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Review
. 2022 Jul 28;27(15):4846.
doi: 10.3390/molecules27154846.

Challenges and Opportunities of Implementing Data Fusion in Process Analytical Technology-A Review

Tibor Casian  1 , Brigitta Nagy  2 , Béla Kovács  3 , Dorián László Galata  2 , Edit Hirsch  2 , Attila Farkas  2
Affiliations
Review

Challenges and Opportunities of Implementing Data Fusion in Process Analytical Technology-A Review

Tibor Casian et al. Molecules. .

Abstract

The release of the FDA's guidance on Process Analytical Technology has motivated and supported the pharmaceutical industry to deliver consistent quality medicine by acquiring a deeper understanding of the product performance and process interplay. The technical opportunities to reach this high-level control have considerably evolved since 2004 due to the development of advanced analytical sensors and chemometric tools. However, their transfer to the highly regulated pharmaceutical sector has been limited. To this respect, data fusion strategies have been extensively applied in different sectors, such as food or chemical, to provide a more robust performance of the analytical platforms. This survey evaluates the challenges and opportunities of implementing data fusion within the PAT concept by identifying transfer opportunities from other sectors. Special attention is given to the data types available from pharmaceutical manufacturing and their compatibility with data fusion strategies. Furthermore, the integration into Pharma 4.0 is discussed.

Keywords: chemometrics; data fusion; process analytical technology; process control.

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Conflict of interest statement

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Figures

Figure 1
Figure 1
Guidelines used for the quality management of pharmaceutical products.
Figure 2
Figure 2
Data types encountered in the pharmaceutical industry.
Figure 3
Figure 3
-DF strategies and data structures.
Figure 4
Figure 4
Evaluation of the best performing DF strategies across different areas of application (a) and their selection according to the data structure used for modeling ((b)-classification, (c)-process control, (d)-regression applications); 0 + 0: fusion of zeroth order data; 0 + 1: fusion of zeroth order data with first order data; 1 + 1: fusion of first-order data; x-axis represents the number of studies.
Figure 5
Figure 5
(Other—1 entry/method: 2D-image based estimator; correlation-based feature selection-CFS; forward selection; IRIV; multivariate curve resolution-alternating least squares (MCR-ALS); PARAFAC; Random frog (RF); Spectral signatures and leaf venation feature extraction; spectral window selection (SWS); T2, Q—derived from NIR-based MSPC; UV; variable selection based on the normalized differences between reference and sample spectral data; Variables Combination Population Analysis and Iterative Retained Information Variable Algorithm—VCPA-IRIV).
Figure 6
Figure 6
Evaluation of modeling methods considered for classification (a); process control (b) and regression purposes (c); x-axis represents the number of studies using a particular method.
See this image and copyright information in PMC

References

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