Preparation of Poly(MePEGCA-co-HDCA) Nanoparticles with Confined Impinging Jets Reactor: Experimental and Modeling Study

Preparation of Poly(MePEGCA-co-HDCA) Nanoparticles with Confined Impinging Jets Reactor: Experimental and Modeling Study FEDERICA LINCE,1 SARA BOLOGNESI,2 DANIELE L. MARCHISIO,1 BARBARA STELLA,2 FRANCO DOSIO,2 ANTONELLO A. BARRESI,1 LUIGI CATTEL2 1

Dipartimento di Scienza dei Materiali e Ingegneria Chimica, Politecnico di Torino, Torino 10129, Italy

2

Dipartimento di Scienza e Tecnologia del Farmaco, Universit`a di Torino, Torino 10125, Italy

Received 24 March 2010; revised 23 June 2010; accepted 18 November 2010 Published online 21 January 2011 in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/jps.22451 ABSTRACT: In this work, the biodegradable copolymer poly(methoxypolyethyleneglycolcyanoacrylate-co-hexadecylcyanoacrylate) is used to prepare nanoparticles via solvent displacement in a confined impinging jets reactor (CIJR). For comparison, nanoparticles constituted by the homopolymer counterpart are also investigated. The CIJR is a small passive mixer in which very fast turbulent mixing of the solvent (i.e., acetone and tetrahydrofuran) and of the antisolvent (i.e., water) solutions occurs under controlled conditions. The effect of the initial copolymer concentration, solvent type, antisolvent-to-solvent ratio, and mixing rate inside the mixer on the final nanoparticle size distribution, surface properties, and morphology is investigated from the experimental point of view. The effect of some of these parameters is studied by means of a computational fluid dynamics (CFD) model, capable of quantifying the mixing conditions inside the CIJR. Results show that the CIJR can be profitably used for producing nanoparticles with controlled characteristics, that there is a clear correlation between the mixing rate calculated by CFD and the mean nanoparticle size, and therefore that CFD can be used to design, optimize, and scale-up these processes. © 2011 Wiley-Liss, Inc. and the American Pharmacists Association J Pharm Sci 100:2391–2405, 2011 Keywords: nanoparticles; mixing; mathematical model; polymeric drug delivery systems; powder technology; precipitation; colloid; light scattering; particle size; pegylation

INTRODUCTION Colloidal drug carriers, such as nanoparticles, can be used to improve the therapeutic index of an active pharmaceutical ingredient (API). In particular, in anticancer therapy, the rationale behind the use of nanoparticles stands in their ability to accumulate in highly porous and irregular solid tumor tissues, thanks to the so-called enhanced retention and permeability effect. Once the target is reached, the API is then released at a controlled rate due to the gradual degradation of the carrier. The final result depends both on the physicochemical properties of the API (e.g., its chemical structure) and on the characteristics of the carrier. Correspondence to: Daniele L. Marchisio (Telephone: +39011-090-4622; Fax: +39-011-090-4699; E-mail: daniele.marchisio@ polito.it) Journal of Pharmaceutical Sciences, Vol. 100, 2391–2405 (2011) © 2011 Wiley-Liss, Inc. and the American Pharmacists Association

Overall, the challenge is in the proper design of these carriers to provide suitable solutions for some of the delivery problems associated with novel classes of molecules, in the improvement of the therapeutic potential of well established APIs, as well as in the scale-up to the pilot and industrial levels of the production processes of these delivery systems. Over the past 20 years, numerous approaches to improve nanoparticles’ blood residence and accumulation in specific tissues have been developed.1 Long-circulating particles have been obtained by synthesizing copolymers containing at least one poly(ethyleneglycol) (PEG) block2 to significantly reduce the opsonization process.3 Apart from the nature of the nanoparticle surface, size is another important parameter for ensuring longer blood circulation times.4–8 The data collected by Moghimi et al.1 highlight an optimal size for the carriers ranging from 20 to 200 nm. Moreover, the size of the carrier particles also affects opsonization.9

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Among the methods used to prepare such particulate systems, one of the most popular starting from the preformed polymer is based on solvent displacement, wherein the polymer constituting the carrier and the API is dissolved into an organic solvent (e.g., acetone), which is then mixed with an antisolvent (e.g., water).10–14 The process is almost instantaneous and the final properties of the particulate carrier are affected by mixing dynamics of the two solutions of solvent and antisolvent. The importance of mixing is twofold because it plays a crucial role when the process is scaled up and it can be used to tune the final carrier properties. In our previous studies,15,16 the relevant mixing issues in the turbulent precipitation of polycaprolactone nanoparticles through solvent displacement were overcome by using efficient passive mixing devices, commonly called confined impinging jets reactors (CIJRs). In this context, the acronym CIJR (that includes the word reactor) is used in a loose sense because with solvent displacement particles are formed without involving any actual chemical reaction. The investigation of CIJRs has been revived by Johnson and Prud’homme,10–13 who quantified their mixing efficiency by using a parallel-competitive reaction scheme. In other works, mixing was quantified in the very same devices by using barium sulfate precipitation and mathematical modeling, resulting in characteristic mixing times of the order of magnitude of milliseconds.17–19 Other innovative and highly efficient mixers such as tee mixers,20 standard vortex reactors,21 and multi-inlet vortex reactors22–24 have also been proposed and employed for a variety of applications.25,26 The main advantage of these mixers/reactors stands in their ability to guarantee reproducible mixing conditions, and therefore as a consequence, reproducible product characteristics, when compared with classical laboratory-scale preparation methods (i.e., flask equipped with magnetic stirrers). Other important advantages are the possibility to run the nanoparticle production process continuously and the easy and simple scalability. CIJRs are used in this work to investigate the production of poly(alkylcyanoacrylate) (PACA) nanoparticles composed by a cyanoacrylate copolymer, namely the poly(methoxypolyethyleneglycolcyanoacrylateco-hexadecylcyanoacrylate), from here onward poly(MePEGCA-co-HDCA). For comparison, nanoparticles constituted by its homopolymer counterpart poly-HDCA (PHDCA) are also studied. These types of polymers were selected for this study because today PACA polymers are considered as suitable materials for biomedical applications because they are biodegradable and nontoxic.27,28 In particular, poly(MePEGCA-co-HDCA) nanoparticles exhibit a bioerodible PACA core and a shell of excretable PEG chains.2,29,30 The presence of the JOURNAL OF PHARMACEUTICAL SCIENCES, VOL. 100, NO. 6, MAY 2011

PEG chains in the polymer structure was found to increase the degradability of the polymer in calf serum2 and to extend the circulation time in the bloodstream without displaying in vivo toxicity.31 Poly(MePEGCA-co-HDCA) nanoparticles have been investigated for the treatment of experimental allergic encephalomyelitis,32 prion diseases,33 brain tumors,34 and other pathologies.35 To our knowledge, this is the first time that this methodology based on the use of CIJRs is applied for these two polymers and more in general to PACA. Investigation of the effect of the different operating parameters [i.e., initial polymer concentration, solvent type, antisolvent-to-solvent ratio (R), mixing rate, as well as the effect of the presence of the PEG segment on the polymeric chains] on the final carrier characteristics was carried out. The polymer nanoparticles were characterized by dynamic light scattering (DLS) and zeta potential measurements, as well as scanning electronic microscopy (SEM) observations. The effect of some of these operating parameters is then explained by means of computational fluid dynamics (CFD) simulations. Results show that CIJRs can be profitably used to continuously produce nanoparticles with desired characteristics, both in terms of their size and surface properties, offering the possibility of easy control and scalability to the pilot and industrial scale levels.

MATERIALS AND METHODS Polymer Synthesis MethoxyPEG (MePEG, molecular weight = 2000 g/ mol, purity >95%) and cyanoacetic acid (purity >99%) were purchased from Fluka Chemical Co., Milan, Italy. The solvents used for the synthesis were of analytical grade and purchased from Carlo Erba Reagenti, Milan, Italy. The poly(MePEGCA-co-HDCA) and the PHDCA were synthesized by tandem Knoevenagel condensation–Michael addition reaction with MePEG cyanoacetate (MePEGCA) and n-hexadecylcyanoacetate (HDCA) for poly(MePEGCA-co-HDCA) and with only HDCA for PHDCA, according to the methodology reported by Peracchia et al.,29 with minor modifications. MePEGCA was prepared by esterification of the cyanoacetic acid with MePEG in dichloromethane (DCM). The reaction takes place in the presence of N-ethyl-N-(dimethylaminopropyl)-carbodiimide (EDC) and 1,4-(dimethylamino)pyridine (DMAP) as catalyst. The reaction was carried out at room temperature in nitrogen atmosphere. Cyanoacetic acid (1.9 g, 22 mmol) and MePEG (22 g, 11 mmol, molar ratio 2:1) were placed into a glass flask and dissolved in 50 mL of DCM and 3 mL of ethylacetate. After the addition of DMAP (catalytic amount), EDC (4.2 g, DOI 10.1002/jps

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22 mmol) dissolved in DCM was added dropwise. After stirring for 24 h, the product was washed with three portions of water (20 mL each). The combined filtrates were concentrated under reduced pressure to leave a viscous oil, which solidified on standing. The synthesis yield is 99%. For the synthesis of HDCA, n-hexadecanol (10 g, 41.2 mmol) dissolved in 100 mL of CH2 Cl2 and cyanoacetic acid (3.8 g, 44.7 mmol) dissolved in ethylacetate were placed into a glass flask. A catalytic amount of DMAP dissolved in CH2 Cl2 was added. The reaction mixture was cooled at 4◦ C and a solution of 1,3-dicyclohexylcarbodiimide (9.2 g, 44.7 mmol) in DCM was added dropwise. The reaction was stirred at room temperature under nitrogen for 6 h. Then, hexane was added and the white solid that formed was filtered off. The mixture was concentrated and purified by flash chromatography (silica gel 60, 230–400 mesh, Merck, Darmstadt, Germany) eluting with hexane/ ethyl acetate (90:10). The ester was obtained as a white solid with a yield of 96% and a melting point of 51◦ C. After the monomers synthesis, condensation of MePEG cyanoacetate with n-hexadecyl cyanoacetate was carried out in ethanol, in the presence of formalin and dimethylamine (DMA). The MePEG cyanoacetate (10.3 g, 5 mmol) and the n-hexadecyl cyanoacetate (6.2 g, 20 mmol, molar ratio 1:4) dissolved in CH2 Cl2 were placed into a glass flask fitted with a rubber septum. Then, 50 mL of ethanol was added. Formalin (37%, w/v, 6.1 mL, 75 mmol) and DMA (40%, w/v, 8.4 mL, 75 mmol) were introduced with a syringe. The reaction was carried out at room temperature under nitrogen. After stirring for 24 h, the reaction mixture was concentrated under reduced pressure and the residue taken into water. The solution was extracted with DCM, and the combined organic phases were dried over MgSO4 . The solvent was evaporated, and the residue was placed several hours under vacuum to give the copolymer as a pale yellow waxy material. The copolymer yield is 98% and the total amount obtained is 16.4 g. The reaction scheme is summarized in Figure 1. The PHDCA was instead obtained by condensation of n-hexadecyl cyanoacetate in presence of formalin and DMA. The reaction scheme is reported in Figure 2. n-Hexadecyl cyanoacetate (3.1 g, 10 mmol) was dissolved in 30 mL of ethanol and 5 mL of CH2 Cl2 , then DMA (40%, w/v, 338 :L, 3 mmol) was added. A total volume of 1.2 mL (15 mmol) of formalin 37% (w/v) is added drop by drop. Then, the reaction was carried out at room temperature under nitrogen for 24 h. The reaction mixture was concentrated under reduced pressure; the residue was taken into water and extracted with DCM. The polymer final quantity produced is 3.2 g with a yield of 98% and a final melting point of 42–43◦ C. DOI 10.1002/jps

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Figure 1. Poly(MePEGCA-co-HDCA) reaction scheme.

Figure 2. PHDCA reaction scheme. JOURNAL OF PHARMACEUTICAL SCIENCES, VOL. 100, NO. 6, MAY 2011

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To check the polymer/copolymer quality, molecular weight measurements (Zetasizer nanoseries ZS 90, Malvern Instruments, Worcestershire, UK) were performed along with characterization via differential scanning calorimetry, DSC, (Q 200, TA Instruments, West Sussex, UK) and nuclear magnetic resonance, NMR, (Bruker AC 200P, 200 MHz; Karlsruhe, Germany). Preparation of the Nanoparticles In this section, the different experiments carried out, the reactants, and the equipments used for the preparation and characterization of the nanoparticles are described in details. Poly(MePEGCA-co-HDCA) and PHDCA synthesized as described in the previous section were used. Acetone and tetrahydrofuran (THF; HPLC grade) were purchased from Sigma–Aldrich Co. (Milan, Italy) and used with micro filtrated water R 4.0 ster(Millipore System, Milli-Q RG, millipack
ile pack, 0.22 :m, Holliston MA, US) to prepare the nanoparticles. Nanoparticles were obtained via the solvent displacement method (or nano-flash precipitation). In practice, the homopolymer or the copolymer was dissolved in the solvent (i.e., acetone or THF), then the obtained solution was mixed with the antisolvent (i.e., MilliQ water) by a syringe pump (KDS200 syringe pump; KD Scientific, Massachusset, US) connected to the CIJR shown in Figure 3. Particle formation occurred spontaneously inside the CIJR and the precipitate was then collected into 10 mL of MilliQ water kept under gentle mechanical stirring. This procedure ensures that nanoparticles are rapidly quenched by dilution, guaranteeing the suppression of secondary processes, such as aggregation, after particles were formed in the CIJR and collected for characterization. These issues have been investigated in our previous work on similar systems15,16 and the quenching procedure was found to be effective also for the system investigated in this work. The particle size distribution (PSD) was determined by DLS (Zetasizer Nano Series ZS90; Malvern Instrument, Worcestershire, UK) that measures in the size range from 0.6 nm to 3 :m and results are here reported in terms of the mean particle size, dm (nm). Zeta potential measurements were performed on the same equipment by electrophoretic measurements, using the same sample. Further dilution with double-distilled water was typically necessary for particle size and zeta potential measurements; the measurements were performed in triplicate. Moreover, the data obtained with DLS were carefully screened to make sure that the requirements for high quality measurements such as particle count, polydispersity, and background noise were met. Only in a few cases, these criteria were not respected and the measurements were indeed rejected and repeated with fresh samples. JOURNAL OF PHARMACEUTICAL SCIENCES, VOL. 100, NO. 6, MAY 2011

Figure 3. Sketch of the confined impinging jet reactor. Quote are in millimeters.

Nanoparticle preparation and characterization were repeated for each experiment from three to six times to assess the overall reproducibility of the preparation and characterization protocol. On the basis of this analysis, the mean values of the mean particle size and zeta potential were estimated and data are reported in terms of mean values and error bars The overall reproducibility for the mean particle size is fairly good except for a few cases (discussed in details in the next section); an overall poorer reproducibility was instead observed for the zeta potential, highlighting that for the operating conditions investigated in this work, these values should be used with great care and only for an estimate of trends (rather than absolute values). Nanoparticles morphology was investigated by SEM as well as by field emission SEM. Samples were prepared after 1:20 dilution by MilliQ water and then a small drop of the diluted solution was deposited on a metal planchet and eventually after solvent evaporation, the sample was ready for the analysis. Different operating conditions were tested to estimate their effect on the final particle characteristics. The flow rate of the pure water stream (FRw ) fed to the CIJR was varied between 3 and 120 mL/min, whereas the flow rate of the solvent stream (i.e., acetone or THF) was varied independently. In a first set of experiments, R was kept equal to 1. In a second DOI 10.1002/jps

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set of experiments, at FRw of 120 mL/min, the solvent flow rate was varied, resulting in final R values ranging from 1 to 8. Different initial polymer/ copolymer concentrations, C0 , in the solvent solution were also investigated, ranging from 2 to 15 mg/mL for poly(MePEGCA-co-HDCA) and from 0.5 to 10 mg/ mL for PHDCA, with slightly different ranges in acetone and THF due to different solubility.

COMPUTATIONAL AND NUMERICAL DETAILS To investigate the flow field and the characteristic mixing timescales, CFD was employed. In this approach the CIJR is discretized through a computational grid (constituted by several thousands computational cells) and the flow field is simulated by solving the relative governing equations. Here, we used the methodology proposed by Liu and Fox.36 We refer readers interested in the details to that paper, whereas in what follows only a very brief summary will be presented. Being the flow field inside the CIJR turbulent, the Reynolds-averaged Navier– Stokes equations (RANS) approach is used37 and mixing is described in terms of the mean mixture fraction and the mixture fraction variance. The mean mixture fraction represents in this case the volume fraction of solvent (i.e., acetone or THF) in the CIJR. This quantity is equal to 1 and 0 near the two inlets, corresponding to the pure solvent and pure antisolvent streams, respectively. When mixing is complete, the mean mixture fraction reaches a value that depends on the R, that is, for example, equals to 0.5 for R equals to 1. The rate at which this value is reached quantifies the level of homogeneity inside the CIJR and is usually written in terms of a macromixing characteristic time. In turbulent systems, however, once the solvent volume fraction (i.e., the mean mixture fraction) reaches the value corresponding to complete mixing, a certain degree of segregation (between solvent and antisolvent molecules) may still be present. This segregation at the molecular level is quantified by the so-called mixture fraction variance that is maximum when the solvent and antisolvent molecules are completely segregated and is null in the case of complete mixing at the molecular level (or in other words, absence of turbulent fluctuations). This problem is known in the literature as micromixing and experimental evidence shows that this is relevant also in solvent displacement processes for the production of organic nanoparticles.38 The rate at which complete mixing at the molecular level is reached is quantified by the so-called micromixing time. The summation of the macromixing and micromixing times is often referred to as global mixing time and is an indicator of the efficiency of mixing in the CIJR. Our previous studies show that this quantity is DOI 10.1002/jps

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related to the mean particle size of the obtained particles; in fact, the faster mixing is (namely, the smaller the global mixing time is) the smaller the obtained particles are.16,17,19,21 The global mixing time is here calculated by using the commercial CFD code FLUENT 6.3. The computational grid for the CIJR was created with GAMBIT, and only half of the real geometry was considered, under the hypothesis of symmetry. Different grids were tested and the final grid consisted of about 200,000 computational cells. As shown in our previous work39 for this type of processes, wherein mixing occurs between two fluids characterized by different densities and viscosities, simulations must be carried out by accounting for the local solvent/antisolvent composition. Because the flow field in the inlet pipes is laminar, inlet velocity profiles were defined by an elliptic paraboloid where the mean velocity is calculated on the basis of the flow rate for the two streams. Turbulence was modeled by using the RANS approach with the standard k–ε model and with standard wall functions. A first-order upwind scheme was employed for spatial discretization, whereas for the pressure–velocity coupling, the SIMPLE algorithm was employed. Simulations were considered converged when the normalized residuals for each variable reached values smaller than 10−6 .

RESULTS AND DISCUSSION Polymer Characterization Let us first discuss the polymer/copolymer characterization tests. As already reported, the molecular weight of the polymer and the copolymer was measured, thanks to the Debye theory through static light scattering. In fact, according to this theory, this information can be extracted by plotting the intensity of the scattered light of diluted samples of the polymer in a solvent at various concentrations and by comparing these measurements with those of the pure solvent and of a standard solvent (i.e., toluene). The scattered intensity was determined by averaging over several single measurements for the pure solvent (acetone) and for the samples at various polymer concentrations. Results for PHDCA are reported in Figure 4 (top) and, as it is possible to see, they are characterized by large uncertainties related to the sensitivity of the measurements to fine and ultrafine dust particles. In this case, only THF was used due to the small solubility of the homopolymer in acetone. The concentrations employed were 6, 10, 15, and 20 mg/mL, resulting in a molecular weight of 30.9 ± 7.54 kDa. This result shows that the polymerization of the homopolymer was very extensive. However, although the positive JOURNAL OF PHARMACEUTICAL SCIENCES, VOL. 100, NO. 6, MAY 2011

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slope of the interpolation line confirmed that THF is a good solvent for the homopolymer, these results should be considered with great care. Results for the copolymer poly(MePEGCA-coHDCA) are reported in Figure 4 (bottom) and, as it is possible to see, much smaller fluctuations are detected in this case. With polymer concentrations of 5, 10, and 15 mg/mL in acetone, a molecular weight of 4.37 ± 1.19 kDa was obtained. The negative slope of the line means that acetone is not a very good solvent for the copolymer. The same measurements were performed by using THF as solvent. In this case, a molecular weight of 4.38 ± 0.67 kDa was obtained. This value is very similar to the one obtained in acetone but in this case the positive slope of the interpolation curves indicates that THF is more affine to the copolymer and the dissolution is faster and more effective. In this case, the concentrations used were

Figure 5. Mean particle size dm (a) and zeta potential ZP (b) versus the water flow rate FRw for particles constituted by poly(MePEGCA-co-HDCA) obtained by mixing water with acetone for an initial polymer concentrations of 10 mg/mL (squares), 4 mg/mL (circles), and 2 mg/mL (triangles up). Mean particle size dm (c) and zeta potential ZP (d) versus the water flow rate FRw for particles constituted by PHDCA obtained by mixing water with THF at initial polymer concentrations of 10 mg/mL (squares), 4 mg/ mL (circles), and 2 mg/mL (triangles up). The antisolventto-solvent ratio is equal to 1.

Figure 4. Mean particle size dm (a) and zeta potential ZP (b) versus the water flow rate FRw for particles constituted by poly(MePEGCA-co-HDCA) obtained by mixing water with acetone (filled symbols) and THF (open symbols), for initial polymer concentrations of 4 mg/mL (squares) and 2 mg/mL (circles); mean particle size dm (c) and zeta potential ZP (d) versus the water flow rate FRw for particles constituted by PHDCA obtained by mixing water with acetone (filled symbols) and THF (open symbols), for initial polymer concentrations of 1.5 mg/mL (squares), 1 mg/mL (circles), and 0.5 mg/mL (triangles); the antisolvent-to-solvent ratio is equal to 1. JOURNAL OF PHARMACEUTICAL SCIENCES, VOL. 100, NO. 6, MAY 2011

6, 10, and 15 mg/mL. Results for the copolymer are in agreement with the theoretical molecular weight of about 3.20 kDa calculated by considering four blocks of hexadecyl and only one block of PEG. From the DSC analysis of PHDCA, reported in Figure 5 (top), an endothermic peak at 36◦ C was detected; this peak corresponds to the melting of the crystalline fraction of the polymer. The area under the peak gives information about the quantity of heat adsorbed for the crystal melt, that is, about 51.05 J/g. The polymer is only partially crystalline and the glass transition temperature at −51.5◦ C can also be detected. The DSC analysis for the copolymer, namely the poly(MePEGCA-co-HDCA), is instead reported in Figure 5 (bottom). As it is possible to see, the copolymer is characterized by two parts, as shown by the presence of two peaks at 36◦ C and 52◦ C in the thermogram, corresponding, respectively, to the HDCA and MePEGCA chains. In addition from the area of the DOI 10.1002/jps

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two peaks, it is possible to state that the heat required to melt the hydrophilic part (PEG chains) is four times higher than the heat necessary to melt the lipophilic part, in particular 85.01 J/g for MePEGCA and 22.65 J/g for HDCA. These data confirmed the efficacy of the synthesis procedure. The copolymer glass transition temperature is around −51.5◦ C. The structures of the obtained substances were confirmed by 1 H NMR. The 1 H NMR was recorded in CDCl3 at room temperature, with SiMe4 as internal standard. All the characteristics peaks are reported for each substance. For the monomer HDCA, the result is the following: 1 H NMR (CDCl3 , 200 MHz) *4.20 (t, 2H, COOCH2 ), 3.46 (s, 2H, CNCH2 ), 1.68 (m, 2H, OCH2 CH2 ), 1.26 (s, 26H, CH2 ), 0.88 (t, 3H, CH3 ). Regarding to the PHDCA: 1 H NMR (CDCl3 , 200 MHz)

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* 4.29 (t, 2H, COOCH2 ), 2.90–2.20 (m, 2H, CCH2 C), 1.75 (m, 2H, OCH2 CH2 ), 1.26 (s, 26H, CH2 ), 0.88 (t, 3H, CH3 ). For MePEGCA: 1 H NMR (CDCl3 , 200 MHz) * 4.28 (t, 2H, COOCH2 ), 3.64 (m, CH2 of PEG), 3.55 (s, 2H, CNCH2 ), 3.37 (s, 3H, OCH3 ). Finally, the copolymer poly(MePEGCA-co-HDCA) shows the following result: 1 H NMR (CDCl3 , 200 MHz) * 4.26 (t, 2H, COOCH2 ), 3.61 (m, CH2 of PEG), 3.37 (s, 3H, OCH3 ), 2.70–2.10 (m, 2H, CCH2 C), 1.72 (m, 2H, OCH2 CH2 ), 1.25 (s, 26H, CH2 of hexadecyl), 0.84 (t, 3H, CH3 ). Nanoparticle Preparation Let us now discuss the results concerning the polymer nanoparticles. Figures 6a and 6b report the effect of the FRW on the final mean particle size and zeta

Figure 6. Mean particle size dm (a), (c) and zeta potential ZP (b), (d) versus the antisolventto-solvent flow rate ratio, R, for particles constituted by poly(MePEGCA-co-HDCA) (left) and PHDCA (right) obtained at water flow rate, FRw , of 120 mL/min, for an initial polymer concentration of 2 mg/mL and for particles prepared with acetone (filled symbols) and THF (open symbols). DOI 10.1002/jps

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potential for two different initial concentrations (C0 = 4 and 2 mg/mL) in acetone and THF, respectively, for the copolymer investigated in this study, namely poly(MePEGCA-co-HDCA). The flow rate ranges from 3 to 120 mL/min. The behavior of the copolymer can be directly compared with that of the homopolymer, namely PHDCA, for three different initial concentrations (C0 = 1.5, 1, and 0.5 mg/mL) again in acetone and THF, reported in Figures 6c and 6d. The concentration range considered for the homopolymer is narrower in comparison with that of the copolymer due to the solubility limits of PHDCA in acetone. For both the copolymer and the homopolymer, the R is kept equal to 1 in this case. Figure 6a shows that the effect of the FRW on the final mean size of poly(MePEGCA-co-HDCA) particles is quite important. It is also interesting to notice that the experiments are characterized by very high reproducibility, as demonstrated by the very small error bars. An increase in the FRW determines a substantial reduction of the mean size, for particles obtained with both acetone and THF as solvents, at all the different initial copolymer concentrations investigated. The effect of the FRw is to improve the overall mixing performance of the CIJR. It is interesting to point out that the effect of FRw on the mean particle size is particularly important for FRw smaller than 40 mL/min, whereas for FRw larger than 40 mL/min, the mean particle size seems to be more or less constant. In fact, as it will be explained by our simulations, an increase in FRw significantly reduces the time required to mix the solvent and the antisolvent solutions, resulting in smaller particles. However, these effects are important in the FRw range between 3 and 40 mL/min, whereas for FRw values greater than 40 mL/min, no significant improvements are expected and detected. We shall come back to this aspect later on, when analyzing the results of the simulations performed in this work. It is also important to highlight that the comparison between the mean size of poly(MePEGCA-coHDCA) particles obtained with acetone and THF as solvents clearly shows that the use of acetone induces the formation of smaller particles. In fact, under all the operating conditions investigated in this work, the size of particles obtained with THF as solvent is almost double of that of the particles obtained with acetone. As it will become clearer below, although the use of different solvents induces different mixing conditions, resulting therefore in different mean particle sizes, this factor alone does not justify the magnitude of the effect of this parameter. Another underlying reason for explaining these very different behaviors when using acetone or THF as solvents can be found in the intermolecular interactions of water/THF molecules and water/acetone molecules. As reported in the work of Katayama and JOURNAL OF PHARMACEUTICAL SCIENCES, VOL. 100, NO. 6, MAY 2011

Ozutsumi,40 the molecular interactions in the two systems are very different. First of all, the molecular structures of THF and acetone are different, the first one being cyclic, whereas the second one being linear; in addition, both solvents are miscible with water but acetone is more polar than THF. Moreover, the diffusion coefficient of acetone in water is much higher than that of THF in water. All these factors result in different types of solvation, as experimental and theoretical findings suggest that around one molecule of acetone seven molecules of water are generally found, whereas around a THF molecule only five molecules of water are usually present. Therefore, when acetone is used as solvent, after mixing with water and during solvent displacement, a rapid change of environment around polymer molecules occurs; polymer molecules quickly find themselves surrounded by an unfriendly environment mainly made of water molecules, inducing a rapid formation of a large number of small particles that do not have the possibility of further growth. In contrast, when particles are formed by using THF and water, the formation process is slower and results in bigger particles. Very interesting are the results concerning the zeta potential measurements, reported in Figure 6b. In fact, although generally this measurement is difficult to be carried out and is affected by a significant experimental uncertainty (quantified by the error bars), the zeta potential contains crucial information regarding the surface of the particles. As a general comment, it is interesting to remind readers that most particles are charged in aqueous dispersion media. This can be due to charged groups on the surface but has been experimentally observed also for nanoparticles made with not charged materials (i.e., polystyrene). The value of the zeta potential can be related to the presence of PEG chains on the surface of the particles. As it is well known, PEG chains are quite mobile and flexible; therefore, when PEG chains are located on the surface of the particles, they have the tendency to shift the plane of shear to a greater distance from the particle, resulting in a reduction of the measured zeta potential. On the opposite, particles without PEG chains on the surface will result in more negative zeta potential values. As it is possible to see from Figure 6b, the FRw used during the preparation of the particles seems not to affect much the final zeta potential, especially in case of acetone as solvent, whereas the effect of the initial polymer concentration and the choice of the solvent are indeed more important. For example, for particles prepared with acetone as solvent, the final zeta potential ranges from −5 to 0 mV and from −27 to −30 mV for initial copolymer concentrations of 4 and 2 mg/mL, respectively. In contrast, for particles prepared by using THF as solvent, zeta potential values are around −15 mV for both concentrations. It is therefore possible to infer that the number of DOI 10.1002/jps

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PEG chains emerging from the core of the particles and located on their surface depends on the operating conditions during their preparation. In particular, at high values of the initial copolymer concentration (with acetone as solvent), some of the hydrophilic PEG chains can be forced inside the hydrophobic core of the particles, resulting in more negative values for the zeta potential. On the opposite, at low initial copolymer concentrations, with acetone, and at any concentration with THF, most of the PEG chains will cover the particle surface, resulting in smaller zeta potential values. These findings are in good agreement with other experimental data reported in the literature.41–44 Some additional information can be obtained by comparing the behavior of the copolymer, reported in Figures 6a and 4b, with that of the homopolymer, reported in Figures 6c and 6d. For the homopolymer, PHDCA, the investigated concentration range is lower than for the copolymer due to the low solubility limit of the polymer in acetone. Also for the homopolymer, the effect of the flow rate is remarkable; again an increase in the FRW determines a reduction in the size of particles obtained with both solvents. Also in this case, it is important to notice that THF seems to be a less effective solvent than acetone, in producing particles of small size. From the initial polymer concentration and the final mean particle size (assuming that all the polymers have precipitated), it is possible to estimate the final total particle number density, resulting in numbers is the range from 1018 to 1020 m−3 . These numbers can be used to establish the role of aggregation. In fact, if the suspension results in very large total particle number densities and the attractive forces overcome the repulsive forces, then the aggregation between particles is likely to happen. In the case of the copolymer, however, the presence of the PEG chains might avoid particles from aggregating due to steric effects and the formation of a hydrophilic-stabilizing layer.QUERY 17!revised These effects are probably absent in the case of the homopolymer, and this could explain the fact that under some operating conditions, the homopolymer produces particles larger than the copolymer. Figure 7 reports two SEM images of poly(MePEGCA-co-HDCA) and PHDCA nanoparticles prepared with an R equals to 1 and with a FRW of 120 mL/min. Particles show a spherical shape with size distributions comparable with those measured with DLS measurements. It is important to remind here that the direct comparison between the copolymer and the polymer is impossible due to the different solubility in acetone and THF, as well as due to the significant difference in the molecular weight. However, to further investigate the effect of the operating parameters on the final product DOI 10.1002/jps

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Figure 7. Debye plots for (top) PHDCA in THF (0.00253 ± 3.53 × 10−4 ) and for (bottom) poly(MePEGCAco-HDCA) in acetone (filled symbols, −0.0104 ± 0.00524) and in THF (open symbols, 0.00111 ± 0.00149).

characteristics (i.e., particle size and zeta potential), additional experiments have been conducted in a wider range of initial polymer concentration. However, owing to the low solubility of the homopolymer in acetone, additional experiments were conducted by using acetone for the copolymer and THF for the homopolymer. Results are summarized in Figure 8, and as it is possible to see (especially for the mean particle size), data are characterized by high reproducibility, as demonstrated by the small error bars. The effect of the FRW on the final mean particle size for the copolymer and the homopolymer is reported in Figures 8a and 8c, respectively. Zeta potential measurement for the copolymer and the homopolymer is reported in Figures 8b and 8d, respectively. Three higher initial concentrations are considered for both the copolymer (i.e., C0 = 15, 10, and 4 mg/mL) and the homopolymer in THF (i.e., C0 = 10, 4, and 2 mg/mL). Also in these cases, the effect of the FRw is to reduce the mean particle size. For example, the data collected for PHDCA in THF at an initial concentration equals to 10 mg/mL show a reduction of the mean particle size from 400 nm, at FRw of 3 mL/min, to nearly 300 nm at FRw of 120 mL/min. The same trend is detectable for the other initial concentrations. JOURNAL OF PHARMACEUTICAL SCIENCES, VOL. 100, NO. 6, MAY 2011

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Figure 8. DSC thermogram for PHDCA (top) and poly(MePEGCA-co-HDCA) (bottom); melting temperatures and glass transition temperatures are indicated.

As clearly evident by the comparison of Figures 8a and 8c, particles constituted by the copolymer are generally smaller than those constituted by the homopolymer. Although the direct comparison is not possible in this case because Figures 8a and 8c refer to particles obtained with different solvents, it is possible to conclude that the presence of the PEG chain probably reduces aggregation, resulting in the formation of smaller particles. In fact, when the PEG

chains are set outward the particles, a hydrophilic layer around the particle is rapidly formed. This hydrophilic layer together with steric effects prevents particles from aggregating. The zeta potential for the copolymer and the homopolymer is reported, respectively, in Figures 8b and 8d. As it is possible to see, although the experimental uncertainty is higher in this case, the zeta potential for the copolymer seems to change when the initial copolymer concentration is varied. This could be again explained in terms of the ability of the PEG to emerge from the hydrophobic core, which, in turn, might be affected by the copolymer concentration during nanoparticle preparation. With respect to this result, it is interesting to point out that the zeta potential of the particles constituted by the homopolymer is less sensitive to its concentration. When particles are loaded with drug molecules, the R is one of the most used parameters to improve the final drug loading. It is therefore interesting to understand what happens to the particle characteristics when this parameter is changed, either for acetone or THF as solvent. The effect of R on the final mean particle size and on the zeta potential is reported in Figures 9a, 9c, 9b, and 9d, respectively, for the copolymer, poly(MePEGCA-co-HDCA), and the homopolymer, PHDCA; both the case of acetone and THF solvent are considered for an initial concentration of 2 mg/mL. The FRW is kept constant and equals to 120 mL/min, whereas the solvent flow rate is reduced to obtain higher values of R. Results show that an increase of the R, for both acetone and THF, causes a slight increase in the mean particle size (see, Fig. 9a), as well as in some cases, a reduction of the negative value of the zeta potential (i.e., zeta potential values closer to zero; see Fig. 9b). Although (especially in some cases) the experimental reproducibility is quite low (as highlighted by the large error bars), these trends consistently emerge.

Figure 9. Poly(MePEGCA-co-HDCA) (left) and PHDCA (right) nanoparticles prepared with R = 1, FRw = 3 mL/min and for the initial concentration of 2 mg/mL. JOURNAL OF PHARMACEUTICAL SCIENCES, VOL. 100, NO. 6, MAY 2011

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It is important to remind here that the effects of a change in this parameter are twofold. On one hand, an increase in R determines an environment inside the CIJR richer in water, but on the other hand, as the FRW is kept constant while the solvent flow rate is reduced, the overall mixing conditions are also different (as confirmed by our simulations). Unfortunately, it is impossible for the selected geometry to study the effect of this parameter without changing the mixing conditions. From what previously reported, we already know that a decrease on the mixing efficiency generally leads to bigger particles; moreover, an environment richer in water might help the PEG chains to display themselves outward, resulting in final particles with more highly stretched PEG chains. This latter hypothesis is also supported by the decrease in zeta potential values. It is interesting to compare the behavior of the copolymer, reported in Figures 9a and 6b, with that of the homopolymer, Figures 6c and 6d. As it is seen, also in this case, an increase in the R causes an increase in the mean particle size and zeta potential values closer to zero. It should be highlighted that especially when acetone is used as solvent, experiments are very difficult to be carried out; as already reported, under these operating conditions, the original homopolymer solution in acetone is very close to the solubility limits. As a matter of fact, under these operating conditions, the experimental error is quite large. CFD Simulations and Mixing Dynamics As evidenced by our previous reports15,16 and by the work of other groups10–13 , the particle formation process is highly influenced by mixing; as a matter of fact, small particles with narrow PSD are obtained only under very efficient mixing conditions. The changes of the operating parameters studied in this work have an influence both on the overall physicochemical conditions, under which the particle formation process occurs, and on the fluid dynamics and mixing conditions of the reactor where the antisolvent and the solvent mix.QUERY 21!revised For example, when the R is varied, the composition of the mixture in which particles are formed is completely modified, together with the dynamics of mixing of the two solutions. The objective of this part of the work is therefore to quantify, by means of CFD, the different mixing performances when the inlet flow rates, the R, and the type of solvent are changed from one experiment to another to identify and separate the effect of mixing from that of the other factors involved. The contour plots for the mean mixture fraction and the mixture fraction variance in the CIJR are reported in Figure 10 at FRW of 20 mL/min (top) and at FRW of 120 mL/min (bottom) for the water/acetone system and in Figure 11 for the water/THF system. As is it seen, the solvent and the antisolvent enter DOI 10.1002/jps

Figure 10. Contour plots of mean mixture fraction and mixture fraction variance at flow rate ratio equals to 1 and for a water flow rate of 20 mL/min (top) and of 120 mL/min (bottom) for the water/acetone system.

the reactor through the inlet pipes, reach the center of the reactor, where impinge and mix. Most of the turbulence is created (and then dissipated) in the impingement plane, which is slightly off-center due to the different densities of water and acetone. As already reported, the mean mixture fraction represents the amount of solvent in the final mixture exiting the reactor. As it is possible to see, this quantity is 0 in one inlet (the left one, where the antisolvent enters) and 1 in the other (the right one, where the solvent enters). When the two streams are completely mixed, the mixture fraction reaches its final value that, for an R equals to 1, is about 0.5. The more uniform the mixture fraction is (and the closer it gets to the value corresponding to complete mixing), the better is the mixing performance in the reactor. However, as the particle formation process is very fast (almost instantaneous), it is also crucial to estimate the mixture fraction variance, namely the average fluctuations of the solvent volume fraction around its local mean value. When the variance is null, antisolvent and solvent are perfectly mixed, whereas when the variance is greater than zero, a certain degree of molecular JOURNAL OF PHARMACEUTICAL SCIENCES, VOL. 100, NO. 6, MAY 2011

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Figure 11. Contour plots of mean mixture fraction and mixture fraction variance at flow rate ratio equals to 1 and for a water flow rate of 20 mL/min (top) and of 120 mL/min (bottom) for the water/ THF system.

segregation exists between the solvent and the antisolvent fluid elements and molecules. Figures 10 and 11 show that an increase in FRW results in faster mixing; in fact, a more uniform value of the mixture fraction is obtained, with a partial segregation of the antisolvent and solvent molecules only in a confined volume around the impingement plane. As already reported, when the solvent is changed, mixing in the reactor changes. The significance of this change can be quantified by comparing the contour plots reported in Figure 10 (for water/acetone) with those reported in Figure 11 (for water/THF). As it is possible to see, as the densities of water and THF are closer to each other than those of water and acetone, the flow field is more symmetric for the water/THF system. Moreover, the contour plots of mean mixture fraction and variance seem to show that mixing is faster and more efficient for the water/THF system in comparison with the water/acetone system. Different R were considered in our experiments (i.e., R = 1, 2, 3, and 8) and Figures 12 and 13 report the results for the simulations carried out unJOURNAL OF PHARMACEUTICAL SCIENCES, VOL. 100, NO. 6, MAY 2011

Figure 12. Contour plots of mean mixture fraction and mixture fraction variance for antisolvent (water) to solvent (acetone) flow rate ratios, R, equal to 2 (top) and 3 (bottom) and for a water flow rate equal to 120 mL/min.

der these conditions for the water/acetone and water/ THF systems, respectively. As shown previously at R equals to 1, the two jets present a comparable velocity, whereas an increase in the value of R determines a shift of the impingement plane close to the solvent inlet. Under these operating conditions, gradients at the molecular level are quickly dissipated, but mixing at the macroscale is not very efficient. As a matter of fact, in many cases, the solution exiting the reactor is not completely mixed, as some gradients in the mean mixture fraction still persist. More quantitative statements can be formulated by analyzing the characteristic timescales involved in the mixing process. In Figure 14, the mean residence time and the volume-averaged macromixing and micromixing time together with the volume-averaged global mixing time are reported versus the inlet FRW for an R equals to 1. As already anticipated, the macromixing time represents the time required to destroy macroscale gradients, the micromixing time represents instead the time required to destroy microscale gradients, and reach complete mixing at the molecular level, whereas the global mixing time is DOI 10.1002/jps

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Figure 14. Characteristics mixing time for different water flow rate values (FRW ): macromixing time, τM (squares); micromixing time, τm (circles); global mixing-time, τtot (triangles); mean residence time, τR (diamonds); open symbols refers to water/THF, whereas filled symbols refers to water/ acetone.

Figure 13. Contour plots of mean mixture fraction and mixture fraction variance for antisolvent (water) to solvent (THF) flow rate ratios, R, equal to 2 (top) and 3 (bottom) and for a water flow rate equal to 120 mL/min.

simply the summation of these two. These timescales should be compared with the residence time, which represents the mean time spent by the fluids in the reactor. As it is possible to see, as the flow rate is increased, mixing becomes faster and faster, as the global mixing time is significantly reduced. This reduction is stronger than that of the mean residence time, guaranteeing smaller and smaller mixture fraction gradients at the reactor outlet. These results well explain the decrease in the mean particle size caused by an increase in the FRW ; in fact, better mixing conditions result in higher supersaturation levels, inducing the formation of smaller and smaller particles. Comparison between the water/acetone and water/ THF systems shows that mixing is generally faster for the latter, although the difference is quite limited. This difference is likely not to have a significant effect on the final mean particle size, and therefore the significant differences in the final mean size for particles obtained by using acetone of THF as solvents must be attributed to factors other than mixing. Figure 15 reports the micromixing, macromixing, and global mixing times together with the mean residence time for a FRW of 120 mL/min as a function DOI 10.1002/jps

of the R. As already reported, the micromixing time seems not to change much when R is changed. On the contrary, the macromixing timescale is significantly increased when R is increased, and this increase seems to be larger for the water/acetone system than for the water/THF system. For this particular case (i.e., significant changes in R), it would appear that particle size is correlated with micromixing time rather than with macromixing (or overall mixing) time; in fact, micromixing time increases only moderately, similarly to particle size (see, Fig. 3). The consistent correspondence between mean particle size and mixing time (for the same antisolvent and solvent, polymer and initial polymer concentration) clearly shows that the mixing time, calculated through CFD simulations, can be used to design novel reactors to scale up and down these processes, as well as to transfer these processes from one mixing equipment to another17 . A more comprehensive approach is instead needed to take into account the effect of the nature of the antisolvent, solvent, and polymer, as well as the effect of the initial polymer concentration.

Figure 15. Characteristics mixing time for different antisolvent-to-solvent flow rate ratios (R): macromixing time, τM (squares); micromixing time, τm (circles); global mixing time, τtot (triangles); mean residence time, τR (diamonds); open symbols refers to water/THF, whereas filled symbols refers to water/acetone. JOURNAL OF PHARMACEUTICAL SCIENCES, VOL. 100, NO. 6, MAY 2011

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CONCLUSIONS In this work, the effect of the most important operating parameters for solvent displacement precipitation processes, namely the initial polymer and copolymer concentration, the antisolvent flow rate (FRW ), the type of solvent (acetone or THF), and the R, on the final PSD, surface properties, and morphology of poly(MePEGCA-co-HDCA) and PHDCA nanoparticles was investigated, analyzed, and explained. For the first time in this work, these particles are prepared with a CIJR able to guarantee repeatable conditions, continuous operation, and ease of scalability. Experimental data show the important effect of mixing on the particle formation process. Keeping all the other operating parameters constant, simply by changing the flow rate of the inlet solutions (and therefore by manipulating the mixing dynamics), the mean particle size can be modified significantly. This result has two important implications. The first one is that mixing in CIJR can be manipulated (by changing the inlet flow rates) to control the final PSD. For a given material and a given recipe, the PSD can be subjected to fine tuning by slightly changing this parameter. The second one is that when transferring a preparation from the laboratory scale to the pilot or industrial scale, and therefore when the mixing conditions are changed by those of a typical laboratoryscale equipment to those of a pilot-scale equipment, the PSD of the particles is bound to change accordingly. Mixing conditions are here quantified in terms of a global mixing time, calculated through CFD simulations, that can be used for scaling up and down CIJRs and similar devices as well as for designing new mixing equipments. The next steps of this work include the development of a more complete mathematical model able to account also for the effect of the other parameters and the investigation of the effect of the presence of the API (i.e., doxorubicin).

ACKNOWLEDGMENTS The authors wish to thank Julia Amici, Giuseppe Casti, and Mauro Raimondo for their contributions to this work. The financial contribution of the Italian Ministry of Higher Education and Scientific Research is also gratefully acknowledged.

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