| Title |
PAT-Based Control of Fluid Bed Coating Process Using NIR Spectroscopy to Monitor the Cellulose Coating on Pharmaceutical Pellets
|
|---|---|
| Published in |
AAPS PharmSciTech, December 2016
|
| DOI | 10.1208/s12249-016-0680-2 |
| Pubmed ID | |
| Authors |
Venkata Ramana Naidu, Rucha S. Deshpande, Moinuddin R. Syed, Piyush Deoghare, Dharamvir Singh, Pravin S. Wakte |
| Abstract |
Current endeavor was aimed towards monitoring percent weight build-up during functional coating process on drug-layered pellets. Near-infrared (NIR) spectroscopy is an emerging process analytical technology (PAT) tool which was employed here within quality by design (QbD) framework. Samples were withdrawn after spraying every 15-Kg cellulosic coating material during Wurster coating process of drug-loaded pellets. NIR spectra of these samples were acquired using cup spinner assembly of Thermoscientific Antaris II, followed by multivariate analysis using partial least squares (PLS) calibration model. PLS model was built by selecting various absorption regions of NIR spectra for Ethyl cellulose, drug and correlating the absorption values with actual percent weight build up determined by HPLC. The spectral regions of 8971.04 to 8250.77 cm(-1), 7515.24 to 7108.33 cm(-1), and 5257.00 to 5098.87 cm(-1) were found to be specific to cellulose, where as the spectral region of 6004.45 to 5844.14 cm(-1)was found to be specific to drug. The final model gave superb correlation co-efficient value of 0.9994 for calibration and 0.9984 for validation with low root mean square of error (RMSE) values of 0.147 for calibration and 0.371 for validation using 6 factors. The developed correlation between the NIR spectra and cellulose content is useful in precise at-line prediction of functional coat value and can be used for monitoring the Wurster coating process. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 67% |
| Switzerland | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 27 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 27 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 8 | 30% |
| Researcher | 3 | 11% |
| Student > Postgraduate | 3 | 11% |
| Student > Ph. D. Student | 3 | 11% |
| Other | 2 | 7% |
| Other | 2 | 7% |
| Unknown | 6 | 22% |
| Readers by discipline | Count | As % |
|---|---|---|
| Pharmacology, Toxicology and Pharmaceutical Science | 5 | 19% |
| Chemistry | 5 | 19% |
| Engineering | 3 | 11% |
| Agricultural and Biological Sciences | 3 | 11% |
| Chemical Engineering | 2 | 7% |
| Other | 2 | 7% |
| Unknown | 7 | 26% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 27 March 2017.
All research outputs
#14,056,410
of 22,961,203 outputs
Outputs from AAPS PharmSciTech
#925
of 1,470 outputs
Outputs of similar age
#223,624
of 420,937 outputs
Outputs of similar age from AAPS PharmSciTech
#11
of 32 outputs
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We're also able to compare this research output to 32 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 56% of its contemporaries.