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Development of Near Infrared (NIR) Spectroscopy-based Process Monitoring Methodology for Pharmaceutical Continuous Manufacturing using an Offline Calibration Approach Evan Hetrick, Zhenqi Shi, Lukas Barnes, Aaron Garrett, Robert Rupard, Timothy Kramer, Tony Cooper, David Myers, and Bryan Castle Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.7b01907 • Publication Date (Web): 31 Jul 2017 Downloaded from http://pubs.acs.org on August 1, 2017

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Analytical Chemistry

Development of Near Infrared (NIR) Spectroscopybased Process Monitoring Methodology for Pharmaceutical Continuous Manufacturing using an Offline Calibration Approach

Evan M. Hetrick, Zhenqi Shi*, Lukas E. Barnes, Aaron W. Garrett, Robert G. Rupard, Timothy T. Kramer, Tony M. Cooper, David P. Myers, and Bryan C. Castle

Eli Lilly and Company, Indianapolis, IN, USA, 46285

*author to whom correspondence should be addressed: [email protected] (Zhenqi Shi) Tel: 1-317-276-9431

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ACS Paragon Plus Environment


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Abstract A near infrared (NIR) calibration was developed using an efficient offline approach to enable a quantitative partial least squares (PLS) chemometric model to measure and monitor the concentration of active pharmaceutical ingredient (API) in powder blends in the feed frame (FF) of a tablet press. The approach leveraged an offline “feed frame table” which was designed to mimic the full process from a NIR measurement perspective, thereby facilitating a more robust model by allowing more sources of variability to be included in the calibration by minimizing the consumption of API and other raw materials. The design of experiment (DOE) for the calibration was established by an initial risk assessment and included anticipated variability from factors related to formulation, process, environment and instrumentation. A test set collected on the feed frame table was used to refine the PLS model. Additional fully independent test sets collected from the continuous drug product manufacturing process not only demonstrated the accuracy and precision of the model, but also illustrated its robustness to material variability and process variability including mass flow rate and feed frame paddle speed. Further, it demonstrated that a calibration can be generated on the offline feed frame table and then successfully implemented on the full process equipment in a robust manner. Additional benefits of using the feed frame table is to streamline model monitoring and maintenance activities in a manufacturing setting. The real-time monitoring enabled by this offline calibration approach can be useful as a key component of the control strategy for continuous manufacturing processes for drug products, including to detect special cause variations such as transient disturbances, enable product collection/rejection based upon pre-determined concentration limits and may play an important role in enabling real-time release testing (RTRt) for manufactured pharmaceutical products.

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Introduction Since the issue of the process analytical technology (PAT) guideline by the FDA in 20041, the use of near infrared spectroscopy (NIRS) for monitoring batch blending uniformity for solid oral dosage forms has become not only one of the most reported, but also one of the most implemented PAT applications in commercial pharmaceutical manufacturing.2-9 Despite its popularity, there is a debate in the field about the true value of using NIRS for real-time monitoring of blend homogeneity for batch processes given the fact that blending is not the last step of powder mixing prior to tablet compression. In fact, the feed frame (FF) of a tablet press has been previously reported to be an additional mixing element.10 Thus, blend uniformity at the end of a batch blending process may not be truly representative of the final uniformity of the compressed tablets. Continuous manufacturing (CM) of solid oral drug products continues to be an area of intense research interest.11 A topic of considerable focus within the field of CM is the ability to detect transient disturbances in API concentration in the powder blend. One approach to transient disturbance detection involves data from loss-in-weight (LIW) feeders combined with a residence-time distribution (RTD) model to predict API content of the blend in the tablet press feed frame based on “fed concentration” calculated from the LIW feeder data. While LIW feeder data combined with an RTD model are able to detect and control potential disturbances originating from the feeders of a CM process, an additional strategy may be required to detect and respond to special-cause variations and process disturbances that occur downstream of the feeders. An example of such a special-cause variation includes potential preferential accumulation of the API or excipients within equipment and piping during operation. It should be noted that these examples of special-cause variations may be product-specific and may not be risk factors for certain formulations. In cases where it is desirable to detect disturbances and special cause variations originating downstream of the feeders, spectroscopy-based PAT applications involving NIR sensors have demonstrated great promise. A promising location for such real-time spectroscopic sensors is the tablet press feed frame.12-14 This location has the advantage of being the location closest to the final tablet compression where a PAT measurement can be performed on powder, and thus provides a representative measure of the API concentration in the compressed tablets. However, at the same time, the feed frame represents a challenging location for a spectroscopic measurement due to the very dynamic nature of the sample (i.e., powder) at that location, such as changes in working distance, measurement angle, and powder physical properties (e.g., density). Although significant efforts have focused on using NIRS for monitoring API concentrations in blends, the CM process brings several unique challenges. For example, an initial approach toward developing a spectroscopic method for disturbance detection focused on the use of qualitative trending,12-14 which has been commonly used for batch blending monitoring. Qualitative trending refers to the use of a metric representative of concentration change (not absolute concentration) to monitor the API concentration.15-17 However, the mass flow rate for a continuous process (which can range from approximately 5 to 75 kg/hr) is much faster than 3



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powder movement in a batch blending process when a bin-blender is rotating. Unless properly accounted for in a calibration, mass flow rate as well as other factors, including feed frame paddle speed, are expected to impact a qualitative metric. In fact, the impact of flowing powder on the quantitation of API content via NIRS in a CM application has been reported before.18,19 In addition, while a common approach for a batch blending process is to build a calibration model using dynamic samples (instead of static samples) across multiple flow conditions,6,20 such an approach executed on the full CM process equipment would likely consume a large quantity of material to collect a representative calibration set. Requiring large quantities of material may limit the amount of variability that can be included in a calibration, potentially jeopardizing the robustness of the subsequent method. Thus, analytical and PAT scientists need efficient approaches for building robust calibration models for real-time monitoring. In this report, we detail the use of an offline “feed frame table” to collect calibration spectra consuming a limited amount of raw materials to generate a NIRS calibration for real-time monitoring of API concentration in the feed frame of a tablet press. To the best of the authors’ knowledge, this study represents the first implementation of an off-line calibration approach at the feed frame of a tablet press to provide quantitative real-time monitoring for a CM process. While the use of the feed frame as a PAT monitoring location combined with qualitative trending approaches have been reported previously,12-14 our experience using qualitative trending has revealed the negative impacts of tablet press operational changes on the robustness of the measurement (Supporting Information Figure S-1). In this report we describe generating a NIRS calibration for a quantitative method using the off-line “feed frame table” to capture dynamic variability expected from the CM process equipment that may impact a multivariate prediction. The impact of various preprocessing approaches on the error statistics of the multivariate model was characterized. The method generated with the feed frame table was successfully used for in-process monitoring at the feed frame of a tablet press on the continuous drug product manufacturing process, demonstrating the equivalency between the offline feed frame table and the full CM process from a spectroscopy monitoring standpoint.

1. Experimental 1.1 Calibration design of experiments (DOE) The purpose of the calibration DOE was to incorporate the most common sources of variability encountered during routine monitoring of API concentration in the feed frame, including material, process, and environmental sources of variability. In order to keep a relatively small number of design points, a supersaturated design (i.e., there are more parameters than blends) via Nearly Orthogonal Arrays21,22 was used to span the calibration range while simultaneously introducing as much excipient variability as was feasible. Specific combinations of factors were chosen so as to get as much balance as possible for pairs of factors (with priority given to the factors of API concentration and lot). For the continuous factors, the allocation of factor levels was chosen to reduce correlation with the %API factor while limiting the correlation with other 4


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continuous factors. In total, seven calibration blends were designed to generate the calibration dataset whereby 2 kg of total blend material was prepared for each blend via gravimetric addition of the constituent components to a tumble bin blender. Detailed information of the DOE is shown in Table 1.

Table 1. Calibration blends used on the offline feed frame table to generate the NIR model

Calibration Blend 1 2 3 4 5 6 7

API % of theoretical concentration 90 100 100 110 80 80 90

Ratio of Diluent 1 : Diluent 2 (% of target ratio) 100 109 100 100 109 91 91

API lot lot A lot B lot B lot C lot C lot A lot B

Diluent 3 particle size (PS) High PS High PS Nominal PS Nominal PS Low PS High PS Low PS

For material variability, both API and excipients were taken into consideration. Three separate lots of API were used (lots A-C in Table 1) in the seven blends to provide a representative range of particle size and bulk density. The blends also included four levels of concentration of API (80, 90, 100, 110% of theoretical concentration) to provide a range of concentrations for the method calibration. Diluent 1 and Diluent 2 are different grades of the same excipient. The ratio between the two grades was varied to simulate potential lot-to-lot variability for each grade with particle size variability and bulk density variability. Lastly, a single lot of Diluent 3 was sieved and recombined to create particle size variability to be representative of expected lot-tolot variability. The remaining three excipients (disintegrant, glidant, and lubricant), were all present at levels less than 5% in the nominal formulation and were kept at a constant relative ratio to each other in each calibration blend.

For process factors, mass flow rate (kg/hr) and feed frame paddle speed were varied to mimic the dynamic powder flow past the probe for each calibration blend during normal press operation. Mass flow was varied at two (low and high) levels to bracket the intended mass flow for the continuous manufacturing process. In addition, two feed frame paddle speeds (15 rpm and 45 rpm) were chosen to bracket the expected range of paddle speeds during routine tablet press operation.

For environmental variability, the entire calibration dataset was collected on four different days across two weeks at both 20% and 45% RH (setpoints) conditions in a humidity-controlled 5



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process suite. At each RH condition, the seven calibration blends were acclimated prior to spectral data collection. Calibration data were collected in a full-factorial fashion with respect to mass flow rate and feed frame paddle speed at both RH setpoints for each of the seven calibration blends.

2.2 NIR data collection The calibration was generated using a NIR spectrometer and non-contact fiber optic probe from Prozess Technologie (St. Louis, MO). The spectrometer was a Prozess 611 NIR Monitoring System consisting of a tungsten-halogen lamp for illumination and a 256-channel InGaAs diodearray detector with a nominal wavelength range of 1100 – 2100 nm with approximately 5 nm wavelength spacing. The probes were non-contact fiber optic probes with a body constructed of 316 stainless steel with an IP65+ rated coating on the fiber optic bundles. Referencing was performed daily on each analyzer with 99% encapsulated fluorilon diffuse reflectance standards (Avian Technologies LLC, Sunapee, NH). Spectral data were generated with NovaPAC software (version 6.0, Prozess Technologie, St. Louis, MO). The integration time was 60 ms with 20 spectral co-adds. All data included in this manuscript was collected in development before transfer to the manufacturing site. 2.3 Feed frame table The calibration DOE was executed on a custom-designed and fabricated feed frame table (Figure 1) manufactured by the Equipment Development Group at Eli Lilly and Co. The feed frame table is a table on which a tablet press feed frame is mounted that allows powder blends to be passed through the feed frame (manually fed) and be collected at the outlet of the feed frame without the tablet press and the feeders, mixer, and surge hopper required for the full CM process. The purpose of the feed frame table was to accurately mimic the dynamic powder flow experienced in routine press operation and collect representative spectral data while reducing consumption of powder blend, which allowed a greater amount of material variability to be included in the calibration, resulting in a more robust method. Two variables were changed on the feed frame table to mimic operating conditions: the feed frame paddle speed and the slide gate position (located at the exit of the feed frame) which controls mass flow rate. The feed frame table motor included a variable frequency drive (VFD) that controls paddle speed by powering a vertical shaft keyed to the feed frame paddles. The paddle speed was set using a dial that equates speed set point (rpm) to motor amperage. The slide gate position was calibrated using the nominal blend before method calibration. The slide gate calibration determined the correlation between slide gate setting (i.e. how far the slide gate was opened) and the mass flow rate of powder through the feed frame (determined gravimetrically over a fixed period of time). The feed frame table was designed to allow manual recycling of each calibration blend for spectral data collection under different conditions of mass throughput, paddle speed, and %RH. A custom-designed probe adapter, manufactured via a 3-D printing process, was used 6


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to position the NIR probe over a sapphire window above the dosing paddle in the feed frame. The probe holder was designed to maintain a constant distance between the tip of the probe to the powder-side of the sapphire window. Containment bags (5 L, EZ Biopacs, ILC Dover, Frederica, Delaware, USA) were used for manual material transfer through connection to the feed frame via a sanitary fitting at both the inlet and outlet. 2.4 Test sets To evaluate the calibration model performance, a test set was collected with blend #3 (nominal blend) on the feed frame table when the paddle speed was adjusted from 15 rpm to 45 rpm at the high mass flow and 45% RH condition. Twenty spectra were collected at 15 and 45 rpm, respectively, while approximately ten spectra were collected during the paddle speed transition from 15 rpm to 45 rpm. In total, fifty spectra were collected as the test set. Spectra collected during this test set were not included in the calibration. Furthermore, the aforementioned spectrometer and probe were subsequently deployed onto the feed frame of a tablet press in the full continuous manufacturing drug product process to collect independent test data across multiple concentration levels at two different mass flow rates and feed frame paddle speeds to determine the accuracy, precision, and robustness of the PLS model when calibrated on the offline feed frame table and deployed on the full CM process equipment.

2.5 Software The calibration model was developed and optimized in MatLab (R2015a) with PLS Toolbox (Eigenvector Research Inc, Manson, WA), and deployed in Unscrambler (X10.3, CAMO, Bridgewater, NJ) for real-time monitoring on the full continuous manufacturing drug product process.

3 Results and Discussion 3.1 Raw Calibration Spectra The DOE described in Section 2.1 contained seven calibration blends. A significant advantage of the feed frame table is the ability to generate representative spectra while consuming much smaller quantities of blends, and thus use significantly less API and excipients, to generate the calibration dataset. Reduced material consumption allows more variability to be included in the calibration model, thereby improving model robustness. Calibration spectra from each blend were collected on the feed frame table at four combinations of flow rate and paddle speed under each of two relative humidity conditions (20% and 45% RH) for a total of 56 design points across the calibration DOE. The spectral variability shown in Figure 2 demonstrates that the use of the table allowed important material, process, and environmental variability to be included 7



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into the calibration with a relatively small quantity of API (especially when compared to the required API amount had the calibration been executed on the full continuous manufacturing process equipment). It is estimated that incorporating this amount of variability by conducting the calibration on the feed frame table used 95% less API than if the same calibration had been performed on the continuous manufacturing process equipment (5 kg used on the feed frame table vs 100 kg estimated for calibration on the process equipment). Twenty spectra were collected at each design point of the calibration yielding 560 spectra at each of the two %RH conditions. In total, the calibration spectral matrix was 1120 by 256, with 1120 representing the total number of calibration spectra obtained and 256 representing the number of wavelength channels on the InGaAs diode array detector of the NIR spectrometer. 3.2 Optimization of Wavelength Range and Spectral Preprocessing To develop a robust calibration model for the dynamic powder samples, the following systematic approach was taken to search for an optimal combination of variable selection (i.e. selection of wavelength region) and spectral preprocessing. Two wavelength ranges evaluated included the full spectral range and a range covering 1626 nm to 1893 nm, which represents the first overtone of C-H vibrations for the target analyte. The first overtone region was selected due to its higher variable importance plot (VIP) values when a PLS model was built across the full wavelength range using SNV as the preprocessing (Figure 3). The choice of the first overtone region was also later confirmed by specificity demonstrated in the final PLS model in Section 3.3, characterized by strong API absorption bands versus relatively weak absorption by the excipients. In addition, seven combinations of preprocessing were investigated: SNV, 1st derivative, 2nd derivative, SNV followed by 1st derivative, 1st derivative followed by SNV, SNV followed by 2nd derivative and 2nd derivative followed by SNV. Savitzky-Golay smoothing with a window width of 15 was used for all related derivative calculations. The error statistics and standard deviations of predicted concentrations across the fifty spectra collected on the feed frame table as the test set were used as figures of merit for comparison across different preprocessing approaches. The graphical comparisons across two wavelength ranges and seven preprocessing combinations are shown in Figure 4 for the first test set collected on the feed frame table. The left Y-axis represents the root mean square error of prediction (RMSEP) vs. the 100% theoretical concentration of the test set blend, and the right Y-axis represents the standard deviation of the 50 predictions for the test set over both paddle speeds across which the 50 spectra were collected. The left half of the plot represents models generated with only the first overtone region, while the right half of the plot represents models using the entire spectrum. The blue bars and red bars indicate the RMSEP and standard deviation, respectively, for each combination of wavelength range and preprocessing. As can be seen, each combination resulted in acceptable figures of merit for the intended purpose of the measurement. The order of SNV and derivative was found to differ for the case of the full versus the narrow wavelength range, which matches previous reports regarding the need to perform case-by-case optimization on such a preprocessing combination given specific dataset(s).23 Within each wavelength region, 8


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SNV preprocessing resulted in the best combination of RMSEP and standard deviation. While SNV preprocessing using the full wavelength range provided a slightly better RMSEP and standard deviation than SNV using the narrow wavelength range, the model employed moving forward used only the narrow wavelength range (1626 nm – 1893 nm). Using only the first overtone region was based on both the increased sensitivity of the model for the API as demonstrated in the VIP plot in Figure 3, and the fact that a model focused on this region may be less sensitive to effects of excipient material variability manifested in other regions of the spectrum. 3.3 Partial Least Squares Model After selecting the optimized wavelength range and preprocessing, the predicted residual error sum of squares (PRESS) plot and correlation plot for the PLS model were generated and are shown in Figure 5. During cross-validation, the approach of random subset was used. The convergence between RMSEC and RMSECV suggested an initial 5 latent variable (LV) model. However, the RMSEP based upon the first test set indicated a 3-LVmodel would capture sufficient variability, with additional LVs beyond 3 not resulting in a significant improvement in RMSEP on the independent test set. Further, the physical interpretation of the score plot (see below) did not illustrate any classification by specific variables within the DOE on the fourth and the fifth LVs. Thus, a 3-LV model was chosen to avoid potential over-fitting. The correlation plot between predicted and measured percent theoretical concentration for the calibration data using a 3-LV PLS model is also shown in Figure 5. In addition, it is worthwhile to note that the test set was collected on nominal blend #3 after the blend was used for collecting calibration spectra. The prediction performance by the model as illustrated by RMSEP in Figure 5 indicates that the use of the feed frame table to collect calibration spectra by recycling the blend did not cause segregation issues or potential over-lubrication due to the amount of shear the blend experienced during calibration. The score plots (2nd vs. 1st LV and 3rd vs. 1st LV) are shown in Figure 6. The first LV explained the majority of concentration variability, the second LV explained the majority of variability caused by paddle speed, and the third LV explained the majority of variability imparted by RH. Meanwhile, the individual clusters formed by each calibration blend represent the unique formula compositions evaluated in the calibration DOE, which originated from the different lots of API, the varied ratio between Diluent 1 and Diluent 2, and the various particle sizes of diluent 3 (created by appropriately combining different sieve cuts). Such a wide range of raw material properties included in the model calibration, along with the process and environmental factors also included, improved the robustness of the model toward future raw material, process, and environmental variability. Since the first LV captured the majority of API concentration variability (83%, data not shown), the loadings of the first LV by the PLS model were plotted against the pure component spectra of the API and the major excipients in Figure 7. It was noted that the 1st loading spectrum contained some high frequency noise. Upon investigation, these features were attributed to 9



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systematic noise from the spectrometer initially used in development. Such features were not evident when a second spectrometer (same make and model) from the same vendor was later deployed at a different manufacturing site by collecting data on the same calibration blends via the use of the feed frame table. By comparing the 1st loading spectra from both spectrometers, it is clear that the alignment between the first LV and the pure component spectrum of the API (especially around 1680 nm and 1750 nm) clearly demonstrated the ability of the PLS models to capture the variability associated with API concentration. This analysis demonstrates the specificity of the PLS model for the API. Additional evidence supporting the specificity of the PLS model is the linearity and the accuracy demonstrated when applying the PLS model onto independent test sets. Detailed info can be found in Figure 8 and Table 2. 3.4 Analysis of Residuals To evaluate the sensitivity of the PLS model to potential material and environmental factors, the residuals were obtained from the difference between the PLS predictions and the API concentration as determined by the dispensed amount (mass) of API and excipients on a wt/wt basis. Supporting Information Figure S-2 shows the residuals of the calibration set plotted against the blend number, RH, and processing parameters (paddle speed and mass flow rate, described as throughput in Figure S-2). Ideally, the residuals for each of the calibration sets would vary around zero (signifying that the model had minimal bias within the calibration set) and low variability (indicative of precise measurements). Blend 2 and 6 showed larger residuals than other blends. Blend 2 had the largest residual range with the residuals at 20% RH being centered about -3.0% and the residuals at 45% RH being centered about 3.0%. This was consistent with the larger Q residuals observed from blend 2 in Figure 9. Blend 6 also possessed larger residuals when data was collected at low paddle speed (15 rpm) and 20% RH, which was also consistent with its larger Hotelling T2 in Figure 9. Therefore, such a residual analysis provides insights as to potential impacts on model performance. Here, the effects of blends, RH and paddle speed appear to have more impact than the mass flow rate set point. 3.5 Model Evaluation on the Continuous Manufacturing Process After generating the calibration on the offline feed frame table as described above, the next step was to test the model on the full continuous manufacturing process. This served to assess the feasibility of generating the calibration on the offline feed frame table and its subsequent ability to produce robust predictions on the full continuous process equipment. Two experiments spanning the process parameters of low and high mass flow rates and low and high feed frame paddle speeds were conducted on the full continuous manufacturing process equipment to test the performance of the optimized PLS model. The concentrations vs. time profiles are shown in Figure 8. Figure 8A was generated at the low mass flow rate (18 kg/h) and Figure 8B was generated at the high mass flow rate (36 kg/h). In both plots, the red trace indicates the theoretical API concentration in the powder blend based on the amount of API and excipients dispensed from the loss-in-weight feeders (prior to mixing). For both experiments, 10


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the feeders were initially set to dispense 100% theoretical concentration, then stepped to dispense 110% concentration, followed by 90% (Figure. 8A) or 85% (Figure. 8B) concentration. The blue lines represent the real-time FF NIR measurement (i.e., real-time prediction of API concentration in the powder blend based on the PLS model described above). It is evident that the FF NIR measurement tracked the expected change in API concentration based on the feeder set points very closely. For example, in both cases, the FF NIR predicted approximately 100% concentration at the start of each run and captured both the increase and subsequent decrease in API concentration due to the intentional feeder set point changes. Compressed tablet cores generated during this experiment were sampled and subsequently analyzed by either transmission NIR spectroscopy or HPLC, and those results are overlaid on the plots as well. There is excellent correspondence between the FF NIR prediction of API concentration and subsequent measure of API concentration by an orthogonal, offline technique (both transmission NIR spectroscopy on the tablet cores and HPLC analysis). The figures of merit (accuracy, precision, and linearity) of the FF NIR measurement of API concentration are summarized in Table 2. Accuracy was determined by calculating the RMSEP between the FF NIR measurement of API concentration and that determined by HPLC analysis of core tablets sampled at the time of the NIR prediction. Precision was determined by calculating the standard deviation of the FF NIR predictions when the process was at steady operation at the 100% level. Linearity was determined by plotting the API concentration as determined by HPLC vs. the FF NIR predicted API concentration and performing a linear regression and calculating the R2. The data summarized in Table 2 illustrate the excellent accuracy, precision, and linearity of the FF NIR measurement. Furthermore, these data demonstrate the success of generating the calibration on the offline feed frame table and subsequent application to the full CM process equipment. Moreover, since the data across Experiments 1 and 2 in Table 2 were collected at two different mass flow rates and feed frame paddle speeds, this experiment demonstrates the robustness of the calibration to process parameters that may be expected to vary during routine production on the full CM equipment. Finally, the overlay plots in Figure. 8 demonstrate the value of such a NIRS-based monitoring approach of powder blends for providing a rapid, realtime measurement of API concentration in the powder blend between HPLC sampling locations/time points throughout a continuous run. Table 2. Figures of merit (accuracy, precision, and linearity) of the FF NIR method based on data presented in Figure 8 from two experiments conducted on the full continuous manufacturing process.

Experiment

Mass Flow Rate

Feed Frame Paddle Speed

Accuracya

Precisionb

Linearityc

1 Low (18 kg/hr) 15 rpm 1.3% 0.3% 0.99 2 High (36 kg/hr) 25 rpm 1.3% 0.3% 0.98 a Accuracy was determined by calculating the RMSEP of the FF NIR prediction vs. offline HPLC analysis of the compressed tablets.

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b

Precision was determined by calculating the standard deviation of the FF NIR prediction during steady operation at the 100% level. c 2 Linearity values represent the R of a linear regression performed on a plot of HPLC results vs. FF NIR prediction. For the experiment conducted at low throughput, the linearity was based upon data collected at nominal levels of 90%, 100% and 110%. For experiment conducted at high throughput, the linearity was based upon data collected at nominal levels of 85%, 100% and 110%. Due to a time gap in the real-time process, the predicted concentration from the feed frame at the 100% nominal level is not shown in Figure 8.

In addition, both the diagnostic plots (i.e., Q vs. Hotelling T2) and score plots (Figure 9) indicated that the entirety of the two experiments were within the calibration model confidence intervals. The diagnostic and score plots demonstrate that calibrating on the offline feed frame table appropriately mimics the actual process and, additionally, demonstrate the robustness of the optimized PLS model for routine monitoring at a range of throughputs and feed frame paddle speeds on the full CM process. These data also show that the feed frame is a suitable point for measuring the API content in the powder prior to tablet compression; the excellent correspondence between the FF NIR prediction and tablet measurements (both transmission NIR spectroscopy and HPLC) confirm that measuring the powder in the feed frame is predictive of the API content in the eventual tablet. Moreover, the excellent correspondence between the FF NIR prediction and subsequent offline HPLC analysis of core tablets (Figure 8 and Table 2) as well as robust diagnostics (Figure 9) demonstrates that the FF NIR measurement could be suitable for real-time release applications. A noteworthy observation is that in the Q vs. T2 plot in Figure 9, the clusters for the two runs on the CM process equipment were located in a region that was not overlapped by calibration data, though none would be classified as outliers at the 99.9% confidence level. The use of the feed frame table was capable of capturing the majority of variability subsequently encountered on the full CM process. At early project stages (i.e., initial deployment of the NIR model on the full CM process after calibration on the offline table), further potential sources of remaining variability may be unknown. Thus, it is recommended to use statistical limits (for example, 99.9% confidence limits or greater) for Q and T2 from the calibration data as diagnostic limits during routine monitoring after initial deployment to avoid potential false positive outliers. When sufficient experience during routine monitoring has accumulated, the diagnostic limits may be adjusted. The adjustment of diagnostic limits has been reported previously.24 4

Conclusions

The generation of a NIR calibration (quantitative PLS model) for the real-time in-line measurement of the concentration of API in a powder blend in a tablet press feed frame for a drug product continuous manufacturing (CM) process was successfully completed. The process involved an upfront risk assessment of factors that may contribute variability, followed by a calibration DOE on an offline feed frame table to enable a quantitative PLS model with limited consumption of API. It is estimated that calibrating on the offline feed frame table consumed 12


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95% less API than would have been required to generate the same robustness if calibration had been conducted on the full CM process equipment. The calibration model was applied to multiple independent test runs, conducted on both the feed frame table and the full CM process equipment, yielding results with excellent accuracy and precision for the intended purpose of detecting potential transient disturbances in API concentration in the CM process. Furthermore, analysis of T2 and Q diagnostics across the independent test runs demonstrated the robustness of the calibration model and the suitability of generating the calibration on the offline feed frame table for subsequent application to the full CM process equipment. The data demonstrated that spectra collected on the offline feed frame table were representative of spectra collected on the full CM process, demonstrating equivalency between the offline table and full CM process from a spectroscopic monitoring perspective. The data presented in this study demonstrate that the offline feed frame table can be used to enable efficient model calibration, validation, and lifecycle maintenance on multiple spectrometers across multiple sites, simplifying the effort needed to implement a spectroscopic PAT tool for continuous manufacturing applications. Furthermore, the accuracy, precision, linearity, and robustness observed when deployed on the full CM process equipment demonstrate that this type of in-line real-time PAT method may likely be suitable for eventual real-time release testing (RTRt) for relevant quality attributes that rely upon a measure of API concentration.

5. Acknowledgements The authors thank Jimmy Engle, Jeffrey D. Hofer, Salvador Garcia Munoz, Ian Leavesley, Ahmad Almaya, Wyatt Roth, Leo Manley, Jose Cintron and Nelson Sando for helpful technical guidance, assistance, and discussions related to the development of this PAT method and application.

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Figure 1. Feed frame table used for offline model calibration and tests. The feed frame table was custom designed and manufactured in the Equipment Development Group at Eli Lilly and Company. The magnified portion represents the probe head holder interface between the feed frame and the reflectance probe. The arced portion in the picture represents the probe bundle holder in order to avoid extreme bending angle.

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Figure 2. Raw (top) and SNV preprocessed (bottom) spectra for the calibration data collected on the iPAS NIR spectrometer and probe under two paddle speed conditions (15 vs. 45 rpm).

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Figure 3. Variable importance plot (VIP) plot of the PLS model built on the full wavelength range using SNV preprocessing.

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Figure 4. Comparison of the statistical prediction errors and standard deviations of the predicted API concentrations on the test set collected on the feed frame table across seven preprocessing approaches for the narrow (1626 nm to 1893 nm) and full wavelength range.

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Figure 5. Predicted residual error sum of squares (PRESS) plot (top) and correlation plot (bottom) for the optimized PLS model. The measured and predicted % theoretical concentrations for the correlation plot were entirely from the calibration data.

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Figure 6. Two-dimensional score plots for the PLS model across the three latent variables (LV). Multiple colors in the plots represent the classification by concentration (top, LV1 vs LV2), paddle speed (middle, LV1 vs LV2) and % RH (bottom, LV1 vs LV3). 19



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1st loading of the development spectrometer

3

1st loading of the manufacturing spectrometer pure API pure diluent-2 pure diluent-3 pure diluent-1

2

1

0

-1

-2

-3

-4

-5

-6 1600

1650

1700

1750

1800

1850

1900

nm

Figure 7. Spectral comparison of the loadings for the 1st latent variable (LV) of the optimized PLS model, pure API and major excipients.

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120 FF NIR

% Theoretical Concentration

A 115

Tablet NIR

110

Tablet HPLC (weightcorrected) Feeder theoretical potency

105

100

95

90

85

80

Time

120

115

B

110

% Theoretical Concentration

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105

100

95

FF NIR Tablet NIR

90

85

Tablet HPLC (weightcorrected) Feeder Theoretical Potency

80

Time

Figure 8. Concentration vs. time profiles of the test data collected from the full continuous manufacturing process equipment at low (A) and high (B) mass flow rates. Feeder theoretical potency was calculated based on the mass flow rates from individual feeders. HPLC and tablet NIR were conducted on individual tablets and serve as reference comparison to the FF NIR results.

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Figure 9. Diagnostic (top) and score plots (bottom) of the calibration data from the feed frame table and test data collected from the continuous manufacturing process. Blue dashed lines in both plots represent the 99.9% confidence intervals.

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6.

References

(1) FDA. http://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/ ucm070305.pdf, 2004, last accessed at 2017/7/28 (2) Cho, J. H.; Gemperline, P. J.; Aldridge, P. K.; Sekulic, S. S. Anal. Chim. Acta. 1997, 348, 303-310. (3) Ma, H.; Anderson, C. A. J. Pharm. Sci. 2008, 97, 3305-3320. (4) Pan, T.; Barber, D.; Coffin-Beach, D.; Sun, Z.; Sevick-Muraca, E. M. J. Pharm. Sci. 2004, 93, 635-645. (5) Popo, M.; Romero-Torres, S.; Conde, C.; Romanach, R. J. AAPS PharmSciTech 2002, 3, E24. (6) Shi, Z.; Cogdill, R. P.; Short, S. M.; Anderson, C. A. J. Pharm. Biomed. Anal. 2008, 47, 738-745. (7) Sulub, Y.; Konigsberger, M.; Cheney, J. J. Pharm. Biomed. Anal. 2011, 55, 429-434. (8) Sulub, Y.; Wabuyele, B.; Gargiulo, P.; Pazdan, J.; Cheney, J.; Berry, J.; Gupta, A.; Shah, R.; Wu, H.; Khan, M. J. Pharm. Biomed. Anal. 2009, 49, 48-54. (9) Ufret, C.; Morris, K. Drug Dev Ind Pharm 2001, 27, 719-729. (10) Liu, Y. In International Forum of Process Analytical Chemistry: Arlington, VA, 2015. (11) Almaya, A.; Belder, L. D.; Meyer, R.; Nagapudi, K.; Lin, H. H.; Leavesley, I.; Jayanth, J.; Bajwa, G.; DiNunzio, J.; Tantuccio, A. J. Pharm. Sci. 2017, 106, 930-943. (12) Liu, Y.; Blackwood, D. Am. Pharm. Rev. 2012, http://www.americanpharmaceuticalreview.com/Featured-Articles/116357-SamplePresentation-in-Rotary-Tablet-Press-Feed-Frame-Monitoring-by-Near-Infrared-Spectroscopy/, last accessed at 2017/7/28 (13) Sasic, S.; Blackwood, D. O.; Liu, A.; Ward, H. W.; Clarke, H. J. Pharm. Biomed. Anal. 2015, 103, 73-79. (14) Ward, H. W.; Blackwood, D. O.; Polizzi, M.; Clarke, H. J. Pharm. Biomed. Anal. 2013, 80, 1823. (15) Maesschalck, R. D.; Sanchez, F. C.; Massart, D. L.; Doherty, P.; Hailey, P. Appl. Spectrosc. 1998, 52, 725-731. (16) Skibsted, E. T. S.; Boelens, H. F. M.; Westerhuis, J. A.; Witte, D. T.; Smiths, D. S. J Pharm. Biomed. Anal. 2006, 41, 26-35. (17) Sekulic, S. S.; Ward, H. W.; Brannegan, D. R.; Stanley, E. D.; Evans, C. L.; Sciavolino, S. T.; Hailey, P. A.; Aldridge, P. K. Anal Chem 1996, 68, 509-513. (18) Martinez, L.; Peinado, A.; Liesum, L.; Betz, G. Eur J Pharm Biopharm 2013, 84, 606-615. (19) Mateo-Ortiz, D.; Colon, Y.; Romanach, R. J.; Méndez, R. J Pharm. Biomed. Anal. 2014, 100, 40-49. (20) Karande, A. D.; Liew, C. V.; Heng, P. W. Int J Pharm 2010, 395, 91-97. (21) Box, G. E. P.; Tyssedal, J. Biometrika 1996, 83, 950-955. (22) Lu, X.; Li, W.; Xie, M. J. Qual Technol 2006, 38, 148-161. (23) Fearn, T. NIR News 2008, 19, 16-17. (24) Bu, D. S.; Wan, B. Y.; McGeorge, G. Chemometr. Intell. Lab. 2013, 120, 84-91. 23



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Figure Legends Figure 1. Feed frame table used for offline model calibration and tests. The feed frame table was custom designed and manufactured in the Equipment Development Group at Eli Lilly and Company. The magnified portion represents the probe head holder interface between the feed frame and the reflectance probe. The arced portion in the picture represents the probe bundle holder in order to avoid extreme bending angle. Figure 2. Raw (top) and SNV preprocessed (bottom) spectra for the calibration data collected on the iPAS NIR spectrometer and probe under two paddle speed conditions (15 vs. 45 rpm). Figure 3. Variable importance plot (VIP) plot of the PLS model built on the full wavelength range using SNV preprocessing. Figure 4. Comparison of the statistical prediction errors and standard deviations of the predicted API concentrations on the test set collected on the feed frame table across seven preprocessing approaches for the narrow (1626 nm to 1893 nm) and full wavelength range. Figure 5. Predicted residual error sum of squares (PRESS) plot (top) and correlation plot (bottom) for the optimized PLS model. The measured and predicted % theoretical concentrations for the correlation plot were entirely from the calibration data. Figure 6. Two-dimensional score plots for the PLS model across the three latent variables (LV). Multiple colors in the plots represent the classification by concentration (left, LV1 vs LV2), paddle speed (middle, LV1 vs LV2) and % RH (right, LV1 vs LV3). Figure 7. Spectral comparison of the loadings for the 1st latent variable (LV) of the optimized PLS model, pure API and major excipients. Figure 8. Concentration vs. time profiles of the test data collected from the full continuous manufacturing process equipment at low (A) and high (B) mass flow rates. Feeder theoretical potency was calculated based on the mass flow rates from individual feeders. HPLC and tablet NIR were conducted on individual tablets and serve as reference comparison to the FF NIR results. Figure 9. Diagnostic (top) and score plots (bottom) of the calibration data from the feed frame table and test data collected from the continuous manufacturing process. Blue dashed lines in both plots represent the 99.9% confidence intervals.

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229x86mm (150 x 150 DPI)


Development of Near Infrared Spectroscopy-based ... - ACS Publications

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