Data fusion strategies for performance improvement of a Process Analytical Technology platform consisting of four instruments: An electrospinning case study

Casian, Tibor; Farkas, Attila; Ilyés, Kinga; Démuth, Balázs; Borbás, Enikő; Madarász, Lajos; Rapi, Zsolt; Farkas, Balázs; Balogh, Attila; Domokos, András; Marosi, György; Tomuță, Ioan; Nagy, Zsombor Kristóf

doi:10.1016/j.ijpharm.2019.118473

Cited by 20 publications

(17 citation statements)

References 46 publications

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“…Thirdly, a dimensionality reduction step is essential to extract relevant features in MLDF [ 91 , 102 , 103 ]. Another justification for this step is to reduce the computational time during model development, i.e., for neural networks [ 104 , 105 ].…”

Section: Data Fusionmentioning

confidence: 99%

“…For more details, the reader is referred to, e.g., [ 116 ] and [ 117 ]. Other feature extraction methods found in the literature are parallel factor analysis (PARAFAC), a generalization of PCA [ 91 ], independent component analysis (ICA) [ 118 ], orthogonal-PLS [ 104 ], or autoencoder [ 119 ].…”

Section: Data Fusionmentioning

confidence: 99%

“…The obtained LVs have been extensively used as relevant features for DF applications [ 118 , 120 , 121 , 122 ]; for overview purposes [ 104 ] and outlier identification [ 72 ].…”

Section: Data Fusionmentioning

confidence: 99%

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

et al. 2022

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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.

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Section: Data Fusionmentioning

confidence: 99%

Section: Data Fusionmentioning

confidence: 99%

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

et al. 2022

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“…At present, electrospinning is restricted to the manufacturing of place mats (two dimensional objects). Owing to the deposition of charge from electrically spun fibres on the collection surface, the resulting material is limited to a thickness of approximately 3-4 mm [40]. Additionally, electrospinning limitations include the dangerous quality of the solvents used, efficiency/productivity and fibre power generation [41][42][43].…”

Section: Electrospinning Material Toxicity and Limitationsmentioning

confidence: 99%

The amber nano fibers development prospects to expand the capabilites of textile 3D printing in the general process of fabrication methods

Viļuma-Gudmona

Ļašenko

Sanchaniya

et al. 2021

Engineering for Rural Development

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The amber nano fibres are bio-active composites which could be applied in three-dimensional printing. The idea of using the amber for creating a new 3D printing material, including technological composition of the innovative amber nano and micro fibres is based on the hypothesis that organic compounds of succinite have an effective impact on living cells, including reparative, stimulating, for sedative effects as well protective properties from electro-magnetic field. To produce amber nano fibres, bioactive components were used that were isolated from the succinite and tested (in vitro) in the scientific laboratories. Three-dimensional printing is an emerging technology, which has been initially used to design and generate three-dimensional structures mainly for transplantation therapies. This process, which can be used for a variety of polymers, is becoming an increasingly essential component of biotechnological advancements. This article discusses the foundations of this technology, namely the processes used in conventional three-dimensional printing; it also discusses tissue engineering, a discipline of which new implementations of this technology are used as bio printing. The shortcomings of tissue engineering are addressed, including the failure of existing technology to manufacture nanofiber-based constructs used in blood vessels, cartilage, artery valves, tendon, cardiac muscle valves, muscle, and cornea. From this need for nanofiber processing, electrospinning is proposed as a possible roadmap for imminent tissue engineering threedimensional printing technologies, and finally, the latest integration of this technology with three-dimensional printing is addressed, highlighting the existing shortcomings in maintaining mandatory nano resolutions.

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“…A data fusion model combing near infrared (NIR) features and process parameters for prediction of drug dissolution from controlled release multiparticulate beads was developed by Ibrahim et al 25 . Casian et al 26 first applied a data fusion strategy to increase the quantitative ability of a process analytical technology (PAT) platform consisting of four sensors. Han et al 27 extracted 145 direct compressed oral disintegrating tablets (ODT) formulation data from 1218 articles in Web of Science database, and built a deep neural network (DNN) model to predict the disintegrating time of ODT formulations.…”

Section: Introductionmentioning

confidence: 99%

Using a material database and data fusion method to accelerate the process model development of high shear wet granulation

Wang

Cao

et al. 2021

Sci Rep

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High shear wet granulation (HSWG) has been wildly used in manufacturing of oral solid dosage (OSD) forms, and process modeling is vital to understanding and controlling this complex process. In this paper, data fusion and multivariate modeling technique were applied to develop a formulation-process-quality model for HSWG process. The HSWG experimental data from both literature and the authors’ laboratory were fused into a single and formatted representation. A material database and material matching method were used to compensate the incomplete physical characterization of literature formulation materials, and dimensionless parameters were utilized to reconstruct process variables at different granulator scales. The exploratory study on input materials properties by principal component analysis (PCA) revealed that the formulation data collected from different articles generated a formulation library which was full of diversity. In prediction of the median granule size, the partial least squares (PLS) regression models derived from literature data only and a combination of literature data and laboratory data were compared. The results demonstrated that incorporating a small number of laboratory data into the multivariate calibration model could help significantly reduce the prediction error, especially at low level of liquid to solid ratio. The proposed data fusion methodology was beneficial to scientific development of HSWG formulation and process, with potential advantages of saving both experimental time and cost.

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Data fusion strategies for performance improvement of a Process Analytical Technology platform consisting of four instruments: An electrospinning case study

Cited by 20 publications

References 46 publications

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

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

The amber nano fibers development prospects to expand the capabilites of textile 3D printing in the general process of fabrication methods

Using a material database and data fusion method to accelerate the process model development of high shear wet granulation

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