A Statistical Approach to Evaluating the Manufacture of Furosemide Tablets - Pharmaceutical Technology

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A Statistical Approach to Evaluating the Manufacture of Furosemide Tablets
The authors evaluated the manufacturing data of 40-mg tablets of furosemide, a potent diuretic.


Pharmaceutical Technology
Volume 35, Issue 3, pp. 112-121

Results and discussion


Table VI: Values of the uniformity of dosage units.
Content of furosemide in the powder mixture. The relative standard deviation of the method was 0.43%. Random 10-g samples of the mixed powder were taken from 10 places within the bin blender (see Figure 1). Table I shows the data of the three consecutive batches. The powder blends met the acceptance criteria and the specifications (90.0–110.0%). The control charts did not show a special cause of variation (see Figure 2). Considering the analysis of locations, the one-way ANOVA was used to test for differences among the locations (see Figure 1). The analysis revealed no statistical difference between the locations (p value = 0.959).


Figure 6: Control chart for individual, move range (MR) and standard deviation (StDev). The subgroup size was 10 (I-MR-R/S) for the uniformity of dosage units. LCL is lower control limit, S is standard deviation, and UCL is upper control limit.
The p value for the Anderson–Darling normality test was 0.069 (> 0.05), indicating a normal distribution (see Table II). This test was developed to be especially sensitive to deviations from normality in the distribution tails. For capability analysis, the tails are the most critical part of the distribution (15). To understand process variation, the authors performed a fully nested ANOVA (see Table III). The result showed that 72.90% of the observed variation in the furosemide content resulted from a batch factor. Thus, to minimize the process variability, the causes of the variation among batches required further investigation.


Figure 7: Capability analysis of the uniformity of dosage units of furosemide tablets. Cp is process capability, Cpk is process-capability index, CL is control level, CPL is process capability relative to lower specification limit, CPU is process capability relative to upper specification limit, LSL is lower specification limit, PPM is parts per million, StDev is standard deviation, and USL is upper specification limit.
The indices of 2.19 and 2.23 for Cpk and Cp, respectively, revealed that this process is statistically centered and robust (see Figure 3). The estimated nonconformity for the powder mixing process was less than 1 ppm. Cp and Cpk indices equal or above 1.0 correspond to a satisfactory low proportion of nonconformity (16). However, for a good process under statistical control, Cpk should be greater than 1.5 (17).


Table VII: Estimated variance component for uniformity of dosage units of furosemide.
Evaluation of the tablet weight. Figure 4 shows the individual tablet weights for each batch taken from the left and right sides of the tablet machine. The tablet weight was chosen as a surrogate for the process stability of the compression step. As shown in Figure 4, the two values above and the five values below the control limits, among 1200 samples analyzed, cannot support the assumption that this process was unstable. The mean tablet weight was 164.22 ±4.25 mg. In addition, all values were within the specification limits. The lower specification limit (LSL) was 152 mg, and the upper specification limit (USL) was 177 mg (see Table IV). However, a few outliers can have a large influence on the process-capability indices, as evidenced in Figure 5. The process capability indices were 1.00 and 0.98 for Cp and Cpk, respectively, revealing a high proportion of nonconformity (2751.21 ppm). Although interbatch variability could have contributed to the results, further investigation to address the high nonconformity was needed. Issues that may cause weight variation are powder flow problems, improper die fill, and powder size distribution.


Table VIII: Values of the % drug released using tablet dissolution of furosemide tablet at the beginning.
In addition, a comparison of the tablet weights at the two sides of the tableting machine was performed (see Table V). The p value (one-way ANOVA) was 0.522, showing that the tablet weights for both sides did not differ significantly. The residuals plot indicated a normal distribution and the absence of special causes of variation. Similar results were reported in a study where a double-station Kilian tableting machine (IMA Kilian, Köln, Germany) was used to manufacture metamizol tablets (18).


Figure 8: Control chart for individual, move range (MR), and standard deviation (StDev). The subgroup size was 18 (I-MR-R/S) for the % released using tablet dissolution. LCL is lower control limit, S is standard deviation, UCL is upper control limit, and X is individual value.
Evaluation of the uniformity of dosage units. Table VI shows the dosage-unit uniformity for each batch for three different time periods: the beginning, middle, and end of the process. No special causes of variability were observed in the control charts (see Figure 6). The p value for the Anderson–Darling test was equal to 0.010, which revealed a non-normal distribution (> 0.05, see Table II). The Box–Cox method was used to transform the values into a normal distribution (λ = 4.5) (13). In a similar way, non-normal process capability indices were determined using a generalized λ distribution described by Pal (19). The authors used spreadsheets to illustrate how easily the necessary calculation can be performed. The transformed values were used to calculate the process-capability indices. Cp and Cpk were 1.43 and 1.27, respectively. The difference in values indicated that the process was not statistically centered. However, the predicted nonconformity rate was low (70.32 units), as shown in Figure 7.


Figure 9: Capability analysis of % released using tablet dissolution. Cp is process capability, Cpk is process-capability index, CPL is process capability relative to lower specification limit, CPU is process capability relative to upper specification limit, LSL is lower specification limit, PPM is parts per million, StDev is standard deviation, and USL is upper specification limit.
The estimated source of variance showed that 54.19% of the observed variability was within a sampling group, 27.83% resulted from the sampling time point, and 17.98% resulted from the batch (see Table VII). To optimize this manufacturing process, the causes of the variations among the batches and the statistically noncentered profile of this process should be investigated.


Table IX: Estimated variance component for the % drug released using tablet dissolution of furosemide.
Evaluation of the dissolution of furosemide tablets. The dissolution of furosemide tablets was evaluated for each batch at the beginning, the middle, and the end of the process (see Table VIII). Figure 8 indicates that the process stability was achieved because no special cause of variation was observed. The p value for the Anderson–Darling test was equal to 0.006, which confirmed a non-normal distribution (see Table II). The transformed data (Box–Cox method, λ = 5) were used to calculate Cp and Cpk, which were 3.46 and 2.29, respectively. The estimated nonconforming proportion was low (0.00 ppm), and the process was slightly not statistically centered (see Figure 9). The estimated cause of variance showed that 40.34% resulted from a batch factor, and 17.23% from the sampling group (see Table IX).


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