Modeling and Optimization of a Tablet Manufacturing Line
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.
KeywordsMultivariate data analysis Partial Least Squares Latent variables Optimization Drug formulations Roller compaction Tablet press
- 2.Tousey MD. The granulation process101: basic technologies for tablet making. Pharmaceutical technology tableting and granulation 2002;8–13.Google Scholar
- 3.de Jong JAH. Tablet properties as a function of the properties of granules made in a fluidized bed process. Powder Tech. 1991;65(1–3):293–303.Google Scholar
- 8.Jaeckle CM, Macgregor JF. Product design through multivariate statistical analysis of process data. Am Inst Chem Eng. 1998;44:1105–18.Google Scholar
- 11.Garcia-Munoz S, MacGregor JF, Neogi D, Latshaw B, Metha S. Optimization of batch operating policies. Part II. Incorporating process constraints. Ind Eng Chem Res. 2007;47:4020–208.Google Scholar
- 13.ProSensus (2010) Software. Retrieved from ProSensus: http://prosensus.ca/software/promv