Real-Time Measurement of Coating Film Thickness

March 2, 2019
Johannes G. Khinast

Johannes G. Khinast is professor at the Institute of Process and Particle Engineering, Graz University of Technology, Austria

,
Stephan Sacher

Stephan Sacher is senior scientist at the Research Center for Pharmaceutical Engineering GmbH in Graz, Austria.

,
Elen Gartshein

Elen Gartshein is senior manager at Pfizer Global Supply, Peapack, NJ, USA.

,
Matthias Wolfgang

Matthias Wolfgang is scientist at the Research Center for Pharmaceutical Engineering GmbH in Graz, Austria.

,
Patrick Wahl

Patrick Wahl is senior scientist at the Research Center for Pharmaceutical Engineering GmbH, Graz, Austria.

Pharmaceutical Technology, Pharmaceutical Technology-03-02-2019, Volume 43, Issue 3
Page Number: 36–47

Optical coherence tomography can improve quality control and development of coated dosage forms by allowing film thickness to be measured in real time.

The pharmaceutical coating process is a well-established unit operation, but accurately measuring the endpoint of the coating process remains a challenge. Knowing the endpoint is crucial, especially for functional coatings, because quality attributes, such as coating layer thickness, have a direct impact on product performance, specifically the dissolution rate. 

This article summarizes results of a study that looked at optical coherence tomography (OCT) as a way to monitor pharmaceutical spray coating processes in-line for tablets and pellets. The study measured coating thickness as a function of time for tablets in a drum coater and for pellets in a fluid bed coater. It also looks into the possible influence of dye, which was found to have no impact on measurements. Coating thickness was determined automatically based on OCT images rather than via chemometric calibration models. In-line data and off-line three-dimensional mapping revealed additional facets of tablet and pellet coating quality (i.e., intra- and inter-particle coating variability).

Results of the study indicate that an industrial-ready OCT system can improve process understanding and assure product quality in pharmaceutical functional coating applications, assisting pharmaceutical scientists with process development, scale-up, transfer, and troubleshooting. In the future, OCT could become a new tool for quality control (QC) release, replacing unreliable and time-consuming testing procedures. One of the technology’s key strengths is that it allows detailed understanding of the source of and presence of defects and can identify poor coating quality, both within a batch and between batches.

Functional coating is a common way to improve patient compliance, prevent counterfeiting, and enhance bioavailability-and with it, the overall functionality of solid oral dosage forms. In some cases “active coatings” are applied that contain one or more APIs to mitigate interactions between different drugs or to account for different release behaviors in a single dosage form. Enteric coatings are typically applied to tablets in pan coating equipment, while beads, mini-tablets, and pellets are typically coated in fluid-bed coating equipment (e.g., for taste-masking applications in pediatric dosage forms). The coating process must be controlled, based on coating thickness, thickness variability (both between different processing lines and within a single coating line), and coating defects (e.g., coloration or surface blemishes and cracks).

Problems with coating are common during development and manufacturing, especially when scaling-up a process or transferring it from one lab or manufacturing site to another. Thus, having technology available that can monitor in real time the critical quality attributes of coatings (including thickness, thickness variability, morphology, porosity, defects, and cracks) can reduce overall development time, accelerate process scale-up, and enable greater precision in determining the root-cause analysis of coating weakness.

A number of techniques are currently used to study oral solid-dosage form coating quality, including optical inspection of cross-sectional cuts and measurement of tablet diameters or weight gain.

However, these approaches are time consuming, error prone, and can only provide approximate results. In addition, these methods cannot be applied in-line to allow for process monitoring or control. 

To overcome these shortcomings, in-line process analyzers have been developed to monitor and assess coating quality non-destructively, each with its own strengths and weaknesses. 

These methods include:

  • Near-infrared (NIR) (1,2) and Raman spectroscopy (3,4) 

  • Terahertz (THz) sensing (5,6), a powerful approach, but one that is not easy to implement in real time

  • Spatial filter velocimetry (7)

  • Dynamic image analysis (8).

In contrast, optical coherence tomography (OCT) allows coating quality of translucent functional coatings to be determined rapidly, in real time, without the need for calibration. It also eliminates the need to develop and maintain chemometric models in order to interpret the data. These models are required for both NIR and Raman (1). Compared with other process analytical technology (PAT) approaches, OCT offers much higher scanning speeds (i.e., up to 250,000 measurements per second vs. approximately 30 for THz and less than one for Raman). Furthermore, OCT provides very high axial and lateral resolution (more than one order of magnitude better than all other approaches).

How OCT works for coating measurement

OCT is an interferometric technique that is used to generate cross-sectional depth-resolved images of coating layer(s). The physical setup and operation of OCT systems have been described in the literature (9–11), with a functional distinction being made between time- and spectral-domain OCT systems. During image acquisition, a light source with high spatial and low temporal coherence is focused on the coating surface. Most light is reflected or diffracted, but a substantial part penetrates the surface and is reflected from interfaces of different materials with distinctive changes in refractive index. The reflected light is detected with a spectrometer (for spectral-domain OCT). By measuring the optical path length between the reflections, it is possible to determine the distance between the interfaces considering the refractive index of the material.

Several studies have already demonstrated the high performance of OCT systems for measuring the coating thickness of pharmaceutical solid dosage forms (12–14). OCT can also be used to analyze coating thickness variations within single particles (intra-particle variation) and between particles (inter-particle variation) during the coating process (15). Recently, OCT has been commercialized as a monitoring technology for good manufacturing practice (GMP) applications, and an ATEX model is available for industrial use. The research examined in this article evaluated the GMP commercial device.

 

Materials and methods

Optical coherence tomography probe.Throughout all experimental work, a commercial spectral-domain OCT system (OSeeT, Phyllon, Austria) was used for measurements and data recording. The OSeeT system works at a central wavelength of 832 nm with a spectral bandwidth of 75 nm, leading to a theoretical axial resolution of 4 µm. 

The base unit can be combined with a one-dimensional (1D) sensor with 14-µm lateral resolution for in-line measurements. This sensor is also part of the additionally available at-line sampling device. A three-dimensional (3D) sensor with 10-µm lateral resolution is available for off-line measurements. Sensors and peripherals can easily be changed due to the use of standard connections such as fiber channel/angled physical contact (FC/APC) interface for the sensors and optical peripherals, and Universal Serial Bus Version 3.0 (USB 3.0) for electrical interfacing. 

This research used a 1D sensor head (Phyllon) to monitor and validate a pan-coating process, and a custom-made 3D sensor (16) in two-dimensional (2D) operation mode (i.e., with one galvo mirror disabled) to monitor the fluid-bed coating experiments. The 3D sensor was also used to measure pellets and tablets periodically drawn from the process, off-line.

Sensor exposure time was set to 15 µs for in-line measurements of tablets (1D) and 30 µs for in-line measurement of pellets (2D) and off-line measurements (3D). The idle time (for read-out and digitalization) was set to 1.9 µs for all measurements. The exposure and idle times resulted in acquisition rates of 59.2 kHz (1D) and 31.3 kHz (2D/3D). The acquisition rate corresponds to the number of single-depth scans per second, resulting in a frame rate of 57.8 frames per second (fps) for the 1D device, and 30.6 fps for the 2D and 3D devices.

Case study one: pan coating. As a model use case, pan coating was evaluated on tablet cores containing acetylsalicylic acid (ASA) similar to the commercial product Thrombo ASS. Coating was performed in a lab-scale pan coater (ProCepT, Zelzate, Belgium) with a reservoir capacity of 1 L, and a 0.8-mm Schlick spray nozzle was used to apply the coating suspension. The batch consisted of 350 g uncoated tablet cores containing 50 mg acetylsalicylic acid, lactose monohydrate, microcrystalline cellulose, highly dispersed silicone dioxide (SiO2) starch, talc, and triacetin. The biconvex-shaped tablet cores had a diameter of 7.15 mm, a height of 3.7 mm, and a radius of cubature of 7.9 mm.

A common enteric coating (Eudragit L30 D-55, Evonik Industries AG, Darmstadt, Germany) was used, and blue colorant Liquitint Blue HP (Milliken & Company, Gent, Belgium) was added to the coating solution to provide a visual indicator of applied coating homogeneity. Following the coating manufacturer’s instructions (17), 504.9 g of coating solution were prepared, and the amount of added spray solution was calculated (18) to achieve a targeted coating thickness of approximately 70 µm.

A total of 150 g of coating suspension was applied to the cores, resulting in an additional 30 g of dry mass on the tablets. Coating parameters and targeted coating thickness were recommended by GL-Pharma, who manufacturers commercial product. The pan speed was set to 40 rpm, and the coating was performed at a spray rate of 3.2 g/min, at an inlet airflow rate of 0.4 m³/min, and a temperature of 50 °C.

Tablets were monitored (19) through the holes of the perforated drum of the pan, and OCT images were continuously acquired and saved for post-processing throughout the whole coating run. An automated evaluation algorithm (19) was applied on all saved images, including:

  • The automatic detection of tablets 

  • Extraction of air/coating and coating/core interfaces

  • Correction of distortions due to tablet speed, oblique orientation, and curvature

  • Calculation of the coating thickness.

The refractive index was assumed to be 1.48, as validated for the same coating material (20). Due to the insignificant influence of appearance-altering colorants on the refractive index, a negligible impact on measurements was expected.

Additional samples of 10–15 tablets were drawn every eight minutes and further analyzed using the OCT 3D sensor head configuration. Results of in-line and off-line measurements, as well as weight of applied coating were compared using Matlab software.

Case study two: fluid-bed coating. This use case aimed to coat relatively large pellets completely and monitor thickness. Pellet coating was performed in an lab-scale air flow technology coater (Romaco Innojet VENTILUS V-2.5, Pharmatechnik GmbH, Karlsruhe, Germany) on extruded calcium stearate pellets, which were composed of a matrix carrier of 75% (w/w) calcium stearate, 20% (w/w) paracetamol (API), and 5% (w/w) glycerol monostearate as plasticizer (21).

The pellets had a mean particle size of 400–2000 µm. 450 g of these pellets were coated with molten Dynasan 118 glyceryl tristearate (Cremer Oleo, Germany) in three replicated experiments (B01–B03), all of which were run at the same process conditions.

The molten coating temperature was roughly 90 °C during all experiments, and the refractive index was assumed to be 1.438 (22). Preliminary tests showed that a 100-µm coating thickness was sufficient to generate a smooth and cohesive film. The coating process lasted for 32 minutes until 218 g of coating mass were sprayed onto the pellets to achieve a theoretical coating thickness of 100 µm. Process conditions were set to a spray rate of 7 g/min, 1.5 bar atomizing pressure, 60 m³/h air flow rate and 35 °C inlet air temperature.

Pellet coating was monitored using the 3D sensor in 2D operation mode (13). At the inspection window, the sensor was protected by a thin sheet of plastic foil during all runs. In-line measurements of pellets were recorded every other minute for a duration of 60 seconds. 

The research then compared automated sampling data to results from manually drawn samples, which were analyzed off-line and evaluated based on 3D-OCT measurements and particle-size analysis using a particle analyzer (Qicpic, Sympatec GmbH, Germany). Results of in-line, 3D, and particle-size measurement were then compared using Matlab software.

 

Results

Case study: pan coating. As shown in Figure 1a, the measured coating thickness of tablets steadily increases with process time. Due to the 4.5-µm axial resolution limit of the 1D sensor (23), reliable thickness measurements are available only after seven minutes. For each analyzed tablet, at least 50 measurements per tablet were performed. Tablets with fewer values were automatically rejected to guarantee accuracy. In total, 1385 tablets were analyzed during 48 minutes of process time and the coating thickness approached a final value of 69.1 ± 4.9 µm, which is a good result compared to the target 70 µm coating thickness.

Coating thickness variation. The collected data also permit the time-dependent analysis of coating thickness variations on single tablets (i.e., the intra-tablet as well as inter-tablet coating variability). This analysis was done based on the relative standard deviations (RSD) of the coating thickness as illustrated in Figures 1b and 1c.

The high RSD at the beginning of the process was due to the inhomogeneous distribution of coating mass on the total number of tablets and on single tablets.  As can be seen, the RSD decreased with process time, and thus, the coating uniformity was enhanced while approaching the process end. A final weight gain of 150 g and an RSD of mean coating thickness (inter-tablet coating variability) of 7.2% could be achieved.

Manually drawn samples at different stages of the process enabled analysis of polymer film integrity and homogeneity on the tablet using off-line evaluation via the 3D sensor. Results agreed with in-line-measurements, which are shown as red circles in Figures 1a and 1b. The RSD of the coating thickness of the 3D-mapped tablets is of the same magnitude as the RSD of the measured in-line data. This shows that deviations of in-line data originate mainly from differences of the coating, whereas other effects, such as random movement of tablets or different parts of the tablets facing the sensor, are of minor impact.

Case study: fluid-bed coating.The quality of real-time OCT images during fluid-bed coating was not as good as it was for the off-line images, due to rapid movement of the pellets and signal attenuation originating from scanning through the protection foil. The results were also affected by a systematic error (13), due to the curved surface of the pellets, which needs to be corrected for. To minimize errors, the algorithm used for automated evaluation of pellet coating thickness must consider these effects. To correct for these effects, a spherical shape was assumed for all pellets, and an ellipsoid fit of the captured OCT images was transformed to an equivalent circle radius. After this correction, the air-coating and coating-core interfaces could be evaluated in the same way as the tablet measurements. Due to the fact that pellets have a more curved surface than typical tablets, the algorithm evaluated only the upper part of the pellets, which are facing the sensor head within an angle of ±30° around the highest point in the image.

Results of the automated data evaluation and reference measurements are shown in Figure 2. While OCT data were acquired for the entire process time, only a subset of data are shown for the sake of clarity. A total number of 15,685 pellets were automatically evaluated with an additional number of 1395 pellets analyzed by particle size analysis (shown as green filled squares in the figure) to provide an independent reference. In-line OCT measurements were only accepted if at least 30 measurements per pellet could be performed.

In terms of mean coating thickness (Figure 2a), results for all coating runs agreed with results from the manual evaluation and showed even better correlation with the results from particle size analysis. As illustrated in Figure 2b, the plots of inter-particle coating variability showed good correlation for all runs. All results of endpoint coating thickness were close to the target coating thickness of 100 µm and correlated well among the applied methods. As expected, the RSD of the coating thickness between particles decreases during the process, following a function of √(1/t) as described by Turton (24). In general, the variability between runs is more pronounced in the beginning, due to higher uncertainty of measurements originating from the resolution limits of the sensor and spectrometer setup. 

The plots of intra-particle coating variability in Figure 2c also show a decrease in process time for all automatically evaluated runs, in contrast to the results from manual evaluation, which did not follow this trend. This behavior can be explained by the fact that the manual evaluation does not need to compensate for the experimental setup and the unknown velocity of the pellets. Additionally, fewer samples could be evaluated manually compared to the automatic evaluations. Therefore, these values should be considered with a high error probability compared to the automated evaluated results. Results of the coating experiments for the different evaluation and reference methods are summarized in Table I.

Discussion

Pan coating. Results of the automatic evaluation of in-line OCT data during tablet coating showed a linear increase of coating thickness with process time, which is in excellent agreement with the reference method (sprayed coating solution) and the off-line 3D mappings. Inter- and intra-tablet coating variabilities were found to decrease with process time, as expected. A minimum of 50 thickness measurements for every tablet guarantees a reliable basis for all automated evaluations and additional statistical results. Note that the non-zero coating thickness during the first 10 minutes originates from a high uncertainty of the measurements as a consequence of the resolution limit of the system (<10 µm). Additionally, only a few tablets were coated in the first few minutes, leading to a much higher spread of thickness readings.

Colorant had no impact on measurements. The addition of 1‰ of a liquid colorant to the spray suspension showed no negative effect on the quality of the readings, nor on the results of in-line OCT measurements, although the visual appearance of the tablets changed significantly with the color changing to dark blue. Note that the blue colorant used was not pharmaceutical grade, and therefore, not suitable for ingestion, but was used to allow visual inspection, and to demonstrate that OCT can also be used to analyze colored coatings.

Analyzing the recorded 3D maps in more detail revealed that the coating layer partially compensated for small surface irregularities of the tablet cores, while larger cavities in the tablet surface persisted throughout the whole coating process as shown in Figure 3. Thus, OCT can be used to develop coating formulations designed to smooth tablet cores, if needed. Defects in the coating and existence of poorly coated regions (e.g., at the edges) can be readily detected, enabling closer engineering and control of the coating process (e.g., by increasing the coater temperature).

Case study two: fluid-bed coating. All three runs showed good correlation with each other and the reference method (dynamic imaging analysis). They are in an even better agreement with results from the manual evaluation, where only three points per pellet were measured and evaluated. This emphasizes the fact that the presented OCT system is capable of extracting the correct interfaces of coated pellets and interprets the data in a reproducible way.

The improved algorithm, fitting an ellipsoid, worked well for the tested experiments, demonstrated by consistent measurements compared with the reference methods. As listed in Table I, the comparison of results from automatic and manual evaluation reveals the advantage of the high number of measured values, with at least 30 thickness measurements per pellet for automated evaluation, because the standard deviation (SD) values are only half of the manual evaluation. This highlights the advantages offered by OCT in terms of precision of automatic thickness measurements due to a higher significance compared to results based on only three manual measurements per pellet. All runs met the targeted coating thickness of approximately 100 µm, which represents a coating amount of 32.6% (weight), which is typical for taste masking of multi-particulates (25) for similar coating systems.

In contrast to the manually evaluated data, OCT data enabled statistical information to be developed that showed intra- and inter-pellet coating variability and the variability decay with process time. In this study, all three experiments showed the same behavior in general, with a decrease of RSD for the inter-pellet coating variability over time. The intra-pellet coating variability of the manual evaluation only deviated from the automated evaluated data in the beginning of the run due to the small number of samples used for the manual evaluation.

 

Conclusion

This study demonstrated that different coating processes could be easily analyzed by means of an industrial OCT system in real-time, both for tablets and pellets, in a coating thickness ranging from 10 µm up to 100 µm. OCT could be applied for inline measurements ranging from lab- to production-scale coaters. It can accurately detect the coating end-point based on coating thickness, independent of spray efficiency, process parameters, and scale. For long-term inline measurements, air purging of the optical window of the 1D sensor worked well, and all in-line equipment is available in hygienic design for use in GMP applications.

OCT as a PAT tool

In this work, the authors focused on investigating the growth of the coating layer, demonstrating that OCT is a suitable PAT tool. No dissolution data were generated. However, by comparing tablets used in these experiments with results for commercially available Thrombo ASS, research showed that the coating thicknesses matched perfectly, with differences of less than 1 µm in coating thickness between the commercial product and our showcase tablets.

Work is now underway to correlate OCT data and dissolution behavior, and will be the focus of future articles. However, preliminary data correlating OCT measured coating thickness and dissolution behavior were presented at the International Forum on Process Analytical Chemistry (IFPAC) meeting in Washington, DC in 2018 (26).

Some limitations seen

Despite OCT’s strengths, the tested OSeeT system showed some limitations.  For example, coatings that contain a high concentration of pigments that are frequently used in cosmetic coatings (e.g., titanium dioxide as a whitener or iron oxide as a colorant) cannot yet be measured. Moreover, thin layers (<10 µm) cannot be accurately resolved. Development efforts are now underway to overcome these limitations.

In addition to results discussed in this article, the 3D-OCT system has been used successfully to test other commercial products, as shown in Table II. All examples could be measured and evaluated automatically. Coating thicknesses for the given examples were in the range of 10 to 250 µm.

The presented results highlight the ability of OSeeT, as the first commercially available OCT system for the pharmaceutical industry, to generate significant statistical data for the mean coating thickness, as well as inter- and intra-tablet variability, as a function of coating time. In addition, inner structure (e.g., porosity, morphology) and defects can be studied, as shown in Figure 3.
 

All of these properties, within the current quality assurance frameworks, can be (and are) analyzed daily. The number of dosage forms tested is low, however, and testing is time-consuming and costly.

In contrast to the current picture, OCT allows a detailed understanding of the critical quality attributes of coatings in real-time, including the source of variability and the presence of defects or poor coating quality, both intra and inter batch. Thus, OCT technology can support pharmaceutical scientist in rationally developing coating formulations and the associated processes, allowing straightforward process transfer, scale-up, and troubleshooting.

Figure 3: Reconstruction of a complete 3D optical coherence tomography (OCT) scan of one of the tablets under test. The coating partly compensates for irregularities in the core-coating interface.

Acknowledgements

This work has been funded by the Austrian Competence Centers for Excellent Technologies (COMET) program, under the auspices of the Austrian Federal Ministry of Transport, Innovation and Technology (BMVIT), the Austrian Federal Ministry of Economy, Family and Youth (BMWFI) and by the State of Styria (Styrian Funding Agency SFG). COMET is managed by the Austrian Research Promotion Agency FFG.

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Article Details

Pharmaceutical Technology
Vol. 43, No. 3
March 2019
Pages: 36, 38, 40, 42–44, 46–47

Citation

When referring to this article, please cite it as M. Wolfgang, et. al. “Real-Time Measurement of Coating Film Thickness," Pharmaceutical Technology 43 (3) 2019.

About the Authors

Matthias Wolfgang is scientist, and Patrick Wahl and Stephan Sacher are senior scientists at the Research Center for Pharmaceutical Engineering GmbH, Graz, Austria; Elen Gartshein is senior manager at Pfizer Global Supply, Peapack, NJ, USA; Johannes G. Khinast* is professor at the Institute of Process and Particle Engineering, Graz University of Technology, Austria, khinast@tugraz.at.

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