Injun Chu jasonfamily13@yahoo.com

Evaluating the Performance of a Fiber Optic NIR Probe to Monitor the Continuous Mixing Process.

Abstract

Recently, pharmaceutical industries have searched for an efficient method to monitor the continuous manufacturing of medicinal drugs. Many techniques, such as high performance liquid chromatography (HPLC) and thermo gravimetric analysis have been attempted and found to be costly, destructive to the material, and time-consuming, as well as impractical in the continuous manufacturing process. Today in the pharmaceutical industries, products are usually made in batches, not in a continuous mixer. However, the problem with batch manufacturing is that the process becomes progressively more impractical as the scale of production increases. In this experiment, we prepared samples of varying known concentrations of an active primary ingredient and analyzed their content using the NIR probe. From this data, a non-linear regressional model (partial least square) was created and used as a model to analyze a variety of arbitrary samples in a continuous manufacturing environment. Then, “Antaris”, the industry’s standard NIR analyzer used for small quantities of particles, was used to collect samples from the continuous manufacture. Although the off-line method, which involves the use of the Antaris machine, is more precise than the online method, we concluded that the fiber optic probe is a more efficient device simply because it can be

used without stopping the manufacturing process.

Introduction

As with all commercial industries, consumer safety is of great concern in pharmaceuticals. In order to ensure that the consumers receive a proper dosage of active ingredients in a drug, concentrations must be monitored throughout the manufacture of the drug. Such monitoring is quite easy in a batch manufacturing environment, but poses the problem of speed, accuracy, and reproducibility. NIR technology could potentially provide all three of these. Some researchers have even attempted to mount an NIR probe to the top of a batch mixer [3]. However, not until recently has a method been developed that could apply this technology to a continuous process.

Scientists have tried to extract small samples during the blend development. Typically, these techniques sporadically disrupt either the state of the product or the progression of the process. For example, in HPLC, the sample must be crushed, dissolved into water, and separated using the solubility of its components, making the manufacturing process very time consuming due to the fact that each sample, which is about 3 times the unit dose, takes several hours to analyze. Likewise, thermo gravimetric analysis, which involves heating the substance in question and recording its change in

Nicholas Szamreta szamreta@optonline.net

Aleksandra Szczuka aszczuka@yahoo.com

Figure 1. The NIR Fiber-optic probe.

mass, is too costly and cannot be used on large samples, making it impossible to be used in continuous manufacture. Furthermore, the methods discussed are only efficient on meager amounts of blend, which makes it impossible to guarantee that the entire sample has the same consistency. In the late 1980’s, the solution to this problem was uncovered by Emil Ciurczak and James Drennen, who redefined the use of Karl Norris’s Near-Infrared (NIR) Spectroscopy in determining food content and applied it to pharmaceutical mixtures [4].

Drennen and Ciurczak used NIR Spectroscopy to monitor the consistency of a given sample in a batch manufacturing environment. Ciurczak was the pioneer in this field, as he used fiber optic probes and spectral analysis to determine if a given mixture was equivalent to the control. Drennen confirmed that this technique was accurate and precise by assessing the homogeneity of hydrochlorothiazide in 1996 [1]. Conversely, using spectroscopy in continuous pharmaceutical manufacturing is a virtually untested procedure, and has yet to be established as a reliable means for quality control. Nevertheless, based on

the current technology available, NIR spectroscopy is the most promising way of ensuring that consumers receive safe and reliable medicine, while still maintaining efficiency and continuity in the manufacturing environment.

NIR spectroscopy has many assets that make it the most reliable way to monitor pharmaceuticals undergoing continuous manufacture. The blend measured by the NIR probe does not require sample preparation, which means that there is no need to destroy it in order to get reliable results. Additionally, the NIR probe constantly sends NIR radiation into the flowing mixture and receives the reflection of the beam, making it very easy to measure the concentrations of various particles in a blend by using its surface directly. As a result of this, the spectroscopic probe can easily become a part of the manufacturing belt, and evaluate the entire blend sample without destroying or moving it during the process, ensuring blend uniformity [5]. NIR spectroscopy therefore proves to be the best solution to the problem posed, due to its ability to analyze with speed, accuracy, and reproducibility. It cannot be overlooked, however, that the NIR probe is a relatively new and novel device, making the purpose of this research to evaluate the performance of a fiber optic NIR probe in monitoring the continuous manufacturing process in the pharmaceutical industry. Background

A typical NIR spectroscopy setup consists of three basic elements: a light source to emit near infrared radiation, a detector to receive reflected radiation, and a diffraction grating to record the intensity of reflected light as the

Figure 2. Samples containing mixture of APAP / Avicel. Samples were all stored in these

vials to avoid contamination.

Figure 3. Antaris. This is the spectroscopic machine that was used to scan offline samples.

wavelength of the light varies [2]. The basic idea behind the probe’s mechanism relates to the bonds within the sample and the way that they vibrate. When near infrared radiation is emitted onto a sample, some of it is used up in making the molecules of a sample vibrate. In acetaminophen, which consists principally of C-H, N-H, C-O, and O-H bonds, each bond has its own unique vibrational fingerprint [3]. Based on how much light is reflected at a certain wavelength, one can determine the degree of vibration. Upon varying concentrations of these molecules, the

degree of vibrations will vary, as will the intensity and the wavelength of the light reflected, creating a spectral graph with wavelength (or wave numbers, which are simply the inverses of wavelength) as the x-coordinates and intensity of light as the y-coordinates. The resulting graphs show the spectrum in the form of the molecular vibration absorbance bonds and peaks. This spectrum is then analyzed using a multivariate calibration set, which consists of a set of samples of various known concentrations [6].

There are two main methods of NIR analysis used in the pharmaceutical industry: the online method, and the offline method. The online method is a probe that can be mounted directly on the manufacturing line. This method is able to collect spectrums while powder is flowing through a system. The offline method consists of a machine in which static samples are analyzed. The name is derived from the fact that the samples must be collected from the manufacturing line, and then analyzed off of the line. This requires the use of batches, or samplings of mixture, which take more time to scan. Therefore, the online method is considered more efficient, as it can readily test mixtures

Figure5. The online setup for scanning the blend with the NIR probe

Figure 4. Small scale continuous manufacturing setup.

without the waste of essential time. Experimental Design The mixture we used in this experiment to test the NIR technology was a binary mixture of acetaminophen (APAP) and Avicel-102 (microcrystalline cellulose). However, before the NIR equipment could be tested on arbitrary samples of powder, a multivariate calibration set had to be prepared. This set simply consisted of 5 gram samples of powder, varying from 0 to 10 percent of APAP. Data was taken from these samples by scanning a small amount of them under an NIR non-contact probe, while making sure that

the surface of the powder was flat. Four scans of each calibration sample were taken to account for variances on the surface of the powder using a chemometric software called OMNIC. A regressional model known as partial least square used on three of the scans to arrange the data in a linear fashion. (Appendix B) The purpose of the fourth calibration scan was to validate the results of the linear model. Scans of the calibration samples were also taken using the offline method (Antaris). This method of analysis, unlike the online method, does not require the samples to be taken out of their containers.

Assuming the calibration models are accurate, they can be used comparatively to analyze other binary samples consisting of APAP and Avicel-102. Various experimental scans were taken in a continuous manufacturing environment. For each experiment, a specific variable was changed, such as the number of scans, the concentration of APAP, the mixing speed, and the flow rate. To begin each experiment, the two feeders at the top of the manufacturer were loaded with APAP and Avicel-102 respectively. The flow rate of each feeder was then adjusted to provide for

Figure 6. A vacuum is typically used to ensure that powder in the air does not

contaminate blends.

the desired concentration for each component. Each experiment began with a start-up time of two minutes to allow the process to become steady. After this period, samples were taken every eight seconds until the end of the test to be tested offline at a later time.

Using this experimental method, two experiments were done at the 3% APAP concentration level in order to determine which gave more precise data; one with 8 total scans from the online probe, and the other with 16 total scans. In the next experiment, the flow rate of APAP was increased, while the flow rate of Avicel-102 was decreased proportionally to yield a concentration of 7% APAP. During this time the number of scans taken per minute did not vary. In the subsequent experiment, the mixing speed was changed from 160 rpm to 250 rpm in order to see whether it affected the performance of the probe. Similarly, in another experiment, the researchers changed the flow rate of both mixers proportionally in order to

increase the overall flow rate without changing the overall concentration. Finally, the angle of the chute was changed.

In order to determine the sample size of powder being scanned by the probe each time, many tests were conducted. The first test was to measure the bulk density of the used powder (as opposed to the tap density). This is done by pouring the powder into a graduated cylinder, recording the volume as well as the mass of the powder, thus yielding density. By multiplying this density by the volume of powder passing through the chute, we determined the size of the sample. In order to determine the volume of sample passing through the chute, the diameter of the spot of light emitted by the probe was taken, and multiplied by the length the chute, as well as by the penetration of the light, which is assumed to be 1 mm. This volume can then be used in conjunction with the density to yield the mass of the sample for each scan taken by the probe. Evaluation of both the online and offline methods of NIR spectroscopy required the researchers to use a variety of analytical techniques. For example, the standard deviation of the data is found as follows:

where S is standard deviation, n is the number of pieces of data, c is an arbitrary piece of data in the set, and

c is the mean of the data. This value gives a general idea of the precision or variance of each piece of data.

1

)(1

2

−

−=∑

=

n

cc

S

n

i

n

c

c

n

ii∑

== 1

Figure 7. Comparison between the spectra of pure APAP and various APAP/Avicel mixtures. Note the change of peak position and height.

On the other hand, the mean of the data, is found as follows:

where c is the mean, c is an arbitrary piece of data, and n is the number of pieces of data. The mean gives a general idea of how accurate the data is overall. However, the best indicator of variance for a situation like this is Relative Standard Deviation, which combines the above distributions. RSD can be defined by the following:

We chose to use RSD not only because it is an effective measure of

precision, but also because it effectively assesses the repeatability of a result. Repeatability is essential in an experiment such as this one, because it assures that the equipment being utilized can continue to be used just as effectively.

Results (Figures located in Appendix A)

All of the calibration scans taken using the fiber optic probe need to be analyzed since their precision is essential to the rest of the experiments. The root mean squared error (appendix B) for the model predicted by the software TQ Analyst was .412. This is then compared to the root mean square error of the actual model, which was .625. Because the two do not differ by much, it is safe to assume that the predictive model is accurate (Figures 8 and 9). The first two experiments at 3% APAP dealt with the impact of the total number of scans taken on the effectiveness of the online method. Data taken with 16 total scans from the online method had a relative standard deviation

c

SRSD =

of .044, whereas similar data taken with 8 total online scans had a relative standard deviation of .15. This data indicates that the experiment in which more scans were taken, there was much less variance. Typically, a smaller degree of variance is good, because it indicates precision. The next experiment was meant to show how changes in concentration would affect the performance of both the online and offline method. When concentration was changed from 3% to 7%, the offline method experienced a decrease in RSD (from .13 to .07). This is an expected trend, as an increase in concentration should provide more to detect within the sample, thus decreasing the variance. However, the online method of analysis did not report a similar trend. Instead, the RSD went up from .044 to .057 when the concentration was increased. This may indicate that the fiber optic probe is not as reliable at higher concentrations. However, the difference in variation between 3% and 7% with the online method was only .013. Because commercial products containing APAP usually do not contain more than 9% acetaminophen, this change is not of great significance (Figure 10). Further experimentation evaluated the effect of changes in mixing speed and flow rate on the data collected by both the online and offline methods. When flow rate was increased for both components (total flow rate was increased from 30kg/h to 45kg/h), the Relative Standard Deviation of both the online and offline methods increased dramatically (online changed from .044 to .15 while offline changed from .13 to .16) (Figures 11 and 12). Despite a great increase in variability, this trend was predicted by the

researchers. A greater flow rate signifies that the powders spend less time in the mixer, meaning that different parts of the mixture are more likely to have different concentrations, thus increasing the variability. This result was not only predicted, but also consistent between the online and offline methods, suggesting that the relative standard deviation went up due to the uneven mixing of powder and not because of flaws in either piece of equipment. Changes in mixing rate provide an eerily similar scenario. Once again, both the online and offline methods saw substantial changes in relative standard deviation when compared to the baseline scans. The online method saw a change from .044 to .17, while offline saw a change from .13 to .30. The change is justified because increasing the speed of the mixer causes more contact between powder molecules. Because all of the particles in the blend are of different sizes, this can greatly detract from the uniformity of the blend. As with the flow rate experiment, any changes in variance were consistent between the online and offline methods. (Figures 13 and 14) The final experiment involved finding the mass of each sample that was scanned by the probe during continuous manufacturing. By utilizing the density of the 3% mixture, the diameter of the light from the probe, the length of the chute, and the penetration of the light, the mass of each sample was determined to be about 12 grams. As far as pharmaceutical testing goes, this number is quite high. Typically, such analysis is done with samples that are no more than three times as large as the dosage of the drug. Most commercial tablets containing APAP are less than

one gram of medication. Therefore, an ideal sample size would be about three grams. However, sample size will always be relatively high when dealing with the continuous manufacturing of a drug. Unlike batch manufacturing, in which only a finite sample can be taken from the mixture, the sample size in a continuous mixer is dictated strictly by the flow rate of the powder. In other words, small sample sizes, like those used during batch manufacturing, cannot be achieved in a continuous mixer without reducing the flow rate of the blend. Related Work We found our work to be in accordance with a few other works, as well as having a few unique aspects. For example, in his paper entitled Analytical Control of Pharmaceutical Production Steps by Near Infrared Reflectance Spectroscopy, Marcelo Blanco analyzes the flexibility of the fiber optic probe, as well as its ability to analyze a sample in a non-contact fashion. He too concluded that NIR spectroscopy is a valuable tool in the pharmaceutical industry, as it is relatively accurate, and can analyze without manipulation of the sample. He also stresses the speed of the technology, but does not mention how it can be applied to continuous manufacturing [2]. This is most likely because Blanco published his paper in 1999, several years before continuous manufacturing became popular in the early part of the 21st century. Conclusion

From varied experimentation with the NIR fiber-optic probe, we have concluded that the probe works well

under conditions that resemble commercial continuous manufacturing. The online method used during continuous manufacture saves time compared to batch processing because batch processing requires scaling in order to manufacture in a larger manner. That poses great risks because changes must be made to the method of manufacture. The probe gets rid of the problem by directly monitoring a large-scale process. Also, spectroscopy is more economical due to its one-time cost and ability to analyze a sample without destroying it. Once installed, the probe needs minimal maintenance, which saves frustration and effort. This made the experiments efficient, reliable, and reproducible.

Further changes were done to the control setup to test the performance of the probe at different conditions. The flow rate and mixing speed were increased in order to see how the probe performed under critical conditions. Generally, both changes increased the relative standard deviation. Out of all the changes, the increase in the flow rate affected the relative standard deviation the most. Thus, the result shows a limit in the performance in the probe. But at the same time, the probe is able to get accurate percentages of samples while running the mixing process.

Also, when the sample size was found using the velocity and the volume of the flowing powder, we found that the sample size was too large to be a unit dose. However, large sample sizes are a drawback attributed to continuous manufacturing, not to the NIR probe.

However, we also found that once the percentage goes out of certain range, the on-line method loses its accuracy. For example, if the percentage is too low, the probe is unable to get

accurate results, since less light will be able to get reflected. Although the off-line method is more precise than the on-line, the on-line is more useful, since industries will be able to monitor the actual process rather than broken parts of the whole. Therefore, the use of these probes in pharmaceutical industries will be very useful, since it will allows the surveillance of the mixing process effectively before production of the tablets. It should replace the batch manufacturing method, since the use of the NIR fiber-optic probe allows manufacturers to monitor continuous manufacturing process, which is more large-scale than the batch method. Acknowledgements

The researchers would like to

thank Aditya Vanarase, their project advisor, for all of his guidance and knowledge throughout the experimental process. They would also like to thank their lab technician Rocio Arroyane for her help with some of the experiments. The researchers would also like to express their gratitude towards their counselors Daniel Cobar and Kenneth Wasserman for escorting them to and from the lab as well as providing moral support when necessary. Special thanks to Allanah Miller for her guidance and expertise in writing. Also, they would like to thank Professor Fernando Muzzio for allowing them to perform the experiments. The researchers also thank the Governor’s School program sponsors: Rutgers University, the Rutgers University School of Engineering, the Motorola Foundation, Morgan Stanley, PSEG, Silver Line Building Products, and the families of 2001-2008 program alumni. Finally, the

researchers would like to thank The NJ Governor's School of Engineering and Technology (Donald M. Brown, Director, and Blase Ur, Program Coordinator), the Rutgers University School of Engineering (Dr. Yogesh Jaluria, Outgoing Interim Dean, and Dr. Thomas Farris, Dean), and the NJ Governor's School Board of Overseers.

References 1. Blanco M., Coello J., and Iturriaga H. (August 1998) “Near-infrared spectroscopy in the pharmaceutical industry” The Analyst 135-150. 2. Blanco M., Eustaquio A., Maspoch S. (1999) “Analytical control of pharmaceutical production steps by near infrared reflectance spectroscopy” Analytica Chimica Acta 237-246. 3. Burns Donald A., and Ciurczak Emil A. (2007) “Handbook of Near-Infrared Analysis” 3rd ed., CRC Press. Retrieved July 17, 2009. 4. Davies T. (1998) “The history of near infrared spectroscopic analysis: Past, present and future- ‘From sleeping technique to the morning star of spectroscopy’” Proche Infrarouge 17-19. 5. Koehler F. (2002) “Near infrared spectroscopy: the practical chemical imaging solution” NIR Spectroscopy 13-19. 6. Li W. and Worosila G. (2005) “Quantitation of active pharmaceutical ingredients and excipients in powder blends using designed multivariate calibration models by near-infrared

spectroscopy.” International journal of pharmaceuticals 213-219. 7. Luyapert J. (2007) “Near-infrared spectroscopy applications in pharmaceutical analysis” Talanta 865-883. 8. Treado P., Levin I., and Lewis E. (1992). "Near-Infrared Acousto-Optic Filtered Spectroscopic Microscopy: A Solid-State Approach to Chemical Imaging". Applied Spectroscopy: 553-559. 9. Tobias, Randall D. “An Introduction to Partial Least Squares Regression” Retrieved July 20, 2009 from <http://ww.ats.ucla.edu/stat/sas/library/pls.pdf>

Figures 8 and 9. The spectrum for varying concentrations of APAP (above). The peaks were analyzed and re-graphed in a linear fashion (below).

Appendix A. Result figures

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8 Scans_3% 16 Scans_3% 16 Scans_7%

RSD=0.057

RSD=0.044

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Figure 10. A graph showing % APAP detected during continuous scan. Shows relative standard deviation based on the contrasts in different frequencies of scans and different concentrations.

Offline Flow Rate

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Figure 11 and 12. Effect of flow rate on the online (above) and offline (below) methods.

Online Flow Rate

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Figure 13. Effect that the mixing speed has on the offline method of analysis.

Mixing Speed Online

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Figure 14. Effect that the mixing speed has in the online method of analysis.

Figure 15. Calibration data as collected by the offline method.

Appendix B – Math ~Partial Least Squares – A regressional data analysis technique that takes a multivariate set of known factors and uses it to predict what other relative values may be [9]. For example, we, during our research, prepared eleven known combinations of two components (APAP and Avicel). The multivariate set of data for these calibration samples consisted of concentration, wavelength, and intensity of light reflected. Our chemometric software then used partial least squares to create a linear model for wavelength and light intensity for all concentration bands. ~Root Mean Squared Error – Root mean squared error is used to express the deviation of a predictive model from what is actually observed. In our experiment, the chemometric software used RMSE as an assessment of the accuracy of the predictive model. The software determines this by relating the validation points (fourth scan of each calibration sample) to the calibration points (first three scans of each calibration sample).