Multiblock PLS analysis of an industrial pharmaceutical process
Article first published online: 24 SEP 2002
Copyright © 2002 Wiley Periodicals, Inc.
Biotechnology and Bioengineering
Volume 80, Issue 4, pages 419–427, 20 November 2002
How to Cite
Lopes, J.A., Menezes, J.C., Westerhuis, J.A. and Smilde, A.K. (2002), Multiblock PLS analysis of an industrial pharmaceutical process. Biotechnol. Bioeng., 80: 419–427. doi: 10.1002/bit.10382
- Issue published online: 24 SEP 2002
- Article first published online: 24 SEP 2002
- Manuscript Accepted: 23 APR 2002
- Manuscript Received: 6 DEC 2001
- Foundation for Science and Technology. Grant Number: PRAXIS XXI BD/18471/98
- pharmaceutical production;
- multiblock PLS;
- multivariate modeling
The performance of an industrial pharmaceutical process (production of an active pharmaceutical ingredient by fermentation, API) was modeled by multiblock partial least squares (MBPLS). The most important process stages are inoculum production and API production fermentation. Thirty batches (runs) were produced according to an experimental planning. Rather than merging all these data into a single block of independent variables (as in ordinary PLS), four data blocks were used separately (manipulated and quality variables for each process stage). With the multiblock approach it was possible to calculate weights and scores for each independent block. It was found that the inoculum quality variables were highly correlated with API production for nominal fermentations. For the nonnominal fermentations, the manipulations of the fermentation stage explained the amount of API obtained (especially the pH and biomass concentration). Based on the above process analysis it was possible to select a smaller set of variables with which a new model was built. The amount of variance predicted of the final API concentration (cross-validation) for this model was 82.4%. The advantage of the multiblock model over the standard PLS model is that the contributions of the two main process stages to the API volumetric productivity were determined. © 2002 Wiley Periodicals, Inc. Biotechnol Bioeng 80: 419–427, 2002.