Encapsulation of exemestane in polycaprolactone nanoparticles: optimization, characterization, and release kinetics
© Springer-Verlag Wien 2013
Received: 5 November 2012
Accepted: 25 March 2013
Published: 25 April 2013
This study was aimed at developing a polymeric drug delivery system for a steroidal aromatase inhibitor, exemestane (exe) intended for sustained targeted delivery of drug through intravenous route. Carboxylated polycaprolactone (cPCL) was synthesized by ring opening polymerization of caprolactone. Exe-loaded cPCL nanoparticles (NPs) were prepared by interfacial deposition of preformed polymer and characterized. A 3-factor, 3-level Box–Behnken design was used to derive a second-order polynomial equation and construct contour and response plots for maximized response of percentage drug entrapment (PDE) with constraints on particle size (PS). The independent variables selected were ratio of exe/cPCL, amount of cPCL, and volume of organic phase. Polymerization of caprolactone to cPCL was confirmed by Fourier transform infrared (FTIR) and gel permeation chromatography. The prepared NPs were evaluated for differential scanning calorimetry (DSC), transmission electron microscopy (TEM), and in vitro release studies. Optimum formulation based on desirability (1.0) exhibited PDE of 83.96 % and PS of 180.5 nm. Check point analysis confirmed the role of the derived polynomial equation and contour plots in predicting the responses. Zeta potential of optimized formulation was −33.8 ± 2.1 mV. DSC studies confirmed the absence of any interaction between drug and polymer. TEM image showed non-aggregated and spherical shaped NPs. Drug release from NPs showed sustained release and followed Korsmeyer–Peppas model, indicating Fickian drug release. Thus, preparation of exe-loaded cPCL NPs with high PDE and desired PS suitable for providing passive targeting could be statistically optimized using Box–Behnken design.
KeywordsExemestane Polycaprolactone Nanoparticles Box–Behnken design
Breast cancer is the leading cause of death among women, with one million new cases in the world each year (McPherson et al. 2000), out of which one third are reported to be hormone dependent (Henderson and Canellos 1980; Theobald 2000). Growth of breast cancer cells is often estrogen dependent. Continuous estrogen suppression in patients with hormone-sensitive breast cancer prevents proliferation of tumor. Aromatase is the key enzyme that converts androgens to estrogens both in pre- and postmenopausal women (Lonning 1998; Strassmer-Weippl and Goss 2003). Exemestane (exe) is a third generation, potent irreversible type I steroidal aromatase inhibitor approved by the Food and Drug Administration for the treatment of breast cancer (Johannessen et al. 1997). It acts as a false substrate for the aromatase enzyme and is processed to an intermediate that binds irreversibly to the active site of the enzyme causing its inactivation, an effect also known as suicide inhibition (Dowsett 1998). Although treatment with orally administered exe has been shown to be well tolerated by patients, the most common adverse events consist of hot flashes, nausea, fatigue, dizziness, increased sweating, headache, body weight change, vaginal dryness, arthralgias, and myalgias (Scott and Wiseman 1999; Clemett and Lamb 2000). The problem with the oral delivery of exe is its inability to target the tumor site. This problem can be overcome by employing delivery systems capable of providing targeted drug delivery. Poly(lactic-co-glycolic acid) (PLGA) nanoparticles (NPs) are already reported to provide passive targeting of anticancer drugs to tumor site (Yallapu et al. 2010; Fonseca et al. 2002).
Polymeric NPs with a diameter of less than 200 nm are one of the carrier systems used for passive targeting and sustained release of drug. NPs regroup both nanocapsules and nanospheres. Polycaprolactone (PCL) is a biodegradable polyester and is prepared by ring opening polymerization of ε-caprolactone. PCL is degraded by hydrolysis of its ester linkages in physiological conditions and has therefore received a great deal of attention for use as a biomaterial for sustained release drug delivery systems (Lam et al. 2008; Aberturas et al. 2011). Different methods reported for preparing NPs using biodegradable polymers include monomer polymerization, interfacial deposition, salting out, nanoprecipitation, emulsification solvent evaporation, etc. (Quintanar-Guerrero et al. 1998). Interfacial deposition of preformed polymer technique is based upon interfacial deposition of a polymer followed by diffusion of a semi-polar and miscible solvent in aqueous medium containing surfactant (Fessi et al. 1989; Barichello et al. 1999). Moraes et al. used this method for preparation of PLGA nanocapsules with particle size (PS) of 123 nm and 69 % drug loading (Moraes et al. 2009). Formulation of NPs by this method involves many important factors which contribute to the outcome of experiment in terms of drug entrapment and PS. Different process variables include stirring speed, temperature, rate of addition of organic phase to aqueous phase, etc. Different formulation variables include drug/polymer ratio, concentration of polymer in organic phase, surfactants, surfactant concentration, volume of aqueous and organic phase, organic solvents, etc.
Optimization by changing one-variable-at-a-time is a complex method to evaluate the effects of different variables on an experimental outcome. This approach assesses one variable at a time instead of all simultaneously. The method is time consuming, expensive, and often leads to misinterpretation of results when interactions between different components are present. Another approach is to accurately evaluate the impact of the independent variables on the dependent variables by varying all the important factors simultaneously in a systematic manner. This approach is known as response surface methodology (RSM). RSM is a statistical technique which can address the present scenario and can be used to establish relationships between several independent variables and one or more dependent variables (Myer and Montogomery 2002; Ray et al. 2009). RSM optimizes multiple variables by systematic variation of all variables in a well-designed experiment with a minimum number of experiments. The RSM optimization process involves the following steps: (1) performing statistically designed experiments, (2) estimating the coefficients of a mathematical model using regression analysis technique, and (3) predicting the response and checking the adequacy of the model. Among the available statistical design methods, a full factorial design (FFD) involves a large number of experiments for accurately predicting the response. At the same time, it is often considered unpractical due to its requirement of more number of experiments as compared with other designs (Box et al. 1978; Myer et al. 1989). Fractional factorial design lacks the ability to accurately predict all positions of the factor space that are equidistant from the centre (rotatability). Based upon the desirable features of orthogonality and rotatability, central composite design (CCD), and Box–Behnken design (BBD) are commonly chosen for the purpose of response optimization (Bae and Shoda 2005; Ray 2006). BBD was successfully used by Rahman et al. for optimization of risperidone-loaded solid lipid NPs (Rahman et al. 2010).
The BBD was specifically selected since it requires fewer runs than three-factor, three-level FFD and CCD when three or more variables are involved. This cubic design is characterized by a set of points lying at the midpoint of each edge and a replicate centre point of the multidimensional cube (George Box 1960). The BBD technique is a three-level design based upon the combination of two-level factorial designs and incomplete block designs. BBD is a spherical design with excellent predictability within the spherical design space. Compared with the CCD method, the BBD technique is considered as the most suitable for evaluating quadratic response surfaces particularly in cases when prediction of response at the extreme level is not the goal of the model. In addition, the BBD technique is rotatable or nearly rotatable regardless of the number of factors under consideration (Myer and Montogomery 2002; Bae and Shoda 2005; Ray 2006). However, it is a very time-consuming method. Hence, deriving a quantitative mathematical relationship between the variables to evaluate its effect on dependent variables is of utmost importance (Seth and Misra 2002; Mehta et al. 2007).
In the present study, exe-loaded carboxylated polycaprolactone (cPCL) NPs were prepared by interfacial deposition of preformed polymer technique and optimized using three-factor, three-level Box–Behnken design. The prepared NPs were characterized for percentage drug entrapment (PDE), particle size, zeta potential, compatibility, morphology, in vitro drug release studies, and release kinetics. It was hypothesized that cPCL-based NPs of exe would be capable of passive targeting to the tumor due to PS of less than 200 nm and provide sustained drug release. This would help to improve clinical utility, decrease the dose and frequency of dosing, reduce side effects, and improve therapeutic efficacy of exe in cancer management.
2 Materials and methods
Exe was obtained as a gift sample from Sun Pharma Advanced Research Centre, Vadodara, India. Poloxamer 188 was a gift sample from BASF, Ludwigshafen, Germany. Capric/caprylic triglyceride (Capmul MCM, C8) was obtained as gift sample from Abitec Corporation, Janesville, WI. Caprolactone monomer was purchased from Sigma-Aldrich, Mumbai, India. All other chemicals were of analytical grade and obtained commercially.
2.2 Synthesis of cPCL
Synthesis of carboxylated PCL was carried out by ring opening polymerization of caprolactone monomer in presence of succinic acid as reported by Zhang et al. (1994) with some modifications. Reaction was carried out at room temperature in presence of tertiary butoxide (4 g) for 24 h instead of heating reaction mixture at 225 °C for 3 h. Polymerization was carried out in a flask sealed with a ball filled with nitrogen. The reactant mixture of succinic acid (23.5 mg) and caprolactone (3.65 g) was added to about 15 ml of dichloromethane in the flask for initiation of polymerization reaction. The reaction was catalyzed using tertiary butoxide (4 g). The reaction was allowed to continue for 24 h. The reaction mixture was precipitated in ice-cold water and precipitates were dissolved in acetone for re-precipitation and purification to remove excess succinic acid. Each reaction step as well as purification step was monitored by TLC using 100 % ethyl acetate as a mobile phase and iodine as a spotting reagent. The reaction was considered to be complete when there was absence of spots for caprolactone monomer and succinic acid from the reaction mixture.
2.3 Fourier transform infrared spectroscopy
The sample (2 mg) was finely grounded with purified potassium bromide (200 mg; to remove scattering effects from large crystals). This powder mixture was then pressed in a mechanical die press to form a pellet. These pellets were scanned and spectra were recorded on Fourier transform infrared (FTIR; Bruker Corporation, Billerica, MA). The scanning range was 400–4,000 cm−1 with the resolution of 2 cm−1.
2.4 Molecular weight determination
Gel permeation chromatography (GPC) was carried out to determine the molecular weight of the formed polymer (Behan et al. 2001). A GPC (Perkin Elmer, Series 200, Shelton, CT) equipped with a Waters 510 pump, 50°, 10–3°, and 10–4°A Phenogel columns serially set (Phenomenex, Torrance, CA) and a Waters 410 differential refractometer were used. The mobile phase was tetrahydrofuran (THF) at a flow rate of 1.0 ml/min; 50 μl of a 2 % polymer solution in THF was injected into the system, and size exclusion chromatogram was recorded.
2.5 Preparation of exe-loaded cPCL NPs
cPCL NPs loaded with exe were prepared by interfacial deposition of preformed polymer (Fessi et al. 1989). Exe (5 mg) was dissolved in oil (400 μl capric/caprylic triglyceride mixture) and added to acetone (8 ml) in which cPCL (100 mg) was dissolved along with sorbitan monooleate (Span 60, 0.05 ml), under moderate magnetic stirring. This solution was then added to an aqueous phase (40 ml distilled water) containing Poloxamer 188 (0.5 %) with continuous stirring on magnetic stirrer at room temperature. Stirring was continued for 3–4 h to allow complete evaporation of organic solvent. The NPs suspension was centrifuged at 50,000×g for 30 min at 4 °C (3K30, Sigma Centrifuge, Osterode, Germany), supernatant was alienated, nanoparticulate pellet was re-dispersed in water (10 ml) and lyophilized (Heto Drywinner, Allerod, Denmark) using sucrose as cryoprotectant (NPs (one part) and cryoprotectant (two parts)). Empty NPs were prepared by the method described above with the exception of adding exe. Based on preliminary experiments, variables like drug/polymer ratio (X1), amount of polymer (X2), and volume of organic phase (X3) were selected as independent variables and PDE and PS were taken as dependent variables. Effect of independent variables on dependent variables was studied using 3 × 3 Box–Behnken design.
2.6 Lyophilization and optimization of cryoprotectant
Lyophilization is the process in which freeze-drying is done to remove solvent from the formulation and therefore improve its stability upon storage. The process of freeze drying is stressful and hence a cryoprotectant is added in the process, which also helps in re-dispersibility of the freeze-dried NPs in a suitable solvent (Chacon et al. 1999). One of the main challenges during the freeze-drying process is preserving or rather increasing the re-dispersibility of the NPs upon reconstitution with distilled water or buffered saline. Cryoprotectants are generally added to the NPs prior to the drying step and also act as re-dispersants. Cryoprotectants such as trehalose, sucrose, and mannitol can be used to increase the physical stability of NPs during the freeze-drying process (Paolicelli et al. 2010). In the present study, trehalose, sucrose, and mannitol were investigated in different ratios and change in PS upon re-dispersion was observed. Nanoparticulate suspension (2 ml) was dispensed in 10 ml semi-stoppered glass vials with rubber closures and frozen for 24 h at −60 °C. Thereafter, the vials were lyophilized (Heto Drywinner, Allerod, Denmark) using different cryoprotectants like trehalose, sucrose, and mannitol in different concentrations. Finally, vials were sealed under anhydrous conditions and stored until being re-hydrated. Lyophilized NPs were re-dispersed in exactly the same volume of distilled water as before lyophilization. NP suspension was subjected to PS measurement as described earlier. Ratio of final PS (Sf) and initial PS (Si) was calculated to finalize the suitable cryoprotectant based on lowest Sf/Si ratio.
2.7 HPLC analysis
Quantitative estimation of exe was done by HPLC as reported by Breda et al. with slight modification in mobile phase which consisted of a filtered and degassed mixture of acetonitrile/0.02 M phosphate buffer (pH 4.0; 75:25) (Breda et al. 1993). The equipment consisted of Shimadzu ultraviolet (UV)–vis detector and reversed phase C-18 column, Lichro Cart-RP8 (250× 4.6 mm, 5 μ). The mobile phase was delivered at a flow rate of 1.0 ml/min, the injection volume was 20 μl, the effluent was monitored at UV detection at 247 nm, and the retention time for exe was 5.0 min.
2.8 Drug content and percentage drug entrapment
2.9 Particle size and zeta potential
The size analysis and polydispersity index of the NPs were determined using a Malvern Zetasizer Nano ZS (Malvern Instrument, Worcestershire, UK). Each sample was diluted ten times with filtered distilled water to avoid multiscattering phenomena and placed in disposable sizing cuvette. Polydispersity index was noted to determine the narrowness of the PS distribution. The size analysis was performed in triplicate, and the results were expressed as mean size ± SD.
Zeta potential distribution was also measured using a Zetasizer (Nano ZS, Malvern instrument, Worcestershire, UK). Each sample was suitably diluted ten times with filtered distilled water and placed in a disposable zeta cell. Zeta limits ranged from −200 to +200 mV. The electrophoretic mobility (μm/sec) was converted to zeta potential by in-built software using Helmholtz-Smoluchowski equation. Average of 3 measurements of each sample was used to derive average zeta potential.
2.10 Experimental design
A three-factor, three-level Box–Behnken statistical design was employed to optimize the process and formulation parameters in preparation of exe-loaded cPCL NPs and evaluate main effects, interaction effects, and quadratic effects of the process parameters on the PDE and PS. The independent variables selected were drug/polymer ratio (X1), amount of cPCL (X2), and volume of organic phase (X3). A design matrix comprising 13 experimental runs was constructed. The design was used to explore quadratic response surfaces and constructing second-order polynomial models and contour plots to predict responses with Design Expert (Version 8.0.3, Stat-Ease Inc., Minneapolis, MN).
2.11 Contour plots
Contour plots are diagrammatic representation of the values of the response. They are helpful in explaining the relationship between independent and dependent variables. The reduced models were used to plot two-dimensional contour plots. Two contour plots for PDE and PS were established between X2 and X3 at fixed levels (−1, 0, and 1) of X1.
2.12 Response surface plots
To understand the main and the interaction effects of two variables, response surface plots were used as a function of two factors at a time, maintaining the third factor at fixed level (Mak et al. 1995). These plots were obtained by calculating the values obtained by one factor where the second varied (from −1 to 1 for instance) with constraint of a given Y value.
2.13 Check point analysis
A check point analysis was performed to confirm the utility of the established contour plots and reduced polynomial equation in the preparation of NPs. Values of independent variables (X2 and X3) were taken from three check points on contour plots plotted at fixed levels of −1, 0, and 1 of X1, and the values of PDE (Y1) and PS (Y2) were calculated by substituting the values in the reduced polynomial equation. Exe-loaded NPs were prepared experimentally by taking the amounts of the independent variables (X1 and X2). Each batch was prepared three times and mean values were determined. Difference in the predicted and mean values of experimentally obtained PDE and PS was compared by using student’s t test.
2.14 Normalized error determination
2.15 Desirability criteria
2.16 In vitro drug release studies
Where, M t /M∞ is the fractional amount of drug released, k is the release constant, n is the release exponent, and t is the time of release.
2.17 Transmission electron microscope studies
A sample of NPs (0.5 mg/ml) was suspended in water and bath sonicated for 30 s; 2 μl of this suspension was placed over a Formvar-coated copper transmission electron microscopy (TEM) grid (150 meshes) and negatively stained with 2 μl uranyl acetate (1 %) for 10 min, allowed to dry, and the images were visualized at 80 kV under TEM (FEI Tecnai G2 Spirit Twin, Czech Republic) and captured using Gatan Digital Micrograph software.
2.18 Differential scanning calorimetric studies
All the samples were dried in desiccators for 24 h before thermal analysis. Differential scanning calorimetry (DSC) studies on pure exe, cPCL, physical mixtures of drug and cPCL and drug-loaded NPs were performed in order to characterize the physical state of drug in the NPs. Thermograms were obtained using DSC model 2910 (TA Instruments, New Castle, DE). Dry nitrogen gas was used as the purge gas through the DSC cell at a flow rate of 40 ml/min. Samples (4–8 mg) were sealed in standard aluminum pans with lids and heated at a rate of 10 °C/min from 20 to 300 °C. Data were analyzed using TA Universal Analysis 2000 software (TA Instruments, New Castle, DE).
3 Results and discussion
Coded values of the formulation parameters of exemestane-loaded cPCL NPs
Box–Behnken experimental design with measured responses for exemestane-loaded cPCL NPs
Y1 (PDE, mean ± SD)
Y2 (PS, mean ± SD)
56.32 ± 1.738
224.10 ± 7.213
73.36 ± 2.081
115.03 ± 3.595
23.94 ± 1.254
263.67 ± 4.549
72.89 ± 1.437
256.27 ± 5.387
57.93 ± 1.728
284.77 ± 4.879
64.68 ± 1.921
185.00 ± 4.424
48.71 ± 0.713
302.63 ± 2.875
82.00 ± 1.101
212.40 ± 3.050
70.91 ± 1.788
185.93 ± 3.502
54.94 ± 1.806
340.43 ± 8.334
74.89 ± 2.644
235.80 ± 6.777
68.95 ± 3.235
350.03 ± 7.214
82.67 ± 1.310
207.40 ± 2.035
Model coefficients estimated by multiple regression analysis for PDE of exemestane-loaded cPCL NPs
X 1 X 2
X 1 X 3
X 2 X 3
X 1 2
X 2 2
X 3 2
Model coefficients estimated by multiple regression analysis for PS of exemestane-loaded cPCL NPs
X 1 X 2
X 1 X 3
X 2 X 3
X 1 2
X 2 2
X 3 2
Analysis of variance of full and reduced models for PDE of exemestane-loaded cPCL NPs
Analysis of variance of full and reduced models for PS of exemestane-loaded cPCL NPs
The goodness of fit of the model was checked by the determination coefficient (R2). In this case, the values of the determination coefficients (R2 = 0.9681 and 0.9634 for PDE and PS, respectively) indicated that over 96 % of the total variations were explained by the model. After reducing the equation, the values of the determination coefficients (R2 = 0.8944 and 0.8558 for PDE and PS, respectively) indicated that over 85 % of the total variations were explained by the model. High R2 values of full model as compared with reduced model are possibly due to the number of factors included. The more the number of factors, the more is the R2 value, even if the factors are not significant (Montgomery 2004). The values of adjusted determination coefficients (adj R2 = 0.8727 and 0.8538 for PDE and PS, respectively) were also very high (>85 %) indicating high significance of the model. Moreover, the high values of correlation coefficients (R = 0.9839 and 0.9815 for PDE and PS, respectively) signify an excellent correlation between the independent variables (Box et al. 1978). All the above considerations indicate an excellent adequacy of the derived regression model (Akhnazarova and Kafarov 1982; Adinarayana and Ellaiah 2002; Box et al. 1978; Yee and Blanch 1993).
3.1 Contour plots
3.2 Response surface plots
Response surface plot of drug/polymer ratio vs. amount of polymer showed nonlinear behavior. With decrease in drug/polymer ratio, no significant change in PS was observed. Simultaneous increase in both drug/polymer ratio as well as polymer concentration showed increased PS. Increase in PS was more influenced by change in amount of polymer than drug/polymer ratio (Fig. 6a). Response surface plot between drug/polymer ratio and volume of organic phase showed no significant change in PS (Fig. 6b). Plot between amount of polymer and volume of organic phase showed increase in PS when amount of polymer increased and volume of organic phase decreased at the same time (Fig. 6c).
3.3 Desirability criteria
From the results, the optimum levels of independent variables were screened out by regression analysis. Since PDE and PS were taken into consideration simultaneously, the results were unable to attend both the dependent variables at a time. The batch with smallest PS of less than 175 nm exhibited only about 69-71 % PDE (at X1 = −0.5 to 0, X2 = −0.8 to −1.0, and X3 = +1.0) while that with highest PDE of more than 80 % had PS of 210 to 300 nm (at X1 = +1, X2 = −0.5 to 0.9, and X3 = 0 to +1.0) (Figs. 3 and 4). Hence, desirability criteria were used to find out optimized formulation parameters. The desirability criteria were obtained using Design Expert software (version 8.0.3). Our criteria included maximum PDE and PS not more than 200 nm. The optimum formulation offered by the Design Expert 8.0.3 software based on desirability was found at 0.43, −0.68, and 0.27 level of X1, X2, and X3 respectively. The calculated desirability factor for offered formulations was 1, which indicated suitability of the designed factorial model. The results of dependent variables from the software were found to yield 83.96 % PDE and 180.51 nm PS at these levels.
3.4 Checkpoint analysis and NE
Check point analysis, t test analysis, and normalized error determination
−0.3 (85 mg)
0.5 (9 ml)
0.2 (110 mg)
−0.8 (6.4 ml)
−0.7 (65 mg)
0.8 (9.6 ml)
3.5 Zeta potential
Zeta potential gives information to predict the storage stability of colloidal dispersions (Thode et al. 2000). High negative values of the zeta potential indicate that the electrostatic repulsion between particles will prevent their aggregation and thereby stabilize the nanoparticulate dispersion (Feng and Huang 2001; Joshi et al. 2010). The zeta potential values ranged between −19.6 and −34.0 mV for all 13 formulations. The surfactant concentration affected the charge on the particle. It was seen that as the surfactant concentration was increased from 0.25 to 0.75 %, there was a decrease in the zeta potential value. This is possibly because with increase in concentration of non-ionic surfactant, total charge on the particle decreases due to increased amount of surfactant coating which also resulted in increased PS (Redhead et al. 2001). However, change in polymer concentration had no effect on zeta potential values. The optimized batch of exe-loaded cPCL NPs was found to have zeta potential of −33.8 ± 2.1 mV. Zeta potential values in the −15 to −30 mV are common for well-stabilized NPs (Musumeci et al. 2006). Hence, it was concluded that the NPs would remain physically stable.
3.6 Lyophilization and optimization of cryoprotectants
Effect of cryoprotectants and their concentration on PS of lyophilized NPs after re-dispersion in distilled water
Final average PS in nm (Sf)
3.7 Transmission electron microscopy
3.8 Differential scanning calorimetry
3.9 In vitro drug release studies
Caprolactone was successfully polymerized in presence of succinic acid to PCL by ring opening polymerization. The present study demonstrated the use of Box–Behnken design as data analysis approach to understand the effect of various formulation variables in the prediction of PDE and PS of exe-loaded cPCL NPs. No significant difference between predicted and observed responses was observed in check point analysis with very less NE. The optimized cPCL NPs of exe had high entrapment and small PS. DSC studies indicated absence of any interaction of exe with cPCL. These NPs exhibited sustained release and followed Fickian diffusion based release kinetics. This sustained release delivery system of exe would reduce the side effects associated with the conventional cancer therapy by reducing dosing frequency and systemic side effects. Thus, our results prove that desirable goals can be achieved by systematic statistical approach in shortest possible time with reduced number of experiments.
Differential scanning calorimetry
Transmission electron microscopy
Percentage drug entrapment
The authors are grateful to All India Council of Technical Education, New Delhi, India for providing National Doctoral Fellowship to Abhinesh Kumar. We also acknowledge Sun Pharma Advanced Research Centre, Vadodara, India for gift sample of exe, Abitec Corporation, Janesville, WI for Capric/caprylic triglyceride (Capmul MCM, C8) and BASF, Ludwigshafen, Germany for Poloxamer 188.
Declaration of interests
The author(s) declare that they have no competing interests.
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