STATISTICAL PROCESS CONTROL AND PROCESS VALIDATION IN PHARMACEUTICALS

STATISTICAL PROCESS CONTROL AND PROCESS VALIDATION IN PHARMACEUTICALS

Statistical process control (SPC), also called statistical quality control and process validation (PV)

SPC comprises the various mathematical tools (histogram, scatter diagram run chart, and control chart) used to monitor a manufacturing process and to keep it within in-process and final product specification limits.

There are three ways of establishing quality products and their manufacturing processes:
1. In-process and final product testing, which normally depends on sampling size (the larger the better). In some instances, nothing short of excessive sampling can ensure reaching the desired goal, i.e., sterility testing.

2. The establishment of tighter control limits (so-called “in-house”) that hold the product and the manufacturing process to a more demanding standard will often reduce the need for more extensive sampling requirements.

3. “Zero defects” by applying tighter control over process variability (Meeting a so-called 6 sigma standard).

Most pharmaceutical products and their manufacturing processes in the United States today, except for sterile processes are designed to meet a 4 sigma limit (which would permit as many as eight defects per 1000 units).

The new approach is to center the process (in which the grand average is roughly equal to 100% of label potency or the target value of a given specification) and to reduce the process variability or noise around the mean or to achieve minimum variability by holding both to the new standard, batch after batch.

A 6 sigma limit may be possible (which is equivalent to not more than three to four defects per 1 million units), also called “zero defects.” The goal of 6 sigma, “zero defects” is easier to achieve for liquid than for solid pharmaceutical dosage forms.

Process characterization: To determine the critical unit operations or processing steps and their process variables, which usually
affect the quality and consistency of the product outcomes or product attributes.

Process ranging represents studies that are used to identify critical process or test parameters and their respective control limits, which normally affect the quality and consistency of the product outcomes of their attributes.

The following process characterization techniques may be used to designate critical unit operations in a given manufacturing process.

1. Constraint Analysis :

A procedure that makes subsystem evaluations and performance qualification trials manageable is the application of constraint analysis. Boundary limits of any technology and restrictions as to what constitutes acceptable output from unit operations or process steps should in most situations constrain the number of process variables and product attributes that require analysis. The constraint analysis principle should also limit and restrict the operational range of each process variable or specification limit of each product attribute.

Constraining process variables usually come from the following sources:
• Previous successful experience with related products/processes
• Technical and engineering support functions and outside suppliers
• Published literature concerning the specific technology under investigation

The constraint analysis comes to us from the Pareto Principle and is also known as the 80–20 rule, which simply states that about 80% of the process output is governed by about 20% of the input variables and that means finding those key variables that drive the process.

The FDA in their proposed amendments to the CGMPs has designated that the following unit operations are considered critical and  their processing variables must be controlled and not disregarded:
• Cleaning
• Weighing/measuring
• Mixing/blending
• Compression/encapsulation
• Filling/packaging/labeling

2. Fractional Factorial Design
An experimental design is a series of statistically sufficient qualification trials that are planned in a specific arrangement and include all processing variables that can affect the expected outcome of the process under investigation.

In the case of a full factorial design, n equals the number of factors or process variables, each at two levels, i.e., the upper (+) and lower (−) control limits. Such a design is known as a 2n factorial.

Using a large number of process variables (say, 9) we could, for example, have to run 29, or 512, qualification trials to complete the full factorial design.
The fractional factorial is designed to reduce the number of qualification trials to a more reasonable number, say, 10, while holding the number of randomly assigned processing variables to a reasonable number as well, say, 9.

The technique was developed as a non-parametric test for process evaluation. Ten is a reasonable number of trials in terms of resource and time commitments and should be considered an upper limit in a practical testing program. This particular design as presented in
Table – 1 does not include interaction effects.

3. OPTIMIZATION TECHNIQUES

Optimization techniques are used to find either the best possible quantitative formula for a product or the best possible set of experimental conditions (input values) needed to run the process. Optimization techniques may be employed in the laboratory stage to develop the most stable, least sensitive formula, or in the qualification and validation stages of scale-up to develop the most stable least variable, robust process within its proven acceptable range(s) of operation,

Optimization techniques may be classified as parametric statistical methods and non-parametric search methods. Parametric statistical methods, usually employed for optimization, are full factorial designs, half factorial designs, simplex
designs, and Lagrangian multiple regression analysis. Parametric methods are best suited for formula optimization in the early stages of product development.

Constraint analysis, described previously, is used to simplify the testing protocol and the analysis of experimental results.

Worst-case conditions:

Trial 1 (lower control limit).

Trial 10 (upper control limit).

X variables were randomly assigned.

The best values to use are the RSD of the data set for each trial.

When adding up the data by columns, + and − are now numerical values, and the sum is divided by 5 (number of +s or −s).
If the variable is not significant, the sum will approach zero.

The steps involved in the parametric optimization procedure for pharmaceuticals consist of the following essential operations:
1. Selection of a suitable experimental design
2. Selection of variables (independent Xs and dependent Ys) to be tested
3. Performance of a set of statistically designed experiments
4. Measurement of responses (dependent variables)
5. Development of a predictor, polynomial equation based on statistical
and regression analysis of the generated experimental data
6. Development of a set of optimized requirements for the formula based on mathematical and graphical analysis of the data generated

Source &Reference-Drug and Pharmaceuticals Sciences (Volume 129)

PILOT SCALE-UP AND PROCESS VALIDATION