This document discusses quality risk management process for aseptic processes. It begins by defining an aseptic process as the manipulation of sterile components in a controlled environment to produce a sterile product. Aseptic processes carry a high risk of contamination, so quality risk management is essential. The document then discusses quality risk management and its uses, including determining the scope of audits, evaluating changes, and identifying critical process parameters. Finally, the document lists several quality management tools like check sheets, control charts, Pareto charts, and histograms that can be used in quality risk management.

1. quality risk management process
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I. Contents of quality risk management process
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This article is part of PharmTech's supplement "Injectable Drug Delivery."
Aseptic processes are some of the most difficult processes to conduct in the pharmaceutical
industry. Because of the nature of aseptic processes, sterile products produced aseptically present
a significantly higher risk to the patient than terminally sterilized products. Because of the high
level of risk, an effective quality-risk-management program is necessary to protect the patient.
An effective risk-management program aids in the careful control of the process, reducing the
risk of contamination as well as wasted effort in controlling insignificant risks.
What is an aseptic process?
Aseptic processing involves manipulation of sterile components in a carefully controlled
environment using careful techniques to produce a sterile product. While aseptic processing
usually involves filling of final drug product, there are other types of aseptic processes, including
aseptic assembly of devices or combination products, aseptic crystallization or aseptic
precipitation of drug product to produce a sterile bulk-drug substance, and aseptic formulation of
final drug product.
One thing all aseptic processes have in common is their high level of risk. They require careful
control of the aseptic environment, of personnel practices and procedures, sterilization of
equipment and components, extensive environmental monitoring, and many other controls. The
number of controls required and the severe consequences of control failure make aseptic
processing one of the highest-risk pharmaceutical processes. Quality risk management is an
essential tool in ensuring product quality.
Quality risk management

2. Although quality risk management (QRM) is a relatively new concept to the pharmaceutical
industry, it has been used in other industries for many decades, with some risk-assessment tools
dating back to the World-War-II era. The pharmaceutical industry has been slow to adopt many
of these tools because of the industry focus on regulatory compliance as the driving force for
quality. This traditional compliance-based approach had its drawbacks that became more evident
as the industry became more diverse and sophisticated. A "one-size-fits-all" approach to quality
became increasingly unworkable, leading the US Food and Drug Administration to develop a
quality systems approach to regulation.
The quality systems approach to the pharmaceutical industry was launched on a large scale with
the FDA publication of Pharmaceutical CGMPs for the 21st Century—A Risk Based
Approach in August 2002 (1). This initiative had the ambitious goal of transforming the FDA
regulatory approach to the pharmaceutical industry into a science-based and risk-based approach
with an integrated quality systems orientation.
In the time since the publication of the concept paper, this initiative has been largely successful,
leading to publication of international guidance documents such as ICH Q9 Quality Risk
Management, and the Parenteral Drug Association (PDA) Technical Report No. 44 on Quality
Risk Management for Aseptic Processes (2, 3). The publication of ICH Q10Pharmaceutical
Quality System has further enhanced the risk-based approach to pharmaceutical manufacturing.
There are many potential uses for quality risk management in the pharmaceutical industry,
including:
 Determining the scope, complexity, and frequency of internal and external audits
 Identifying, evaluating, and communicating the potential quality impact of quality defects,
complaints, trends, and non-conformances
 Providing a framework for evaluation of environmental monitoring data
 Evaluating the impact of changes to the facility, equipment, or process on product quality
 Establishing appropriate specifications and identifying critical process parameters during product
and process development
 Assisting facility design (e.g., determining appropriate material, equipment, and personnel flows,
appropriate level of cleanliness for processing areas)
 Determining the scope and extent of qualification of facilities, buildings, and production
equipment and/or laboratory instruments (including proper calibration methods)
 Determining acceptable cleaning validation limits
 Determining revalidation frequency
 Determining the extent of computerized system validation
 Identifying the scope and extent of verification, qualification, and validation activities
 Determining the critical and noncritical steps in a process to assist in the design of process
validation.
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III. Quality management tools
1. Check sheet

3. The check sheet is a form (document) used to collect data
in real time at the location where the data is generated.
The data it captures can be quantitative or qualitative.
When the information is quantitative, the check sheet is
sometimes called a tally sheet.
The defining characteristic of a check sheet is that data
are recorded by making marks ("checks") on it. A typical
check sheet is divided into regions, and marks made in
different regions have different significance. Data are
read by observing the location and number of marks on
the sheet.
Check sheets typically employ a heading that answers the
Five Ws:
 Who filled out the check sheet
 What was collected (what each check represents,
an identifying batch or lot number)
 Where the collection took place (facility, room,
apparatus)
 When the collection took place (hour, shift, day
of the week)
 Why the data were collected
2. Control chart
Control charts, also known as Shewhart charts
(after Walter A. Shewhart) or process-behavior
charts, in statistical process control are tools used
to determine if a manufacturing or business
process is in a state of statistical control.
If analysis of the control chart indicates that the
process is currently under control (i.e., is stable,
with variation only coming from sources common
to the process), then no corrections or changes to
process control parameters are needed or desired.
In addition, data from the process can be used to
predict the future performance of the process. If
the chart indicates that the monitored process is
not in control, analysis of the chart can help

4. determine the sources of variation, as this will
result in degraded process performance.[1] A
process that is stable but operating outside of
desired (specification) limits (e.g., scrap rates
may be in statistical control but above desired
limits) needs to be improved through a deliberate
effort to understand the causes of current
performance and fundamentally improve the
process.
The control chart is one of the seven basic tools of
quality control.[3] Typically control charts are
used for time-series data, though they can be used
for data that have logical comparability (i.e. you
want to compare samples that were taken all at
the same time, or the performance of different
individuals), however the type of chart used to do
this requires consideration.
3. Pareto chart
A Pareto chart, named after Vilfredo Pareto, is a type
of chart that contains both bars and a line graph, where
individual values are represented in descending order
by bars, and the cumulative total is represented by the
line.
The left vertical axis is the frequency of occurrence,
but it can alternatively represent cost or another
important unit of measure. The right vertical axis is
the cumulative percentage of the total number of
occurrences, total cost, or total of the particular unit of
measure. Because the reasons are in decreasing order,
the cumulative function is a concave function. To take
the example above, in order to lower the amount of
late arrivals by 78%, it is sufficient to solve the first
three issues.
The purpose of the Pareto chart is to highlight the
most important among a (typically large) set of
factors. In quality control, it often represents the most
common sources of defects, the highest occurring type
of defect, or the most frequent reasons for customer

5. complaints, and so on. Wilkinson (2006) devised an
algorithm for producing statistically based acceptance
limits (similar to confidence intervals) for each bar in
the Pareto chart.
4. Scatter plot Method
A scatter plot, scatterplot, or scattergraph is a type of
mathematical diagram using Cartesian coordinates to
display values for two variables for a set of data.
The data is displayed as a collection of points, each
having the value of one variable determining the position
on the horizontal axis and the value of the other variable
determining the position on the vertical axis.[2] This kind
of plot is also called a scatter chart, scattergram, scatter
diagram,[3] or scatter graph.
A scatter plot is used when a variable exists that is under
the control of the experimenter. If a parameter exists that
is systematically incremented and/or decremented by the
other, it is called the control parameter or independent
variable and is customarily plotted along the horizontal
axis. The measured or dependent variable is customarily
plotted along the vertical axis. If no dependent variable
exists, either type of variable can be plotted on either axis
and a scatter plot will illustrate only the degree of
correlation (not causation) between two variables.
A scatter plot can suggest various kinds of correlations
between variables with a certain confidence interval. For
example, weight and height, weight would be on x axis
and height would be on the y axis. Correlations may be
positive (rising), negative (falling), or null (uncorrelated).
If the pattern of dots slopes from lower left to upper right,
it suggests a positive correlation between the variables
being studied. If the pattern of dots slopes from upper left
to lower right, it suggests a negative correlation. A line of
best fit (alternatively called 'trendline') can be drawn in
order to study the correlation between the variables. An
equation for the correlation between the variables can be
determined by established best-fit procedures. For a linear

6. correlation, the best-fit procedure is known as linear
regression and is guaranteed to generate a correct solution
in a finite time. No universal best-fit procedure is
guaranteed to generate a correct solution for arbitrary
relationships. A scatter plot is also very useful when we
wish to see how two comparable data sets agree with each
other. In this case, an identity line, i.e., a y=x line, or an
1:1 line, is often drawn as a reference. The more the two
data sets agree, the more the scatters tend to concentrate in
the vicinity of the identity line; if the two data sets are
numerically identical, the scatters fall on the identity line
exactly.
5.Ishikawa diagram
Ishikawa diagrams (also called fishbone diagrams,
herringbone diagrams, cause-and-effect diagrams, or
Fishikawa) are causal diagrams created by Kaoru
Ishikawa (1968) that show the causes of a specific
event.[1][2] Common uses of the Ishikawa diagram are
product design and quality defect prevention, to identify
potential factors causing an overall effect. Each cause or
reason for imperfection is a source of variation. Causes
are usually grouped into major categories to identify these
sources of variation. The categories typically include
 People: Anyone involved with the process
 Methods: How the process is performed and the
specific requirements for doing it, such as policies,
procedures, rules, regulations and laws
 Machines: Any equipment, computers, tools, etc.
required to accomplish the job
 Materials: Raw materials, parts, pens, paper, etc.
used to produce the final product
 Measurements: Data generated from the process
that are used to evaluate its quality
 Environment: The conditions, such as location,
time, temperature, and culture in which the process
operates
6. Histogram method

7. A histogram is a graphical representation of the
distribution of data. It is an estimate of the probability
distribution of a continuous variable (quantitative
variable) and was first introduced by Karl Pearson.[1] To
construct a histogram, the first step is to "bin" the range of
values -- that is, divide the entire range of values into a
series of small intervals -- and then count how many
values fall into each interval. A rectangle is drawn with
height proportional to the count and width equal to the bin
size, so that rectangles abut each other. A histogram may
also be normalized displaying relative frequencies. It then
shows the proportion of cases that fall into each of several
categories, with the sum of the heights equaling 1. The
bins are usually specified as consecutive, non-overlapping
intervals of a variable. The bins (intervals) must be
adjacent, and usually equal size.[2] The rectangles of a
histogram are drawn so that they touch each other to
indicate that the original variable is continuous.[3]
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