Scale-up of a Pharmaceutical Roller Compaction ... - ACS Publications

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Scale-up of a Pharmaceutical Roller Compaction Process Using a Joint-Y Partial Least Squares Model Zheng Liu,† Mark-John Bruwer,† John F. MacGregor,*,† Samarth S. S. Rathore,‡ David E. Reed,‡ and Marc J. Champagne‡ † ‡

ProSensus Inc., Ancaster, Ontario, Canada L9G 4V5 Eli Lilly and Corporation, Indianapolis, Indiana 46285, USA ABSTRACT: Garcia-Munoz et al. [Garcia-Munoz, S.; Kourti, T.; MacGregor, J. F. Chemom. Intell. Lab. Syst. 2005, 79, 101114] proposed a new latent variable regression methodology, joint-Y partial least squares (JYPLS), for product transfer between plants. In this paper, this method is used for product scale-up from a type of laboratory-scale roller compactor, a Fitzpatrick IR220, to a type of full-scale roller compactor, a Fitzpatrick IR520, in the pharmaceutical industry. A JYPLS model is first built with the data set collected from historical experiments on these two types of compactors. The JYPLS model relates API mass fraction, excipient mass factions, and roller compaction process measurements to ribbon properties. A constrained optimization is then formulated to invert the JYPLS model to find the key process settings of the Fitzpatrick IR520 to make the same quality of ribbon using the same raw materials formulation as the ribbon that had been produced on the Fitzpatrick IR220.

1. INTRODUCTION Latent variable empirical models built with historical data have proven their efficiency and robustness in modeling the region in which a process normally operates.1 Jaeckle and MacGregor2,3 proposed a methodology based on inversion of latent variable models to estimate the required process conditions for a plant to yield a desired set of properties in the final product. They extended this method to the problem of transferring products between plants.5 In this product-transfer problem, the desired grade has already been produced at a source plant site (plant a) and the operating conditions in the target plant (plant b) are the ones to be estimated (e.g., it is desired to produce in plant b one of the grades already produced in plant a). Figure 1 illustrates the typical data structure for the product-transfer problem. The product scale-up problem is a special case of the producttransfer problem but refers to the problem of estimating the operating conditions in a full-scale plant in order to yield a final product with the same set of final properties as were produced in a pilot (or laboratory-scale) plant. For the product scale-up problem, Xa and Ya represent the process conditions and product qualities of a pilot plant, respectively, and Xb and Yb represent the process conditions and product qualities of a full-scale plant. A pilot plant usually has many measurements on the system (with well-calibrated and precise sensors), and the runs are performed under tight control and with few unwanted disturbances entering the system. The full-scale plant, however, may have fewer observations and measurements, and the system will likely be exposed to more unwanted disturbances.2 If the grades produced in plant a and plant b are not common but share the same correlation structure, then the scores of the latent variable model for each plant will span a region in a common latent variable space. This suggests that there should be a way to transfer information from Xa to Xb through a common latent space defined by Ya, Yb and by Xa, Xb. On the basis of this r 2011 American Chemical Society

Figure 1. Typical data structure for product transfer.

observation, Garcia-Munoz et al.4 proposed a new latent variable regression method, joint-Y partial least squares (JYPLS), for product transfer between plants. The common plane defined by the joint quality matrix ([YaT YbT]) is a key concept in the JYPLS model structure. In this paper, this method is used for product scale-up of a roller compaction process in the pharmaceutical industry. Roller Compaction Process. In tablet production, fine powdered materials are granulated to obtain material of intermediate size. Granulation of powdered material provides the following benefits: it prevents segregation of components, decreases dusting, and improves flow properties. It also increases poured density and helps ensure a uniform distribution of ingredients. Wet and dry granulations are the two common granulation methods in the pharmaceutical industry.6,7 Roller compaction is a particle size enlargement technique using compaction of dry Received: November 16, 2010 Accepted: July 27, 2011 Revised: July 22, 2011 Published: July 27, 2011 10696

dx.doi.org/10.1021/ie102316b | Ind. Eng. Chem. Res. 2011, 50, 10696–10706

Industrial & Engineering Chemistry Research powder. By contrast, wet granulation uses aqueous or solventbased solutions and elevated temperatures during subsequent drying, which may lead to chemical deterioration of the active ingredient.8 During roller compaction (Figure 2), mixtures of active pharmaceutical ingredient (API) and excipients, e.g., binders, disintegrant, diluents, and lubricants, are mixed in a blender. The powder mixtures are then fed via a screw to counterrotating rolls, drawn into the nip region, and compacted into strips know as a ribbon. The ribbon is subsequently milled to form granules. There are many factors that can affect the quality of the compacted material, including machine design parameters (e.g., feed system, roll diameter, and roll surface configuration); process parameters (e.g., roll speed, screw speed, and roll gap); and physical properties of the feed materials (e.g., frictional properties at the roll surface, bulk density, and flowability). In addition, the effect of air within the pores can influence the feeding of the powders and the resulting ribbon compact properties.8,9 In a recent paper, Soh et al.10 used multivariate statistical modeling to understand the effect of raw material properties and roller compaction operating parameters on ribbon quality.

Figure 2. Schematic diagram of the roller compaction process.

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In most cases, scale-up for roller compaction occurs in several stages. Small-scale laboratory development from 0.5 to 2 kg/ batch can be scaled up to 510 kg/batch and then to 20100 kg/batch on a pilot scale. Production scale can typically range from 200 kg/batch to greater than 1000 kg/batch. There are many scale-up approaches for roller compaction unit operation reported in the literature1113 The suitability of each approach depends upon the specific formulation, the blending process selected, roller compaction equipment design, and most importantly the roller compaction equipment operating condition settings. Maintaining the same type of vendor equipment, feed design, roll design, and operating conditions may help to minimize issues with scale-up. However, very often it is not feasible to maintain these conditions. Moreover, scaling factors may not be proportional. For example, as the roll diameter increases, the nip shape and size change disproportionately, as does the roll gap.9 This makes scale-up of roller compaction more challenging. All of these methods are based on first principles analysis of the roller compaction process. Very little attention has been given to using the knowledge gained from actual data from past experiments performed during new drug product development and scale-up. In this paper, we will use the historical data from a type of laboratory-scale roller compactor, the Fitzpatrick IR220 roller compactor, and from a type of full-scale roller compactor, the Fitzpatrick IR520 roller compactor, to build a JYPLS model. This model is then used for scaling up several grades of ribbon that have been made on the Fitzpatrick IR220 roller compactors. The paper is organized as follows: section 2 introduces the joint-Y PLS methodology; section 3 describes the data set and the regular PLS models built from those data; section 4 builds the JYPLS model on this data set; section 5 outlines the formulation of the optimization problem to invert the JYPLS model for product scale-up; section 6 gives the simulation results; and section 7 summarizes the simulation and discusses the strengths and weaknesses of this method.

2. JOINT-Y PLS MODELING METHODOLOGY For a data set structure as illustrated in Figure 1, the joint-Y PLS model4 is defined by " # " # Ya Ta YJ ¼ ð1Þ ¼ Q T þ EYJ Yb Tb J

Figure 3. Block diagram for the joint-Y PLS method. 10697

dx.doi.org/10.1021/ie102316b |Ind. Eng. Chem. Res. 2011, 50, 10696–10706

Industrial & Engineering Chemistry Research

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Figure 4. Data structure of the data set collected from the two types of roller compactors and illustration of the scale-up problem.

Table 1. Summary of the Separate PLS Models for Each Type of Roller Compactor no. of PCs

R2Y(CUM)

Q2Y(CUM)

PLS model of IR220

2

0.957

0.949

PLS model of IR520

3

0.866

0.846

Xa ¼ Ta PaT þ EXa

ð2Þ

Xb ¼ Tb PbT þ EXb

ð3Þ

Ta ¼ Xa Wa

ð4Þ

Tb ¼ Xb Wb " # Ua ¼ Y J QJ UJ ¼ Ub

ð5Þ ð6Þ

where Wa* and Wb* are transformations14 of the model weights (Wa, Wb) to compute the scores directly from the original Xa and Xb. Figure 3 illustrates the loadings and scores structure of the JYPLS model. Examining the formula, one can find that the only difference between a JYPLS model and two regular separate PLS models on each plant is in formula 1, where QJ, which is calculated with [Ya; Yb]T, replaces Qa and Qb, which are calculated with Ya and Yb separately. The augmented matrix [Ya; Yb]T defines the joint quality plane. This common plane is a key concept in the development of the JYPLS model.4 So, one prerequisite of using JYPLS is that the attributes of the products manufactured in the two plants, Ya and Yb, must have a common correlation structure. As to the X space, there is no prerequisite for JYPLS models. The number and type of process variables describing the operating conditions in the two plants may be different; the two plants may even be of different design and manufacture; and the number of grades produced in the two plants may differ. JYPLS model is a powerful method that can model both plants simultaneously under such conditions. The NIPALS algorithm for JYPLS, which is similar to the one for ordinary PLS, was formulated by Garcia-Munoz et al.4,15 The major advantage of the NIPALS algorithm would be that it can easily handle missing data in the X and Y matrices.

3. DATA SET The data set in this study is collected from a laboratory-scale Fitzpatrick IR220 roller compactor and from a full-scale Fitzpatrick IR520 roller compactor at Eli Lilly Corp. in Indianapolis, IN: 37 samples from past experiments on the Fitzpatrick IR220 roller compactor and 101 samples on the Fitzpatrick IR520 roller compactor. For each sample, the X space includes three sources of information: API mass fractions, excipient mass fractions, and roller compaction process measurements; the Y space includes two key properties that are associated with roller compaction: ribbon density (RD) and ribbon solid fraction (RSF). Three API’s (API-1, API-2, and API-3) were used in both IR220 and IR520 runs, and the other (API-4) was only used for some runs of the IR520. Six excipients, (EXP-1EXP-6) were commonly used in several of the formulations processed in both machines. EXP-9 was only used in formulations run on the IR220, and EXP-7 and EXP-8 were only used in formulations run on the IR520. EXP-1, a type of diluent, and EXP-6, a type of disintegrant, are commonly used in all samples. Figure 4 illustrates the data structure. To understand the relationship between the X space and the Y space of each type of roller compactor, separate PLS models were built for the samples of IR220 and IR520 using ProMV Version 10.08, a multivariate statistical software package developed by ProSensus Inc. (ProSensus, 2010).16 In each model, unimportant variables were excluded. The PLS model for IR220 was fitted with two principal components (refer to Table 1). Figure 5 shows the joint plot w*q1 vs w*q2 of the X and Y space weights (w*’s and q’s, respectively) for the PLS model for the IR220. We can see that the density of ribbon is mainly positively correlated with roll force and mass fractions of API-1, EXP-4, EXP-5, and EXP-9 and negatively with the mass fractions of most of the remaining excipients and API’s. The solid fraction of ribbon, although highly positively correlated with the ribbon density, shows some different dependencies on the roller compaction variables and formulation variables, namely, a stronger positive correlation with EXP-9, a weaker correlation with roll force, and a negative correlation with roll speed and with the screw speed variables. Although these correlations are all apparent in the joint loading plot of Figure 5, they are also clearly shown for each y variable separately in PLS regression coefficient plots shown in Figure 6a. The PLS model for the predicted y’s can 10698



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Figure 5. Plot of PLS weights w*q1 vs w*q2 for the PLS model for IR220.

be rewritten in regression form as follows: Y = TQ = (XW*)Q = XB where the regression coefficients are given by B = W*Q, a function of the w*’s and q’s shown in Figure 5. However, when interpreting these coefficient plots, it is important to note that the PLS model for the IR-220 data is only two-dimensional, so all of the 14 and 15 coefficients in Figure 6 cannot be interpreted as having independent effects. Rather one should interpret the coefficient plots to show that certain combinations of the variables being higher or lower than average will lead to higher ribbon densities or solids fraction. Figure 6a shows ^ a = X aB). We the coefficient plots for the IR220 model (B in Y can see that (1) RD will increase as vertical screw speed (VFS), VFS/HFS (vertical to horizontal screw speed), and roll force increase and as roll RPM decreases; (2) RSF will increase as VFS, VFS/HFS, and roll RPM decrease, but roll force appears to have no effect; and (3) both RD and RSF are strongly correlated in a similar way with the API and excipient weight fractions. For the PLS model built with the samples from Fitzpatrick IR520 roller compactor, three principal components were fitted (refer to Table 1). Figure 6b shows the coefficient plots. Because RSF and RD are strongly correlated (refer to Figure 6b) in the IR520 compactor, we can see that their coefficients are quite similar. RD and RSF both increase as roll force increases and as VFS/HFS and roll gap decrease. Roll RPM and VFS have little effect on RD and RSF on the IR520. They were excluded from this model and, therefore, not shown in the plots. Again the effects of API and excipient mass fractions on RD and RSF are significant and affect both response variables in a similar manner.

Comparing the coefficients from these two PLS models, there are many similarities, but also some major differences. The effects of the excipient and API weight fractions are for the most part of similar sign and relative importance in the two processes. However, there are some differences related to the importance and magnitudes of some of the machine variables between the two machines, in particular the gap, the roll speed, and the screw speeds. Differences in the control strategies of the IR220 and IR520 machines could be the reason for differences in their regression coefficients. The Fitzpatrick IR520 is a production-scale roller compactor that has the option for gap control, which means that the HFS is adjusted automatically through a feedback loop in order to achieve the desired gap. On the other hand, the Fitzpatrick IR220 does not have a gap control option, meaning the horizontal feed screw is manually set to achieve the desired gap and then the VFS is set to be about 46 times the HFS. As a result of the above analysis and discussion, it is clear that the relationship among the machine variables is different between the machines, so the X space model, at least for these variables, must be modeled separately for each machine. When two plants/machines produce similar product, but have different process measurements or the process measurements have different effects, a JYPLS model may be used to model both plants simultaneously.4,15 However, to use a JYPLS model, we need to first check if Ya and Yb have the same correlation structure. Figure 7 shows the scatter plot of RD and RSF of the samples from both the IR220 and the IR520. One can see that the plots of RD versus RSF for each machine are essentially parallel to one another (the small offsets are due to drug product differences that will be accounted 10699



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Figure 6. (a) Coefficient plots for RD and SF for the PLS model of IR220. (b) Coefficient plots of the PLS model of IR520.

for in the models by the API and excipient formulation part of the X space), implying that the two machines do produce product

with the same correlation structure in the Y space. The IR520 data cover a larger range of Y than the IR220 data. 10700



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Figure 7. RSF vs RD.

4. JYPLS MODEL A JYPLS model with three PCs was fitted to this combined data of IR220 and IR520. The model was built with the software package JYPLS toolbox (Version 1.0) developed by ProSensus Inc. Figure 8 shows the bar plot of the cumulative fractions of the sum of squares of the X space explained for the two processes R2Xa(cum.) and R2Xb(cum.). We can see that the X space of IR220 is better explained, with the R2Xa(cum.) = 0.983, than the X space of IR520, with the R2Xb(cum.) = 0.643. Figure 9 shows the bar plot of R2Y(cum.), which shows that the Y space is well-explained by the model. Figure 10 shows the observed vs predicted plot of each y. Figure 11 and Figure 12 show the coefficient plots. One can observe that the coefficient plots are quite similar to the coefficients plots of the separate PLS models, which were discussed in section 3. This is because Ya and Yb follow the same correlation structure.

Figure 8. R2X(cum.) of the JYPLS model for (a) IR220 and (b) IR520. 0

5. FORMULATION OF THE OPTIMIZATION FOR PRODUCT SCALE-UP The objective of product scale-up is to find the process settings of the full-scale equipment to achieve the same quality of product that has been made on the laboratory-scale equipment. This can be achieved by solving an optimization problem to invert the JYPLS model, subject to the constraints of the model parameters and the actual constraint of the plant settings. The formulation is expressed as follows: n min ðy bdes x bnew W b Q J T ÞT G1 ðy bdes x bnew W b Q J T Þ x bnew

0 þ G2 @

A

∑

a¼1

x bnew w ba sba

!2 1 A

þ ðx bnew x bnew W b Pb T ÞT G3 ðXbnew x bnew W b Pb T Þ s:t: :

Rx bnew e r

o

@

A

∑

a¼1

x bnew w ba sba

!2 1 A e 95% conf lim

ðx bnew xbnew W b Pb T ÞT ðxbnew x bnew W b Pb T Þ e 95% conf lim

ð7Þ

where Wb*, Pb, and QJ are the parameters of the JYPLS model and G1, G2, and G3 are weighting parameters, A is the number of components of the JYPLS model; R and r are coefficients describing the hard constraints that need to be respected on xbnew, for example, upper or lower limits on the variable settings.17,18 xbnew is the vector of key manipulated variables of the full-scale plant that need to be decided before running the process. The optimization is performed by searching over these variables, but the search is confined to the space of the latent variable model through the constraints in the objective function 7. One can understand the optimization formulation in this way: Minimizing the first item of the objective function means that we are looking for the process settings which will manufacture the 10701



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Figure 9. R2Y(cum.) of the JYPLS model.

product as close as possible to the specified product quality; minimizing the second item of the objective function means that the solution should be as close as possible to the average settings of the samples which are used for training the model; and minimizing the third item of the objective function means that we need the solution to be as close as possible to the hyper plane defined by the loadings of the JYPLS model. The two hard constraints on Hotelling’s T2 and SPEX guarantee that the solution will not be a significant extrapolation from the validity space of the JYPLS model. The hard constraints on xbnew guarantee that the solution will be feasible from the viewpoint of process design. In many cases one might only use either the soft constraints (those on T2 and SPE in the quadratic objective function) or the hard constraints, but not both. Since the dimension of the Y space is only 2, while the PLS model dimension and the dimension of Xb are 3 (see Figure 8), then there is a one-dimensional null space in Xb within which the solution for Y does not change. This null space and the role that the soft and hard constraints in the objective function play in forcing a unique solution have been discussed elsewhere.2,17 The manipulated variables Xbnew in this study include: VFS/HFS, roll force, and roll gap. The constraints on those variables for the Fitzpatrick IR520 roller compactor are listed in Table 2.

6. RESULT For various reasons, it was not possible at this time to implement results of the scale-ups obtained from the optimization studies for additional products. Hence, to evaluate the method, we considered the scale-up to the IR520 of seven existing products that had been made on the IR220, and then compared the results with the closest existing results that had already been achieved on the IR520. We specified the API and excipient mass fractions to be those used in the IR220 runs and solved the optimization as formulated in eq 7 to find the process settings for the IR520 to achieve the same ribbon quality as was made with the IR220 using those formulations. The optimization was solved using the software package JYPLS toolbox (Version 1.0) developed by ProSensus Inc. The results are shown in Table 3 for the seven scale-ups (drug-1 products,

Figure 10. Observed vs predicted of the JYPLS model for (a) RD and (b) RSF.

SU-54, SU-55, and SU-62; drug-2 products, SU-68 and SU-78; and drug-3 products, SU-131 and SU-134). The numbers in the rows for the adjustable variables VFS/HFS, roll force, and gap (boldfaced rows) are the solutions from the optimization. The predicted and target ribbon quality results are shown in the boldfaced rows below these. Figure 13 illustrates the locations relative to the training samples from the IR520 in tb1/tb2 score space when the solutions from the scale-up of SU-54 through SU-134 are projected to the score space of the JYPLS model. For the drug-1 samples run on the IR220 machines, only one of them (SU-54) had an almost identical formulation that had also been run on an IR520 machine (Obs-43). Therefore the scale-up of SU-54 serves as a direct test of the JYPLS scale-up methodology through comparison with the Obs-43 results that were actually achieved on the IR520. Comparison of the optimization solution for SU-54 with the known process setting for Obs-43 is shown in Table 4. The predicted ribbon properties of SU-54 are very close to the actual ribbon properties of Obs-43, and the settings of the adjustable variables 10702



Figure 11. Coefficient for (a) RD and (b) RSF of the JYPLS model for IR220.

(VFS/HFS, roll force, and gap) found by the optimization are quite close to those actually used in the IR520. This comparison is an excellent direct validation of scale-up methodology. However, it is only for one scale-up condition. Therefore, we also show some scale-up comparisons for other drug-1 formulations even though the formulations are somewhat different between the selected IR220 runs and the runs actually performed on the IR520 machines. IR220 run no. 62 was selected as a scale-up candidate to the IR520 (SU-62) because it lay close to three drug-1 observations of IR520 in the training data set, i.e., Obs-41, Obs-47, and Obs-48, in the tb1/tb2 plot of Figure 13, even though there were differences in the formulations used. Table 5 shows the comparisons of the scaled-up process conditions and the predicted ribbon properties for SU-62 with the actual formulations and process conditions used for observations 41, 47, and 48 and the actual ribbon properties achieved in those runs. It is observed that the predicted ribbon properties of SU-62 are close to those achieved in the three IR520 runs. The values of the optimized X variables settings from the scale-up are slightly

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Figure 12. Coefficient for (a) RD and (b) RSF of the JYPLS model for IR520.

Table 2. Constraints on the Key Manipulated Variables of IR520 lower boundary

upper boundary

VFS/HFS

4.00

100.00

roll force (KN/cm) gap (mm)

3.00 2.00

12.00 4.00

different from those used in the observed IR520 runs, but this difference and the difference among the settings for the IR520 runs might be expected from the fact that the formulations run is somewhat different. To counterbalance the formulation differences, the process conditions need to be slightly different. However, because the scores tb1 and tb2 are the combined effect of the process variables and formulation conditions that are most correlated to ribbon properties, the closeness of those samples in tb1/tb2 plot indicates that, from a multivariate viewpoint, they should be considered to be similar, at least 10703



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Table 3. Solutions for the Simulated Scale-up Samples drug-1

drug-2

drug-3

SU-54

SU-55

SU-62

SU-68

SU-78

SU-131

SU-134

API-1

0.210

0.210

0.120

0.027

0.033

0.000

0.000

API-2

0.000

0.000

0.000

0.084

0.077

0.000

0.000

API-3

0.000

0.000

0.000

0.001

0.000

0.311

0.397

API-4

0.000

0.000

0.000

0.027

0.021

0.000

0.000

excipient-1

0.137

0.191

0.095

0.533

0.398

0.212

0.271

excipient-2

0.000

0.000

0.000

0.000

0.000

0.008

0.010

excipient-3

0.000

0.000

0.000

0.008

0.000

0.006

0.000

excipient-4 excipient-5

0.007 0.016

0.007 0.016

0.007 0.016

0.000 0.000

0.003 0.004

0.000 0.000

0.000 0.000

excipient-6

0.000

0.000

0.000

0.018

0.009

0.040

0.051

excipient-7

0.000

0.000

0.000

0.027

0.014

0.000

0.000

excipient-8

0.000

0.000

0.000

0.027

0.033

0.000

0.000

VFS/HFS

8.571

8.686

6.180

9.229

4.265

7.944

7.609

roll force (KN/cm)

5.499

5.419

7.076

7.497

6.814

7.660

8.563

gap (mm)

2.420

2.332

2.893

2.995

3.352

4.000

4.000

density of ribbon (TARGET)

1.109

1.109

1.132

0.975

0.992

0.971

0.994

density of ribbon

1.106

1.106

1.127

0.896

0.941

0.970

0.994

0.738

0.735

0.766

0.645

0.670

0.706

0.723

0.740

0.738

0.768

0.665

0.700

0.705

0.720

(PREDICTED) solids fraction (TARGET) solids fraction (PREDICTED)

Figure 13. Location of the solutions for the seven scale-up (SU) samples when projected to the t1/t2 score space of the JYPLS model.

with respect to ribbon properties. This example is used to illustrate that this scale-up methodology can be used even if the formulations are not identical as long as the latent variable model spans the scores of the point to be scaled up. In a sense, this estimation of optimal settings for the formulation of SU-62 is more an optimization of the IR520 for a new formulation, based on the JYPLS model, than it is a direct scale-up. However, if one wanted to consider optimization or scale-up

of the process for completely different API’s and excipients (as opposed to restricting oneself to the existing set of ingredients as done in this paper), then one would need to have detailed property information on each of these raw materials and incorporate these properties into the latent variable models and into the optimization as done by Muteki et al.19,20 in their product development work. The scale-up of runs 68 and 78 from the IR220 are shown to illustrate an attempt to scale-up formulations that were very different from any that had been run before in the IR520 machines (they do not span the score space of the IR520 model). The optimization solution of SU-68 and SU-78 projected onto the JYPLS tb1 /tb2 score plot in Figure 13 are far outside the 95% confidence limit, implying they are indeed major extrapolations for the model. The reason for the extrapolation is shown in Table 6, where it is seen that the weight percent of API-2 used in the formulations for SU-68 and SU-78 are very different than anything previously used for that drug in any IR520 runs (typical values used in previous IR520 runs 9093 are shown in Table 6. Similarly, several of the excipient values were quite different than ever used before on the IR520. With such an extrapolation, one should not expect that the predicted ribbon properties would closely match the target properties specified in the optimization, but the extrapolation should be an intelligent one based on the knowledge incorporated into the JYPLS model based on all the existing data. In particular, it should provide a good first guess at the scale-up conditions for the IR520 runs using those formulations. Referring to Table 3, one can see that indeed the predicted ribbon properties of those two samples 10704



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Table 4. Optimization Solution of SU-54 vs Actual Process Setting of IR520 for Obs-43 API-1

API-2

API-3

API-4

EXP-1

EXP-2

EXP-3

EXP-4

EXP-5

EXP-6

SU-54

0.210

0.000

0.000

0.000

0.137

0.000

0.000

0.007

0.016

0.000

Obs_43

0.212

0.000

0.000

0.000

0.137

0.000

0.000

0.008

0.014

0.000

VFS/

roll force

gap

density of ribbon—


solid fraction of ribbon—

solid fraction of

EXP-7

EXP-8

HFS

(KN/cm)

(mm)

TARGET

PREDICTED

TARGET

ribbon—PREDICTED

SU-54

0.000

0.000

6.500

4.000

2.400

1.109

1.080

0.738

Obs-43

0.000

0.000

6.667

4.000

2.500

0.727

1.067

0.714

Table 5. Optimization Solution of SU-62 vs Actual Process Setting of IR520 for Obs-41, -47, and -48 API-1

API-2

API-3

API-4

EXP-1

EXP-2

EXP-3

EXP-4

EXP-5

EXP-6

SU-62 Obs-41

0.120 0.027

0.000 0.000

0.000 0.000

0.000 0.000

0.095 0.183

0.000 0.000

0.000 0.000

0.007 0.007

0.016 0.014

0.000 0.0000

Obs-47

0.285

0.000

0.000

0.000

0.137

0.000

0.000

0.006

0.012

0.0000

Obs-48

0.285

0.000

0.000

0.000

0.137

0.000

0.000

0.010

0.016

0.0000

EXP-7

EXP-8

VFS/

roll force

gap



solid fraction of ribbon—

solid fraction of

HFS

(KN/cm)

(mm)

TARGET

PREDICTED

TARGET

ribbon—PREDICTED

1.127

0.766

0.768

SU-62

0.000

0.000

6.180

7.076

2.893

1.132

Obs-41

0.0000

0.0000

6.8182

8.500

3.000

1.136

0.772

Obs-47 Obs_48

0.0000 0.0000

0.0000 0.0000

6.2500 6.2500

6.500 6.500

3.100 3.100

1.171 1.165

0.770 0.766

Table 6. Mass Fraction of API-2 for SU-68 and SU-78 vs Mass Fraction of API-2 of Selected Samples in the Training Data Set

primary ID API-2

drug-2 samples from

some drug-2 samples

validation data set

from training data set

SU-68 0.111

SU-78 0.111

Obs-90 Obs-91 Obs-92 Obs-93 0.008 0.011 0.008 0.006

did not achieve the specified ribbon properties. However, the predicted properties are clearly showing lower values for the ribbon solids fraction and density as specified by their targets in the optimization, and hence the solutions are at least in the correct direction. Muteki and MacGregor 19 in their sequential DOE approach to the rapid development of new products recommended that such moderate extrapolations be used as experiments in a sequential manner. Under this approach, the new extrapolated recipe and conditions are run as an experiment, the results obtained from it are then used to update the model (with heavy weight given to the new observation), and the whole process is repeated until the desired results are achieved. Results19 showed that this approach can be very successful with a very small number of experiments since one is always maximizing the information available at every stage.

7. CONCLUSION In this paper, a latent variable modeling methodology for product transfer and scale-up (JYPLS model proposed by Garcia-Munoz et al.4) is applied to drug material scale-up from a Fitzpatrick IR220 roller compactor to a Fitzpatrick IR520 roller compactor. The methodology is demonstrated by running optimizations for the scale-up of several existing runs from the IR220 machine to the IR520 machine and comparing the results with

actual runs that had been performed on the IR520 machines. The results confirmed that the methodology can provide very good scale-up of drug formulations and machine settings between these roller compactors to achieve specified ribbon properties (ribbon density and ribbon solids fraction) provided that the scale-up is not a major extrapolation from the existing data. For extrapolations to new formulations, an iterative approach to scale-up is discussed.

’ AUTHOR INFORMATION Corresponding Author

*E-mail: [email protected].

’ ACKNOWLEDGMENT We acknowledge Allen Greg Steffler, Jeffrey D. Hofer, Jon Hilden, and Richard H. Meury for assisting in the data collection process for the paper and John Chlapik for providing technical guidance on roller compaction operation. We also acknowledge Jeanette M. Buckwalter and Dale E. Greenwood for reviewing the paper. ’ REFERENCES (1) MacGregor, J. F.; Yu, H.; Garcia-Munoz, S.; Flores-Cerrillo, J. Data-base Latent Variable Methods for Process Analysis, Monitoring and Control. Comput. Chem. Eng. 2005, 29, 1217–1223. (2) Jaeckle, C. M.; MacGregor, J. F. Product Design through Multivariate Statistical Analysis of Process Data. Am. Inst. Chem. Eng. J. 1998, 44, 1105–1118. (3) Jaeckle, C. M.; MacGregor, J. F. Industrial Applications of Product Design through the Inversion of Latent Variable Models. Chemom. Intell. Lab. Syst. 2000, 50, 199–210. (4) Garcia-Munoz, S.; Kourti, T.; MacGregor, J. F. Product Transfer between Sites Using Joint-Y PLS. Chemom. Intell. Lab. Syst. 2005, 79, 101–114. 10705



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(5) Jaeckle, C. M.; MacGregor, J. F. Product Transfer between Plants Using Historical Process Data. Am. Inst. Chem. Eng. J. 2000, 46, 1989–1997. (6) Mansa, R. F.; Bridson, R. H.; Greenwood, R. W.; Barker, H.; Seville, J. P. K. Using Intelligent Software To Predict the Effects of Formulation and Processing Parameters on Roller Compaction. Powder Technol. 2008, 181, 217–225. (7) Iveson, S. M.; Litster, J. D.; Hapgood, K.; Ennis, B. J. Nucleation, Growth and Breakage Phenomena in Agitated Wet Granulation Processes: A Review. Powder Technol. 2001, 117, 3–39. (8) Bindhmadhavan, G.; Seville, J. P. K.; Adams, M. J.; Greenwood, R. W.; Fitzpatrick, S. Roll Compaction of a Pharmaceutical Excipient: Experimental Validation of Rolling Theory for Granular Solids. Chem. Eng. Sci. 2005, 60, 3891–3897. (9) Teng, Y.; Qiu, Z.; Wen, H. Systematical Approach of Formulation and Process Development Using Roller Compaction. Eur. J. Pharm. Biopharm. 2009, 73, 219–229. (10) Soh, J. L.; Wang, F.; Boersen, N.; Pinal, R.; Peck, G. E.; Carvajal, M. T.; Cheney, J.; Valthorsson, H.; Pazdan, J. Utility of Multivariate Analysis in Modeling the Effects of Raw Material Properties and Operating Parameters on Granule and Ribbon Properties Prepared in Roller Compaction. Drug Dev. Ind. Pharm. 2008, 34 (10), 1022–1035. (11) Dehont, F. R.; Hervieu, P. M. Briquetting and Granulation by Compaction: New Granulator-Compactor for the Industry. Drug Dev. Ind. Pharm. 1989, 15 (1416), 2245–2263. (12) Sheskey, P.; Pacholke, K.; Sackett, G.; Maher, L.; Polli, J. Effect of Process Scale-up on Robustness of Tablets, Tablet Stability and Predicted in Vivo Performance. Pharm. Technol. 2000, 24, 30–52. (13) Nkansah, P.; Wu, S.; Sobotka, S.; Yamamoto, K.; Shao, Z. J. A Novel Method for Estimating Solid Fraction of Roller Compacted Ribbons. Drug Dev. Ind. Pharm. 2008, 34, 142–148. (14) Dayal, B.; MacGregor, J. F. Improved PLS Algorithms. J. Chemom. 1997, 11, 73–85. (15) Garcia-Munoz, S. Batch Process Improvement Using Latent Variable Methods. Ph.D. Thesis, McMaster University, Ontario, Canada, 2004. (16) ProSensus. 2010 Software. Retrieved from ProSensus, http:// prosensus.ca/software/promv. (17) García-Mu~noz, S.; Kourti, T.; MacGregor, J. F.; Apruzesse, F.; Champagne, M. Optimization of Batch Operating Policies. Part I. Handling Multiple Solutions. Ind. Eng. Chem. Res. 2006, 45, 7856–7866. (18) Garcia-Munoz, S.; MacGregor, J. F.; Neogi, D.; Latshaw, B. E.; Mehta, S. Optimization of Batch Operating Policies. Part II. Incorporating Process Constraints and Industrial Applications. Ind. Eng. Chem. Res. 2007, 47, 4202–4208. (19) Muteki, K.; MacGregor, J. F. Sequential Design of Mixture Experiments for the Development of New Products. J. Chemom. 2007, 21, 496–505. (20) Muteki, K.; MacGregor, J. F.; Ueda, T. On the Rapid Development of New Polymer Blends: The Optimal Selection of Materials and Blend Ratios. Ind. Eng. Chem. Res. 2006, 45, 4653–4660.

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