Modeling of feed-forward control using the partial least squares regression method in the tablet compression process

Evaluation of the effect of granule size of raw tableting materials on critical quality attributes of tablets during the continuous tablet manufacturing process using near-infrared spectroscopy

OBJECTIVE

The effects of granule size of raw materials on tablet hardness (TH) and weight (TW) in the continuous tablet manufacturing process (CTMP) were investigated using near-infrared spectroscopy (NIRS).

METHODS

Granule materials of different sizes were prepared by extrusion granulation from a standard granule formula powder containing lactose/starch and 4.5% acetaminophen. Large-, small-, and medium-sized granules were sequentially filled in a hopper, and tablets were produced continuously using a single-shot tableting machine. After arranging approximately 500 tablets in order, the tablets were subjected to NIRS. A total of 450 NIRS datasets were divided into three groups of 150 each (calibration, validation 1, and validation 2 datasets).

RESULTS

The best fitted calibration models for predicting TH and TW were obtained, with sufficient accuracy, based on NIRS using the partial least squares regression, and comprised both physical and chemical information. The regression and loading vectors of the calibration models suggested that the models used to predict TH and TW involve physical information based on geometrical factors of the tablet and chemical information related to binder-related intermolecular interactions.

CONCLUSIONS

The changes in the predicted value profiles of TH and TW using NIRS reflected the changes in the measured values depending on the raw granule size during CTMP.

PAT implementation for advanced process control in solid dosage manufacturing - A practical guide

The implementation of continuous pharmaceutical manufacturing requires advanced control strategies rather than traditional end product testing or an operation within a small range of controlled parameters. A high level of automation based on process models and hierarchical control concepts is desired. The relevant tools that have been developed and successfully tested in academic and industrial environments in recent years are now ready for utilization on the commercial scale. To date, the focus in Process Analytical Technology (PAT) has mainly been on achieving process understanding and quality control with the ultimate goal of real-time release testing (RTRT). This work describes the workflow for the development of an in-line monitoring strategy to support PAT-based real-time control actions and its integration into solid dosage manufacturing. All stages are discussed in this paper, from process analysis and definition of the monitoring task to technology assessment and selection, its process integration and the development of data acquisition.

Development of a Controlled Continuous Low-Dose Feeding Process

This paper proposes a feed rate control strategy for a novel volumetric micro-feeder, which can accomplish low-dose feeding of pharmaceutical raw materials with significantly different powder properties. The developed feed-forward control strategy enables a constant feed rate with a minimum deviation from the set-point, even for materials that are typically difficult to accurately feed (e.g., due to high cohesion or low density) using conventional continuous feeders. Density variations observed during the feeding process were characterized via a displacement feed factor profile for each powder. The characterized effective displacement density profile was applied in the micro-feeder system to proactively control the feed rate by manipulating the powder displacement rate (i.e., computing the feed rate from the powder displacement rate). Based on the displacement feed factor profile, the feed rate can be predicted during the feeding process and at any feed rate set-point. Three pharmaceutically relevant materials were used for the micro-feeder evaluation: di-calcium phosphate (large-particle system, high density), croscarmellose sodium (small-particle system, medium density), and barium sulfate (very small-particle <10 μm, high density). A significant improvement in the feeding performance was achieved for all investigated materials. The feed rate deviation from the set-point and its relative standard deviation were minimal compared to operations without the control strategy.

Pharmaceutical application of multivariate modelling techniques: a review on the manufacturing of tablets

The tablet manufacturing process is a complex system, especially in continuous manufacturing (CM). It includes multiple unit operations, such as mixing, granulation, and tableting. In tablet manufacturing, critical quality attributes are influenced by multiple factorial relationships between material properties, process variables, and interactions. Moreover, the variation in raw material attributes and manufacturing processes is an inherent characteristic and seriously affects the quality of pharmaceutical products. To deepen our understanding of the tablet manufacturing process, multivariable modeling techniques can replace univariate analysis to investigate tablet manufacturing. In this review, the roles of the most prominent multivariate modeling techniques in the tablet manufacturing process are discussed. The review mainly focuses on applying multivariate modeling techniques to process understanding, optimization, process monitoring, and process control within multiple unit operations. To minimize the errors in the process of modeling, good modeling practice (GMoP) was introduced into the pharmaceutical process. Furthermore, current progress in the continuous manufacturing of tablets and the role of multivariate modeling techniques in continuous manufacturing are introduced. In this review, information is provided to both researchers and manufacturers to improve tablet quality.

Multivariate feed forward process control and optimization of an industrial, granulation based tablet manufacturing line using historical data

The purpose of this work was to understand the variability in disintegration time and tableting yield of high drug load (>60%) tablets prepared by batch-wise high shear wet granulation. The novelty of the study is the use of multivariate methods (Batch Evolution Models - BEMs and Batch Level Models - BLMs) to enhance process control, with a feed forward component, using prediction models built from a historical dataset acquired for 95 industrial scale batches. Time dependent process variables and significant influences on investigated parameters were identified. Prediction of output from input was tested with Partial Least Squares (PLS) and Artificial Neural Network (ANN) modeling. A reliable prediction ability was achieved for granulation water amount (±2 kg in a 16-31 kg range), tableting speed (±5000 tablets/h in a 23,000-72,500 tabl./h range) and disintegration time of cores (±100 s; in a 250-900 s range). Offsets from the optimal process evolution and certain raw material properties were correlated with differences observed in the output variables. Improvement options were identified for 80% of the batches with high disintegration time. Hence, the trained models can be applied for systematic process improvement, enabling feed forward control.

Partial Least Squares Regression-Based Robust Forward Control of the Tableting Process

In this study, we established a robust feed-forward control model for the tableting process by partial least squares regression using the near-infrared (NIR) spectra and physical attributes of the granules to be compressed. The NIR spectra of granules are rich in information about chemical attributes, such as the compositions of any ingredients and moisture content. Polymorphism and pseudo-polymorphism can also be quantitatively evaluated by NIR spectra. We used the particle size distribution, flowability, and loose and tapped density as the physical attributes of the granules. The tableting process was controlled by the lower punch fill depth and the minimum distance between the upper and lower punches at compression, which were specifically related to the tablet weight and thickness, respectively. The feed-forward control of the process would be expected to provide some advantages for automated and semi-automated continuous pharmaceutical manufacturing. As a result, our model, using a combination of NIR spectra and the physical attributes of granules to control the distance between punches, resulted in respectable agreement between the predicted process parameters and actual settings to produce tablets of the desired thickness.

Data fusion strategies for performance improvement of a Process Analytical Technology platform consisting of four instruments: An electrospinning case study

The aim of this work was to develop a PAT platform consisting of four complementary instruments for the characterization of electrospun amorphous solid dispersions with meloxicam. The investigated methods, namely NIR spectroscopy, Raman spectroscopy, Colorimetry and Image analysis were tested and compared considering the ability to quantify the active pharmaceutical ingredient and to detect production errors reflected in inhomogeneous deposition of fibers. Based on individual performance the calculated RMSEP values ranged between 0.654% and 2.292%. Mid-level data fusion consisting of data compression through latent variables and application of ANN for regression purposes proved efficient, yielding an RMSEP value of 0.153%. Under these conditions the model could be validated accordingly on the full calibration range. The complementarity of the PAT tools, demonstrated from the perspective of captured variability and outlier detection ability, contributed to model performance enhancement through data fusion. To the best of the author's knowledge, this is the first application of data fusion in the field of PAT for efficient handling of big-analytical-data provided by high-throughput instruments.

Feedforward and Feedback Control of a Pharmaceutical Coating Process

This work demonstrates the use of a combination of feedforward and feedback loops to control the controlled release coating of theophylline granules. Feedforward models are based on the size distribution of incoming granules and are used to set values for the airflow in the fluid bed processor and the target coat weight to be applied to the granules. The target coat weight of the granules is controlled by a feedback loop using NIR spectroscopy to monitor the progress of the process. By combining feedforward and feedback loops, significant variation in the size distributions and ambient conditions were accommodated in the fluid bed coating of the granules and a desired dissolution profile was achieved. The feedforward component of the control system was specifically tested by comparing the performance of the control system with and without this element by Monte Carlo simulation.

Short-Term Load Forecasting with Tensor Partial Least Squares-Neural Network

Short-term load forecasting is very important for power systems. The load is related to many factors which compose tensors. However, tensors cannot be input directly into most traditional forecasting models. This paper proposes a tensor partial least squares-neural network model (TPN) to forecast the power load. The model contains a tensor decomposition outer model and a nonlinear inner model. The outer model extracts common latent variables of tensor input and vector output and makes the residuals less than the threshold by iteration. The inner model determines the relationship between the latent variable matrix and the output by using a neural network. This model structure can preserve the information of tensors and the nonlinear features of the system. Three classical models, partial least squares (PLS), least squares support vector machine (LSSVM) and neural network (NN), are selected to compare the forecasting results. The results show that the proposed model is efficient for short-term load and daily load peak forecasting. Compared to PLS, LSSVM and NN, the TPN has the best forecasting accuracy.

Raman Spectroscopy for Process Analytical Technologies of Pharmaceutical Secondary Manufacturing

As the process analytical technology (PAT) mindset is progressively introduced and adopted by the pharmaceutical companies, there is an increasing demand for effective and versatile real-time analyzers to address the quality assurance challenges of drug manufacturing. In the last decades, Raman spectroscopy has emerged as one of the most promising tools for non-destructive and fast characterization of the pharmaceutical processes. This review summarizes the achieved results of the real-time application of Raman spectroscopy in the field of the secondary manufacturing of pharmaceutical solid dosage forms, covering the most common secondary process steps of a tablet production line. In addition, the feasibility of Raman spectroscopy for real-time control is critically reviewed, and challenges and possible approaches to moving from real-time monitoring to process analytically controlled technologies (PACT) are discussed.

The Multivariate Regression Statistics Strategy to Investigate Content-Effect Correlation of Multiple Components in Traditional Chinese Medicine Based on a Partial Least Squares Method

Amultivariate regression statisticstrategy was developed to clarify multi-components content-effect correlation ofpanaxginseng saponins extract and predict the pharmacological effect by components content. In example 1, firstly, we compared pharmacological effects between panax ginseng saponins extract and individual saponin combinations. Secondly, we examined the anti-platelet aggregation effect in seven different saponin combinations of ginsenoside Rb1, Rg1, Rh, Rd, Ra3 and notoginsenoside R1. Finally, the correlation between anti-platelet aggregation and the content of multiple components was analyzed by a partial least squares algorithm. In example 2, firstly, 18 common peaks were identified in ten different batches of panax ginseng saponins extracts from different origins. Then, we investigated the anti-myocardial ischemia reperfusion injury effects of the ten different panax ginseng saponins extracts. Finally, the correlation between the fingerprints and the cardioprotective effects was analyzed by a partial least squares algorithm. Both in example 1 and 2, the relationship between the components content and pharmacological effect was modeled well by the partial least squares regression equations. Importantly, the predicted effect curve was close to the observed data of dot marked on the partial least squares regression model. This study has given evidences that themulti-component content is a promising information for predicting the pharmacological effects of traditional Chinese medicine.