title: Semi-quantitative prediction of a multiple API solid dosage form with a combination of vibrational spectroscopy methods.
abstract: Quality control (QC) in the pharmaceutical industry is a key activity in ensuring medicines have the required quality, safety and efficacy for their intended use. QC departments at pharmaceutical companies are responsible for all release testing of final products but also all incoming raw materials. Near-infrared spectroscopy (NIRS) and Raman spectroscopy are important techniques for fast and accurate identification and qualification of pharmaceutical samples. Tablets containing two different active pharmaceutical ingredients (API) [bisoprolol, hydrochlorothiazide] in different commercially available dosages were analysed using Raman- and NIR Spectroscopy. The goal was to define multivariate models based on each vibrational spectroscopy to discriminate between different dosages (identity) and predict their dosage (semi-quantitative). Furthermore the combination of spectroscopic techniques was investigated. Therefore, two different multiblock techniques based on PLS have been applied: multiblock PLS (MB-PLS) and sequential-orthogonalised PLS (SO-PLS). NIRS showed better results compared to Raman spectroscopy for both identification and quantitation. The multiblock techniques investigated showed that each spectroscopy contains information not present or captured with the other spectroscopic technique, thus demonstrating that there is a potential benefit in their combined use for both identification and quantitation purposes.
authors.affiliation: Quality Operations, Laboratory for Liquid Products, Merck Serono, Darmstadt, Germany; Institute for Biotechnology and Biosciences, Instituto Superior Técnico, Universidade de Lisboa, Portugal. Electronic address: alexandra.hertrampf@merckgroup.com.
authors.forename: A
authors.initials: A
authors.lastname: Hertrampf
authors.affiliation: 4Tune Engineering Ldt., Lisbon, Portugal. Electronic address: rms@4tuneengineering.com.
authors.forename: R M
authors.initials: RM
authors.lastname: Sousa
authors.affiliation: Institute for Biotechnology and Biosciences, Instituto Superior Técnico, Universidade de Lisboa, Portugal. Electronic address: cardoso.menezes@tecnico.ulisboa.pt.
authors.forename: J C
authors.initials: JC
authors.lastname: Menezes
authors.affiliation: Quality Operations, Laboratory for Liquid Products, Merck Serono, Darmstadt, Germany. Electronic address: thorsten.herdling@merckgroup.com.
authors.forename: T
authors.initials: T
authors.lastname: Herdling
mesh_terms: D004304:Dosage Forms; D016018:Least-Squares Analysis; D008962:Models, Theoretical; D019265:Spectroscopy, Near-Infrared / Q000379:methods*; D013059:Spectrum Analysis, Raman / Q000379:methods*; D014732:Vibration
chemical_list: D004304:Dosage Forms
keywords: Multiblock partial least squares regression; Multivariate data analysis; Principal component analysis; Raman spectroscopy; Sequential-orthogonalised partial least squares regression; near infrared spectroscopy