Slowing the growth rate of Ibuprofen crystals using. the polymeric additive Pluronic F127. Supplemental Material

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1 Slowing the growth rate of Ibuprofen crystals using the polymeric additive Pluronic F127 Supplemental Material Thomas Vetter, Marco Mazzotti,, and Jörg Brozio Institute of Process Engineering, ETH Zurich, CH-8092 Zurich, Switzerland, and Pharmaceutical & Analytical Development, Novartis Pharma AG, CH-4056 Basel, Switzerland Phone: Fax: Note to reader References to tables and figures in the main manuscript are given as Mi where i is the number of the corresponding figure or table. 1 Concentration and solubility measurement with ATR-FTIR In this section the procedures to calibrate the ATR-FTIR signal and to measure the solubility of IBU in a 50/50 wt% mixture of ethanol and water are described. Figure 1(a) shows the fingerprint region of IR spectra for four combinations of IBU and PF127 (see caption for details) against a pure ethanol background. One sees that the effect of PF127 on the spectra cannot be described in To whom correspondence should be addressed Institute of Process Engineering, ETH Zurich, CH-8092 Zurich, Switzerland Pharmaceutical & Analytical Development, Novartis Pharma AG, CH-4056 Basel, Switzerland 1

2 a straightforward manner. However, the spectral region between 1480 and 1580 cm 1 was found to be suitable for calibration after correcting for external signal shifts with linear baselining, thus yielding the spectra illustrated in Figure 1(b). Limiting ourselves to a spectral region where the additive does not distort the spectrum in a nonlinear way enables us to calibrate the ATR-FTIR signal without having to repeat the calibration for different concentrations of PF Calibration In order to obtain quantitative information on the IBU concentration in solution the ATR-FTIR signal is calibrated using the procedure shown in Figure 2(a). For every IBU concentration the initially clear solution is cooled down from an initial temperature, that was chosen based on the starting concentration, thereby moving from undersaturation to supersaturation. During this process, spectra are recorded continuously until a steep increase in the number of short chord lengths is detected by the FBRM, indicating nucleation. The samples recorded after this point are rejected, since the concentration in the solution decreased by the formation of nuclei and concomitant crystal growth. This leads to a calibration set covering a large range of temperatures and concentrations, which is used in a multivariate calibration model as described in detail elsewhere. 3,6,9,10 The multivariate calibration model built in this paper uses the spectral region between around 1480 and 1580 cm 1, therefore leading to a set of 54 variables (wavenumbers) for the 862 samples (spectra) collected during the calibration procedure. The variables in the IR spectra are obviously highly correlated, which makes the relation between the dependent and independent data an ill-conditioned one. For problems like this, partial least squares regression (PLSR) was shown to be a suitable method. 3,4,7 In PLSR, a small number (1-10) of so called latent variables is introduced, which are linear combinations of the original variables that still represent the essential features present in the original data. To determine the number of latent variables, the quality of the calibration model for a certain number of latent variables is estimated by a cross-validation procedure, i.e. part of the samples are left out and the resulting calibration model is used to estimate their concentration. By comparing the determined concentration to the actual concentration the error of the calibration model can be 2

3 estimated. If this procedure is repeated for different subsets of the calibration set, the root mean square error of cross-validation (RMSECV) can be calculated. The RMSECV generally decreases when more latent variables are added to the model. Therefore, the "best" number of latent variables is the one where the RMSECV does not anymore decrease significantly when the next latent variable is added. For the data set at hand, a model built with three latent variables shows a good performance. The RMSECV corresponds to 1.03% of the mean concentration of the calibration set. 1.2 Solubility measurement The calibration model described in the previous section is used to determine the solubility of IBU in a 50/50 wt% mixture of ethanol and water. The presence of significant amounts of other species can obviously alter solubility in an unknown way. Therefore, the solubility is measured for solutions containing 0, 0.04 and 0.08 kg PF127/kg solvent. To measure the solubility, an excess amount of IBU crystals is added to the mixture of PF127, ethanol and water. The resulting suspension is heated up at a rate of 5 C/hr, which causes crystals to dissolve, so as the solution remains saturated. The dissolution of the crystals and the corresponding concentration increase is continuously measured with the ATR-FTIR. After every hour, corresponding to a temperature increase of 5 C, the temperature is held constant for 30 minutes. Since the temperature is constant, the ATR-FTIR signal and therefore the concentration in solution should also be constant over these 30 minutes. Figure 2(b) shows that this is indeed the case. If the concentration curve lagged behind in comparison to the temperature curve, one would simply decrease the heating rate, such that it becomes the rate-limiting step for the increase of concentration. The resulting solubility lines are shown in Figure M4. It was found that the amount of PF127 in the solution clearly increases the solubility for all the temperatures in the range investigated. Similar effects were reported for para-substituted acetanilides 2, the drug substance indomethacin 5 and aliphatic and aromatic hydrocarbons 8 using slightly different Pluronics, where the ratio between the polyoxyethylene and polyoxypropylene blocks differed. To obtain a continuous expression for the solubility lines, a fourth order polyno- 3

4 H 2 O/IBU H 2 O/IBU/PF absorbance [a.u.] absorbance [a.u.] H O H O/IBU 2 0 H 2 O/PF127 H 2 O/PF127/IBU wavenumber [cm 1 ] (a) wavenumber [cm 1 ] (b) Figure 1: Spectra obtained by ATR-FTIR at 30 C: (a) Influence of the additive on the IR spectrum of IBU. The solvent mixture is 50/50 wt% ethanol and water. The IBU and PF127 concentrations are 0.05 kg/kgsolvent and 0.08 kg/kgsolvent, respectively. (b) Baselined spectra in the spectral region used for the calibration model with and without PF127. The concentrations used are the same as in (a). 4

5 mial is fitted through the datapoints. The coefficients of these polynomials are reported in Table M1, where a centered and scaled temperature, θ, has been used. The fitted polynomial is only valid for the temperature range reported and should not be used for extrapolation. Another noteworthy point is that the addition of PF127 seems to facilitate the formation of a liquid-liquid phase equilibrium, which prohibits the solubility measurement at higher temperatures. algorithm LSQNONLIN contained in the MATLAB optimization toolbox. The objective function to be minimized was defined as a weighted sum of squared residuals between the experimental and simulated supersaturation profiles. Weighting was necessary because the individual experiments have a different number of data points, so as the experiments with more data points would exert an unduly large influence on the estimated parameters. Using the relative error between the supersaturation profiles instead of the absolute error yields very similar results because the range of supersaturation values in the experiments is rather narrow. Φ = N T Ni E i=1 j=1 Ni, D j k=1 1 N E i N D i, j ( S exp i, j,k Ssim i, j,k) 2 (1) where Φ is the sum of squared residuals, N T is the number of different temperatures within the whole set of experiments, N E i is the number of experiments performed for the i-th temperature and Ni, D j is the number of data points recorded for the j-th experiment at the i-th temperature. The symbols S exp i, j,k and Ssim i, j,k represent the experimental and calculated values of the supersaturation. The values of the constants in the growth rate expressions estimated from the experimental data are reported in Tables M3 and M4 for the birth and spread and for the mass transfer controlled growth rate expression, respectively. Confidence intervals for the parameters can be estimated by calculating the derivative of Φ in the vicinity of the estimated values with respect to the model parameters. 1 The resulting sensitivity matrix can be used to calculate the covariance matrix. The standard error, s m for the m-th model parameter is given by the square root of the m-th diagonal element of the covariance matrix. To get a certain level of confidence, α, the standard error has to be multiplied by the relevant quantile of Student s t-distribution, t α. The parameter and its confidence interval can then be expressed as 5

6 concentration [kg IBU/kg solvent] temperature [ C] time [h] (a) t concentration IBU [g/kg solvent ] temperature [ C] 8 (b) Figure 2: (a) Concentrations and temperatures used for the calibration set. The initially undersaturated solutions (open circles) are cooled down until nucleation is detected by FBRM or the final temperature has been reached (closed circles). (b) Procedure used for the solubility measurement of IBU in a 50/50 wt% mixture of ethanol and water. A solution containing an excess amount of IBU crystals is slowly heated up, therefore the solution concentration increases. Saturation is checked by adding plateaus in the temperature profile for which a concentration plateau is found at the same time. 6

7 k m ±t α s m (2) The confidence intervals reported in Tables M3 and M4 were calculated for a 95% confidence. The calculation of confidence intervals for k 4 and k 5 was not possible since the derivative of Φ in the vicinity of the estimated values of k 4 and k 5 was close to zero, yielding an ill-conditioned estimation of the covariance matrix. The quality of the fit of the two models for each experiment was quantified using the coefficient of determination, R 2 (see Table M2), as follows: R 2 = 1 N D i=1 ( S exp i S exp i Si sim S exp ) 2 (3) where N D is the number of experimental points in a specific experiment and S exp is the average supersaturation during that experiment, defined as S exp = 1 N D N D i=1 S exp i (4) It should be noted that the additional indices, j and k, from Eq. (1) were dropped for the sake of simplicity. 2 Crystal purity analysis The influence of the additive used in this work seems to be by adsorption on the crystal surface. Since the polymer is chemically very different from the solute molecule, a first guess would be that the polymer cannot be incorporated into the crystal. However, since the purification property of crystallization is of paramount importance, this needs to be checked. To this end, we have analyzed the purity of samples of final crystals obtained at all three concentrations of PF127. After filtration to remove the residual solvent and most of the PF127, 5 g of the crystals were resuspended in 1 L pure water and stirred for two hours. Thereafter, the crystals were filtered again and washed with 4 L water to remove any remaining polymer adsorbed on the crystal surface. 7

8 Afterwards, the crystals were dissolved and their purity was analyzed as described in Section M2.4. The values found and reported in Table 1 indicate that the purity of the crystals is not significantly decreased for concentrations of 0.04 and 0.08 kg PF127/kg solvent and that the washing procedure as described above is highly effective in removing the adsorbed polymer. It should be noted that the samples without added PF127 were not treated with the washing procedure above and thus show a somewhat reduced purity in comparison to the original substance. In fact, the samples that have been washed, indicate purities higher than 100%, which means that the final crystals are purer than the purchased material. It can thus be concluded from this study that PF127 is not extensively incorporated into the crystal lattice and that no or very few inclusions in the crystals are formed. Table 1: Purity measured by HPLC of the crystals produced at different Pluronic F127 concentrations. samples where the polymer was washed off. c PF127 [kg kg 1 solvent ] Run Purity [%] ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± 0.6 Notation c concentration [kg kg 1 solvent ] c solubility [kg kg 1 solvent ] k i parameter in the crystal growth rate varies 8

9 N D number of data points in the j-th experiment at the i-th temperature [-] Ni E number of experiments at temperature i [-] N T number of different temperatures in the experimental set [-] R 2 coefficient of determination [-] s standard error varies S supersaturation [-] S exp mean supersaturation during an experiment [-] t time [s] t α quantile of Student s t-distribution [-] T temperature [K] α confidence level [-] Φ sum of squared residuals [-] References (1) Beck, J. V., Arnold, K. J., Parameter Estimation in Engineering and Science, 1st ed., John Wiley & Sons, New York, (2) Collett, J. H., Tobin, E. A., Relationships between poloxamer structure and the solubilization of some para-substituted acetanilides. Journal of Pharmacy and Pharmacology, 31 (3), (3) Cornel, J., Lindenberg, C., Mazzotti, M., Quantitative application of in situ ATR-FTIR and Raman spectroscopy in crystallization processes. Industrial & Engineering Chemistry Research, 47 (14), (4) Geladi, P., Kowalski, B. R., Partial Least-Squares Regression - a Tutorial. Analytica Chimica Acta, 185 (1-3),

10 (5) Lin, S. Y., Kawashima, Y., The Influence of 3-Poly(oxyethylene)poly(oxypropylene) surface-active block copolymers on the solubility behavior of Indomethacin. Pharmaceutica Acta Helvetiae, 60 (12), (6) Lindenberg, C., Krättli, M., Cornel, J., Mazzotti, M., Brozio, J., Design and Optimization of a Combined Cooling/Antisolvent Crystallization Process. Crystal Growth & Design, 9 (2), (7) Nadler, B.; Coifman, R. R., Partial least squares, Beer s law and the net analyte signal: statistical modeling and analysis. Journal of Chemometrics, 19 (1), (8) Nagarajan, R., Barry, M., Ruckenstein, E., Unusual Selectivity in Solubilization by Block Copolymer Micelles. Langmuir, 2 (2), (9) Pollanen, K., Hakkinen, A. W., Reinikainen, S. P., Louhi-Kultanen, A., A study on batch cooling crystallization of sulphathiazoles Process monitoring using ATR-FTIR and product characterization by automated image analysis. Chemical Engineering Research and Design, 84 (A1), (10) Togkalidou, T., Tung, H. H., Sun, Y. K., Andrews, A., Braatz, R. D., Solution concentration prediction for pharmaceutical crystallization processes using robust chemometrics and ATR-FTIR spectroscopy. Organic Process Research & Development, 6 (3),

LabMax ReactIR FBRM. Relative Supersaturation Control in Batch Cooling Crystallization. Application Note. 1 Abstract.

LabMax ReactIR FBRM. Relative Supersaturation Control in Batch Cooling Crystallization. Application Note. 1 Abstract. Jörg Worlitschek Terry Redman Herbert Briggeler Olivier Ubrich Paul Barrett Brian O Sullivan Claude Didierjean Mettler Toledo GmbH, AutoChem Address Sonnenbergstrasse 74 PO Box, CH-8603 Schwerzenbach Phone