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Use Statistical Process Control
(SPC) as a Tool of Understanding
and Managing Variability
Jane Weitzel
Independent Consultant
PHARMAQUAL 360ºFebruary 22 – 24, 2017
Orlando, FL
Jane Weitzel
Jane Weitzel Biosketch
Jane Weitzel has been working in analytical chemistry for
over 40 years for pharmaceutical and other companies with
the last 5 years at the director/associate director level. She
is currently a consultant, auditor, and educator. Jane has
applied Quality Systems and statistical techniques, including
the estimation and use of measurement uncertainty, in a
wide variety of technical and scientific businesses. She has
obtained the American Society for Quality Certification for
both Quality Engineer and Quality Manager.
Jane has assisted laboratories with implementing the
Lifecycle Approach to Analytical Procedures
For the 2015 – 2020 cycle, Jane is a member of the USP
Statistics Expert Committee, Expert Panel on Method
Validation and Verification, and Expert Panel on General
Chapter <11>.
Disclaimer
This presentation reflects the speaker’s
perspective on this topic and does not
necessarily represent the views of USP or
any other organization.
Topics Introduction to Lifecycle
Analytical Target Profile (ATP) & Decision Rules
(DR) &Target Measurement Uncertainty (TMU)
Continued Process Verification
Risk
What risks are
How to Identify and Assess Their Impact
Analytical Control Strategy (ACS)
Control Charts
When are they appropriate?
Different types of Control Charts & their use
Interactive Exercise
Participants use risk tools to perform a risk
analysis and design a risk management for a
procedure.
Lifcycle is Fantastic
The lifecycle approach to
analytical procedures
addresses problems industry
we have all struggled with.
That is why it is fantastic.
Do we release the lot?
Is the variability acceptable?
We know the lot is OK, but how
do we show that?
Example - Do you release the lot?
A lot of drug substance is ready to be
released.
Specification is 90.0 to 110.0%
Value is 95.7%.
Do you release the lot?
Example – Release of a Lot
Today?
The lot can be
released because the
chance of it being Out
Of Specification, OOS,
is low.
Potency is 95.7%
Now we can answer the following
questions – with numbers
What is significant?
What is critical?
When is a control needed?
When is a control not needed?
What is good enough?
USP Stimuli Articles
Proposed New USP General Chapter: The Analytical Procedure
Lifecycle <1220>;USP PF 42(6)
Fitness for Use: Decision Rules and Target Measurement
Uncertainty; USP PF 42(2)
Analytical Target Profile: Structure and Application Throughout the
Analytical Lifecycle; USP PF 42(5)
Analytical Control Strategy; USP 42(5)
Proposed new USP General Chapter <1210> Statistical Tools for
Method Validation; USP PF 42(5)
Proposed New USP General Chapter: The Analytical Procedure
Lifecycle ⟨1220⟩USP PF 43(1)
USP.ORG
Register once at no cost
References
Analytical Methods and control Strategies; The Forgotten Interface?,
Phil Borman, Matt Popkin, Nicola Oxby, Marion Chatfield, David
Elder, Phar Outsourcing, January/February 2015;16(1)
Using the Guard Band to Determine a Risk-Based Specification,
Christopher Burgess, Pharmaceutical Technology, October 1, 2014
M. Schweitzer, M. Pohl et al.: QbD Analytics. Implications and
Opportunities of Applying QbD Principles to Analytical
Measurements, Pharmaceutical Technology, Feb. 2010, 2-8
http://pharmtech.findpharma.com/pharmtech/article/articleDetail.jsp?id=654746
Number of articles in IVT & GXP publications http://www.ivtnetwork.com/
More References
Setting and Using Target Measurement Uncertainty;
https://www.eurachem.org/index.php/publications/guides
References regarding misclassification:
Confidence intervals for misclassification rates in a gauge R&R
study; Burdick RK, Park Y-J, Montgomery DC, Borror CM..J Qual
Tech. 2005;37(4):294–303.
Design and Analysis of Gauge R&R Studies; Making Decisions with
Confidence Intervals in Random and Mixed ANOVA Models.;
Burdick RK, Borror CM, Montgomery DC.; ASA-SIAM Series
on Statistics and Applied Probability, SIAM, Philadelphia, ASA,
Alexandria, VA, 20005
Coming Q12
Technical and
Regulatory
Considerations for
Pharmaceutical
Product Lifecycle
Management
Include analytical
procedures
Scientifically Sound
and Appropriate
Where do these concepts
fit in?
CFR has always required
use of sound science
Decision rules,
measurement uncertainty,
risk and probability have
been used in many
scientific areas for many
years.
We can leverage this
experience to more
effectively meet cGMP
requirements.
Sound Science
Metrological approach
to measurements
Measurement
uncertainty
Target measurement
uncertainty
Completely
characterises the
variability
http://www.fda.gov/ScienceResearch/FieldScience/LaboratoryManual/ucm171878.htm
Measurement Uncertainty
non-negative parameter characterizing the
dispersion of the quantity values being attributed
to a measurand, based on the information used
(VIM III 2.2.6)
EURACHEM CITAC Guide - Quantifying
Uncertainty In Analytical Measurements
http://www.bipm.org/en/
publications/guides/vim.
html
Euarachem.org
QUAM2012:P1
I will call QUAM
Process to Estimate MU
A well designed robustness DOE is a good start
• Identify the measurand
• (set up the final concentration calculation equation)1
• List the steps in the analytical process2
• Identify potential sources of random variability in each step – uncertainty components3
• Design a process that permits an estimate of each source of random variability or of a group of sources or look for the data
4
• Combine the different estimates of random variability to get the overall uncertainty estimate5
Statistics and Validation
The agency does not
provide specific
prescriptions on how
to meet requirements.
We need to use:
Good science
Metrology
Statistical Tools
For example, book
by Lynn Torbeck
Lifecycle Uses Quality by Design
ICH Q8
A systematic approach to development that
begins with predefined objectives and emphasizes
product and process understanding and process
control, based on sound science and quality risk
management.
(Process understanding- The recollection and
comprehension of process knowledge such that process
performance can be explained logically and/or scientifically
as a function of process parameters/inputs.)
What is Quality by Design
Understand your desired output
Understand your inputs
Understand the relationship between inputs and
outputs
Control your inputs to the degree required to
assure you achieve your designed outputs
Person
Reportable
Value
Why apply QbD to Analytical
Procedures? FDA Manufacturing Science White Paper - Innovation
and Continuous Improvement in Pharmaceutical
Manufacturing
Variability and/or uncertainty in a measurement system can pose
significant challenges when OOS results are observed.
Measurement system variability can be a significant part of total
variability.
Similar and repeating OOS observations for different products
across the industry and a less than optimal understanding of
variability
Continuous improvement is difficult, if not impossible.
Why apply QbD to Analytical
Procedures? Extensive deployment of lean and six sigma
methodologies
Increasing adoption of Quality by Design
approaches to process development (focus
on science and risk based strategies)
Why apply QbD to Analytical
Procedures? Focus has been on compliance rather than
science
ICHQ2 often applied in the laboratory in a
checkbox manner without the effect of the
validation parameter on the fitness for
purpose of the procedure being thoroughly
understood
QbD for methods 1. Define desired method performance
2. Ensure chosen method is designed to meet this
requirement and is aligned with first intents where
possible
3. Systematically identify all potential method input
variables
4. Based on risk determine what experimentation is
needed to understand how variation in inputs could
effect outputs
5. Ensure controls defined in the method are based on this
understanding
6. Maintain and use this understanding through the
lifecycle.
Three Stage Approach to Analytical
Lifecycle
Stage 1
Procedure Design and Development
Stage 2
Procedure Performance Qualification
Stage 3
Continued Procedure Performance Verification
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Stage 3 – Continued Procedure
Performance Verification To provide ongoing assurance that the
analytical procedure remains in a state of
control throughout its lifecycle
Routine Monitoring: an ongoing program to collect
and process data that relate to method
performance, e.g.
from analysis / replication of samples or standards during
batch analysis
by trending system suitability data
by assessing precision from stability studies
[J. Ermer et al.: J. Pharm. Biomed. Anal. 38/4 (2005) 653-663]
Continual Improvements (Changes)
Risk assessment to evaluate
Impact of the respective change
Required actions to demonstrate (continued)
appropriate performance
Feedback loop
As needed, return to Stage 2 or Stage 1
ATP establishes criteria for acceptability
Risk Assessment Design of experiments (DOE) is a fundamental methodology for the
QRM process. It is a systematic method to determine the
relationships between variables affecting a process, and it is used to
find cause-and-effect relationships
Understand the procedure variables and
their impact on the reportable value
Detect presence and degree of variation
Understand the impact of variation on the
analytical procedure performance and
ultimately on data attributes
Report and Manage Post Marketing
Changes to an Approved NDA,
ANDA and BLA Lifecycle approach provides a structure, a
language and techniques to evaluate,
manage and report changes
These will be understood by industry and
regulatory bodies
All understand the probability, the risk, the evaluations
Understand how they are identified, evaluated and
managed
Wording for ATP
Assay
The procedure must be able to quantify the
analyte in presence of (X, Y, Z) over a range
of A% to B% of the nominal concentration
with an accuracy and uncertainty so that the
reportable result falls within ±C% of the true
value with at least P% probability.
The variables in orange are specific for each
reportable result.
Target
Measurement
Uncertainty
(TMU)
TMU and ATP
The target measurement uncertainty
becomes part of the analytical target profile.
The TMU defines the acceptance criteria for
the method.
Remember, the uncertainty includes all random
effects (including the uncertainty of the bias).
Analytical Target Profile (ATP)
A predefined objective that states the
performance requirements for the analytical
procedure
The output of the procedure is a reportable
result that must be fit for its purpose.
Applies throughout the life of the analytical
procedure, including stage 3
Report and manage post marketing changes
to an approved NDA, ANDA and BLA
DECISION RULES
Decision rules and their relevance to analytical procedure
qualification will be presented
Used to explain fitness for intended
purpose as part of change control
What is the role manufacturing and
clinical play in defining that use?
Fitness for intended use needs to be known
Decision rules proved that link
Through probability
Types of Decisions
What is the drug concentration in the blood
(during a clinical study)?
Does this batch of drug product meet
specification for potency?
Does this lot of drug substance meet
specification for impurity A?
Does this in-process solution have correct
concentration, e.g. for excipient concentration?
Is this environmental monitoring sample in
specification?
HOW THE USER OF THE
DATA IS KEY TO DEFINING
THE DECISION RULE
Ensuring the reportable result is fit for use
Purpose of a decision Rule
During change control and reporting
to regulatory bodies, it is clear the
user of the data is involved.
The USER Develops the Decision
Rule For an example, consider the case for a
brand new measurement
The best source for the prescription of the
decision rule is the person/organization that
will use the output of the analytical procedure
Can be one person (the expert) or a group of
people (clinical studies, production, stability)
The group can include management (financial
risks)
Called Decision Makers in ICH Q8
Analytical may develop decision rule
If the end user of the data is not available
E.g. often the case for a commercial contract
laboratory
The laboratory can create a decision rule to
assist with ensuring its test results are
suitable.
This is especially useful for testing according
to USP monographs.
The approach provides a language for
communication.
Decision Rule
A documented rule ... that describes how
measurement uncertainty will be allocated
with regard to accepting or rejecting a
product according to its specification and the
result of a measurement.
ASME B89.7.3.1-2001 (reaffirmed 2006)
Decision Rule – Acceptance or
RejectionDecision rules give a
prescription for the
acceptance or rejection of
a product based on the
measurement result, its
uncertainty and the
specification limit or limits,
taking into account the
acceptable level of the
probability of making a
wrong decision.
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Meaning of Product
The term product in the decision rule
definition refers to the whatever is tested.
Does not mean drug product lot only.
Could be:
In-process sample (buffer solution)
Lot of drug substance
Batch of drug product
Lot of excipient
Environmental monitoring sample
Why Use Decision Rules
Decision rules clearly state the intended use
of the procedure
Risk and probability are used to develop the
decision rule
This means there is a defined process to define
the intended use of the procedure
Risk and Probability are consistent with QbD
A guard band can be created using the
uncertainty
Decision RuleTo decide whether
a result indicates
compliance or
non-compliance
with a
specification, it is
necessary to take
into account the
measurement
uncertainty.
Upper
Limit
1
Result is
above the
limit.
Limit is below
expanded
uncertainty.
2
Result above
the limit.
Limit is within
the expanded
uncertainty.
3
Result is
below the
limit.
Limit is within
the expanded
uncertainty.
4
Result is
below the
limit.
Limit is above
expanded
uncertainty.
Decision RuleTo decide whether
a result indicates
compliance or
non-compliance
with a
specification, it is
necessary to take
into account the
measurement
uncertainty.
Upper
Limit
1
Result is
above the
limit.
Limit is below
expanded
uncertainty.
2
Result above
the limit.
Limit is within
the expanded
uncertainty.
3
Result is
below the
limit.
Limit is within
the expanded
uncertainty.
4
Result is
below the
limit.
Limit is above
expanded
uncertainty.
How much overlap is
acceptable? That is the
acceptable probability of
making a wrong decision.
Decision Rules Require 4 Components
Decision rules give a prescription for the
acceptance or rejection of a product based
on
1. the measurement result,
2. its uncertainty and
3. the specification limit or limits,
4. taking into account the acceptable level of
the probability of making a wrong
decision.
Example Decision Rule
The lot of drug
substance will be
considered compliant
with the specification
of 95.0% to 105.0% if
the probability of
being above the
upper limit is less
than 2.5% and below
the lower limit is less
than 2.5%.
Lower Limit 95
Nominal Concentration (Central Value) 100
Upper Limit 105
Measurement Uncertainty 2.5
% Below Lower Limit Total % Outside Limits % Above Upper Limit
2.28% 4.55% 2.28%
85 90 95 100 105 110 115
Concentration
LL
UL
Setting TMU
This document discusses how
to set a maximum admissible
uncertainty, defined in the
third edition of the
International Vocabulary of
Metrology as the “target
uncertainty”, to check whether
measurement quality
quantified by the
measurement uncertainty is fit
for the intended purpose.
Eurachem.org
CONTINUED PROCESS
VERIFICATION
Stage 1
Procedure Design and Development
Stage 2
Procedure Performance Qualification
Stage 3
Continued Procedure Performance Verification
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Analytical Control Strategy (ACS)
the ACS is a planned set of controls, derived
from understanding the requirements for
fitness for purpose of the reportable value,
the understanding of the analytical procedure
as a process, and the management of risk,
that assures the performance of the
procedure and the quality of the reportable
value, in alignment with the ATP, on ongoing
basis
TMU
The role of the Analytical Control Strategy is
to ensure that the TMU is met on consistent
basis over the entire lifecycle of the analytical
procedure therefore the reportable value
conforms to the ATP.
Development of ACS
The development of the
Analytical Control Strategy
requires consideration of all
aspects of an analytical
procedure that might impact
the reportable value.
A unit operation is any part of
potentially multiple-step
process which can be
considered to have a single
function with clearly defined
boundary.
For an analytical procedure
three distinct unit operations
can be identified.
Replication Strategy
USP General Notices states
(7. TEST RESULTS 7.10.
Interpretation of Requirements)
“The reportable value, which
often is a summary value for
several individual
determinations, is compared
with the acceptance criteria.
The reportable value is the end
result of a completed
measurement procedure, as
documented.
See Appendix in USP stim
article on ACS PF 42(5)
sem =s/√nSem = standard error of the mean
s = standard deviation for a single
value
n = number of values averaged
Examples of Control Strategy (Operational Control)
Specific instructions in procedure
Strict control of time or temperature
Training
Specifying grades of materials
System suitability
Control charts
Harm Hazard Risk
Hazard: The potential source of harm
(ISO/IEC Guide 51).
Harm: Damage to health, including the
damage that can occur from loss of product
quality or availability.
Risk: The combination of the probability of
occurrence of harm and the severity of that
harm (ISO/IEC Guide 51).
Definitions from ICH Q9
Risk
Hazard - aspect of an analytical procedure
that might impact the reportable value
Harm – how can it impact the Critical Quality
Attributes (CQA) of the reportable value
Eg. bias and uncertainty (accuracy & precision)
Risk is a variable that has significant impact
on bias and uncertainty
Quality Risk Management
The QRM for an analytical
procedure is a systematic
process for the assessment,
control, communication and
review of risk to the quality of
the reportable value across the
analytical procedure lifecycle.
the risk refers the quality of the
reportable value, which is the
product of the analytical
procedure
85 90 95 100 105 110 115
Concentration
LL
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Risk
Risk is a combination of
Probability
Severity
Detectability
ACS
Steps to eliminate risk or control risk
Severity cannot change
Reduce probability
Increase detectability
B
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s
U, k=2
Temperature
of column
Time of extraction
Concentration
Analyst’s
Knowledge
85 90 95 100 105 110 115
B
i
a
s
U, k=2
Temperature
of column
Time of extraction
Concentration
Analyst’s
Knowledge
85 90 95 100 105 110 115
B
i
a
s
U, k=2
Temperature
of column
Time of extraction
Concentration
Analyst’s
Knowledge
85 90 95 100 105 110 115
B
i
a
s
U, k=2
Temperature
of column
Time of extraction
Concentration
Analyst’s
Knowledge
Has impact & needs
control
Restrict temp to narrow
range
B
i
a
s
U, k=2
Temperature
of column
Time of extraction
Concentration
Analyst’s
Knowledge
Has impact & needs
control
Restrict temp to narrow
range
No Risk
No control
Risk Assessment
1. Risk identification
1. What might go wrong? (hazard & harm)
2. Risk analysis
1. Estimate of the risk
2. What is the likelihood (probability) it will go
wrong?
3. Risk evaluation
1. What are the consequences (severity)?
Use of Uncertainty in Risk Analysis
The Eurachem Guide “Quantifying
Uncertainty” (QUAM) is valuable for providing
a QRM process directly for analytical
procedures.
Measurand
Quantity intended to be measured (VIM 2.3)
Unambiguous and detailed description
Includes analyte
Eurachem Guide Terminology in Analytical
Measurement (1.11)
The measurand definition helps identify
hazards in the risk analysis
Estimate uncertainty
1. Specify Measurand
2. Identify u sources
3. Group sources
4. Quantify groups
5. Quantify ungrouped
6. Convert to standard
deviation
7. Combine u
Compare u to TMU
By comparing the estimated uncertainty to
the TMU, you can demonstrate if the
analytical procedure is fit for intended
purpose
AND
You can know which uncertainty components
will have an impact on the reportable value
and which will not
These are the risks and they are quantified
Potential u sources (risks)
QUAM , Section 6, deals with Identifying
Uncertainty Sources
Start with the formula for calculating the
reportable value
Uses Cause and Effect diagram
Lists potential u sources
Sampling
Storage
Instrument effects
Reagent Purity
Assumed
stochiometry
Measurement
conditions
Sample effects
Computational effects
Blank correction
Operator effects
Random effects
Note how some of
these are unique to
analytical procedures
The u is used to determine impact
Risk includes
assessment of impact
When is impact
significant?
u and decision rules
can determine that
Use u to evaluate the risk
1. Risk evaluation
1. What are the consequences (severity)?
QUAM section 7, Quantifying the uncertainty
Evaluate the uncertainty for each individual
source (examples A1 to A3 in QUAM)
Determine directly the combined contribution to
uncertainty using method performance data
This is your analytical procedure development and
qualification experimental values
Eg. Repeatability, robustness, intermediate precision, LOD
study
Bias Study
Risk to the reportable
value includes bias
QUAM section 7.7.4
deals with bias
studies
Use of Reference
Materials
Comparison to
another method
Spikes, etc.
Picture when there
is no bias
When is bias
significant?Lower Limit 90
Nominal Concentration (Central Value) 100
Upper Limit 110
Measurement Uncertainty 2
% Below Lower Limit Total % Outside Limits % Above Upper Limit
0.00% 0.00% 0.00%
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Concentration
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UL
Bias is significant
Bias is significant
because % Below the
limit, as set by
decision rule, is not
acceptable
(Return to method
development &
eliminate bias is best)
If bias cannot be
eliminated, some
form of control
strategy is required
Eg/ bias is caused by
interferent; control that
interferent
Lower Limit 90
Nominal Concentration (Central Value) 93
Upper Limit 110
Measurement Uncertainty 2
% Below Lower Limit Total % Outside Limits % Above Upper Limit
6.68% 6.68% 0.00%
85 90 95 100 105 110 115
Concentration
LL
UL
When is u significant
If :
the TMU is
not met
requirements
of decision
rule are not
met
(%outside
the limits >
than DR)
Reduce u
Use ACS
Lower Limit 90
Nominal Concentration (Central Value) 100
Upper Limit 110
Measurement Uncertainty 5
% Below Lower Limit Total % Outside Limits % Above Upper Limit
2.28% 4.55% 2.28%
85 90 95 100 105 110 115
Concentration
LL
UL
Eg/ Use replication
u = 5
Use Duplicates
u = 5/√2 = 3.5
Duplicates can be
control charted!
Lower Limit 90
Nominal Concentration (Central Value) 100
Upper Limit 110
Measurement Uncertainty 3.5
% Below Lower Limit Total % Outside Limits % Above Upper Limit
0.21% 0.43% 0.21%
85 90 95 100 105 110 115
Concentration
LL
UL
ANALYTICAL CONTROL
STRATEGY (ACS)
Control Charts
When are they appropriate?
Different types of Control Charts & their use
ICH Q9 list of control charts
I.9 Supporting Statistical Tools
A listing of some of the principal statistical
tools commonly used in the pharmaceutical
industry is provided:
Control Charts, for example:
Acceptance Control Charts (see ISO 7966);
Control Charts with Arithmetic Average and Warning
Limits (see ISO 7873);
Cumulative Sum Charts (see ISO 7871);
Shewhart Control Charts (see ISO 8258);
Weighted Moving Average.
Control Charts and Statistical Process
Control - References
FDA Field Science
and Laboratories
describe control
charts in their SOP
online at:
http://www.fda.gov/Scie
nceResearch/FieldScie
nce/LaboratoryManual/
ucm171889.htm
Control Charts
Used to determine and demonstrate the measurement
system is still in control
The chart allows you to distinguish patterns
The chart graphically displays the data and compares it
to an average or expected value and an expected range.
Common practice uses warning limits at ± 2 standard
deviations and control limits at ± 3 standard deviations.
A normal distribution allows us to calculate the
probability of getting a result above the warning or
control limits.
Control Charts
Control charts also allow you to detect trends,
such as more random variability or a gradual
downward trend.
Some types of charts are Mean Chart, Shewhart
Chart, Range Chart
Based on the probabilities, rules for assessing
and reacting to trends can be used.
There are several types of rules, select yours
and follow them.
Construction Of Shewhart Control
Chart (1)
Ensure measurement process is in statistical
control
Include reference material in measurement
runs (early in method)
Collect reference sample data from a
minimum of 20 results from routine runs
Construction Of Shewhart Control
Chart (2)
Calculate mean and standard deviation (s) of
at least 20 reference sample results collected
Construct graph with lines at mean, +/- 2s
(UWL & LWL) and at +/- 3s (UCL & LCL)
Plot subsequent reference material results as
they are obtained
Normal Distribution Curve
Basis for Chart
-4 -3 -2 -1 0 1 2 3 4
z
Percentage of Results Between z Factors for a Normal Distribution
68%
95%
99.7%
Normal Distribution Curve
Basis for Chart
UCL
UWL
LWL
LCL
-4-3
-2-1
01
23
4
z
Pe
rcen
tage
of
Re
sult
s B
etw
een
z F
acto
rs fo
r a
No
rmal
Dis
trib
uti
on
68
%
95
%
99
.7%
Control Charts Are For The Analyst
The analyst uses the control chart as part of
the checks to confirm the method performed
as expected and there are no trends to
investigate.
Shewhart Control Chart Rules (1)
No more than 5% (1 in 20) of the values
should be outside the UWL and LWL.
No points should fall outside the UCL and
LCL.
2 successive points outside the UWL and/or
the LWL signifies a possible loss of control.
Shewhart Control Chart Rules (2)
More than 4 successive points on one side or
the other of the mean signifies a drift or a
bias.
97.0
98.0
99.0
100.0
101.0
102.0
103.0
1 6 11 16 21 26 31 36
Va
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Control ChartIn-House Quality Conrol Standard
Average
LCL
In House Value
UCL
Shewhart Control Chart Rules (3)
A sudden increase in variation of values
about the mean signifies a loss of precision.
97.0
98.0
99.0
100.0
101.0
102.0
103.0
1 6 11 16 21 26 31 36
Va
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Control ChartIn-House Quality Conrol Standard
Average
LCL
In House Value
UCL
Control Chart and Bias
If the control chart uses the data from
measuring a Certified Reference Material:
You can add the Certified Reference Value
(CRV) to the control chart to demonstrate the
accuracy.
Is there a bias? Does the central value in the
control chart agree with the CRV?
Control Chart for a Reference Material
97.0
98.0
99.0
100.0
101.0
102.0
103.0
1 6 11 16 21 26 31 36
Valu
e
Run
Control ChartIn-House Quality Conrol Standard
Average
LCL
In House Value
UCL
Does Bias Exist?
Remember that the Certified Reference
Value for a Certified Reference Material has
an uncertainty associated with it.
Let’s look at a chart to see how that
uncertainty can be used.
Does a Bias Exist?
The lab used EXCEL.
They plotted the values they obtained for the
reference material.
Reference
Value
Does a Bias Exist?
The lab used EXCEL.
They plotted the values they obtained for the
reference material.
Reference
Value
Uncertainty of
reference value
Example - Do you release the lot?
A lot of drug substance is ready to be
released.
Specification is 90.0 to 110.0%
Value is 93.7%.
Do you release the lot?
Example – Release of a Lot
Common Usage
The lot can be
released because the
chance of it being Out
Of Specification, OOS,
is low.
Potency is 95.7%
Statistical Usage
The lot can be
released because the
probability of the
potency being OOS is
< 0.3%.
Potency is
95.7% ± 4.0 % with a
coverage factor of 3 for a
99.7% level of confidence
Picture of Release the Lot
85 90 95 100 105 110 115
Concentration
Upper Limit Lower Limit
95.7
Normal
Distribution
Probability
More Details - Probability
Nominal Concentration (Central Value) 95.7
Target Measurement Uncertainty (Standard Deviation) 2.00 Enter the largest standard deviation which results in the acceptable
Lower Limit 90.0 "Total outside limits".
Upper Limit 110.0
% below Lower Limit Standard Deviation 2.00 % above upper limit
0.22% Total outside limits 0.22% 0.00%
85 90 95 100 105 110 115
Concentration
Upper Limit Lower Limit
Uses
EXCEL
Calculates
Probability
INTERACTIVE EXERCISE
Participants use risk tools to perform a risk analysis and design a
risk management for a stability procedure.
Prep of a Cd Calibration Standard
QUAM Example A1
A calibration standard is prepared from a high
purity metal (cadmium) with a concentration
of Ca.1000 mg L-1.
Combined standard uncertainty uc
Discuss what is
significant
What needs
control strategy
Conlusion
Lifecycle approach
covers analytical
procedure completely
Risk analysis
ACS
Control Charts
