Control of three different continuous pharmaceutical manufacturing processes: Use of soft sensors

Rehrl, Jakob; Karttunen, Anssi-Pekka; Nicolaï, Niels; Hörmann, Theresa; Horn, Martin; Korhonen, Ossi; Nopens, Ingmar; Beer, Thomas De; Khinast, Johannes G.

doi:10.1016/j.ijpharm.2018.03.027

Cited by 58 publications

(28 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Process analytical technology (PAT) is an important aspect of solid oral dosage manufacturing control strategies, including both non-spectroscopic (soft sensor) and spectroscopic-based PAT [1,2]. Spectroscopic PAT, such as NIR and Raman, has been used successfully for real-time monitoring of uniformity and mixing, moisture content measurements in oral solid unit operations, physical form, and identification [3][4][5][6][7][8][9][10].…”

Section: Advantages Of Spectroscopic Patmentioning

confidence: 99%

Process Control of Drug Product Continuous Manufacturing Operations—a Study in Operational Simplification and Continuous Improvement

et al. 2020

View full text Add to dashboard Cite

Purpose The purpose of this manuscript is to demonstrate that implementation of gravimetric measurements provides the same assurance of product quality and process control as spectroscopic measurements (1) for control of drug content in a fixed-dose combination (FDC) tablet and (2) for identification of non-conforming material. Methods A wet granulation continuous tableting line was used to make the FDC drug product batches. Comparative data was generated for ten batches using near-infrared (NIR) spectroscopy for core tablets, and gravimetric in-process control measurements (IPCs) applied to the ratio control of intra- and extra-granular blend (IG and EG). HPLC reference data were collected to further demonstrate uniformity at each stage of the production process, including IG, final blend, and core tablets. All possible sources of variation not directly detectable by the gravimetric measurements were considered and quantified. Results The two IPC measurement techniques showed excellent agreement where both were within 2% of the target drug concentrations and within 2% of each other for the ten comparative batches. The NIR was more sensitive to material and process variations than the gravimetric IPCs; thus, it was more variable within and across batches. Gravimetric IPCs were demonstrated as an effective replacement for spectroscopic measurements for continuous tableting operations, capable of ensuring on target manufacturing and detection of non-conforming material. Conclusions As pharmaceutical companies continue to push toward operational simplicity and sustainable manufacturing processes, soft-sensor and gravimetric controls as alternatives to their spectroscopic counterparts will be applied more broadly for process monitoring and control.

show abstract

Section: Advantages Of Spectroscopic Patmentioning

confidence: 99%

Process Control of Drug Product Continuous Manufacturing Operations—a Study in Operational Simplification and Continuous Improvement

et al. 2020

View full text Add to dashboard Cite

show abstract

“…The chemical composition of herbs may vary depending on the species, location of growth, age, harvesting season, drying conditions, and other factors (Heinrich, 2015). Therefore, comprehensive studies of effective analytical methods are required to make quick and reliable quality control at any stage of herbal medicine (HM) production as well as during the storage process, to obtain feedback (Bostijn et al, 2018;Rehrl et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Herbal Medicine Characterization Perspectives Using Advanced FTIR Sample Techniques – Diffuse Reflectance (DRIFT) and Photoacoustic Spectroscopy (PAS)

2020

View full text Add to dashboard Cite

This study demonstrates the significant potential of the Fourier transform infrared spectroscopy (FTIR) sampling methods: cantilever-enhanced Fourier transform infrared photoacoustic spectroscopy (FTIR PAS) and diffuse reflectance infrared spectroscopy (FTIR DRIFT) in the field of herbal medicines (HM). In the present work we investigated DRIFT and PAS sampling methods because they do not require sample preparation, samples may be opaque or dark, require small amounts, both liquid and solid samples can be measured, and solid samples can be analyzed on a small scale. Experiments conducted prove high sensitivity, reproducibility and capability in combination with an unsupervised multivariate analysis technique to discriminate important characteristics of HM, such as the identification of plant parts, differentiation of samples by types, and determination of the concentration of extractable compounds in HM.

show abstract

“…Zabadaj et al [10] proposed an effective soft sensor for the supervisory control of biotransformation production, and the efficiency of the approach was demonstrated. Rehrla et al [11] developed a soft sensor method for estimating the active pharmaceutical ingredient concentration from the system data and the soft sensor model was tested in the three different continuous production lines. A novel approach of supervised latent factor analysis was proposed based on system data regression modelling, which can effectively predict heterogeneous variances, and soft sensors are established for quality estimate in the two case studies [12].…”

Section: Introductionmentioning

confidence: 99%

Development of a Novel Soft Sensor with Long Short-Term Memory Network and Normalized Mutual Information Feature Selection

Li¹,

Sun

2020

Mathematical Problems in Engineering

View full text Add to dashboard Cite

In this paper, a novel soft sensor is developed by combining long short-term memory (LSTM) network with normalized mutual information feature selection (NMIFS). In the proposed algorithm, LSTM is designed to handle time series with high nonlinearity and dynamics of industrial processes. NMIFS is conducted to perform the input variable selection for LSTM to simplify the excessive complexity of the model. The developed soft sensor combines the excellent dynamic modelling of LSTM and precise variable selection of NMIFS. Simulations on two actual production datasets are used to demonstrate the performance of the proposed algorithm. The developed soft sensor could precisely predict the objective variables and has better performance than other methods.

show abstract

Control of three different continuous pharmaceutical manufacturing processes: Use of soft sensors

Cited by 58 publications

References 19 publications

Process Control of Drug Product Continuous Manufacturing Operations—a Study in Operational Simplification and Continuous Improvement

Process Control of Drug Product Continuous Manufacturing Operations—a Study in Operational Simplification and Continuous Improvement

Herbal Medicine Characterization Perspectives Using Advanced FTIR Sample Techniques – Diffuse Reflectance (DRIFT) and Photoacoustic Spectroscopy (PAS)

Development of a Novel Soft Sensor with Long Short-Term Memory Network and Normalized Mutual Information Feature Selection

Contact Info

Product

Resources

About