FRESH PERSPECTIVES NP1 /NP2 placebos and NP2 actives is quite defi nitive (Figure 16); hence,
an output of “placebo” for the third tier model should more accurately be considered as “placebo – maybe” with the fourth tier model providing a much more conclusive designation.
Classes for modeling – NP1 and NP2 placebos (I); NP2 active (II) Classes excluded: tablet and capsule placebos, all actives except NP2
The fi nal hierarchical approach is summarized in the fl owchart in Figure 17.
The established hierarchical model is ideal for classifying 14 different drug products and their placebos; however, testing new products would require further evaluation to determine if the products are properly classified. In one instance, new placebo products sharing a similar unit formula as those included in the model should be accurately predicted and may not require their addition to the model. Likewise, the active products should be evaluated for significant API spectral contributions to determine if they will provide an appropriate response for the model to classify as an active.
and similar to products NP1
In the final scenario, and NP2
where
the products and placebos share the same formulation, the model is recommended to be updated with the inclusion of the new products and their placebos which may require additional hierarchical steps for accurate classification. Simply put, there is no universal answer to the question as to whether the classification model in place needs to be updated with the introduction of new products to the slate; this needs to be addressed on a case-by-case basis.
Conclusion
Implementation of a chemometric Raman spectral library for positively identifying placebos offers several benefits. Unlike a product-specific negative HPLC identification test, a chemometric spectral library testing approach offers a specificity advantage by comparing sample results to several products at once. In addition, because transmission Raman testing is executed on whole tablets and capsules, laboratory analysis time is reduced (e.g., shorter setup, less instrument run time, simpler documentation, etc.).
In our example,
the testing of the calibration, validation and test samples set (400+) required approximately three days. Once the model was completed as a hierarchical model, testing demonstrated 100% correct classification of placebos and actives which included 14 different products and their respective placebos (capsules and tablets) at multiple strengths where active strengths ranged from 0.5 mg (0.25% drug load) to 300 mg (30% drug load).
References 1.
H. Martens, M. Høy, B.M. Wise, R. Bro and P.B. Brockhoff , “Pre-whitening of data by covariance- weighted pre-processing,” J. Chemom., 17(3), 153-165, 2003. Shaver, J.M., Gallagher, N.B, Wise, B.M., “Soft vs OrthogonalizatSoft vs. Hard Orthogonalization Filters in Classifi cation Modeling,” Federation of Analytical Chemistry and Spectroscopy Societies, Louisville, KY, October 18–22, 2009.
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