FRESH PERSPECTIVES
• Run Time: 90 seconds
Samples tested included 12 unique active drugs and their placebos, covering a total of 34 diff erent strengths for tablets and capsules. Data analysis was performed using the chemometrics software package Solo (Eigenvector Research, Inc.).
Results and Discussion
Drug product placebos are made to match the active product physical properties such as color, shape and size in order to facilitate blinded clinical studies. However, in some cases, the manufactured placebo may be prepared using a standard formulation that diff ers from the active drug product due to not only the absence of the active compound, but also with respect to the use of other raw materials and excipients. In other cases, the placebo formulation may be identical to the active formulation with the obvious exception of absence of the active drug.
In this study, the model used included both tablets and capsules which also have diff erent unit formulas. In summary, the completed positive placebo identifi cation model used contained a library of products and placebos which varied in formulation, shape, size, color, and presentation (e.g., capsule versus tablets).
Positive placebo identifi cation tests were executed by initially creating a library of data from 12 active and placebo tablets and capsules covering 34 diff erent dosage strengths ranging from 0.5 mg (0.25% drug load) to 300 mg (30% drug load). The initial model included library items of both tablets and capsule placebo formulations that diff ered from the active formulations. Once created, the model was challenged with new lots of the same products and placebos, and the model was later challenged by addition of two products and placebos sharing the same unit formula.
The initial examination included principal component analysis (PCA) that evaluated the full Raman spectral range to determine if a diff erence could be observed between the diff erent samples types (e.g., placebos, actives, tablets, capsules). Limited selectivity was observed; however, improvement was obtained when spectral ranges were subsequently narrowed to include regions containing active peaks from the products and no or very few peaks present for the placebos (Figure 1). In most cases, the products containing actives consistently produced peaks in the selected regions of 650 – 840 cm-1
and 1480 to 1780 cm-1 which are consistent with compounds
containing aromatic rings, carbonyls and amines. Excipients may also have responses in these regions; however, they did not prove to have signifi cant spectral contributions (Figure 2). Excipients showing peaks in this region include lactose and starch.
Reducing the spectral region to 650 – 840 cm-1 and 1480 to 1780 cm-1
demonstrated signifi cant separation of actives and placebos; however, PCA analysis did not show complete discrimination as one active sample was present in the placebo “cluster” (Figure 3). Further evaluation showed this product to contain a low level active (2.5 mg), and similar formulation components to the placebos making discrimination challenging.
Additional work explored using partial least squares discriminant analysis (PLSDA). Using the same spectral range and pre-processing parameters, PLSDA separated all placebos and actives (Figure 4). As demonstrated in the plot, the 95% confi dence ellipse of the actives and placebos are clearly separated along principle component (LV 1).
The completed model included approximately 56 calibration active and placebo samples which was then validated with new samples (Figure 5). The validated model successfully classifi ed the placebo and actives.
62 American Pharmaceutical Review | Fresh Perspectives 2013
Figure 1 - Spectral ranges were selected based on the presence of active peaks in regions where no placebo peaks were observed. Typically these peaks were due to aromatic, carbonyl and/or amine stretches.
Figure 2 - Spectral regions demonstrating active and excipient peaks
Once the model calibration and validation were complete, the validation data were combined with the calibration data and an independent sample set including replicates over multiple days (> 300 sample runs) was tested against the model. Without fail, the model accurately classifi ed the active and placebo capsules and tablets (Figure 6 & Figure
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28 |
Page 29 |
Page 30 |
Page 31 |
Page 32 |
Page 33 |
Page 34 |
Page 35 |
Page 36 |
Page 37 |
Page 38 |
Page 39 |
Page 40 |
Page 41 |
Page 42 |
Page 43 |
Page 44 |
Page 45 |
Page 46 |
Page 47 |
Page 48 |
Page 49 |
Page 50 |
Page 51 |
Page 52 |
Page 53 |
Page 54 |
Page 55 |
Page 56 |
Page 57 |
Page 58 |
Page 59 |
Page 60 |
Page 61 |
Page 62 |
Page 63 |
Page 64 |
Page 65 |
Page 66 |
Page 67 |
Page 68 |
Page 69 |
Page 70 |
Page 71 |
Page 72 |
Page 73 |
Page 74 |
Page 75 |
Page 76 |
Page 77 |
Page 78 |
Page 79 |
Page 80 |
Page 81 |
Page 82 |
Page 83 |
Page 84 |
Page 85 |
Page 86 |
Page 87 |
Page 88 |
Page 89 |
Page 90 |
Page 91 |
Page 92