Furosemide is a potent loop diuretic used in the treatment of edematous states associated with cardiac, renal, and hepatic
failure and for the treatment of hypertension. Complications, such as erratic systemic drug availability from the oral route
of administration and from unpredictable responses to a given dosage, appear frequently. Furosemide's exact mechanism of action
is not fully understood, but the drug is believed to act at the luminal surface of the ascending limb of the loop of Henle
by inhibiting the active reabsorption of chloride ions. The response to a given dosage is modulated by the individual's fluid
and electrolyte balance (1). Constant biopharmaceutical quality of furosemide tablets is therefore imperative to minimize
undesirable in vivo variability.
Regulatory agencies around the world require proof of product consistency and a high degree of assurance that a product will
meet all specifications (2–4). Thus, process evaluations need to follow scientific and statistical rationales. Simple test
comparisons might not be sufficient. FDA strongly emphasizes that the pharmaceutical industry must understand process variation,
including all sources and degrees of variation, and ultimately the effect of variation on product attributes (5). Furthermore,
FDA guidance states that these data should be collected from the process-design stage through final-product manufacturing.
This recommendation is a clear shift toward a product life-cycle approach including quality by design (5).
The adoption of statistical tools to evaluate data from an existing process can expose variability that might reveal that
this process is not robust. On the other hand, a non-optimized process that is on its target value might be modified to improve
productivity or increase robustness. Process variability can easily be revealed using control charts and statistical analysis.
Figure 1: Sampling scheme of the bin blender. (ALL FIGURES ARE COURTESY OF THE AUTHORS)
A prerequisite for process evaluation is the determination of the analytical method's variability. This determination can
be accomplished using repeatability and reproducibility (RR) studies. Repeatability, sometimes called "equipment variation," is the ability of the measurement system to provide consistent readings when used
by a single technician or operator. Reproducibility, sometimes referred to as "appraiser variation," is the ability to achieve consistent results for multiple operators. In general,
an interval of 10% < %RR < 30% is considered adequate (6).
Figure 2: Control chart for individual (I), move range (MR), and standard deviation (StDev). The subgroup size was 10 (I-MR-R/S)
for furosemide content in the powder mixture. LCL is lower control limit, S is standard deviation, UCL is upper control limit,
and X is individual values.
Current FDA guidance embraces a risk-based approach, and the International Conference on Harmonization's Pharmaceutical Quality Systems recommends continuous improvement of the process performance and product quality (7–9). Statistical tools can enhance process
understanding and foster innovative approaches to process validation and pharmaceutical development (5, 10). Among the statistical
tools, the process-capability indices (i.e., Cp and Cpk) measure the process's ability to manufacture products that meet specifications
and requirements. These indices greatly simplify the management of statistically controlled processes and have been used with
the fundamental assumptions that the data are distributed normally, that the process is stable, and that its variability is
known (6). The goal of the data evaluation was to assess the potential process capability index (i.e., Cp) and the actual
process capability index (i.e., Cpk) using the content homogeneity of the powder mixture, tablet weight, dosage-unit uniformity,
and dissolution behavior of 40-mg furosemide tablets.
Table I: Furosemide content in the powder blend.