Process Analytical Chemistry

Workman, Jerry; Lavine, Barry K.; Chrisman, Ray W.; Koch, Mel

doi:10.1021/ac200974w

Cited by 88 publications

(62 citation statements)

References 176 publications

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“…In practice, concentration measurements require sampling, sample preparation and subsequent offline analysis, which is expensive in terms of both cost and time. In comparison, the recent development of Process Analytical Technologies (PAT) has opened up new avenues for exploiting spectroscopic data, which indirectly provide non-destructive concentration measurements of the species in-situ/online during the course of a reaction [12]. Accurate spectroscopic measurements are available at low cost and short sampling times, and are free of any delay.…”

Section: List Of Symbolsmentioning

confidence: 99%

Extent-based kinetic identification using spectroscopic measurements and multivariate calibration

Billeter¹,

Srinivasan²,

Bonvin³

2013

Analytica Chimica Acta

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Section: List Of Symbolsmentioning

confidence: 99%

Extent-based kinetic identification using spectroscopic measurements and multivariate calibration

Billeter¹,

Srinivasan²,

Bonvin³

2013

Analytica Chimica Acta

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“…The QbD‐based thinking is a perfect opportunity for the pharmaceutical community to take the manufacturing sciences into the new millennium. It has to be, however, emphasized that the concept of PAT is not entirely new, as process analysis/control has been an important area of chemical engineering for decades . Nevertheless, PAT introduced the idea of real‐time process control and real‐time quality assurance (QA) in pharmaceutical manufacturing, being the basis for modern process engineering.…”

Section: Introductionmentioning

confidence: 99%

The Future of Pharmaceutical Manufacturing Sciences

Rantanen

Khinast

2015

Journal of Pharmaceutical Sciences

363

152

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The entire pharmaceutical sector is in an urgent need of both innovative technological solutions and fundamental scientific work, enabling the production of highly engineered drug products. Commercial‐scale manufacturing of complex drug delivery systems (DDSs) using the existing technologies is challenging. This review covers important elements of manufacturing sciences, beginning with risk management strategies and design of experiments (DoE) techniques. Experimental techniques should, where possible, be supported by computational approaches. With that regard, state‐of‐art mechanistic process modeling techniques are described in detail. Implementation of materials science tools paves the way to molecular‐based processing of future DDSs. A snapshot of some of the existing tools is presented. Additionally, general engineering principles are discussed covering process measurement and process control solutions. Last part of the review addresses future manufacturing solutions, covering continuous processing and, specifically, hot‐melt processing and printing‐based technologies. Finally, challenges related to implementing these technologies as a part of future health care systems are discussed. © 2015 The Authors. Journal of Pharmaceutical Sciences published by Wiley Periodicals, Inc. and the American Pharmacists Association J Pharm Sci 104:3612–3638, 2015

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“…1 It eliminates some of the drawbacks of traditional methods that involve cumbersome sampling and delayed quality control, and facilitates online and real-time quality control.…”

Section: Introductionmentioning

confidence: 99%

“…Partial least-squares regression (PLS) is the most widely applied method for regression tasks. [1][2][3] It assumes an approximate linear relationship between the quality parameter and the intensity of its absorption bands, and is thus feasible for spectral data that follow Beer's law. Moreover, PLS is particularly suited when data are high dimensional and multicollinear, which is often the case with spectral data.…”

Section: Introductionmentioning

confidence: 99%

Application of Support Vector Regression for Simultaneous Modelling of near Infrared Spectra from Multiple Process Steps

Tange

Rasmussen

Taira

et al. 2015

Journal of Near Infrared Spectroscopy

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Near infrared (NIR) spectroscopy in combination with partial least-squares regression (PLS) is widely applied in process control for non-destructive measurement of quality parameters during production. PLS assumes an approximate linear relationship between the parameter to be estimated and the intensity of its absorption bands. Spectra, however, may contain non-linearities for various reasons such as differences in viscosity, temperature, pH, particle size and chemical composition of the sample matrix. In such cases, PLS might not predict the parameter of interest sufficiently well, and one must find other methods for the calibration task. Support vector machine regression (SVR) has been gaining interest within chemometrics in recent years. SVR is capable of modelling highly non-linear data, also when data are of very high dimensions. The aim of this study was to develop calibration models of NIR spectra from four different process steps in a raw-sugar factory. The models were intended for monitoring two quality parameters at the individual process steps.Our goal was to obtain global calibration models covering all four process steps in order to obtain simple calibration maintenance. SVR was used for the calibration task, since all of the above-mentioned sources of non-linearities were present. SVR results were benchmarked against PLS. SVR had a better prediction performance than PLS (1) for models built on individual process steps, (2) for global models covering all four process steps and (3) when the global models were evaluated on the individual process steps. Moreover, the majority of SVR models had prediction errors close to reference uncertainty and hence were close to being optimal. Finally, the global SVR models predicted the individual process steps better than the corresponding local PLS models. We conclude that the nonlinear modelling method SVR was able to model non-linearities caused by pooling NIR spectra from multiple different process steps.Implementation of the global SVR models would have several advantages over the local PLS models. First, they would allow simple calibration maintenance because only one model per quality parameter would have to be maintained. Second, they would allow more precise estimation of the quality parameters and therefore better process monitoring.

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Process Analytical Chemistry

Cited by 88 publications

References 176 publications

Extent-based kinetic identification using spectroscopic measurements and multivariate calibration

Extent-based kinetic identification using spectroscopic measurements and multivariate calibration

The Future of Pharmaceutical Manufacturing Sciences

Application of Support Vector Regression for Simultaneous Modelling of near Infrared Spectra from Multiple Process Steps

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