A validation of discrete-element model simulations for predicting tablet coating variability

Achieving an even coating distribution on tablets during the coating process can be challenging, not to mention the challenges of accurately measuring and quantifying inter-tablet coating variability. Computer simulations using the Discrete Element Method (DEM) provide a viable pathway towards model-predictive design of coating processes. The purpose of this study was to assess their predictivity accounting for both experimental and simulation input uncertainties. To this end, a comprehensive set of coating experiments covering various process scales, process conditions and tablet shapes were conducted. A water-soluble formulation was developed to enable rapid spectroscopic UV/VIS analysis of coating amounts on a large number of tablets. DEM predictions are found to lie within the experimentally inferred confidence intervals in all cases. A mean absolute comparison error of 0.54 % was found between model predictions of coating variability and respective sample point estimates. Among all simulation inputs the parameterization of spray area sizes is considered the most significant source for prediction errors. However, this error was found significantly smaller in magnitude compared to experimental uncertainties at larger process scales underlining the value of DEM in the design of industrial coating processes.

In-line cosmetic end-point detection of batch coating processes for colored tablets using multivariate image analysis

In this study, an in-line Process Analytical Technology (PAT) for cosmetic (non-functional) coating unit operations is developed using images of the tablet bed acquired in real-time by an inexpensive industrial camera and lighting system. The cosmetic end-point of multiple batches, run under different operating conditions, is automatically computed from these images using a Multivariate Image Analysis (MIA) methodology in conjunction with a stability determination strategy. The end-points detected by the algorithm differed, on average, by 3% in terms of total batch time from those identified visually by a trained operator. Since traditional practice typically relies on a coating overage to ensure full batch aspect homogeneity in the face of disturbances, the current in-line method can be used to reduce coating material and processing time (over 40% for the operating policy adopted in this work). Additionally, monitoring of the color features calculated by the algorithm allowed the identification of abnormal process conditions affecting visible coating uniformity. This work also addresses practical challenges related to image acquisition in the harsh environment of a pan coater, bringing this tool closer to a state of maturity for implementation in production units and opening the path for their optimization, monitoring, and automatic control.

Optimization of Critical Quality Attributes in Tablet Film Coating and Design Space Determination Using Pilot-Scale Experimental Data

In this study, the novel high-speed tablet film coating process in the continuous manufacturing was investigated. The influence of key process variables (inlet air flow rate, inlet air temperature, and suspension spray rate) were investigated using a Box-Behnken experimental design method. Statistical regression models were developed to predict the outlet air temperature and relative humidity, the coating efficiency, the tablet moisture content, and coating uniformity. The effects of the three key process variables were comprehensively investigated based on mathematical analysis, contour plots, and interaction plots. The results indicate that all the process responses are affected by changing the inlet air flow rate, temperature, and suspension spray rate. A design space (DS) in terms of failure probability was determined based on specifications for tablet moisture content (< 3.5%) and coating uniformity (tablet weight standard deviation < 4 mg for tablet weight of 200 mg) using Monte Carlo simulations. Independent experiments were carried out and successfully validated the robustness and accuracy of the determined DS for the investigated tablet film coating process. All the data were generated using an industrial pilot-scale novel high-speed tablet coating unit from a continuous manufacturing line. The work facilitates the quality by design implementation of continuous pharmaceutical manufacturing.

Effect of Sampling Frequency for Real-Time Tablet Coating Monitoring Using Near Infrared Spectroscopy

While the sampling of pharmaceutical products typically follows well-defined protocols, the parameterization of spectroscopic methods and their associated sampling frequency is not standard. Whereas, for blending, the sampling frequency is limited by the nature of the process, in other processes, such as tablet film coating, practitioners must determine the best approach to collecting spectral data. The present article studied how sampling practices affected the interpretation of the results provided by a near-infrared spectroscopy method for the monitoring of tablet moisture and coating weight gain during a pan-coating experiment. Several coating runs were monitored with different sampling frequencies (with or without co-adds (also known as sub-samples)) and with spectral averaging corresponding to processing cycles (1 to 15 pan rotations). Beyond integrating the sensor into the equipment, the present work demonstrated that it is necessary to have a good sense of the underlying phenomena that have the potential to affect the quality of the signal. The effects of co-adds and averaging was significant with respect to the quality of the spectral data. However, the type of output obtained from a sampling method dictated the type of information that one can gain on the dynamics of a process. Thus, different sampling frequencies may be needed at different stages of process development.

Setting the process parameters for the coating process in order to assure tablet appearance based on multivariate analysis of prior data

Designing efficient, robust process parameters in drug product manufacturing is important to assure a drug's critical quality attributes. In this research, an efficient, novel procedure for a coating process parameter setting was developed, which establishes a prediction model for setting suitable input process parameters by utilizing prior manufacturing knowledge for partial least squares regression (PLSR). In the proposed procedure, target values or ranges of the output parameters are first determined, including tablet moisture content, spray mist condition, and mechanical stress on tablets. Following the preparation of predictive models relating input process parameters to corresponding output parameters, optimal input process parameters are determined using these models so that the output parameters hold within the target ranges. In predicting the exhaust air temperature output parameter, which reflects the tablets' moisture content, PLSR was employed based on prior measured data (such as batch records of other products rather than design of experiments), leading to minimal new experiments. The PLSR model was revealed to be more accurate at predicting the exhaust air temperature than a conventional semi-empirical thermodynamic model. A commercial scale verification demonstrated that the proposed process parameter setting procedure enabled assurance of the quality of tablet appearance without any trial-and-error experiments.