Scale up, optimization and stability analysis of Curcumin C3 complex-loaded nanoparticles for cancer therapy
© Ranjan et al.; licensee BioMed Central Ltd. 2012
Received: 10 May 2012
Accepted: 23 August 2012
Published: 31 August 2012
Nanoparticle based delivery of anticancer drugs have been widely investigated. However, a very important process for Research & Development in any pharmaceutical industry is scaling nanoparticle formulation techniques so as to produce large batches for preclinical and clinical trials. This process is not only critical but also difficult as it involves various formulation parameters to be modulated all in the same process.
In our present study, we formulated curcumin loaded poly (lactic acid-co-glycolic acid) nanoparticles (PLGA-CURC). This improved the bioavailability of curcumin, a potent natural anticancer drug, making it suitable for cancer therapy. Post formulation, we optimized our process by Reponse Surface Methodology (RSM) using Central Composite Design (CCD) and scaled up the formulation process in four stages with final scale-up process yielding 5 g of curcumin loaded nanoparticles within the laboratory setup. The nanoparticles formed after scale-up process were characterized for particle size, drug loading and encapsulation efficiency, surface morphology, in vitro release kinetics and pharmacokinetics. Stability analysis and gamma sterilization were also carried out.
Results revealed that that process scale-up is being mastered for elaboration to 5 g level. The mean nanoparticle size of the scaled up batch was found to be 158.5 ± 9.8 nm and the drug loading was determined to be 10.32 ± 1.4%. The in vitro release study illustrated a slow sustained release corresponding to 75% drug over a period of 10 days. The pharmacokinetic profile of PLGA-CURC in rats following i.v. administration showed two compartmental model with the area under the curve (AUC0-∞) being 6.139 mg/L h. Gamma sterilization showed no significant change in the particle size or drug loading of the nanoparticles. Stability analysis revealed long term physiochemical stability of the PLGA-CURC formulation.
A successful effort towards formulating, optimizing and scaling up PLGA-CURC by using Solid-Oil/Water emulsion technique was demonstrated. The process used CCD-RSM for optimization and further scaled up to produce 5 g of PLGA-CURC with almost similar physicochemical characteristics as that of the primary formulated batch.
KeywordsScale up Optimization PLGA nanoparticles Cancer Response surface methodology (RSM) Curcumin C3 complex Central composite design (CCD)
Curcumin is one of the most promising natural anti-cancer agents and hence been much investigated for the past few decades [1, 2]. Several phase I and phase II clinical trials indicate that curcumin is quite safe and may exhibit therapeutic efficacy [3–5]. A purified form of curcumin which consists of three main components: curcumin (76.07%); bisdemethoxy curcumin (3.63%); and demethoxy curcumin (20.28%) is defined as curcumin C3 complex . Henceforth, curcumin C3 complex will be referred to as curcumin in this paper. Poor water solubility, poor physiochemical properties and low bioavailability continue to pose major challenges in developing a curcumin formulation for clinical efficacy. Lower serum and tissue levels of curcumin are observed irrespective of the route of administration due to extensive intestinal and hepatic metabolism and rapid elimination, thus restraining bioavailability of curcumin [7–10]. To improve its potential application in the clinical arena, several formulation strategies like nanoparticles, liposomes, complex with phospholipids, cyclodextrins and solid dispersions are being developed to improve bioavailability of curcumin and increasing its therapeutic efficacy [10–17]. Among these approaches, biodegradable polymeric nanoparticle based delivery systems offer significant advantage over other nanocarrier platforms as there is tremendous versatility in the choice of polymer matrices that can be used for tailoring nanoparticle properties to meet various drug delivery needs.
Although much research emphasis are presently being dedicated to various nanoparticle formulations in the pharmaceutical industry, especially towards particle design and targeting, very few results have ever been published on process scale-up. Scaling up of the formulation process is essential for clinical use. In this paper, we have made an effort towards optimizing and scaling up PLGA nanoparticles encapsulating curcumin (PLGA-CURC) by using Solid-Oil/Water (S-O/W) an emulsification-solvent-evaporation/diffusion technique. The major goals in designing polymeric nanoparticles as a delivery system are to control particle size and polydispersity, maximize drug encapsulation efficiency and drug loading, optimize surface properties and tailor release of pharmacologically active agents to achieve a site specific action of the drug at the therapeutically optimal desired rate and dose regimen [18, 19]. Optimization becomes especially important when the formulation needs to be scaled up for industrial production. The organic solvent used in the formulation becomes critical for pilot and industrial scale production and hence only class 3 solvents are preferred for formulation while scaling up. In our formulation, we used ethyl acetate as the organic solvent. Partially hydrolyzed PVA was used as emulsion stabilizer as it prevents redispersibility problems .
For the optimization process, our aim was to use Response Surface Methodology (RSM) in conjunction with Central Composite Design (CCD) to establish the functional relationships between three chosen operating variables: polymer (PLGA) concentration, stabilizer (PVA) concentration and volume of organic phase (ethyl acetate). Four responses were identified namely, mean particle size, polydispersity, encapsulation efficiency (EE) and drug loading (DL) of PLGA-CURC for this study. The optimization procedure involved systematic formulation designs to minimize the number of trials, and analyze the response surfaces in order to realize the effects of factors and to obtain the appropriate formulations with target goals [21, 22]. Further, for analyzing the responses to the variables, mathematical model equations were derived by using Design-Expert® 5.0 software. For a better understanding of the three variables or the optimal PLGA-CURC performance, the models were presented as three-dimensional contour response surface graphs.
Once the optimized batch was determined, classical scale up was followed to produce gram amounts of nanoparticle formulation. The nanoparticles obtained from the scale up were then characterized for particle size, polydispersity, drug loading and morphology and compared with non-scaled up optimized batch, thereby establishing successful process scale-up. Nanoparticles from the scaled up batch were further evaluated for percentage cumulative release, functional assays, cellular uptake in different cancer cell lines and storage stability.
Materials and methods
Poly (D,L-lactide-co-glycolide) 50:50; i.v. 0.77 dL/g (~0.5% w/v in chloroform at 30o C); m.w. 124 kDa was purchased from Lakeshore Biomaterials (Birmingham, AL). Curcumin c3 complex was a kind gift from Sabinsa Corporation (East Windsor,NJ), Polyvinyl alcohol (m.w. 9,000-10,000; 80% hydrolyzed), ethyl acetate, ethanol, nile red, D(+) trehalose, sucrose, were purchased from Sigma Aldrich (St. Louis, MO). The human prostate cancer cell line - DU 145, breast cancer cell line - MDA-MB-231 and pancreatic cancer cell line MiaPaCa were obtained from ATCC (Manassas, VA). RPMI 1640, DMEM and FBS was obtained from Gibco, Invitrogen (Carlsbad, CA). Gold antifade mounting agent with 4’-6-diamidino-2-phenylindole (DAPI) was purchased from Invitrogen (Carlsbad, CA). Double-distilled deionized water was used for all the experiments.
Experimental design for optimization of formulation
Relationship between coded and actual values of the variables used for PLGA-CURC formulation
Coded level of variables
X1 = Amount of PLGA (mg)
X2 = PVA (% w/v)
X3 = Ethyl acetate (ml)
Y1 = Particle Size (nm)
Y2 = Polydispersity
Y3 = Encapsulation efficiency (%)
Y4 = Drug loading (%)
Observed responses in central composite design for PLGA-CURC formulation
Wt. of PLGA (mg)
PVA conc. (% w/v)
Ethyl Acetate (ml)
Particle Size (nm)
Encapsulation efficiency (%)
Drug loading (%)
Where Y is the measured response associated with each factor level combinations; bo is the Intercept; bi ‘s (for i = 1,2, and 3) are the linear effects, the bii are the quadratic effects, the bij ‘s (for i,j = 1,2,and 3, i < j) are the interaction between the ith and jth variables.
Data were analyzed by using analysis of variance (ANOVA), which helped determine if the factors and the interactions between factors were significant. To test whether the terms were statistically significant in the regression model, t-tests were performed using a 95% (α = 0.05) level of significance. An F-test was used to determine whether there was an overall regression relationship between the response variable Y and the entire set of X variables at a 95% (α=0.05) level of significance. The coefficient of multiple determinations was denoted by R2, which measured the proportionate reduction of total variation in Y associated with the use of the set of X variables. In addition, the validity of the regression model was assessed according to statistical assumptions and lack of fit test. The statistical analysis was performed using the software Design Expert (Version 5) (Stat-Ease, Inc, Minneapolis, MN).
Determination of desirability coefficient
One may look upon the di as the value of the response on a new scale between zero and unity. The exponent (weighting factor) defines curvature of the interpolation equation. For example, when wi = 1, the interpolation is linear. Since the di values are in the range 0 ≤ di ≤1, the desirability coefficient is also in the range 0 ≤ δ ≤1. The index n equals ∑wi. The contour plot of desirability coefficients reported here is based on the di values computed for four variables, namely, for particle size (d 1 ), polydispersity (d 2 ), encapsulation efficiency (d 3 ) and drug loading (d 4 ). The goals considered were minimizing particle size and polydispersity, maximizing encapsulation efficiency and drug loading. The desirability coefficient δ was computed in this fashion and the contours of equal δ values were plotted. To obtain the condition on the design variables that maximize δ, a three-dimensional graph of the response against any two factors was plotted, from which the region corresponding to optimum values for δ was yielded.
Characterization of PLGA-CURC
Particle size and polydispersity
Particle size measurements and polydispersity of PLGA-CURC were determined by laser diffraction using a Nanotrac system (Mircotrac, Inc., Montgomeryville, PA). Lyophilized PLGA-CURC were dispersed in double distilled water as described elsewhere [14, 25] and analyzed in triplicates with three readings per nanoparticle sample. The polydispersity was also calculated based on the volumetric distribution of particles.
Determination of curcumin associated with PLGA-CURC
Lyophilized PLGA-CURC (5 mg) was dissolved in 2 ml acetonitrile to extract curcumin into acetonitrile for determining the encapsulation efficiency. The samples in acetonitrile were gently shaken on a shaker for 4 h at room temperature to completely extract out curcumin from the nanoparticles into acetonitrile. These solutions were centrifuged at 14,000 rpm (Centrifuge 5415D, Eppendorf AG, Hamburg, Germany) and supernatant was collected. Suspension (20 μl) was dissolved in ethanol (1 ml) and was used for the estimations. The curcumin concentrations were measured spectrophotometrically at 450 nm. A standard plot of curcumin (0–10 μg/ml) was prepared under identical conditions.
Percentage drug loading
In vitro drug release study
The in vitro drug release profiles of optimized PLGA-CURC formulations were determined by measuring the cumulative amount of drug released from the nanoparticle over predetermined time intervals as described elsewhere .
To study and better understand the release mechanism of curcumin from nanoparticle formulation, data obtained from in vitro drug release studies were fitted in different kinetic models [26, 27]. For zero order, cumulative amount of drug released was plotted versus time; for first order, log cumulative percentage of drug remaining was plotted versus time; for Higuchi’s model, cumulative percentage of drug released was plotted versus the square root of time and for Hixson–Crowell cube root model, cumulative percentage of drug release was plotted versus cube root of time. Plotted data were fitted using a linear equation; the regression coefficient (r2) was calculated from the appropriate graphs. Selection of the best model was based on the comparisons of the relevant correlation coefficients.
External morphology studies
Transmission electron microscopy (TEM)
Scanning electron microscopy (SEM)
The surface morphology of the formulated nanoparticle was measured by scanning electron microscopy (SEM) (EM- LEO 435VP, Carl Zeiss SMT Inc., NY) equipped with 15 kV, SE detector with a collector bias of 300 V. The lyophilized samples were carefully mounted on an aluminum stub using a double stick carbon tape. Samples were then introduced into an automated sputter coater and coated with a very thin film of gold before scanning the samples under SEM.
In vitro cellular uptake of PLGA-CURC in cancer cells
Curcumin is intrinsically fluorescent; this facilitated the visualization of the PLGA-CURC uptake into cells under confocal microscope. To observe the internalization of nanoparticles under a confocal microscope, DU-145, MDA-MB 231 and MiaPaCa were grown under standard cell culture conditions. Cellular uptake of Nile red-labeled PLGA-CURC was determined using a confocal microscope (Zeiss LSM 510 META attached to a Zeiss Axiovert 200 inverted microscope) (Carl Zeiss MicroImaging, Inc., Thornwood, NY). For these experiments, cells were placed on a cover slip in a 6-well tissue culture plate and incubated at 37°C until they reached sub-confluent levels. The cells were then exposed to 1 mg/ml concentrations of nile red labled PLGA-CURC. After 2 h, the treated cells were fixed with standard paraformaldehyde (4%) and fixed using Gold antifade mounting agent with 4’-6-diamidino-2-phenylindole (DAPI). The slides were viewed under the microscope to determine the extent of intracellular nanoparticle uptake.
Western blot analysis to determine the functional integrity
For Western blot, 30–50 μg of nuclear and cytoplasmic and protein extracts, prepared by the nuclear extraction kit (Pierce, USA) protocol, were resolved on 10% SDS-PAGE gel. After electrophoresis, the proteins were electrotransferred to a nitrocellulose membrane, blocked with 5% non-fat milk, and probed with antibodies against the p65 subunit of NFκβ. Thereafter, the blot was washed, exposed to HRP-conjugated secondary antibodies for 1 hour, and finally detected by ECL chemiluminescence reagents (Amersham Pharmacia Biotech, Arlington Heights, IL).
Scale up for large batch of nanoparticles production
Formulation factors and parameter variations for process scale up
Ethyl acetate (ml)
Aqueous phase (ml)
Excess Water (ml)
Sonication Time (s)
Stirrer Speed (rpm)
Stirrer Time (h)
Primary optimized conditions
First stage – 500 mg
Second stage – 1 g
Third stage- 2 g
Fourth stage – 5 g
Scale up results for large batch production of PLGA-CURC
Particle size (nm)
Encapsulation efficiency (%)
Drug loading (%)
Primary optimized conditions
129.5 ± 6.9
0.138 ± 0.023
91.4 ± 2.3
12.68 ± 3.5
First stage – 500 mg
135.4 ± 9.4
0.139 ± 0.012
91.12 ± 1.5
11.98 ± 2.5
Second stage – 1 g
142.3 ± 8.9
0.142 ± 0.024
92.13 ± 3.5
11.12 ± 2.6
Third stage- 2 g
148.6 ± 7.7
0.137 ± 0.025
90.67 ± 2.8
10.92 ± 2.3
Fourth stage – 5 g
158.5 ± 9.8
0.141 ± 0.011
90.34 ± 3.2
10.32 ± 1.4
Pharmacokinetic studies were carried out to analyze the bioavailability of curcumin following intravenous (i.v.) administration of PLGA-CURC. For this study, male Sprague Dawley rats weighing 250–300 g were used in a protocol approved by the IACUC committee of UNTHSC, Fort Worth, Texas, USA. Eight animals were administered PLGA-CURC (7.5 mg curcumin equivalent/kg of bodyweight) by tail vein injection. Blood samples (200μL) were collected into heparinized microcentrifuge tubes at predetermined time points. After each blood sampling, same amount of normal saline was administered to compensate for the blood loss. Plasma was separated by centrifuging the blood samples at 3,500 rcf for 10 min at 4°C. To 100 μL aliquot of acetonitrile containing 0.15 μg/mL of internal standard was added to 100 μL of each plasma samples in order to precipitate the plasma protein. Samples were vortexed for 5 min and then subjected to centrifugation at 2,500 rcf for 15 min to remove any precipitated material. Finally, samples were injected into the HPLC system through the autosampler. The concentration of curcumin was determined by HPLC analysis and quantitated with previous calibrations .
PLGA-CURC (20 mg) was kept in four sealed glass vials and maintained at 4°C for a period of 6 months. Nanoparticles were characterized for change in particle size, encapsulation efficiency and percent drug loading according to the above mentioned protocols.
Gamma irradiation of nanoparticles
Gamma irradiation is recommended by European Pharmacopoeia for the purpose of sterilizing pharmaceutical products. Such studies for our nanoparticles were carried out by Steris Isomedix Services, IL, USA. Drug loaded nanoparticles were γ-irradiated using 60Co as irradiation source and received a dose of either 16.8 kGy for 241 minutes (Low), 25.3 kGy for 179 minutes (Medium) or 35.8 kGy for 241 minutes (High). Non-irradiated samples were kept as reference for further comparison.
Results and discussion
Optimization of PLGA-CURC using central composite design
Response Surface Methodology (RSM) using the Central Composite Design (CCD) model is a well-suited experimental design strategy that offers the possibility of investigating a high number of variables at different levels with only a limited number of experiments . The methodology was originally developed by Box and Wilson and improved by Box and Hunter. This is an ideal tool for process optimization , and its rotatable characteristic enables identification of optimum responses around its center point without changing the predicting variance. RSM is a collection of mathematical and statistical techniques based on the fit of a polynomial equation to the experimental data, which must describe the behavior of a data set with the objective of making statistical provisions. CCD has been successfully used to optimize the technology or production condition for drug delivery systems such as sustained release tablets, liposomes, microspheres, nanoparticles in recent years [29–35]
The ranges for each of the variables in Table 1 were chosen taking into account our preliminary experiments. Table 2 shows the experimental results concerning the tested variables on mean diameter of particle size, polydispersity drug loading percentage and encapsulation efficiency. These four responses were individually fitted to a second order polynomial model. For each response, the model which generated a higher F value was identified as the best fitted model. Each obtained model was validated by ANOVA. Three dimensional response surface plots were drawn for the optimization of PLGA-CURC formulation. These types of plots are useful in studying the effects of two factors on the response at one time, when the third factor is kept constant.
Influence of formulation variables on particle size
A positive value in regression equation for a response represents an effect that favors the optimization (synergestic effect), while a negative value indicates an inverse relationship (antagonistic effect) between the factors and the response .
The reduced quadratic model was found to be significant with an F value of 30.87 (p < 0.0001), which indicates that response variable Y1 and the set of formulation variables were significantly related. The high R2 value indicated that 96.53% of variation in particle size was explained by the regression on formulation factors (Additional file 1: Table S1).
Influence of preparation factors on polydispersity index
Comparison of the experimental and predicted values of PLGA-CURC prepared under the predicted optimum conditions
Y 1 , Particle size (nm)
Y 2 , Polydispersity index
Y 3 , Encapsulation efficiency (%)
Y 4 , Drug loading (%)
Influence of preparation factors on encapsulation efficiency
Influence of preparation factors on percentage drug loading
Optimization by desirability function
In order to evaluate the predictive power of this model and desirability coefficient, PLGA-CURC was prepared under the optimal conditions. The results comparing the experimentally obtained and model predicted values of all four responses are presented in Table 5. The experimental values of the multiple batches prepared under the optimal conditions were very close to the predicted values, with low percentage bias, suggesting that the optimized formulation was reliable and reasonable. It has been shown that the highest encapsulation efficiency and drug loading with commensurate minimum mean particle size and size distribution was achieved by using the optimal conditions of 85 mg PLGA, 1% (w/w) of PVA and 4.25 ml volume of ethyl acetate.
Scale up for large batch production of PLGA-CURC
Scaling up the nanoformulation process to produce large batches of nanoparticles is the key to effective clinical use of nanoparticle based drugs. To translate this formulation into large scale production, we investigated seven critical parameters and their correlation in four sequential stages. Each stage was optimized to get the best parameter combination in terms to target response of particle size, polydispersity, encapsulation efficiency and drug loading. The parameters chosen include polymer solvent ratio, aqueous phase volume, organic and aqueous phase ratio, sonication tip diameter, sonication time, stirrer speed and stirring time. Our goal was to produce PLGA-CURC with similar physicochemical characteristics in a scaled up batch production. Results from all the different stages of scale up production are compiled in Tables 3 and 4 which shows the parameter variations and optimized scale up outcomes of PLGA-CURC formulation.
In the first scale-up optimization stage, polymer amount was increased from 85 mg to 500 mg but the volume of ethyl acetate was increased to only 5 mL from 4.25 mL. This was a very critical step as we needed to minimize the amount of organic solvent needed to prevent problems of solvent evaporation. This resulted in an increase in viscosity of the organic phase leading to larger particle size. To overcome this, the sonication time was increased from 60 sec to 120 sec keeping the sonication tip diameter and stirring speed and stirring time same. This resulted in average particle size of 135.4 nm, an increase of about 6 nm from the primary optimized batch. The drug loading in the first stage scale-up dropped by 0.7% while encapsulation efficiency remained almost the same. Once we achieved the first stage, next we scaled up ~10 times for producing 1 g of PLGA-CURC in the second stage. Doubling the amount of polymer and volume of solvents required increasing the sonication power to get nanoparticles in the same particle size range. For that, the sonication tip diameter was increased from 2 mm to 14 mm. Further, stirring speed was increased from 2,000 rpm to 3,000 rpm and stirring time was increased by 2 h. The resulting optimized batch had an average particle size of 142.3 nm and 11.12% of drug loading. In the third stage (~20X) of scale-up, the aqueous phase was optimized at 60 ml. To keep the nanoparticle size comparable, the sonication time was increased to 180 sec and the stirring speed was doubled to 6,000 rpm. Accounting for more than double of total volume from stage one to three, the exposed surface area for evaporation of solvents was increased by using an open mouthed vessel during stirring. With all these parameter combinations, the final optimized batch for this stage showed a 0.2% decrease in drug loading only and similar ~6 nm increase in particle size from previous stage. In the final stage, we scaled up to ~50 times to produce 5 g of PLGA-CURC. Here, the aqueous phase was increased to 150 mL with the excess water being increased to 200 mL to account for 5 g of polymer being used. Such a large volume of liquid phase needed high sonication power which was brought about by increasing the sonication time from 180 sec to 300 sec, increasing the stirring speed to 8,000 rpm and increasing the stirring time to 8 h. This resulted in nanoparticles having an average particle size of 158.5 nm, a total increase of only 29.4 nm from the primary optimized batch. Also, the drug loading decreased by 2.36% which is minimal considering ~50X scale-up. The encapsulation efficiency and polydipersity was found to be similar to the primary optimized batch. We have successfully produced PLGA-CURC in 5 g quantities through this route and identified critical parameters for scaling up the formulation process.
Characterization and evaluation of the optimized scaled-up formulation
In vitro release studies for PLGA-CURC prepared under optimal conditions
Different kinetic models and regression coefficients of PLGA-CURC formulations a
R2value for burst release (%)
R2value for sustained release (%)
Cellular uptake of nanoparticle prepared under optimal conditions
Western blot analysis
Storage stability of PLGA-CURC nanoparticles
Gamma irradiation PLGA-CURC nanoparticles
Scaling up of the nanoformulation process is essential for the future development of nanoparticle based drug delivery technologies. In this paper, we have made a successful effort towards formulating, optimizing and scaling up PLGA-CURC by using Solid-Oil/Water emulsion technique. Once formulation was achieved, we optimized our process by successful use of RSM using CCD model and scaled up the formulational process in four stages with final scale-up process yielding 5 g of PLGA-CURC. The major goals while designing the scale up stages were to control particle size and polydispersity while maximizing drug encapsulation efficiency and drug loading which were adequately achieved. PLGA-CURC, under the optimized conditions were found to have a particle size of 158.5 ± 9.8 nm, polydispersity of 0.141 ± 0.011, encapsulation efficiency of 90.34 ± 2.3% and drug loading of 10.32 ± 1.4%. Morphological studies of the final scaled up batch showed that the PLGA-CURC were smooth, spherical with a uniform surface. The release kinetics from PLGA-CURC exhibited a biphasic pattern with an initial burst release followed by a slower diffusion controlled drug release for a period of 10 days. Intracellular uptake studies revealed excellent uptake in prostate, breast and pancreatic cell lines. Pharmacokinetic studies illustrated AUC0-∞ of PLGA-CURC after i.v. injection to be 6.139 mg/L h which is higher than most reported in literature for curcumin based formulation. Stability analysis showed long term physicochemical stability and gamma irradiation studies showed no significant changes after sterilization of the PLGA-CURC formulation. In conclusion, our nanoformulation, PLGA-CURC, significantly overcame the limitation of the lack of aqueous solubility of curcumin and thereby improved its bioavailability. The formulation process was successfully optimized using CCD-RSM and scaled up to produce 5 g of PLGA-CURC with similar physicochemical characteristics as that of the primary formulated batch. This scale-up process can be further elaborated to produce higher quantities which would prove beneficial for efficient manufacturing at an industrial scale.
This research was partially supported by SignPath Pharma Inc., Pennsylvania, USA. Anindita Mukerjee was supported by Susan G. Komen postdoctoral grant (KG101213). The authors would like to thank Sanjay Thamake for his help in animal handling for the pharmacokinetics experiment. We would also thank Laurie Mueller for processing the SEM samples at High Resolution Scanning Electron Microscopy Facility at University of Texas Southwestern Medical Center, Dallas, USA.
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