Journal of Pharmaceutical and Biomedical Analysis 89 (2014) 176–182
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Reflectance infrared spectroscopy for in-line monitoring of nicotine during a coating process for an oral thin film Florian Hammes a,b,∗ , Thomas Hille a , Thomas Kissel b a b
LTS Lohmann Therapie-Systeme AG, Andernach, Germany Department of Pharmaceutics and Biopharmacy, Philipps University Marburg, Germany
a r t i c l e
i n f o
Article history: Received 19 September 2013 Received in revised form 28 October 2013 Accepted 31 October 2013 Available online 15 November 2013 Keywords: Process analytical technology (PAT) In-line monitoring Active pharmaceutical ingredient (API) determination Oral thin film (OTF) Reflectance infrared spectroscopy
a b s t r a c t A process analytical method using reflectance infrared spectrometry was developed for the in-line monitoring of the amount of the active pharmaceutical ingredient (API) nicotine during a coating process for an oral thin film (OTF). In-line measurements were made using a reflectance infrared (RI) sensor positioned after the last drying zone of the coating line. Real-time spectra from the coating process were used for modelling the nicotine content. Partial least squares (PLS1) calibration models with different data pre-treatments were generated. The calibration model with the most comparable standard error of calibration (SEC) and the standard error of cross validation (SECV) was selected for an external validation run on the production coating line with an independent laminate. Good correlations could be obtained between values estimated from the reflectance infrared data and the reference HPLC test method, respectively. With in-line measurements it was possible to allow real-time adjustments during the production process to keep product specifications within predefined limits hence avoiding loss of material and batch. © 2013 Elsevier B.V. All rights reserved.
1. Introduction Over the past few years the use of process analytical technology (PAT) to monitor and control pharmaceutical unit operations has received increased attention. PAT is a key element of the “Pharmaceutical Current Good Manufacturing Practices (cGMPs) for the 21st Century-a Risk Based Approach” initiative announced by the Food and Drug Administration (FDA) to improve and modernize pharmaceutical manufacturing [1]. The FDA considers PAT as a system for designing, analysing and controlling manufacturing through timely measurements of critical quality and performance attributes of raw and in-process materials and processes. The goals of the PAT framework include real-time monitoring and control of manufacturing processes based on the knowledge generated in development or implementation and designing well-understood processes that will consistently ensure a predefined quality [2,3]. Process analysers are indispensable PAT tools for real-time process monitoring and control as they supply data from relevant processes. Tools are available for univariate process measurements such as pH, temperature and pressure and those that provide multivariate information related to biological, physical and
∗ Corresponding author at: Pharmaceutical Development, Research & Development, LTS Lohmann Therapie-Systeme AG, Lohmannstr. 2, D-56626 Andernach, Germany. Tel.: +49 (0) 2632 99 2767; fax: +49 (0) 2632 99 1767. E-mail address: [email protected] (F. Hammes). 0731-7085/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.jpba.2013.10.047
chemical attributes of the materials being processed, e.g. a reflectance infrared sensor. In a PAT environment real-time process measurements can be • At-line: measurements where the sample is removed, isolated from and analyzed in close proximity to the process stream. • On-line: measurements where the sample is diverted from the manufacturing process and may be returned to the process stream. • In-line: measurements where the sample is not removed from the process stream [2,4].
Following aspects should be considered when implementing process analysers into relevant process streams: • selection of a suitable process analyser or combination of complementary analysers able to monitor the desired critical process and product information • determination of the locations in the process stream where and how process analysers should be and can be implemented to monitor required information • determination of the optimal measurement conditions for the process analyser to obtain useful data • validation of the performance of process stream analysers [4]
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The main advantage of process analysers against off-line analysis in analytical laboratories is the possibility for continuous in-process monitoring and the possibility of real-time quality control. The examined product of the present study was a nicotine containing single-layer laminate as an intermediate product for an oral thin film (OTF). Oral thin films, also called oral wafers, wafers or strips, are flat and edible films for the oral or transmucosal delivery of APIs. OTFs are placed on the patient’s tongue or any oral mucosal tissue. Instantly wetted by saliva the film hydrates and adheres onto the site of application. After application the OTF rapidly disintegrates and dissolves to release the medication for oromucosal absorption or will maintain the quick-dissolving aspects allowing for gastrointestinal absorption to be achieved when swallowed. The first pass effect can be avoided by oromucosal absorption, so a reduction in the dose is possible which can potentially lead to reduction of side effects associated with the molecule or metabolites, respectively [5–7]. The matrix system of an OTF is based on hydrophilic and/or lipophilic polymers and contains the active pharmaceutical ingredient (API) in amorphous, liquid or crystalline phase. Further compounds of an oral thin film formulation are usually plasticizers, saliva stimulating agents, surfactants, sweetening agents, flavours, colours or fillers [8]. The manufacturing process of an OTF typically consists of three steps: preparation of mass solution, coating and drying and fabrication. The drug containing mass is coated on a release liner for proper wetting and handling. During the coating process water and/or process solvents are evaporated in a drying tunnel. Drying locks the API within polymer matrix in desired state. During fabrication the dried film is processed to desired dosage size by slitting and pouching into appropriate packaging material. To implement process analysers for real-time monitoring of the manufacturing process of an OTF critical quality attributes (CQAs) that are being affected by critical process parameters (CPPs) of the considered process step have to be identified [3]. CQAs are physical, chemical, biological or microbiological properties or characteristics that should be within an appropriate limit, range or distribution to ensure the desired product quality. CQAs are generally associated with the drug substance, excipients, intermediates (in-process materials) and drug product [9]. In coating and drying operations of oral thin films CQAs that could be monitored include product coat weight and drug/excipient identification and/or assay. CPPs which could be monitored are drying temperature, dryer air flow/exhaust, line speed and or pump speed of the mass. With suitable process analysers it is possible to monitor and operate in real-time the impact of CPPs on CQAs. To obtain a homogeneous drug containing laminate after the coating and drying process with regard to the nicotine content, it is important that the laminate is as homogeneous as possible in terms of coat weight, moisture and residual solvents and API content to fulfil the requirements of the European Pharmacopoeia in the monograph “Uniformity of dosage units”[10]. With the current in-house in-process control (IPC) the coat weight is determined gravimetrically at the beginning and at the end of the coating process of each master roll. It is in general possible to take more in-process control samples with the current gravimetric determination, however sampling means destruction of the material and requires interruption of the process. Since starting of the coating process after stopping bears a certain risk towards the quality, stopping the process should be avoided whenever possible. The nicotine content is controlled by the surrogate parameter coat weight of the laminate. The real nicotine content is tested on the single oral thin film after fabrication. Even after drying of the water and ethanol based liquid mass the laminate contains a significant amount of residual water and residual solvents which contributes to the coat
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Table 1 Ingredients of the coating mass. Ingredients Nicotine Methacrylic acid–ethyl acrylate copolymer 1:1, Type A Triethyl citrate Peppermint flavour Sodium hydrogen carbonate Aqua purificata Ethanol Sucralose
weight. Therefore, the correct potency which corresponds to a coat weight of the drug containing laminate is not easily determined [11]. Objective of the present study was to develop a process analytical method for monitoring in-line the CQA nicotine content during the coating and drying process of a single-layer laminate for an oral thin film independent of the current gravimetrically in-process control. There are studies about using near infrared spectroscopy in the field of the pharmaceutical application of oral thin films [12–16] but no studies were reported using reflectance infrared spectroscopy for in-line monitoring of an API during the coating and drying process of an oral thin film. By using reflectance infrared spectrometry a process analytical method was developed which enables, without destroying the laminate, the monitoring and determination of the nicotine content during the coating and drying process independent of the currently performed gravimetrically in-process control. With continuous gathering of real-time data it is possible to allow real-time process adjustments to keep the nicotine content within predefined limits and hence decrease loss of material and batch. 2. Materials and methods 2.1. Production process mass preparation For calibration and validation purposes a mass batch was manufactured with the ingredients listed in Table 1. In the first production step a pre-mix solution consisting of aqua purificata (VWR International GmbH, Darmstadt, Germany), sodium hydrogen carbonate (Merck KGaA, Darmstadt, Germany) and sucralose (Merck KGaA, Darmstadt, Germany) was manufactured. Aqua purificata was weighed in portions into a transportable stainless steel container and sodium hydrogen carbonate was transferred under stirring. After stirring for an appropriate time sucralose was weighed in and the pre-mix solution was stirred. For the main solution ethanol (Merck KGaA, Darmstadt, Germany) and triethyl citrate (Merck KGaA, Darmstadt, Germany) were weighed in a stainless steel stirring container and were stirred. Subsequently methacrylic acid–ethyl acrylate copolymer (Evonik Industries AG, Essen, Germany) was transferred slowly in portions into the solution while agitating. After transferring the copolymer peppermint flavour was weighted in. The pre-mix solution was dosed by using a special pump. Nicotine (Siegfried AG, Zofingen, Switzerland) was weighted in a separate stainless steel container und was transferred into the solution under stirring. After stirring the Nicotine containing mass was purged with nitrogen. Half of the produced batch size was used for generating the calibration samples. The remaining amount was used for the external validation run. 2.2. Production process coating and drying To create a quantitative spectrometric determination model of nicotine and for the external validation of the chosen calibration
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Fig. 1. Schematic presentation of the coating line and the drying profile.
model two independent laminates with different coat weights were manufactured. In correlation to the changing coating weight different levels of nicotine were obtained. The coating process was performed on a production coating line (Olbrich GmbH, Bocholt, Germany) consisting of four drying zones and a total drying tunnel length of approximately 6 m. With defined heating plates and air nozzles in respective drying zones it was possible to set up a predefined drying profile. The drug containing mass was transferred from a stainless steel stirring container into the coating head of the coating line. The coating head consists of two rollers, one fixed roller and one moveable roller which enable different gap set ups of the coating head. The drug containing mass was coated on a polyethylene terephthalate (PET) foil 100 m (Mitsubishi Polyester Film GmbH, Wiesbaden, Germany) with a defined coating speed. To achieve different coat weights, different gaps of the coating head were set up. The reflectance infrared sensor was positioned at the end of drying zone 4 in the middle of the coated and dried laminate. Fig. 1 shows a general scheme of the used production coating line and the drying profile of the drying channel. 2.2.1. Collecting of calibration samples For collecting calibration samples six different gap set ups of the coating head were used. From each gap set up five real-time spectra during the coating process were taken. After recording one spectrum a 6.51 cm2 sample was punched out from the laminate with a die-cutting tool and was immediately sealed in a pre-manufactured pouch. In total 30 spectra were recorded. All samples were weighted and the nicotine content from each sample was determined according to the reference HPLC test method. Table 2 shows the weight of the calibration samples and the corresponding nicotine content. 2.2.2. Collecting of validation samples The spectrometric model which was chosen to be validated by an external validation run was uploaded in the test and measurement lab software of the reflectance infrared (RI) sensor. The model was used for monitoring in-line the nicotine content during the whole coating and drying process of the validation run. From the spectrometric software a data file with predicted data of the nicotine content for the chronological sequence of the production process of the validation run was generated. For validation purposes like the calibration run six different gap set ups were used. From each gap set up four validation samples were taken. For each validation sample a 6.51 cm2 sample was punched out with a diecutting tool and was immediately sealed in a pre-manufactured pouch. The time of sampling was noted. In total 24 validation
samples were gained. All samples were weighed and the nicotine content from each sample was determined according the reference HPLC test method. 2.3. Analytical methods 2.3.1. Reflectance infrared measurements A reflectance infrared (RI) sensor from Honeywell (RIS 3-4810, Honeywell International Inc., Morristown, USA) was used to record real-time spectra during the calibration process and for generating real-time data during the validation run. The system was operated by the RI manufacture test and measurement lab software from Honeywell. The spectra were recorded over a spectral range of 1600–4100 nm with a spectral resolution of 20 nm and a measurement speed of 100 Hz. The reflectance infrared sensor is measuring absorption bands in the near infrared region 1600–2500 nm related to overtones and combinations of fundamental vibrations of CH, NH, OH (and SH) functional groups and absorption bands of fundamental vibrations in the middle infrared region 2500–4100 nm due to CH, NH, OH (and SH) stretching [17,18]. Measurement principle of the sensor is transflectance. Infrared radiation penetrates the laminate, is partially absorbed and partially reflected. Light which cross the laminate is reflected by a diffuse gold standard which is positioned behind the coated laminate. The light cross the laminate again und the sensor measures the diffuse reflected light of the sample. For measurement purposes real-time spectra were taken from the process stream. 2.3.2. HPLC reference test method for nicotine content Calibration and validation samples with a defined area of 6.51 cm2 were punched out of the examined laminate area and were immediately sealed in single pre-manufactured pouches. The weight of each sample was noted and all samples were analyzed according the reference in-house HPLC test method to gain the real value for nicotine content. 2.3.3. Chemometric data analysis of calibration models All multivariate calibrations for quantification of the nicotine content were performed with the Chemom Model tool software from Honeywell. Partial least squares (PLS1) models were developed according to the following approach: All spectra from the calibration run were selected for generating calibration models. Real values for the nicotine content were calculated by the reference HPLC test method. Models based on no data pre-treatment and different data pre-treatments were developed. Spectral data
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Table 2 Weight of the calibration samples and the corresponding nicotine content. Calibration spectrum no.
Weight (mg/6.51 cm2 )
Nicotine (mg/6.51 cm2 )
Calibration spectrum no.
Weight (mg/6.51 cm2 )
Nicotine (mg/6.51 cm2 )
1st set up 1 2 3 4 5
61.29 60.55 61.41 60.49 61.35
2.472 2.432 2.452 2.414 2.438
4th set up 16 17 18 19 20
60.14 60.47 60.67 60.21 60.35
2.395 2.406 2.373 2.414 2.408
2nd set up 6 7 8 9 10
64.17 62.87 62.92 63.45 63.2
2.545 2.488 2.492 2.515 2.507
5th set up 21 22 23 24 25
58.21 57.7 57.91 58.06 57.81
2.328 2.312 2.320 2.323 2.312
3rd set up 11 12 13 14 15
66.27 66.35 66.9 67.03 66.38
2.612 2.608 2.628 2.632 2.613
6th set up 26 27 28 29 30
54.74 54.22 54.54 54.88 54.77
2.213 2.188 2.197 2.211 2.204
were pre-treated with multiplicative scatter correction (MSC), first derivative (1st) and combinations of those. All constructed calibration models were internal validated by cross-validation. As quality criteria of the constructed calibration models the standard error of calibration SEC and the standard error of cross validation SECV using Eq. (1) were used. The optimum number of PLS factors to be included in the model was indicated by a minimum and comparable SEC and SECV [19,20]. In addition to the calculation of SEC and SECV the explained variation of x data (analytical data) and y data (spectral data) of the PLS factors was analyzed. Also R-squared (R2 ) values are given which correspond to the coefficients of determination. The calibration model with the most comparable SEC and SECV was chosen for an external validation run on the production coating line with an independent laminate.
SE =
n ( i=1 i
− ˆ i )
2
n−1
(1)
Eq. (1) Standard error (SE) with yi : nicotine content calculated by the HPLC reference test method, yˆ i : nicotine content predicted by the calibration model 2.3.4. Data analysis of validation samples To evaluate the quality of prediction of the examined spectrometric model the time of sampling for every validation sample was noted. With the chronological data file generated by the spectrometric software and the noted time of sampling the nicotine content predicted by the calibration model could be compared with the real nicotine content calculated by the reference HPLC test method. The standard error of prediction SEP using Eq. (1) was calculated and was compared with the SEC and SECV of the model. In addition R-squared (R2 ) values are given. 3. Results
Table 3 Quantities of materials used for the manufacturing of masses under lab scale conditions. Mass
Nicotine content (g)
Methacrylic acid–ethyl acrylate copolymer 1:1/ethanol 47% (g)
1 2 3 4 5 6
0 1 2 3 4 5
25 24 23 22 21 20
The masses consist of nicotine and methacrylic acid–ethyl acrylate copolymer 1:1 solved in ethanol. The detailed quantities of substances used for preparation are given in Table 3. In the first manufacturing step methacrylic acid–ethyl acrylate copolymer was weighted in a stirring container. After transferring Nicotine the solution was stirred for 20 min. Each mass was coated with a 600 m coating knife on a polyethylene terephthalate (PET) foil 100 m (Mitsubishi Polyester Film GmbH, Wiesbaden, Germany) and was dried in a drying chamber for 15 min at 60 ◦ C. The coat weight of each laminate was nearly constant in a range of ±2 g/m2 . For qualitative issues from each laminate a 10 cm2 sample was punched out with a die cutting tool and a spectrum was taken. All spectra were pre-processed with the data pre-treatment combination of first derivative (1st) and multiplicative scatter correction (MSC). The regression coefficients for nicotine in the spectral region 1600–4100 nm were calculated. The spectral region between 2250 and 2761 nm was identified to be significant for nicotine. This result leads to the conclusion that nicotine can be quantified by reflectance infrared spectroscopy. Fig. 2 shows the recorded spectra pre-treated with the combination of 1st and MSC and the regression coefficients of nicotine. The Chemom Model tool software indicates the spectral range 1600–4100 nm on the x-axis as wavelength channels 1–127. Each wavelength channel equates 19.69 nm.
3.1. Applicability of reflectance infrared spectroscopy for the quantification of nicotine in oral thin films
3.2. Reflectance infrared calibration models
To assess the feasibility of a quantitative spectrometric determination model of nicotine six masses with a defined concentration range of nicotine were manufactured under lab scale conditions to identify the characteristic spectral region for nicotine. For measurement purposes the same reflectance infrared sensor from Honeywell was used as for the trials on the production coating line.
For calibration purposes all spectra from the calibration run were used. In total 5 calibration models based on the PLS1 algorithm were designed. Models based on no data pre-treatment, multiplicative scatter correction (MSC), first derivative (1st) and combinations of those were generated. In addition to the calculation of the SEC and SECV the explained variation of the x data
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Fig. 2. Recorded spectra of the laboratory test pre-treated with the combination of 1st and MSC and calculated regression coefficients for nicotine.
Table 4 Parameters of calibration models. Model no.
Data pre-treatment
Spectral region (nm)
SEC (mg/6.51 cm2 )
SECV (mg/6.51 cm2 )
R2 C
R2 CV
No. of PLS factors
1 2 3 4 5
None MSC 1st 1st + MSC MSC + 1st
1600–4100 1600–4100 1600–4100 1600–4100 1600–4100
0.0176 0.0172 0.0152 0.0147 0.0145
0.0200 0.0176 0.0176 0.0161 0.0157
0.983 0.984 0.988 0.988 0.989
0.978 0.983 0.983 0.986 0.986
2 1 1 1 1
Bold values are parameters/values for calibration model no.2 mentioned in Section 3.2. Table 5 Explained variation of the x and y data of the PLS factors. Model no.
Data pre-treatment
Spectral region (nm)
Explained variation of x data (%)
Explained variation of y data (%)
No. of PLS factors
1 2 3 4 5
None MSC 1st 1st + MSC MSC + 1st
1600–4100 1600–4100 1600–4100 1600–4100 1600–4100
98.3 98.4 98.7 98.8 98.9
99.6 95.7 77.7 84.9 84.6
2 1 1 1 1
Bold values are parameters/values for calibration model no.2 mentioned in Section 3.2.
(analytical data) and y data (spectral data) of the PLS factors was analyzed to support the right selection of the calibration model for an external validation run. Tables 4 and 5 give an overview of the constructed models including the standard error of calibration SEC and the standard error of cross validation SECV and the explained variation of the x and y data of the PLS factors. Calibration model no. 2 with MSC as data pre-treatment shows the most comparable SEC and SECV with 0.0172 and 0.0176 mg/6.51 cm2 . The deviation between the calculated SEC and SECV was the lowest. The calibration model showed the best result for the explained variation of the y data for the first PLS factor with 95.7%. The ratio between explained variation of the x and y data was the best. The most intense regression coefficients identified in the MSC pre-treated model corresponding to the nicotine content were in the spectral region between 2289 and 2722 nm (Fig. 3). Compared to the spectral region 2250–2761 nm from the laboratory test it could be stated that nicotine can be monitored with the generated model. Calibration model no. 2 was chosen for an external validation run on the production coating line with a separate produced laminate. 3.3. Validation results of calibration model With an external validation run the calibration model with MSC as data pre-treatment was validated. For estimating the standard error of prediction SEP the deviation between the real nicotine
content and the predicted nicotine content for all 24 validation samples was calculated. Table 6 shows the calculated deviations. Fig. 4 summarizes these data graphically. The highest deviation was 0.030 mg/6.51 cm2 for validation sample 21, the lowest was −0.027 mg/6.51 cm2 for validation sample 9. The calculated standard error of prediction SEP was estimated with 0.0175 [mg/6.51 cm2 ] and is comparable with the standard error of calibration SEC and standard error of cross validation SECV with 0.0172 and 0.0.176 [mg/6.51 cm2 ]. R2 for the validation run was 0.981. A paired t test was used to check whether the predicted values and the real values were significantly different. Table 7 shows the results of the test; as can be seen, texp was smaller than ttab , so the predicted values and reference values were not significantly different. A correct predictive ability for the MSC pre-treated calibration model could be confirmed. 4. Discussion The present study applied for the first time reflectance infrared spectroscopy for in-line monitoring of the CQA nicotine content during the coating process for an oral thin film. With a feasibility study under lab scale conditions the general applicability of reflectance infrared spectroscopy for the quantification of nicotine in a methacrylic acid-ethyl acrylate copolymer matrix was successfully demonstrated [21]. The spectral region between 2250 and 2761 nm was identified to be relevant for the determination of the nicotine content in OTF. Using real time data
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Table 6 Real values for the nicotine content of the validation samples and deviations (real nicotine content-predicted nicotine content by calibration model 2). Validation sample 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Real nicotine content y (mg/6.51 cm2 ) 2.558 2.519 2.553 2.548 2.458 2.493 2.473 2.456 2.409 2.439 2.380 2.418 2.357 2.346 2.359 2.337 2.323 2.301 2.308 2.312 2.293 2.272 2.297 2.284
Predicted nicotine content yi (mg/6.51 cm2 ) 2.561 2.510 2.560 2.540 2.473 2.497 2.486 2.473 2.436 2.445 2.401 2.442 2.333 2.333 2.332 2.323 2.303 2.287 2.291 2.285 2.263 2.264 2.277 2.269
Deviation y − yi
Weight sample (mg/6.51 cm2 )
(mg/6.51 cm2 )
(%)
−0.003 0.009 −0.007 0.008 −0.015 −0.004 −0.013 −0.017 −0.027 −0.006 −0.021 −0.024 0.024 0.013 0.027 0.014 0.020 0.014 0.017 0.027 0.030 0.008 0.020 0.015
−0.12 0.36 −0.27 0.31 −0.61 −0.16 −0.53 −0.69 −1.12 −0.25 −0.88 −0.99 1.02 0.55 1.14 0.60 0.86 0.61 0.74 1.17 1.31 0.35 0.87 0.66
63.44 63.44 64.56 64.25 62.26 62.15 61.94 60.64 60.46 60.44 58.31 60.97 58.53 58.60 59.19 58.21 58.01 57.03 58.13 58.03 57.02 56.39 56.90 56.24
Fig. 3. Regression coefficients of the calibration model with MSC as data pre-treatment.
Fig. 4. Predicted nicotine content vs. real nicotine content for the validation samples of calibration model with MSC as data pre-treatment.
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Table 7 Paired t-test on predicted values and reference values for calibration model 2. No. of samples (n) SEP texp ttab
24 0.0175 1.27 2.07
gained from a coating run on a production coating line different mathematical models based on the PLS1 algorithm were evaluated. Calibration model no. 2 with MSC as data pre-treatment and one PLS factor did not show the lowest but the most comparable SEC and SECV with 0.0172 and 0.0176 mg/6.51 cm2 in comparison to the other generated calibration models. The model showed the best result for the explained variation of the y data for the first PLS factor with 95.7% and the best ratio between explained variation of the x and y data. Identified absorption bands in the spectral region 2289–2722 nm might result from C H/C H2 and C H/C C combination bands and C H stretching vibration [22]. The whole spectral region from 1600 to 4100 nm was included in the construction of the calibration model to identify possible changes between the coating process of the calibration and validation run according to changes in the residual moisture and/or water content of the dried laminate. Changes in the drying profile could result in a shift of the identified spectral region 2289–2722 nm which might result in an incorrect predictive ability of the calibration model. With an external validation run and an independent laminate the prediction accuracy of model no. 2 was evaluated. With a paired t test a correct predictability of the nicotine content in oral thin films could be confirmed for the MSC pre-treated calibration model [23]. According the goals of the PAT framework a process analytical method was developed which enables real-time monitoring and control of a critical quality attribute during a manufacturing process [3,24]. Until now there was no possibility to monitor and control the nicotine content during the whole coating and drying process apart from the currently used gravimetric in-process control. The nicotine content was up to now controlled by the surrogate parameter coat weight of the laminate. With the implementation of the new process analytical method the continuous collection of data during the coating and drying process is possible which enables real-time process adjustments to keep the nicotine content within predefined limits. The present study showed that reflectance infrared spectroscopy is a rapid in-line process analytical method once it has been validated but the requisite calibration and validation runs are time-consuming. It requires the production of a range of samples under production conditions that differ in concentration. Modelbuilding also requires an optimization step including the choice of a regression method and spectral pre-treatments strongly informed by the used data set. A final point to bear in mind is that calibration models include the errors of the reference method mainly due to sample preparation and handling [23].
5. Conclusion On basis of a nicotine containing laminate manufactured at a production coating line, it could be shown that the nicotine content can be determined quantitatively in-line with a reflectance infrared sensor during the coating process for an oral thin film. A new process analytical method was developed which enables, without destroying the laminate, the monitoring and determination of the nicotine content during the coating and drying process independent of the currently performed gravimetrically in-process control. With continuous gathering of real-time data it is possible to allow real-time process adjustments to keep the nicotine content within predefined limits and hence decrease loss of material and batch. References [1] U.S. Department of Health and Human Services, Food and Drug Administration (FDA), 2004. [2] U.S. Department of Health and Human Services, Food and Drug Administration (FDA), Center for Drug Evaluation and Research (CDER), Center for Veterinary Medicine (CVM), Office of Regulatory Affairs (ORA), 2004, pp. 1–19. [3] A.S. Rathore, R. Bhambure, V. Ghare, Anal. Bioanal. Chem. 398 (2010) 137–154. [4] B.T. De, A. Burggraeve, M. Fonteyne, L. Saerens, J.P. Remon, C. Vervaet, Int. J. Pharm. 417 (2011) 32–47. [5] S. Saini, A. Nanda, M. Hooda, Komal, Pharmacologyonline (2011) 919–928. [6] A. Arya, A. Chandra, V. Sharma, K. Pathak, Int. J. ChemTech Res. 2 (2010) 576–583. [7] S. Kalyan, M. Bansal, Int. J. PharmTech Res. 4 (2012) 25–33. [8] M.N. Siddiqui, G. Garg, P.K. Sharma, Adv. Biol. Res. 5 (2011) 291–303. [9] , in: International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use, Pharmaceutical Development Q8 (R2) (2009). [10] European Pharmacopoeia, Uniformity of Dosage Units, 2008, pp. 357–359. [11] LTS Lohmann Therapie-Systeme AG, Expert Knowledge. [12] L. Sievens-Figueroa, A. Bhakay, J.I. Jerez-Rozo, N. Pandya, R.J. Romanach, B. Michniak-Kohn, Z. Iqbal, E. Bilgili, R.N. Dave, Int. J. Pharm. (Amsterdam, Neth.) 423 (2012) 496–508. [13] C. Beck, L. Sievens-Figueroa, K. Gartner, J.I. Jerez-Rozo, R.J. Romanach, E. Bilgili, R.N. Dave, Powder Technol. 236 (2013) 37–51. [14] R. Susarla, L. Sievens-Figueroa, A. Bhakay, Y. Shen, J.I. Jerez-Rozo, W. Engen, B. Khusid, E. Bilgili, R.J. Romanach, K.R. Morris, B. Michniak-Kohn, R.N. Dave, Int. J. Pharm. (Amsterdam, Neth.) 455 (2013) 93–103. [15] M. Haag, M. Brüning, K. Molt, Anal. Bioanal. Chem. 395 (2009) 1777–1785. [16] W. Fountain, K. Dumstorf, A.E. Lowell, R.A. Lodder, R.J. Mumper, J. Pharm. Biomed. Anal. 33 (2003) 181–189. [17] B. Stuart, Infrared Spectroscopy: Fundamentals and Applications, John Wiley & Sons, Ltd., Chichester, UK, 2004. [18] G. Reich, Adv. Drug Deliv. Rev. 57 (2005) 1109–1143. [19] M. Blanco, M. Bautista, M. Alcala, AAPS PharmSciTech 9 (2008) 1130–1135. [20] R.P. Cogdill, C.A. Anderson, M. Delgado, R. Chisholm, R. Bolton, T. Herkert, A.M. Afnan, J.K. Drennen 3rd, AAPS PharmSciTech 6 (2005) E273–E283. [21] R.P. Cogdill, C.A. Anderson, M. Delgado-Lopez, D. Molseed, R. Chisholm, R. Bolton, T. Herkert, A.M. Afnan, J.K. Drennen 3rd, AAPS PharmSciTech 6 (2005) E262–E272. [22] J. Workman Jr., L. Weyer, Practical Guide and Spectral Atlas for Interpretive Near-infrared Spectroscopy, CRC Press LLC, New York, USA, 2012. [23] Y. Roggo, P. Chalus, L. Maurer, C. Lema-Martinez, A. Edmond, N. Jent, J. Pharm. Biomed. Anal. 44 (2007) 683–700. [24] K.A. Bakeev, Process Analytical Technology: Spectroscopic Tools and Implementation Strategies for the Chemical and Pharmaceutical Industries, Wiley, Oxford, UK, 2005.
Contents lists available at ScienceDirect
Journal of Pharmaceutical and Biomedical Analysis journal homepage: www.elsevier.com/locate/jpba
Reflectance infrared spectroscopy for in-line monitoring of nicotine during a coating process for an oral thin film Florian Hammes a,b,∗ , Thomas Hille a , Thomas Kissel b a b
LTS Lohmann Therapie-Systeme AG, Andernach, Germany Department of Pharmaceutics and Biopharmacy, Philipps University Marburg, Germany
a r t i c l e
i n f o
Article history: Received 19 September 2013 Received in revised form 28 October 2013 Accepted 31 October 2013 Available online 15 November 2013 Keywords: Process analytical technology (PAT) In-line monitoring Active pharmaceutical ingredient (API) determination Oral thin film (OTF) Reflectance infrared spectroscopy
a b s t r a c t A process analytical method using reflectance infrared spectrometry was developed for the in-line monitoring of the amount of the active pharmaceutical ingredient (API) nicotine during a coating process for an oral thin film (OTF). In-line measurements were made using a reflectance infrared (RI) sensor positioned after the last drying zone of the coating line. Real-time spectra from the coating process were used for modelling the nicotine content. Partial least squares (PLS1) calibration models with different data pre-treatments were generated. The calibration model with the most comparable standard error of calibration (SEC) and the standard error of cross validation (SECV) was selected for an external validation run on the production coating line with an independent laminate. Good correlations could be obtained between values estimated from the reflectance infrared data and the reference HPLC test method, respectively. With in-line measurements it was possible to allow real-time adjustments during the production process to keep product specifications within predefined limits hence avoiding loss of material and batch. © 2013 Elsevier B.V. All rights reserved.
1. Introduction Over the past few years the use of process analytical technology (PAT) to monitor and control pharmaceutical unit operations has received increased attention. PAT is a key element of the “Pharmaceutical Current Good Manufacturing Practices (cGMPs) for the 21st Century-a Risk Based Approach” initiative announced by the Food and Drug Administration (FDA) to improve and modernize pharmaceutical manufacturing [1]. The FDA considers PAT as a system for designing, analysing and controlling manufacturing through timely measurements of critical quality and performance attributes of raw and in-process materials and processes. The goals of the PAT framework include real-time monitoring and control of manufacturing processes based on the knowledge generated in development or implementation and designing well-understood processes that will consistently ensure a predefined quality [2,3]. Process analysers are indispensable PAT tools for real-time process monitoring and control as they supply data from relevant processes. Tools are available for univariate process measurements such as pH, temperature and pressure and those that provide multivariate information related to biological, physical and
∗ Corresponding author at: Pharmaceutical Development, Research & Development, LTS Lohmann Therapie-Systeme AG, Lohmannstr. 2, D-56626 Andernach, Germany. Tel.: +49 (0) 2632 99 2767; fax: +49 (0) 2632 99 1767. E-mail address: [email protected] (F. Hammes). 0731-7085/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.jpba.2013.10.047
chemical attributes of the materials being processed, e.g. a reflectance infrared sensor. In a PAT environment real-time process measurements can be • At-line: measurements where the sample is removed, isolated from and analyzed in close proximity to the process stream. • On-line: measurements where the sample is diverted from the manufacturing process and may be returned to the process stream. • In-line: measurements where the sample is not removed from the process stream [2,4].
Following aspects should be considered when implementing process analysers into relevant process streams: • selection of a suitable process analyser or combination of complementary analysers able to monitor the desired critical process and product information • determination of the locations in the process stream where and how process analysers should be and can be implemented to monitor required information • determination of the optimal measurement conditions for the process analyser to obtain useful data • validation of the performance of process stream analysers [4]
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The main advantage of process analysers against off-line analysis in analytical laboratories is the possibility for continuous in-process monitoring and the possibility of real-time quality control. The examined product of the present study was a nicotine containing single-layer laminate as an intermediate product for an oral thin film (OTF). Oral thin films, also called oral wafers, wafers or strips, are flat and edible films for the oral or transmucosal delivery of APIs. OTFs are placed on the patient’s tongue or any oral mucosal tissue. Instantly wetted by saliva the film hydrates and adheres onto the site of application. After application the OTF rapidly disintegrates and dissolves to release the medication for oromucosal absorption or will maintain the quick-dissolving aspects allowing for gastrointestinal absorption to be achieved when swallowed. The first pass effect can be avoided by oromucosal absorption, so a reduction in the dose is possible which can potentially lead to reduction of side effects associated with the molecule or metabolites, respectively [5–7]. The matrix system of an OTF is based on hydrophilic and/or lipophilic polymers and contains the active pharmaceutical ingredient (API) in amorphous, liquid or crystalline phase. Further compounds of an oral thin film formulation are usually plasticizers, saliva stimulating agents, surfactants, sweetening agents, flavours, colours or fillers [8]. The manufacturing process of an OTF typically consists of three steps: preparation of mass solution, coating and drying and fabrication. The drug containing mass is coated on a release liner for proper wetting and handling. During the coating process water and/or process solvents are evaporated in a drying tunnel. Drying locks the API within polymer matrix in desired state. During fabrication the dried film is processed to desired dosage size by slitting and pouching into appropriate packaging material. To implement process analysers for real-time monitoring of the manufacturing process of an OTF critical quality attributes (CQAs) that are being affected by critical process parameters (CPPs) of the considered process step have to be identified [3]. CQAs are physical, chemical, biological or microbiological properties or characteristics that should be within an appropriate limit, range or distribution to ensure the desired product quality. CQAs are generally associated with the drug substance, excipients, intermediates (in-process materials) and drug product [9]. In coating and drying operations of oral thin films CQAs that could be monitored include product coat weight and drug/excipient identification and/or assay. CPPs which could be monitored are drying temperature, dryer air flow/exhaust, line speed and or pump speed of the mass. With suitable process analysers it is possible to monitor and operate in real-time the impact of CPPs on CQAs. To obtain a homogeneous drug containing laminate after the coating and drying process with regard to the nicotine content, it is important that the laminate is as homogeneous as possible in terms of coat weight, moisture and residual solvents and API content to fulfil the requirements of the European Pharmacopoeia in the monograph “Uniformity of dosage units”[10]. With the current in-house in-process control (IPC) the coat weight is determined gravimetrically at the beginning and at the end of the coating process of each master roll. It is in general possible to take more in-process control samples with the current gravimetric determination, however sampling means destruction of the material and requires interruption of the process. Since starting of the coating process after stopping bears a certain risk towards the quality, stopping the process should be avoided whenever possible. The nicotine content is controlled by the surrogate parameter coat weight of the laminate. The real nicotine content is tested on the single oral thin film after fabrication. Even after drying of the water and ethanol based liquid mass the laminate contains a significant amount of residual water and residual solvents which contributes to the coat
177
Table 1 Ingredients of the coating mass. Ingredients Nicotine Methacrylic acid–ethyl acrylate copolymer 1:1, Type A Triethyl citrate Peppermint flavour Sodium hydrogen carbonate Aqua purificata Ethanol Sucralose
weight. Therefore, the correct potency which corresponds to a coat weight of the drug containing laminate is not easily determined [11]. Objective of the present study was to develop a process analytical method for monitoring in-line the CQA nicotine content during the coating and drying process of a single-layer laminate for an oral thin film independent of the current gravimetrically in-process control. There are studies about using near infrared spectroscopy in the field of the pharmaceutical application of oral thin films [12–16] but no studies were reported using reflectance infrared spectroscopy for in-line monitoring of an API during the coating and drying process of an oral thin film. By using reflectance infrared spectrometry a process analytical method was developed which enables, without destroying the laminate, the monitoring and determination of the nicotine content during the coating and drying process independent of the currently performed gravimetrically in-process control. With continuous gathering of real-time data it is possible to allow real-time process adjustments to keep the nicotine content within predefined limits and hence decrease loss of material and batch. 2. Materials and methods 2.1. Production process mass preparation For calibration and validation purposes a mass batch was manufactured with the ingredients listed in Table 1. In the first production step a pre-mix solution consisting of aqua purificata (VWR International GmbH, Darmstadt, Germany), sodium hydrogen carbonate (Merck KGaA, Darmstadt, Germany) and sucralose (Merck KGaA, Darmstadt, Germany) was manufactured. Aqua purificata was weighed in portions into a transportable stainless steel container and sodium hydrogen carbonate was transferred under stirring. After stirring for an appropriate time sucralose was weighed in and the pre-mix solution was stirred. For the main solution ethanol (Merck KGaA, Darmstadt, Germany) and triethyl citrate (Merck KGaA, Darmstadt, Germany) were weighed in a stainless steel stirring container and were stirred. Subsequently methacrylic acid–ethyl acrylate copolymer (Evonik Industries AG, Essen, Germany) was transferred slowly in portions into the solution while agitating. After transferring the copolymer peppermint flavour was weighted in. The pre-mix solution was dosed by using a special pump. Nicotine (Siegfried AG, Zofingen, Switzerland) was weighted in a separate stainless steel container und was transferred into the solution under stirring. After stirring the Nicotine containing mass was purged with nitrogen. Half of the produced batch size was used for generating the calibration samples. The remaining amount was used for the external validation run. 2.2. Production process coating and drying To create a quantitative spectrometric determination model of nicotine and for the external validation of the chosen calibration
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Fig. 1. Schematic presentation of the coating line and the drying profile.
model two independent laminates with different coat weights were manufactured. In correlation to the changing coating weight different levels of nicotine were obtained. The coating process was performed on a production coating line (Olbrich GmbH, Bocholt, Germany) consisting of four drying zones and a total drying tunnel length of approximately 6 m. With defined heating plates and air nozzles in respective drying zones it was possible to set up a predefined drying profile. The drug containing mass was transferred from a stainless steel stirring container into the coating head of the coating line. The coating head consists of two rollers, one fixed roller and one moveable roller which enable different gap set ups of the coating head. The drug containing mass was coated on a polyethylene terephthalate (PET) foil 100 m (Mitsubishi Polyester Film GmbH, Wiesbaden, Germany) with a defined coating speed. To achieve different coat weights, different gaps of the coating head were set up. The reflectance infrared sensor was positioned at the end of drying zone 4 in the middle of the coated and dried laminate. Fig. 1 shows a general scheme of the used production coating line and the drying profile of the drying channel. 2.2.1. Collecting of calibration samples For collecting calibration samples six different gap set ups of the coating head were used. From each gap set up five real-time spectra during the coating process were taken. After recording one spectrum a 6.51 cm2 sample was punched out from the laminate with a die-cutting tool and was immediately sealed in a pre-manufactured pouch. In total 30 spectra were recorded. All samples were weighted and the nicotine content from each sample was determined according to the reference HPLC test method. Table 2 shows the weight of the calibration samples and the corresponding nicotine content. 2.2.2. Collecting of validation samples The spectrometric model which was chosen to be validated by an external validation run was uploaded in the test and measurement lab software of the reflectance infrared (RI) sensor. The model was used for monitoring in-line the nicotine content during the whole coating and drying process of the validation run. From the spectrometric software a data file with predicted data of the nicotine content for the chronological sequence of the production process of the validation run was generated. For validation purposes like the calibration run six different gap set ups were used. From each gap set up four validation samples were taken. For each validation sample a 6.51 cm2 sample was punched out with a diecutting tool and was immediately sealed in a pre-manufactured pouch. The time of sampling was noted. In total 24 validation
samples were gained. All samples were weighed and the nicotine content from each sample was determined according the reference HPLC test method. 2.3. Analytical methods 2.3.1. Reflectance infrared measurements A reflectance infrared (RI) sensor from Honeywell (RIS 3-4810, Honeywell International Inc., Morristown, USA) was used to record real-time spectra during the calibration process and for generating real-time data during the validation run. The system was operated by the RI manufacture test and measurement lab software from Honeywell. The spectra were recorded over a spectral range of 1600–4100 nm with a spectral resolution of 20 nm and a measurement speed of 100 Hz. The reflectance infrared sensor is measuring absorption bands in the near infrared region 1600–2500 nm related to overtones and combinations of fundamental vibrations of CH, NH, OH (and SH) functional groups and absorption bands of fundamental vibrations in the middle infrared region 2500–4100 nm due to CH, NH, OH (and SH) stretching [17,18]. Measurement principle of the sensor is transflectance. Infrared radiation penetrates the laminate, is partially absorbed and partially reflected. Light which cross the laminate is reflected by a diffuse gold standard which is positioned behind the coated laminate. The light cross the laminate again und the sensor measures the diffuse reflected light of the sample. For measurement purposes real-time spectra were taken from the process stream. 2.3.2. HPLC reference test method for nicotine content Calibration and validation samples with a defined area of 6.51 cm2 were punched out of the examined laminate area and were immediately sealed in single pre-manufactured pouches. The weight of each sample was noted and all samples were analyzed according the reference in-house HPLC test method to gain the real value for nicotine content. 2.3.3. Chemometric data analysis of calibration models All multivariate calibrations for quantification of the nicotine content were performed with the Chemom Model tool software from Honeywell. Partial least squares (PLS1) models were developed according to the following approach: All spectra from the calibration run were selected for generating calibration models. Real values for the nicotine content were calculated by the reference HPLC test method. Models based on no data pre-treatment and different data pre-treatments were developed. Spectral data
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Table 2 Weight of the calibration samples and the corresponding nicotine content. Calibration spectrum no.
Weight (mg/6.51 cm2 )
Nicotine (mg/6.51 cm2 )
Calibration spectrum no.
Weight (mg/6.51 cm2 )
Nicotine (mg/6.51 cm2 )
1st set up 1 2 3 4 5
61.29 60.55 61.41 60.49 61.35
2.472 2.432 2.452 2.414 2.438
4th set up 16 17 18 19 20
60.14 60.47 60.67 60.21 60.35
2.395 2.406 2.373 2.414 2.408
2nd set up 6 7 8 9 10
64.17 62.87 62.92 63.45 63.2
2.545 2.488 2.492 2.515 2.507
5th set up 21 22 23 24 25
58.21 57.7 57.91 58.06 57.81
2.328 2.312 2.320 2.323 2.312
3rd set up 11 12 13 14 15
66.27 66.35 66.9 67.03 66.38
2.612 2.608 2.628 2.632 2.613
6th set up 26 27 28 29 30
54.74 54.22 54.54 54.88 54.77
2.213 2.188 2.197 2.211 2.204
were pre-treated with multiplicative scatter correction (MSC), first derivative (1st) and combinations of those. All constructed calibration models were internal validated by cross-validation. As quality criteria of the constructed calibration models the standard error of calibration SEC and the standard error of cross validation SECV using Eq. (1) were used. The optimum number of PLS factors to be included in the model was indicated by a minimum and comparable SEC and SECV [19,20]. In addition to the calculation of SEC and SECV the explained variation of x data (analytical data) and y data (spectral data) of the PLS factors was analyzed. Also R-squared (R2 ) values are given which correspond to the coefficients of determination. The calibration model with the most comparable SEC and SECV was chosen for an external validation run on the production coating line with an independent laminate.
SE =
n ( i=1 i
− ˆ i )
2
n−1
(1)
Eq. (1) Standard error (SE) with yi : nicotine content calculated by the HPLC reference test method, yˆ i : nicotine content predicted by the calibration model 2.3.4. Data analysis of validation samples To evaluate the quality of prediction of the examined spectrometric model the time of sampling for every validation sample was noted. With the chronological data file generated by the spectrometric software and the noted time of sampling the nicotine content predicted by the calibration model could be compared with the real nicotine content calculated by the reference HPLC test method. The standard error of prediction SEP using Eq. (1) was calculated and was compared with the SEC and SECV of the model. In addition R-squared (R2 ) values are given. 3. Results
Table 3 Quantities of materials used for the manufacturing of masses under lab scale conditions. Mass
Nicotine content (g)
Methacrylic acid–ethyl acrylate copolymer 1:1/ethanol 47% (g)
1 2 3 4 5 6
0 1 2 3 4 5
25 24 23 22 21 20
The masses consist of nicotine and methacrylic acid–ethyl acrylate copolymer 1:1 solved in ethanol. The detailed quantities of substances used for preparation are given in Table 3. In the first manufacturing step methacrylic acid–ethyl acrylate copolymer was weighted in a stirring container. After transferring Nicotine the solution was stirred for 20 min. Each mass was coated with a 600 m coating knife on a polyethylene terephthalate (PET) foil 100 m (Mitsubishi Polyester Film GmbH, Wiesbaden, Germany) and was dried in a drying chamber for 15 min at 60 ◦ C. The coat weight of each laminate was nearly constant in a range of ±2 g/m2 . For qualitative issues from each laminate a 10 cm2 sample was punched out with a die cutting tool and a spectrum was taken. All spectra were pre-processed with the data pre-treatment combination of first derivative (1st) and multiplicative scatter correction (MSC). The regression coefficients for nicotine in the spectral region 1600–4100 nm were calculated. The spectral region between 2250 and 2761 nm was identified to be significant for nicotine. This result leads to the conclusion that nicotine can be quantified by reflectance infrared spectroscopy. Fig. 2 shows the recorded spectra pre-treated with the combination of 1st and MSC and the regression coefficients of nicotine. The Chemom Model tool software indicates the spectral range 1600–4100 nm on the x-axis as wavelength channels 1–127. Each wavelength channel equates 19.69 nm.
3.1. Applicability of reflectance infrared spectroscopy for the quantification of nicotine in oral thin films
3.2. Reflectance infrared calibration models
To assess the feasibility of a quantitative spectrometric determination model of nicotine six masses with a defined concentration range of nicotine were manufactured under lab scale conditions to identify the characteristic spectral region for nicotine. For measurement purposes the same reflectance infrared sensor from Honeywell was used as for the trials on the production coating line.
For calibration purposes all spectra from the calibration run were used. In total 5 calibration models based on the PLS1 algorithm were designed. Models based on no data pre-treatment, multiplicative scatter correction (MSC), first derivative (1st) and combinations of those were generated. In addition to the calculation of the SEC and SECV the explained variation of the x data
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Fig. 2. Recorded spectra of the laboratory test pre-treated with the combination of 1st and MSC and calculated regression coefficients for nicotine.
Table 4 Parameters of calibration models. Model no.
Data pre-treatment
Spectral region (nm)
SEC (mg/6.51 cm2 )
SECV (mg/6.51 cm2 )
R2 C
R2 CV
No. of PLS factors
1 2 3 4 5
None MSC 1st 1st + MSC MSC + 1st
1600–4100 1600–4100 1600–4100 1600–4100 1600–4100
0.0176 0.0172 0.0152 0.0147 0.0145
0.0200 0.0176 0.0176 0.0161 0.0157
0.983 0.984 0.988 0.988 0.989
0.978 0.983 0.983 0.986 0.986
2 1 1 1 1
Bold values are parameters/values for calibration model no.2 mentioned in Section 3.2. Table 5 Explained variation of the x and y data of the PLS factors. Model no.
Data pre-treatment
Spectral region (nm)
Explained variation of x data (%)
Explained variation of y data (%)
No. of PLS factors
1 2 3 4 5
None MSC 1st 1st + MSC MSC + 1st
1600–4100 1600–4100 1600–4100 1600–4100 1600–4100
98.3 98.4 98.7 98.8 98.9
99.6 95.7 77.7 84.9 84.6
2 1 1 1 1
Bold values are parameters/values for calibration model no.2 mentioned in Section 3.2.
(analytical data) and y data (spectral data) of the PLS factors was analyzed to support the right selection of the calibration model for an external validation run. Tables 4 and 5 give an overview of the constructed models including the standard error of calibration SEC and the standard error of cross validation SECV and the explained variation of the x and y data of the PLS factors. Calibration model no. 2 with MSC as data pre-treatment shows the most comparable SEC and SECV with 0.0172 and 0.0176 mg/6.51 cm2 . The deviation between the calculated SEC and SECV was the lowest. The calibration model showed the best result for the explained variation of the y data for the first PLS factor with 95.7%. The ratio between explained variation of the x and y data was the best. The most intense regression coefficients identified in the MSC pre-treated model corresponding to the nicotine content were in the spectral region between 2289 and 2722 nm (Fig. 3). Compared to the spectral region 2250–2761 nm from the laboratory test it could be stated that nicotine can be monitored with the generated model. Calibration model no. 2 was chosen for an external validation run on the production coating line with a separate produced laminate. 3.3. Validation results of calibration model With an external validation run the calibration model with MSC as data pre-treatment was validated. For estimating the standard error of prediction SEP the deviation between the real nicotine
content and the predicted nicotine content for all 24 validation samples was calculated. Table 6 shows the calculated deviations. Fig. 4 summarizes these data graphically. The highest deviation was 0.030 mg/6.51 cm2 for validation sample 21, the lowest was −0.027 mg/6.51 cm2 for validation sample 9. The calculated standard error of prediction SEP was estimated with 0.0175 [mg/6.51 cm2 ] and is comparable with the standard error of calibration SEC and standard error of cross validation SECV with 0.0172 and 0.0.176 [mg/6.51 cm2 ]. R2 for the validation run was 0.981. A paired t test was used to check whether the predicted values and the real values were significantly different. Table 7 shows the results of the test; as can be seen, texp was smaller than ttab , so the predicted values and reference values were not significantly different. A correct predictive ability for the MSC pre-treated calibration model could be confirmed. 4. Discussion The present study applied for the first time reflectance infrared spectroscopy for in-line monitoring of the CQA nicotine content during the coating process for an oral thin film. With a feasibility study under lab scale conditions the general applicability of reflectance infrared spectroscopy for the quantification of nicotine in a methacrylic acid-ethyl acrylate copolymer matrix was successfully demonstrated [21]. The spectral region between 2250 and 2761 nm was identified to be relevant for the determination of the nicotine content in OTF. Using real time data
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Table 6 Real values for the nicotine content of the validation samples and deviations (real nicotine content-predicted nicotine content by calibration model 2). Validation sample 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Real nicotine content y (mg/6.51 cm2 ) 2.558 2.519 2.553 2.548 2.458 2.493 2.473 2.456 2.409 2.439 2.380 2.418 2.357 2.346 2.359 2.337 2.323 2.301 2.308 2.312 2.293 2.272 2.297 2.284
Predicted nicotine content yi (mg/6.51 cm2 ) 2.561 2.510 2.560 2.540 2.473 2.497 2.486 2.473 2.436 2.445 2.401 2.442 2.333 2.333 2.332 2.323 2.303 2.287 2.291 2.285 2.263 2.264 2.277 2.269
Deviation y − yi
Weight sample (mg/6.51 cm2 )
(mg/6.51 cm2 )
(%)
−0.003 0.009 −0.007 0.008 −0.015 −0.004 −0.013 −0.017 −0.027 −0.006 −0.021 −0.024 0.024 0.013 0.027 0.014 0.020 0.014 0.017 0.027 0.030 0.008 0.020 0.015
−0.12 0.36 −0.27 0.31 −0.61 −0.16 −0.53 −0.69 −1.12 −0.25 −0.88 −0.99 1.02 0.55 1.14 0.60 0.86 0.61 0.74 1.17 1.31 0.35 0.87 0.66
63.44 63.44 64.56 64.25 62.26 62.15 61.94 60.64 60.46 60.44 58.31 60.97 58.53 58.60 59.19 58.21 58.01 57.03 58.13 58.03 57.02 56.39 56.90 56.24
Fig. 3. Regression coefficients of the calibration model with MSC as data pre-treatment.
Fig. 4. Predicted nicotine content vs. real nicotine content for the validation samples of calibration model with MSC as data pre-treatment.
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Table 7 Paired t-test on predicted values and reference values for calibration model 2. No. of samples (n) SEP texp ttab
24 0.0175 1.27 2.07
gained from a coating run on a production coating line different mathematical models based on the PLS1 algorithm were evaluated. Calibration model no. 2 with MSC as data pre-treatment and one PLS factor did not show the lowest but the most comparable SEC and SECV with 0.0172 and 0.0176 mg/6.51 cm2 in comparison to the other generated calibration models. The model showed the best result for the explained variation of the y data for the first PLS factor with 95.7% and the best ratio between explained variation of the x and y data. Identified absorption bands in the spectral region 2289–2722 nm might result from C H/C H2 and C H/C C combination bands and C H stretching vibration [22]. The whole spectral region from 1600 to 4100 nm was included in the construction of the calibration model to identify possible changes between the coating process of the calibration and validation run according to changes in the residual moisture and/or water content of the dried laminate. Changes in the drying profile could result in a shift of the identified spectral region 2289–2722 nm which might result in an incorrect predictive ability of the calibration model. With an external validation run and an independent laminate the prediction accuracy of model no. 2 was evaluated. With a paired t test a correct predictability of the nicotine content in oral thin films could be confirmed for the MSC pre-treated calibration model [23]. According the goals of the PAT framework a process analytical method was developed which enables real-time monitoring and control of a critical quality attribute during a manufacturing process [3,24]. Until now there was no possibility to monitor and control the nicotine content during the whole coating and drying process apart from the currently used gravimetric in-process control. The nicotine content was up to now controlled by the surrogate parameter coat weight of the laminate. With the implementation of the new process analytical method the continuous collection of data during the coating and drying process is possible which enables real-time process adjustments to keep the nicotine content within predefined limits. The present study showed that reflectance infrared spectroscopy is a rapid in-line process analytical method once it has been validated but the requisite calibration and validation runs are time-consuming. It requires the production of a range of samples under production conditions that differ in concentration. Modelbuilding also requires an optimization step including the choice of a regression method and spectral pre-treatments strongly informed by the used data set. A final point to bear in mind is that calibration models include the errors of the reference method mainly due to sample preparation and handling [23].
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