Augmentation of near infrared diffuse reflectance and transmittance spectral data for the development of robust PLSBC models for classifying double blind clinical trial tablets

• Published in: Journal of pharmaceutical and biomedical analysis 2006-10, Vol.42 (4), p.517-522
• Main Authors: Van den Kerkhof, T., De Maesschalck, R., Vanhoutte, K., Coene, M.C.
• Format: Article
• Language: English
• Subjects: Analysis; Analytical, structural and metabolic biochemistry; Biological and medical sciences; Chemistry, Pharmaceutical; Clinical trial studies; Clinical Trials as Topic; Data augmentation; Double-Blind Method; Fructose - analogs & derivatives; Fructose - chemistry; Fructose - classification; Fundamental and applied biological sciences. Psychology; Galantamine - chemistry; Galantamine - classification; General pharmacology; Humidity; Least-Squares Analysis; Medical sciences; Models, Chemical; Models, Statistical; NIR; Pharmacology. Drug treatments; PLSBC; Reproducibility of Results; Spectroscopy, Fourier Transform Infrared - methods; Tablets - chemistry; Tablets - classification; Water - chemistry
• ISSN: 0731-7085; 1873-264X
• DOI: 10.1016/j.jpba.2006.05.007

The water content of clinical trial tablets can be different between and within different tablet batches, depending on the relative humidity conditions during their production, packaging, storage and analysis. These water variations lead to important spectral variations in the near infrared spectral region which can lead to a wrong identification if the classification model was based on unrepresentative data towards the water content. As model development for clinical trial studies needs to be extremely fast – within one working day – with generally only one batch available, the principle of data augmentation has to be applied to render more robust classification models. Therefore, tablets available for constructing the model are being processed in order to increase or decrease their water content and to make them more representative for tablets to be tested in the future. The inclusion of a deliberate water variation is the most efficient way to develop a model, for which no additional model redevelopment will be required to pass the system suitability tests and to obtain a correct identification.