Quality-by-Design III: Application of Near-Infrared Spectroscopy to Monitor Roller Compaction In-process and Product Quality Attributes of Immediate Release Tablets
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The objective of this study is to use near-infrared spectroscopy (NIRS) coupled with multivariate chemometric models to monitor granule and tablet quality attributes in the formulation development and manufacturing of ciprofloxacin hydrochloride (CIP) immediate release tablets. Critical roller compaction process parameters, compression force (CFt), and formulation variables identified from our earlier studies were evaluated in more detail. Multivariate principal component analysis (PCA) and partial least square (PLS) models were developed during the development stage and used as a control tool to predict the quality of granules and tablets. Validated models were used to monitor and control batches manufactured at different sites to assess their robustness to change. The results showed that roll pressure (RP) and CFt played a critical role in the quality of the granules and the finished product within the range tested. Replacing binder source did not statistically influence the quality attributes of the granules and tablets. However, lubricant type has significantly impacted the granule size. Blend uniformity, crushing force, disintegration time during the manufacturing was predicted using validated PLS regression models with acceptable standard error of prediction (SEP) values, whereas the models resulted in higher SEP for batches obtained from different manufacturing site. From this study, we were able to identify critical factors which could impact the quality attributes of the CIP IR tablets. In summary, we demonstrated the ability of near-infrared spectroscopy coupled with chemometrics as a powerful tool to monitor critical quality attributes (CQA) identified during formulation development.
KEY WORDSchemometrics crushing force disintegration near-infrared spectroscopy partial least square principal component analysis quality by design roller compaction
The authors would like to acknowledge the financial assistance from the FDA and CIPET. Drs. Bancha Chuasuwan, Ramesh Dandu, and Walter Xie for their technical assistance with the CIP assay. Thanks to Ryan McCann for his assistance with roller compaction at Purdue University, School of Pharmacy.
The views expressed in this manuscript are the personal opinion of the authors and do not necessarily reflects the views or policies of the FDA
Analysis of variance
Volume mean diameter
Process analytical technology
Principal component analysis
Principal component regression
Partial least squares
Partial least square regression
Prediction residual error-sum squares
Standard error of calibration
Standard error of cross-validation
Standard error of prediction
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