Applying Statistical Process Control Pharmaceutical Manufacturing The use of applied statistics in studying a pharmaceutical manufacturing process is examined in the work of Tiani (2004) reports that health care quality is critically important in society and the quality of health care is important to all individuals. It is important that treatment is given in an accurate manner and this is particularly true of medications given to patients as it is expected that "the bottle of medicine has the specified number of tablets and that each tablet contains the specified quantity of the correct drug." (Tiani, 2004)

Legal and Regulatory Framework

There are legal and regulatory requirements set out in the law of the United States that the quality of medications be controlled in the pharmaceutical industry. The regulations are contained in federal statutes and outline "a quality control functions that emphasizes inspection and defect detection, and pharmaceutical quality control technology." (Tiani, 2004) It is reported that the drug product "features are the easiest to understand, measure, and control" is that falling under the classification of the medication chemical or physical attributes." (Tiani, 2004) According to Tiani (2004) the United States Pharmacopoeia (USP) is inclusive of "standards for drugs and pharmaceutical substances" referred to as Official Monographs. The USP standards are "official and required by law in the U.S. under the 'Food, Drug, and Cosmetic Act'. " (2004)

Process Analytical Technology (PAT)

Process Analytical Technology has been developed to provide support for the development of pharmaceutical manufacturing and quality assurance. There are two reported components to the PAT framework:

(1) A set of scientific principles and tools supporting innovation; and (2) A strategy for regulatory implementation that will accommodate innovation. (Janardhan, 2011, 2011)

Quality problems in the petrochemical industries "need to be corrected before they contaminate large volume of products." (Janardhan, 2011 ) Pharmaceutical companies are reported to suffer from "excessive rework and scrap, high work-in-process, low capacity utilization, prolonged cycle times and laboratory bottlenecks." (Janardhan, 2011) The AMR reports that the "industry average for both rework and discarded product is about 50%" and that on-hold product inventories "are at the 40 to 60 day level." (Janardhan, 2011) Plant utilization is reported as being "in the range of40 to 50%" and average cycle times stated in the 30 to 90 day range." (Janardhan, 2011)

PAT Solutions

Some of the PAT solutions for research development, scale-up and manufacturing of drug substances are the following areas: (1) identification of raw material; (2) monitoring of online manufacturing processes; and (3) online analysis. (Janardhan, 2011) Key modules of the PAT solution include: (1) acquisition of data; (2) storage of data; (3) mining, visualization and multivariate analysis of data; and (4) control. (Janardhan, 2011) Process parameters reported as used include: (1) Infrared (IR); (2) ultraviolet-visible spectrophotometry (UV-VIS), (3) Raman, (4) High Performance Liquid Chromatography (HPLC) and (5) Mass Spectroscopy. (Janardhan, 2011) The following chart shows the worldwide use of PAT instrumentation and software.

Figure 1

Worldwide Use of PAT Instrumentation and Software

Source: Janardhan, 2011

A survey is reported in the work of Shanley (2011) and stated specifically is that pharmaceutical manufacturers reported the use of process capability analysis and statistical process control" as well as the trends as follows: (1) interest in continuous processing has increased: (2) there is better alignment between IT and process control operations and operational excellence goals; (3) there is slowed installation of new technology platforms due to plant closures and off shoring of operations; and (4) there is an increased use of wireless monitoring and greater interest in process applications. (Shanley, 2011)When participants in the survey were asked if continual improvement and operational excellent programs such as six sigma served to guide automation and IT goals the following responses were given:

Question 2010-2009

Progress being made 30% 38%

Yes, more or less 26% 15%

Not really 18% 22%

Top priority 16% 19%

Not at all 9% 6% (Shanley, 2011)

When participants...

(Shanley, 2011) According to the U.S. Department of Health and Human Services Food and Drug Administration in the work entitled "Guidance for Industry Process Validation: General Principles and Practices" the written protocol specifying the manufacturing conditions, controls, testing and expect outcomes should include the following elements: (1) The manufacturing conditions, including operating parameters, processing limits, and component (raw material) inputs; (2) The data to be collected and when and how it will be evaluated; (3) Tests to be performed (in-process, release, characterization) and acceptance criteria for each significant processing step; (4) The sampling plan, including sampling points, number of samples, and the frequency of sampling for each unit operation and attribute. The number of samples should be adequate to provide sufficient statistical confidence of quality both within a batch and between batches. The confidence level selected can be based on risk analysis as it relates to the particular attribute under examination. Sampling during this stage should be more extensive than is typical during routine production. (U.S. Department of Health and Human Services Food and Drug Administration, 2011)
Statistical Process Control

Statistical process control is reported as being used to "make a process stable over time" and keeping the process stable unless changes to plans are made." (U.S. Department of Health and Human Services Food and Drug Administration, 2011) Changes in stability over time require that that "pattern of variation remain stable, not that there be no variation in the variable measured." (U.S. Department of Health and Human Services Food and Drug Administration, 2011) Statistical quality control involves a process that has one control and has only common cause variation, which is the "inherent variability of the system, due to many small causes that are always present." (U.S. Department of Health and Human Services Food and Drug Administration, 2011) When the process is disturbed by an event that is not predictable, 'special cause 'variation is added to the common cause variation." (U.S. Department of Health and Human Services Food and Drug Administration, 2011)

Control charts are reported to work through "distinguishing the always-present common cause variation in a process form the additional variation that suggests that the process has been disturbed by a special cause. A control chart sounds an alarm when it sees too much variation." (U.S. Department of Health and Human Services Food and Drug Administration, 2011) It is necessary to estimate the process mean and standard deviation from previous data on the process and under these monitoring conditions there are many observations and control in the process has been maintained. Formulation of control charts begins by taking samples spaces at regular intervals and because the observations in the sample will be so close to one another making it possible to assume that the process, during the brief period of monitoring has remained stable. Variation identified with in the same sample provides a benchmark for the common cause variation in the process and the process standard deviation speaks of the standard deviation within the specified timeframe spanning one sample. When control in the process is maintained the same describes the standard deviation of observations during any specified period of time and the control chart assist in making a decision if this has been the case.

Finally, it is necessary…