title: In-Process Control Assay of Pharmaceutical Microtablets Using Hyperspectral Imaging Coupled with Multivariate Analysis.
abstract: Monitoring the amount of active pharmaceutical ingredient (API) in finished dosage form is important to ensure the content uniformity of the product. In this report, we summarize the development and validation of a hyperspectral imaging (HSI) technique for rapid in-line prediction of the active pharmaceutical ingredient (API) in microtablets with concentrations varying from 60 to 130% API (w/w). The tablet spectra of different API concentrations were collected in-line using an HSI system within the visible/near-infrared (vis/NIR; 400-1000 nm) and short-wave infrared (SWIR; 1100-2500 nm) regions. The ability of the HSI technique to predict the API concentration in the tablet samples was validated against a reference high-performance liquid chromatography (HPLC) method. The prediction efficiency of two different types of multivariate data modeling methods, that is, partial least-squares regression (PLSR) and principle component regression (PCR), were compared. The prediction ability of the regression models was cross-validated against results generated with the reference HPLC method. The results obtained from the PLSR models showed reliable performance for predicting the API concentration in SWIR region. The highest coefficient of determination (R2p) was 0.96, associated with the lowest predicted error and bias of 4.45 and -0.35%, respectively. Furthermore, the concentration-mapped images of PLSR and PCR models were used to visually characterize the API concentration present on the tablet surface. Based on these results, we suggest that HSI measurement combined with multivariate data analysis and chemical imaging could be implemented in the production environment for rapid in-line determination of pharmaceutical product quality.
authors.affiliation: Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University , 99 Daehak-ro, Yuseong-gu, Daejeon 34134, South Korea.
authors.forename: Lalit Mohan
authors.initials: LM
authors.lastname: Kandpal
authors.affiliation: Process Analytical Technology, Analytical Development, Biogen , Cambridge, Massachusetts, United States.
authors.forename: Jagdish
authors.initials: J
authors.lastname: Tewari
authors.affiliation: Formulation Development, Biogen , Cambridge, Massachusetts, United States.
authors.forename: Nishanth
authors.initials: N
authors.lastname: Gopinathan
authors.affiliation: Process Analytical Technology, Analytical Development, Biogen , Cambridge, Massachusetts, United States.
authors.forename: Pierre
authors.initials: P
authors.lastname: Boulas
authors.affiliation: Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University , 99 Daehak-ro, Yuseong-gu, Daejeon 34134, South Korea.
authors.forename: Byoung-Kwan
authors.initials: BK
authors.lastname: Cho
mesh_terms: D002851:Chromatography, High Pressure Liquid; D004339:Drug Compounding; D016018:Least-Squares Analysis; D015999:Multivariate Analysis; D004364:Pharmaceutical Preparations / Q000032:analysis*; D025341:Principal Component Analysis; D013607:Tablets / Q000737:chemistry*
chemical_list: D004364:Pharmaceutical Preparations; D013607:Tablets