Modeling and Optimization of a Tablet Manufacturing Line

Abstract

This paper investigates an approach to modeling and optimizing an industrial tablet manufacturing line for different API and excipient formulations. Multi-block partial lease square (PLS) models are built from historical data on a given class of drug products. The data blocks consisted of data on the mass fractions of API and 11 excipients used in the different formulations, the roller compaction process variables, the tablet press settings and the measured final product quality attributes (tablet weight, hardness, and disintegration time). More than 400 runs are used in the modeling. The multi-block PLS models are first used to show which process blocks and which variables in each of the process blocks are most influential on the product quality variables. An optimization is then performed in the latent variable space of the PLS model to find the optimal combination of settings to use for the critical to quality roller compaction and tablet press variables in order to achieve the desired final tablet properties for a specified drug formulation. This optimization can be used to set up the tableting line prior to running a new formulation or can be used in an on-line mode for making small corrections to the operation of the tablet presses in response to small variations in formulations, raw material properties, and roller compaction operation.

Latent variable modeling to analyze the effects of process parameters on the dissolution of paracetamol tablet

“…As the same to VIP, a threshold equal to 1 was applied to assess whether a block was important in prediction of the response. 2 The bar plot of BIP indexes of the 4 blocks used to build the MBPLS model is reported in Fig. 6.…”

“…1 Generally, the production process of tablet mainly consists of several steps: blending, granulating, drying and compaction. 2 The target of tablet production process is to get desired quality. According to quality by design (QbD) principles evolved with the International Conference on Harmonization of Technical Requirements for Registration of Pharmaceuticals for Human Use (ICH) Q8 guidance, 3 it is of great significance to realize that the quality should be designed into the product.…”

“…5 In recent years, several contributions have been reported to develop new processes under the framework of QbD using modeling methods including first-principle models, 6,7 response surface models based on design of experiments 8,9,10 and multivariate statistical models. 2,11,12 For developing a tablet production process which consists of multiple units, it is difficult to establish a deterministic model to describe the process comprehensively. However, multivariate statistical models are useful tools to improve understandings via identifying the critical material attributes (CMAs) and critical process parameters affecting the quality of the final product, and identifying the most important mechanisms of the production process.…”

Advanced Process Decision Making Using Multivariate Latent Variable Methods

“…Liu et al [53] used an MB-PLS approach to model and optimize an industrial tablet manufacturing line for different API and excipient formulations. The multiblock approach was justified by presence of four different sources of variability for the X space, namely the mass fraction of the API, the mass fractions of the excipients (combinations of 11 different excipients were considered), the process settings of the roller compactor and the process settings of the tablet press.…”

“…As in Liu et al [53], an MB-PLS approach was used, where the X space considered 6 blocks (raw materials, granulation, drying, blending, compression and coating), whereas the Y space considered a single block containing the critical to quality attributes (tablet friability, API content uniformity, dissolution and drug-related impurities). For the designed process to be robust, the objective function of the optimization problem was formulated as [45]:…”