Adaptive modeling optimized by the data fusion strategy: Real-time dying cell percentage prediction using capacitance spectroscopy.

Autor: Wu S; Duquesne Center for Pharmaceutical Technology, Duquesne University, Pittsburgh, Pennsylvania, USA.; Duquesne University Graduate School for Pharmaceutical Sciences, Pittsburgh, Pennsylvania, USA., Ketcham SA; Manufascutring Science and Technology, Bristol-Myers Squibb, Devens, Massachusetts, USA., Corredor C; Pharmaceutical Development, Bristol-Myers Squibb, New Brunswick, New Jersey, USA., Both D; Pharmaceutical Development, Bristol-Myers Squibb, New Brunswick, New Jersey, USA., Zhao Y; Global Product Development and Supply, Bristol-Myers Squibb, Devens, Massachusetts, USA., Drennen JK; Duquesne Center for Pharmaceutical Technology, Duquesne University, Pittsburgh, Pennsylvania, USA.; Duquesne University Graduate School for Pharmaceutical Sciences, Pittsburgh, Pennsylvania, USA., Anderson CA; Duquesne Center for Pharmaceutical Technology, Duquesne University, Pittsburgh, Pennsylvania, USA.; Duquesne University Graduate School for Pharmaceutical Sciences, Pittsburgh, Pennsylvania, USA.
Jazyk: angličtina
Zdroj: Biotechnology progress [Biotechnol Prog] 2024 Mar-Apr; Vol. 40 (2), pp. e3424. Date of Electronic Publication: 2024 Jan 04.
DOI: 10.1002/btpr.3424
Abstrakt: The previous research showcased a partial least squares (PLS) regression model accurately predicting cell death percentages using in-line capacitance spectra. The current study advances the model accuracy through adaptive modeling employing a data fusion approach. This strategy enhances prediction performance by incorporating variables from the Cole-Cole model, conductivity and its derivatives over time, and Mahalanobis distance into the predictor matrix (X-matrix). Firstly, the Cole-Cole model, a mechanistic model with parameters linked to early cell death onset, was integrated to enhance prediction performance. Secondly, the inclusion of conductivity and its derivatives over time in the X-matrix mitigated prediction fluctuations resulting from abrupt conductivity changes during process operations. Thirdly, Mahalanobis distance, depicting spectral changes relative to a reference spectrum from a previous time point, improved model adaptability to independent test sets, thereby enhancing performance. The final data fusion model substantially decreased root-mean squared error of prediction (RMSEP) by around 50%, which is a significant boost in prediction accuracy compared to the prior PLS model. Robustness against reference spectrum selection was confirmed by consistent performance across various time points. In conclusion, this study illustrates that the data fusion strategy substantially enhances the model accuracy compared to the previous model relying solely on capacitance spectra.
(© 2024 American Institute of Chemical Engineers.)
Databáze: MEDLINE
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