use statistical process control (spc) as a tool of ... lifecycle...use statistical process control...

Use Statistical Process Control

(SPC) as a Tool of Understanding

and Managing Variability

Jane Weitzel

mljweitzel@msn.com

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>.

mljweitzel@msn.com 3

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

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.

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

• 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

INTRODUCTION TO

LIFECYCLE

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

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.

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)

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

tical C

trol S

Changes

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

ANALYTICAL TARGET

PROFILE

Wording for ATP

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 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 AND

TARGET MEASUREMENT

UNCERTAINTY (TMU)

Intended Use can be Linked to

Clinical Requirement

Intended Use can be Linked to

Clinical Requirement - MU

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 Rules – References

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.

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.

Result is

above the

limit.

Limit is below

expanded

uncertainty.

Result above

the limit.

Limit is within

the expanded

uncertainty.

Result is

below the

limit.

Limit is within

the expanded

uncertainty.

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.

Result is

above the

limit.

Limit is below

expanded

uncertainty.

Result above

the limit.

Limit is within

the expanded

uncertainty.

Result is

below the

limit.

Limit is within

the expanded

uncertainty.

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

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

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

tical C

trol S

Changes

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

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

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

What they are

How to Identify and

Assess Their Impact

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

Hazard Harm 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

Risk is a combination of

Probability

Severity

Detectability

Steps to eliminate risk or control risk

Severity cannot change

Reduce probability

Increase detectability

U, k=2

Temperature

of column

Time of extraction

Concentration

Analyst’s

Knowledge

85 90 95 100 105 110 115

U, k=2

Temperature

of column

Time of extraction

Concentration

Analyst’s

Knowledge

85 90 95 100 105 110 115

U, k=2

Temperature

of column

Time of extraction

Concentration

Analyst’s

Knowledge

85 90 95 100 105 110 115

U, k=2

Temperature

of column

Time of extraction

Concentration

Analyst’s

Knowledge

Has impact & needs

control

Restrict temp to narrow

U, k=2

Temperature

of column

Time of extraction

Concentration

Analyst’s

Knowledge

Has impact & needs

control

Restrict temp to narrow

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)?

USP ACS PF42(5) article

Provides a useful example for HPLC

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

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

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

Upper Limit 110

Measurement Uncertainty 2

0.00% 0.00% 0.00%

85 90 95 100 105 110 115

Concentration

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

Upper Limit 110

6.68% 6.68% 0.00%

85 90 95 100 105 110 115

Concentration

When is u significant

the TMU is

not met

requirements

of decision

rule are not

(%outside

the limits >

than DR)

Reduce u

Use ACS

Lower Limit 90

Upper Limit 110

2.28% 4.55% 2.28%

85 90 95 100 105 110 115

Concentration

Eg/ Use replication

Use Duplicates

u = 5/√2 = 3.5

Duplicates can be

control charted!

Lower Limit 90

Upper Limit 110

Measurement Uncertainty 3.5

0.21% 0.43% 0.21%

85 90 95 100 105 110 115

Concentration

ANALYTICAL CONTROL

STRATEGY (ACS)

Control Charts

When are they appropriate?

Different types of Control Charts & their use

Many types of

controls

Focus on control

charts

CONTROL CHARTS

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.

ASQ.ORG is a great resource

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

Percentage of Results Between z Factors for a Normal Distribution

Normal Distribution Curve

Basis for Chart

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

2 successive points outside the UWL and/or

the LWL signifies a possible loss of control.

More than 4 successive points on one side or

the other of the mean signifies a drift or a

1 6 11 16 21 26 31 36

Control ChartIn-House Quality Conrol Standard

Average

In House Value

A sudden increase in variation of values

about the mean signifies a loss of precision.

1 6 11 16 21 26 31 36

Average

In House Value

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

1 6 11 16 21 26 31 36

Average

In House Value

Do You Have A Bias!

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

Do You Have A Bias!

Does a Bias Exist?

The lab used EXCEL.

They plotted the values they obtained for the

reference material.

Reference

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

chance of it being Out

Of Specification, OOS,

is low.

Potency is 95.7%

Statistical Usage

The lot can be

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

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

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.

Identify uncertainty sources

Quantification of u components

Combined standard uncertainty uc

Discuss what is

significant

What needs

control strategy

Conlusion

Lifecycle approach

covers analytical

procedure completely

Risk analysis

Control Charts

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