and the sample injection volume was set at 5 µL. The mobile phase solvent A was 0.3% (v/v) triethylamine in water and solvent B was 0.3% (v/v) triethylamine in acetonitrile. The solvent gradients were as follows: 0–37 min, 85%-76% B; 37–38 min, 76%-60% B; 38–48 min, 60%-100% B. The re-equilibrium time was 10 min. The ELSD operation parameters were as follows: the nebulizer temperature was set at 65 ^oC, evaporator temperature was set at 60 ^oC, and gas flow rate was set at 1.8 L/min. A typical chromatogram of Astragali radix concentrate was shown in Figure S2.

Dry matter content was determined using a gravimetric method as described in previous work[25]. Each sample of the concentrates was diluted with water to a solution of 2% dry matter content. The conductivity of the diluted concentrates was measured using a portable conductivity meter (DDBJ-350, Hangzhou Qiwei Instrument Co., Ltd.) at 25 ^oC.

Data processing

The calculation formulas of active component purity and dry matter removal are as follows:

$$Active component\text{ purity }=\frac{A{C}{s}}{{DM}{S}}$$

$$Dry matter removal=(1-\frac{{m}{s}\times D{M}{s}}{{m}{c}\times D{M}{c}})\times 100\text{%}$$

where m, AC, and DM stand for mass, active component contents, and dry matter contents, respectively; subscripts S and C represent supernatants and concentrates, respectively.

To identify CMAs, Eq. (

) was used to model material attributes and process CQAs.

$$Y={a}{0}+{\sum }{k=1}^{8}{c}{k}{Z}{k}$$

where Y is the process CQAs; a₀ is a constant; Z_k represents a material attribute; and c_k is the partial regression coefficient. Insignificant variables were removed by stepwise regression. The significance levels for adding terms and removing terms were both set to 0.1. The material attributes remaining in the model after stepwise regression were considered to be the CMAs.

Quadratic models were developed based on the definitive screening designed experiment results. Eq. (

) was used to model CPPs, CMAs, and process CQAs.

$$Y={a}{0}+{\sum }{i=1}^{n}{b}{i}{X}{i}+{\sum }{i=1}^{n}{b}{ii}{X}{i}^{2}+{\sum }{i=1}^{n-1}{\sum }{j=i+1}^{n}{b}{ij}{X}{i}{X}{j}+{\sum }{k=1}^{m}{d}{k}{Z}_{k}^{C}$$

where n and m are the number of CPPs and CMAs, respectively; b and d are the partial regression coefficients; X_i is a potential CPP; and Z^C is a CMA. Stepwise regression was performed as before decribed. Data analysis was performed by Design Expert (version 11.0.0, Stat-Ease Inc., USA).

Material attributes

The quality attributes of different Astragali radix concentrates are shown in Table 2. The electrical conductivity was between 1442 μS/cm and 2390 μS/cm, indicating different electrolyte contents in concentrates. The content of Astragaloside IV and Astragaloside Ⅱ was lower than 2000 μg/g dry matter. The content of CG among the three flavonoid contents was the highest, which can exceed 1600 μg/g. The other flavonoids were less than 1000 μg/g dry matter. The sucrose content was higher than the D-fructose content, which could be more than 700 mg/g dry matter. At most occasions, sucrose was the main component of dry matter. The D-fructose content was lower than 30 mg/g dry matter.

Table 2. Quality attributes different batches of Astragali radix concentrates

Concentrate number	Electrical conductivity (dry matter content of 2%) (Z₈, μS/cm )	Contents of flavonoids and saponins (μg/g dry matter)					Sugar contents (dry matter)
Concentrate number		Astragaloside IV (Z₁)	Astragaloside Ⅱ (Z₂)	CG (Z₃)	PG (Z₄)	IFG (Z₅)	D-fructose (Z₆)	Sucrose (Z₇)
N1	1944	1062	1200	1119	641.6	471.5	25.03	568.0
N2	1832	1020	763.4	1490	688.7	725.6	27.26	489.0
N3	2390	1887	1948	1550	1000	881.2	11.07	616.2
N4	1995	928.6	1093	1693	794.2	603.4	25.48	631.8
N5	1490	390.0	478.9	1121	272.9	228.6	18.54	714.4
N6	1667	247.1	314.3	1390	303.0	250.3	24.35	732.7
N7	1597	321.2	558.7	839.2	190.0	288.4	19.65	706.5
N8	1442	314.7	291.5	1067	207.4	179.2	20.02	695.6
N9	1655	284.5	562.1	1401	341.9	191.4	15.82	670.0
N10	1534	371.4	178.3	1445	299.3	198.8	23.66	754.0

The identification of CMAs

The results of the CMA identification experiments are shown in Table 3. Though process conditions were fixed, the experimental results were quite different, indicating that the material attributes significantly affected the performance of Astragali radix ethanol precipitation process.

Table 3 CMA identification results

Experimental No.	Concentrates	Purity of flavonoids and saponins in the supernatant (μg/g dry mater)					Dry matter removal (Y₆)
Experimental No.	Concentrates	Astragaloside IV (Y₁)	Astragaloside Ⅱ (Y₂)	CG (Y₃)	PG (Y₄)	IFG (Y₅)	Dry matter removal (Y₆)
1	N1	1997	2294	2198	1408	1047	0.594
2	N2	1406	1024	2116	1161	973.0	0.464
3	N3	2985	2905	2501	1722	1527	0.471
4	N4	1366	1519	2468	1266	951.5	0.566
5	N5	563.4	661.2	1972	501.2	372.6	0.702
6	N6	415.2	420.0	2145	451.3	420.7	0.436
7	N7	749.2	722.0	1350	420.9	573.4	0.616
8	N8	437.2	457.8	1754	354.4	305.0	0.422
9	N9	614.0	781.3	2324	579.7	472.4	0.619
10	N10	424.6	239.4	1967	427.1	302.9	0.343

The correlation analysis of material attributes was carried out to find attributes with similar trends [34]. The Pearson coefficients are shown in Table 4. The Pearson coefficients among Astragaloside IV content (Z₁), Astragaloside Ⅱ content (Z₂), PG content (Z₄), IFG content (Z₅) and electrical conductivity (Z₈) was higher than 0.90. It means that one of them can roughly represent other three material attributes because they contained similar information. Electrical conductivity (Z₈) was selected as the potential CMAs in the four material attributes because it is easy to measure. Other potential CMAs are CG (Z₃), D-fructose content (Z₆), and sucrose content (Z₇).

Table 4 Pearson correlation coefficient of materials attributes and P value of significance test

	Z₁	Z₂	Z₃	Z₄	Z₅	Z₆	Z₇
Z₂	0.948 (0.000)
Z₃	0.454 (0.188)	0.359 (0.308)
Z₄	0.947 (0.000)	0.906 (0.000)	0.660 (0.038)
Z₅	0.941 (0.000)	0.867 (0.001)	0.530 (0.115)	0.947 (0.000)
Z₆	-0.243 (0.499)	-0.371 (0.291)	0.099 (0.786)	-0.077 (0.833)	-0.062 (0.866)
Z₇	-0.665 (0.036)	-0.583 (0.077)	-0.287 (0.421)	-0.705 (0.023)	-0.761 (0.011)	-0.247 (0.492)
Z₈	0.943 (0.000)	0.960 (0.000)	0.547 (0.102)	0.953 (0.000)	0.907 (0.000)	-0.241 (0.502)	-0.585 (0.075)

Stepwise regression method was used to determine CMAs [35]. In this method, the term left in linear equations after stepwise regression indicates a CMA [35]. The ANOVA results of multiple linear regression analysis of each CQA using Eq. (3) are shown in Table 5. The determination coefficient (R²) of each model was higher than 0.70, indicating that the models can explain most of the variation of experimental data. However, these potential CMAs have no significant effect on the dry matter removal. It means that the determined material attributes were not main factors that influencing dry matter removal. According to the terms left in models, the CG content (Z₃), the sucrose content (Z₇), and the electrical conductivity (Z₈) were found to be CMAs.

Table 5 Regression coefficient values, determination coefficients and ANOVA results

Process parameters	Y₁		Y₂		Y₃		Y₄		Y₅
Process parameters	Coefficient	P value	Coefficient	P value	Coefficient	P value	Coefficient	P value	Coefficient	P value
Constant	-3401.126		-3301.511		628.697		-277.322		-487.500
Z₃	-0.8486	0.0381*	-1.1012	0.0251*	1.1062	0.0021*			-0.2446	0.0129*
Z₇							-1.8769	0.0148*	-1.1572	0.0024*
Z₈	3.1972	<0.0001**	3.3332	< 0.0001**			1.3343	< 0.0001**	1.2904	< 0.0001**
R²	0.9482		0.9335		0.7126		0.9584		0.9915

* p<0.05

** p<0.01

The effects of CMAs and CPPs

The partial regression coefficients and variance analysis results of the models are shown in Table 6. The P value of each model was less than 0.05, indicating that the model was significant. The adjusted determination coefficient (R²_adj) of each model was higher than 0.84. The contour plots were obtained to analyze the effects of CPPs on CQAs, as shown in Figure 3-6. The dry matter removal increased as dry matter contents increased. The purity of CG decreased as as temperature increased. The purity of Astragaloside IV was mainly affected by CMAs.The dry matter removal was mainly affected by CPPs. The purity of other flavonoids and saponins was affected by both CPPs and CMAs.

Table 6. ANOVA results for multiple regression models

Process parameters	Y₁		Y₂		Y₃		Y₄		Y₅		Y₆
Process parameters	Coefficient	P value	Coefficient	P value	Coefficient	P value	Coefficient	P value	Coefficient	P value	Coefficient	P value
Constant	-346.607		325052.805		21493.781		-15523.097		5054.318		-0.7618
X₁					-54.739	0.6925	683.552	0.6011	-80.341	0.3744	-0.0014	0.0170
X₂			-9928.885	0.0200	-9026.701	0.0203	1888.456	0.7591	-2716.876	0.7914	0.1024	0.0017
X₃			-7009.760	0.0804	-221.201	0.6957					0.0131	0.0062
X₄					-181.541	0.0020	-10.598	0.0174			-0.0449	0.3841
Z₃			-0.614	0.0108
Z₇	-2.816	0.0324					-1.946	0.0147	-3.419	0.0037
Z₈	1.846	0.0006	2.889	<0.0001			0.691	0.0053	0.810	0.0025
X₁X₂									60.954	0.0350
X₁X₄					3.365	0.0834					0.0005	0.0532
X₂X₃			110.205	0.0045	152.158	0.0213
X₁²							-7.643	0.0386
X₂²					1543.544	0.0278	-622.660	0.0826
X₃²			37.338	0.0102
X₄²											0.0007	0.0128
R²	0.8703		0.9872		0.9431		0.9844		0.9449		0.9262
R²_adj	0.8443		0.9744		0.8633		0.9625		0.9005		0.8524
P value	<0.0001		<0.0001		0.0076		0.0003		0.0003		0.0036

Design space development

A Monte Carlo method was performed using a self-coded MATLAB program (R2016a, Version 9.0, The Math Works Inc., USA) to calculate the design space based on the specific goals of process CQAs. The calculation process was introduced in previous work [33]. The acceptable ranges of the CQAs and the probability requirements for compliance are shown in Table 7. 1000 simulations were carried out to get the probability of every possible condition.

Table 7. The lower limits of process CQAs and probability requirements for compliance

Process CQAs	Minimum	Acceptable probability of design space
Dry matter removal (%)	40	≥90%
Purity of Astragaloside IV (μg/g)	800
Purity of Astragaloside Ⅱ(μg/g)	700
Purity of CG (μg/g)	1800
Purity of PG (μg/g)	600
Purity of IFG (μg/g)	600

The conditions of design space were listed in Table S2, and shown in Figure 7(a) - (d). The design space was an irregular region.

Control strategy of Astragali radix concentrates

In order to obtain a satisfactory supernatant, Inequalities (1) should be satisfied for CQA requirements listed in Table 7.

where superscripts refer to dry matter removal, purity of Astragaloside IV, purity of Astragaloside Ⅱ, purity of CG, purity of PG, purity of IFG, and dry matter removal, respectively. The values of regression coefficients in Inequalities (1) can be found in Table 6. If the CMAs of a batch of Astragali radix concentrates meet Inequalities (1), the batch of Astragali radix concentrates is considered to be acceptable for ethanol precipitation. For a batch of acceptable Astragali radix concentrates, feasible process parameters can be chosen after calculation or selected from Table S2. A batch of Astragali radix concentrates is considered to be unacceptable when Inequalities (1) cannot be satisfied.

In industry, the process parameters are usually fixed. If the process parameters are fixed as follows: the ECR is 1.5 g/g, the dry matter content of concentrates is 45%, the ethanol solution concentration is 92% (v/v), and the temperature is 15 ^oC, Inequalities (1) can be simplified to Inequalities (2).

If a batch of Astragali radix concentrates with CMAs meeting Inequalities (2), this batch of Astragali radix concentrates is considered to be high quality material for the current parameter fixing process. If not, it is considered a low-quality material and should not be released for ethanol precipitation directly.

Examples of material quality control and verification experiments

The CMAs of 3 batches of Astragali radix concentrates were measured and are shown in Table 8. According to Inequalities (2), Astragali radix concentrates of N12 were low quality Astragali radix concentrates, and it should not be released. The design space calculation results of N12 was show in Figure S3. The results show that when the materials were unqualified, no matter how to change the CPP, the standards of CQA can not be achieved with a high probability.

Table 8. CMAs of Astragali radix concentrates for validation

concentrates No.	Electrical conductivity (Z₈, μS/cm)	CG contents (Z₃, μg/g dry matter)	Sucrose contents (Z_7,mg/g dry matter)	Quality grade
N11	1736	1292	617.6	High quality
N12	1568	1384	595.2	Low quality
N13	1631	1166	638.9	High quality

N11 and N13 were high quality Astragali radix concentrates. The verification experiment conditions and results are listed in Table 9 and Fig. 7(e) and 7(f). All the predicted values were close to the experimental values, indicating that the models had good predictive performance.

Table 9. Verification conditions and results（n=3）

CPPs and CQAs		V1	V2	V3
concentrates		N11	N13	N11
Is it in the design space?		Yes	Yes	No
DM (%)		45	45	45
ECR (g/g)		1.9	1.9	1.2
The ethanol solution concentration (%)		93	94	90
temperature (^oC)		5	5	5
Dry matter removal (%)	Experimental value*	49.23±0.64	50.95±2.46	37.88±1.20
Dry matter removal (%)	Predicted value	49.82	51.14	38.71
Purity of Astragaloside IV (μg/g)	Experimental value*	1007.19±60.07	899.73±73.14	1083.41±50.90
Purity of Astragaloside IV (μg/g)	Predicted value	1119.77	865.77	1119.77
Purity of Astragaloside II (μg/g)	Experimental value*	948.76±45.37	984.53±28.71	893.08±20.73
Purity of Astragaloside II (μg/g)	Predicted value	911.93	867.57	821.78
Purity of CG (μg/g)	Experimental value*	2691.32±133.66	2733.53±97.48	2451.62±24.05
Purity of CG (μg/g)	Predicted value	2471.65	2539.55	2350.18
Purity of PG (μg/g)	Experimental value*	1054.72±46.97	765.27±25.26	939.22±25.75
Purity of PG (μg/g)	Predicted value	1044.77	930.65	1074.02
Purity of IFG(μg/g)	Experimental value*	887.45±82.71	692.58±23.22	869.80±19.20
Purity of IFG(μg/g)	Predicted value	783.24	625.17	765.00

Experimental value*: average value ±SD

The quality variations of most of Chinese medicines are from raw materials[36]. These variations may transmit from upstream intermediates to drug products. As a purification process, ethanol precipitation process is usually expected to reduce these variations.

The RSD of each active component contents in the concentrates and ethanol precipitation supernatant of Experiment 1–10 were calculated, which are listed in Table 10. The RSD of active component contents were not decreased significantly. Active component contents in the precipitate of Experiment 2 was also analyzed. Astragaloside IV, Astragaloside Ⅱ, CG, PG, and IFG were not found in the precipitate. It indicated that the variation of non-precipitated component contents could not be significantly reduced by ethanol precipitation process.

Table 10

RSD of flavonoids and saponins contents
Materials	RSD of flavonoids and saponins contents
Materials	Astragaloside IV	Astragaloside Ⅱ	CG	PG	IFG
Concentrates	0.782	0.731	0.201	0.598	0.632
Supernatants	0.778	0.795	0.166	0.610	0.589

For a Chinese medicine prepared with a series of unit operations containing ethanol precipitation, it is not enough to ensure the quality consistency of drugs only by the control of ethanol precipitation process parameters. Strict quality control of raw materials is sometimes more important. Mixing different batches of raw materials was an effective way to improve the quality consistency of raw materials. This strategy was introduced in many published works[36, 37].

In this study, a membrane dispersion continuous ethanol addition device which can achieve rapid cooling was developed for Astragali radix ethanol precipitation. The ethanol precipitation process was then optimized according to QbD concept. The experiments were carried out with different concentrates under fixed process conditions to identified the CMAs. CG content, the sucrose content, and the electrical conductivity were found to be CMAs. Definitive screening design was used to investigate the relationships among CPPs, CMAs, and CQAs. After model development, it is found that dry matter removal was mainly affected by CPPs. The purity of Astragaloside IV was mainly affected by CMAs. The purity of Astragaloside Ⅱ, PG, and IFG were affected by both CPPs and CMAs. The design space was then calculated according to the probability of attaining process CQA standards. A material quality control strategy was proposed. High quality and low quality Astragali radix concentrates can be discriminated by the inequalities. Low quality Astragali radix concentrates should not be released for ethanol process directly. Verification experiments were carried out for high quality Astragali radix concentrates. The experimental results agreed well with the prediction results. The control strategy proposed in this work is promising to be used in other processes to improve batch-to-batch consistency of TCMs or herbal medicines.

Abbreviations
TCMs	traditional Chinese medicines
QbD	quality by design
CMAs	critical material attributes
CQAs	critical quality attributes
CPPs	critical process parameters
CG	Calycosin-7-O-β-D-glucoside
PG	9,10-dimethoxypterocarpan-3-O-β-D-glucoside
IFG	2-hydroxy-3',4'-dimethoxyisoflavane-7-O-β-D-glucoside
ECR	the mass ratio of ethanol solution to concentrate
AC	active component contents
DM	dry matter contents
Superscript
S	supernatants
C	concentrates

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Availability of data and materials

All data generated or analyzed during this study are included in this published article and its supplementary information files.

Competing interests

The authors declare that they have no competing interests.

Funding

The authors would like to acknowledge the support of the National S&T Major Project of China (2018ZX09201011-002), and the Basic Public Welfare Research Program of Zhejiang Province (LGG18H280001).

Authors’ contributions

Data curation, J.P.; methodology, X.G.; software, J.P., and X.G.; formal analysis, J.P. and X.G.; resources, X.G. and H.Q.; writing—original draft preparation, J.P. and Y.T.; writing—review and editing, J.P., Y.T., X.G.; supervision, X.G.

Acknowledgement

Not applicable

Zhang L, Gong XC, Wang YF, and Qu HB. Solubilities of protocatechuic aldehyde, caffeic acid, d-galactose, and d-raffinose pentahydrate in ethanol–water solutions. J. Chem. Eng. Data. 2012; 57(7): 2018-22.
Zhang L, Gong XC, and Qu HB. Optimizing the Alcohol Precipitation of Danshen by Response Surface Methodology. Separation Science & Technology. 2013; 48(6): 977-83.
Chen Y, Li YR, Jin YC, Cai S, Cheng YY, and Qu HB. Research progress and thinking on the process and equipment of Chinese medicine alcohol precipitation. Modernization of Traditional Chinese Medicine and Materia Medica -Word Science and Techenology. 2007; 9(5): 16-19.
Soares PA, Vaz AF, Correia MT, Pessoa Jr A, and Carneiro-da-Cunha MG. Purification of bromelain from pineapple wastes by ethanol precipitation. Separation and purification technology. 2012; 98: 389-95.
Boulet M, Britten M, and Lamarche F. Dispersion of food proteins in water-alcohol mixed dispersants. Food Chem. 2001; 74(1): 69-74.
Ku Y, Jansen O, Oles CJ, Lazar EZ, and Rader JI. Precipitation of inulins and oligoglucoses by ethanol and other solvents. Food Chem. 2003; 81(1): 125-32.
Xu B, Wu Z, Lin Z, Sui C, Shi X, and Qiao Y. NIR analysis for batch process of ethanol precipitation coupled with a new calibration model updating strategy. Anal. Chim. Acta. 2012; 720: 22-28.
Hu X, Liu C, Jin Z, and Tian Y. Fractionation of starch hydrolysate into dextrin fractions with low dispersity by gradient alcohol precipitation. Separation and Purification Technology. 2015; 151: 201-10.
Bermejo DV, Ibáñez E, Reglero G, Turner C, Fornari T, and Rodriguez-Meizoso I. High catechins/low caffeine powder from green tea leaves by pressurized liquid extraction and supercritical antisolvent precipitation. Separation and Purification Technology. 2015; 148: 49-56.
Wei Y, Wang L, Wang D, Wang D, Wen C, Han B, et al. Characterization and anti-tumor activity of a polysaccharide isolated from Dendrobium officinale grown in the Huoshan County. Chinese medicine. 2018; 13(1): 1-11.
Koh GY, Chou G, and Liu Z. Purification of a water extract of Chinese sweet tea plant (Rubus suavissimus S. Lee) by alcohol precipitation. J. Agric. Food Chem. 2009; 57(11): 5000-6.
Shao F, Yu M, Jiang M, Yang M, Shang Y, Liu R, et al. Establishment of Determination of Fractal Dimension of Ethanol -precipitated Flocs of Two Root Medicinal Herbs. Chinese Journal of Experimental Traditional Medical Formulae. 2019; 22: 1-5.
Li H, Dai Q, Ren J, Jian L, Peng F, Sun R, et al. Effect of structural characteristics of corncob hemicelluloses fractionated by graded ethanol precipitation on furfural production. Carbohydrate polymers. 2016; 136: 203-09.
Pan J, Shao J, Qu H, and Gong X. Ethanol precipitation of Codonopsis Radix concentrate with a membrane dispersion micromixer. Journal of Cleaner Production. 2020; 251: 119633.
Gong XC, Shen JC, and Qu HB. [Application of continuous mixing technology in ethanol precipitation process of Salvia miltiorrhiza by using micromixer]. China journal of Chinese Materia Medica. 2016; 41(23): 4356-61.
Yu LX. Pharmaceutical Quality by Design: Product and Process Development, Understanding, and Control. Pharm. Res. 2008; 25(4): 781.
Tomba E, Facco P, Bezzo F, and Barolo M. Latent variable modeling to assist the implementation of Quality-by-Design paradigms in pharmaceutical development and manufacturing: A review. Int. J. Pharm. 2013; 457(1): 283-97.
Lawrence XY, Amidon G, Khan MA, Hoag SW, Polli J, Raju G, et al. Understanding pharmaceutical quality by design. The AAPS journal. 2014; 16(4): 771-83.
Kan S, Lu J, Liu J, Wang J, and Zhao Y. A quality by design (QbD) case study on enteric-coated pellets: Screening of critical variables and establishment of design space at laboratory scale. asian journal of pharmaceutical sciences. 2014; 9(5): 268-78.
García Aponte OF, Vallejo Díaz BM, and Mora Huertas CE. Quality by design: Principles and opportunities for the pharmaceutical industry. Estudios Gerenciales. 2015; 31(134): 68-78.
li J, Qiao Y, and Wu Z. Nanosystem trends in drug delivery using quality-by-design concept. J. Control. Release. 2017; 256: 9-18.
Huang P, Zhang Q, Pan H, Luan L, Liu X, and Wu Y. Optimization of integrated extraction-adsorption process for the extraction and purification of total flavonoids from Scutellariae barbatae herba. Separation and Purification Technology. 2017; 175: 203-12.
Gong X, Wang S, Li Y, and Qu H. Separation characteristics of ethanol precipitation for the purification of the water extract of medicinal plants. Separation and Purification Technology. 2013; 107: 273-80.
Zhang L, Yan B, Gong X, Yu LX, and Qu H. Application of quality by design to the process development of botanical drug products: a case study. AAPS PharmSciTech. 2013; 14(1): 277-86.
Gong X, Yan A, and Qu H. Optimization for the ethanol precipitation process of botanical injection: indicator selection and factor influences. Separation Science and Technology. 2014; 49(4): 619-26.
Gong X, Li Y, Guo Z, and Qu H. 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. Separation and Purification Technology. 2014; 132: 126-37.
Xu ZL, Huang WH, Gong XC, Ye TT, Qu HB, Song YG, et al. Design space approach to optimize first ethanol precipitation process of Dangshen. China Journal of Chinese Materia Medica. 2015; 40(22): 4411-16.
Yan A, Gong XC, and Qu H. Method for discriminating key quality control indicators of concentrated solution before traditional Chinese medicine ethanol precipitation. China Journal of Chinese Materia Medica. 2012; 37(11).
Zhang H, Yan A, Gong X, and Qu H. A study on the quality indicators for concentration process of supernatant obtained in the first ethanol precipitation in the production of Danshen Injection. China Journal of Chinese Materia Medica. 2011; (11): 36-40.
Jones B, and Nachtsheim CJ. A Class of Three-Level Designs for Definitive Screening in the Presence of Second-Order Effects. Journal of Quality Technology. 2011; 43(1): 1-15.
Xiao L, Liu Y, Wu D, Chen X, Liu P, Wei H, et al. Optimization of water extraction and alcohol precipitation process of Huangqi Tongbian granules by multi index comprehensive scoring method. Jiangxi Journal of Traditional Chinese Medicine. 2019; 50(03): 59-63.
Luo Y, Li W, Huang W, Liu X, Song Y, and Qu H. Simultaneous determination of six components in water extract and alcohol precipitation liquid in Astragali Ｒadix by HPLC-UV-ELSD. China Journal of Chinese Materia Medica. 2016; 41(5): 850-58.
Shao JY, Cao W, Qu HB, Pan JY, and Gong XC. A novel quality by design approach for developing an HPLC method to analyze herbal extracts: A case study of sugar content analysis. PLoS One. 2018; 13(6): e0198515.
Sun M-f, Yang J-y, Cao W, Shao J-y, Wang G-x, Qu H-b, et al. Critical process parameter identification of manufacturing processes of Astragali Radix extract with a weighted determination coefficient method. Chinese Herbal Medicines. 2019.
Liu S, Shen J, Li W, Qu H, and Gong X. Comparison on three methods for identification of critical process parameters. Chinese Traditional and Herbal Drugs. 2016; 47(18): 3193-98.
Zhong W, Chen S, Zhang J, Wang Y, and Liu A. Which one is more important, raw materials or productive technology?-a case study for quality consistency control of Gegen Qinlian decoction. China Journal of Chinese Materia Medica. 2016; 041(006): 1027-32.
Beek T A V MP. Chemical analysis and quality control of Ginkgo biloba leaves, extracts, and phytopharmaceuticals. J. Chromatogr. A. 2009; 1216(11): 2002-32.

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Optimization of membrane dispersion ethanol precipitation process with a set of temperature control improved equipment

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Abstract

Background

Methods

Results

Conclusions

Figures

1 Background

2 Methods

3 Results

Material attributes

The identification of CMAs

The effects of CMAs and CPPs

Design space development

Control strategy of Astragali radix concentrates

Examples of material quality control and verification experiments

4 Discussions

5 Conclusion

List Of Abbreviations

Declarations

References

Supplementary Files

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