Manufacturing, and Seeking Order in Complexity

Manufacturing, Measurements & Quality
Systems: Seeking Order in Complexity
Richard L. Friedman
Division of Manufacturing & Product Quality
Center for Drug Evaluation & Research
Quality Risk Management Process
Risk Assessment
Risk Identification
Risk Analysis
Risk Evaluation
Risk Control
Risk Reduction
Risk Acceptance
Output / Result of the
Quality Risk Management Process
Risk Review
Review Events
Risk Management Tools
Risk Communication
Pharmaceutical Manufacturing
“Conventional pharmaceutical manufacturing is
generally accomplished using batch processing with
laboratory testing conducted on collected samples to
evaluate quality.”
“…significant opportunities exist for improving
development, manufacturing, and quality assurance
through innovation in product and process
development, process analysis, and process control.”
Guidance for Industry: PAT – A Framework for Innovative
Pharamceutical Development, Manufacturing, and Quality Assurance (2004)
The Quality System:
Foundation for Assuring an Ongoing State of Control
 Materials System
 Equipment & Facilities
 Production
 Laboratory
 Packaging & Labeling
 Quality System
Adaptation: Process Control and Improvement
“Control procedures shall be established to monitor the
output and to validate the performance of those
manufacturing processes that may be responsible for
causing variability in the characteristics of in-process
material and the drug product” (211.110a)
Firm must review, at least annually, the quality standards of
each drug product to determine the need for changes in
drug product specifications or manufacturing or control
procedures. (211.180)
Goal of a Manufacturing Organization
 Provide a consistent, defect-free product to
the marketplace via consistent
manufacturing operations
 Assure safety & efficacy
…But not every company has established adequate quality
practices, or achieved predictable output. Some companies,
products, or processes make more mistakes /defective units than
others. Why?
“Putting out fires is not
improvement of the
- W. Edward Deming
Each production day, each dose,
each patient
If you are operating at 3.8 Sigma, you are getting it right
99 percent of the time... It turns out that even a 1
percent error can add up to a lot of mistakes pretty fast.
Getting it right 99 percent of the time is the equivalent of
20,000 lost articles of mail every hour. It’s 5,000
botched surgical procedures every week. It’s four
accidents per day at major airports...
If you can answer when, where and how often the defects
occur you have what you need... But don’t just focus on
the symptoms of the problem. Find the root causes.
Chowdury, 2001
Current state of Pharmaceutical Manufacturing
Operations in Pharmaceuticals Compare Poorly to Other Industries
The pharmaceutical industry lags similar industries in key measures of operations performance…Many of the
shortcomings reflect poor quality practices and represent cost savings opportunities... Estimates are from
McKinsey Operations Practice.
Overall equipment
10% to 60%
70% to 85%
50% to 70%
80% to 90%
70% to 90%
Annual productivity
1% to 3%
5% to 15%
5% to 10%
1% to 3%
5% to 15%
First-pass yield - zero defects
90% to 99%
70% to 90%
90% to 99%
90% to 99%
Production lead times in days
120 to 180
1 to 7
7 to 120
5 to 10
3 to 7
Finished goods inventory in
60 to 90
3 to 30
3 to 30
5 to 50
10 to 40
Labor value-add time
60% to 70%
60% to 70%
60% to 70%
60% to 90%
Direct/indirect labor ratio
The Gold Sheet, January 2009
Why Measure?
“The basis of all scientific work is
the conviction that the world is an
ordered and comprehensible
– Einstein
For a Measurement to provide meaningful evaluation
of quality, it should:
Provide a direct measure of the critical in vitro attribute
that is the surrogate for the in vivo clinical attribute (ideally
with IVIVC)
2. Provide very high assurance that process reproducibly
meets its limits & specifications for each batch
3. Have a purpose (why measure?)
to detect and prevent non-conforming in-process and finished
product output – i.e., assure effective control of the process
…and thus greatly reduce risk of any defective product reaching
the consumer
Be sufficient and timely (how, where, when & how often?)
5. Be reliable. Measurement capability (good analysis
provides the truth; allows for correct decisions)
How, where, when and how often to measure?
 Qualitative vs. quantitative
 Instrument and operator interfaces
 to process
 to sample
 Timeliness and frequency
 Method of sampling
 destructive or non-destructive
 at-line (sample removed), offline (sample temporarily diverted
off-stream) or in-line (on-stream from a distance)
 invasion of process stream can affect measurement
 representative of the batch or providing worst case
 periodic, continuous, stratified to risk
Sources of Variability
When developing or evaluating a process, essential
questions include:
 What variables in the process are influential, and require careful
control and intensified monitoring?
 Why and how can a particular variable affect final product
 How should cGMP procedures be designed to control that
variable? Each significant variable can be measured in one or
more ways. As variables are measured, a reliable picture of
batch quality will emerge.
How Good Are Our
Quality System Detection of Variation &
Defects before Distribution
 Test of a firm’s Quality System is if it will promptly
catch a problem in a batch vs. discovering only
after it is marketed.
1. Mistakes are, in many cases, not caught by the
individual making the error, but instead through final
inspection or QC test!
2. QC testing is of limited sample size intended to assess a
chemical, microbiological, or physical attribute.
3. To avoid detecting mistakes or defects only after a drug
product has been distributed:
 Use Redundancy of Controls, or PAT
Product Quality:
How good is batch-to-batch QC testing?
 Specifications alone can not assure product quality.
 Quality must be built in (21 CFR §§ 211.100, 211.110)
 QC testing regimens may be found during lifecycle to be
insufficient. Enhanced process control or new/modified
QC tests might be needed.
 Passing result for finished product potency, dissolution
or content uniformity, on its own, does not provide high
assurance that it will pass again.
 Sample sizes are not always sufficient, and processes
are not always stable.
Potential for Dissolution Variability
 If a given lot just barely passes the USP
or approved criteria once, what is the
probability of passing 2 to 8 more times?
 Depending on the overall inherent lot
variability, it can range from 0.3% to 100%
Case Study #1
Dissolution Failure (multivariate cause?)
 It was concluded that the cause of the dissolution
failure was a combination of factors, including a
formulation change, specifically a 1% increase in
lubricant, a subtle change in the effective density of
the tablets, the bulk density of microcrystalline
cellulose and the process which changed mixing
principle (v-blender vs. shaker mixer).
 Dissolution testing on stability ultimately detected
the problem.
 3 batches failed (but only 1 batch/year typically
placed on stability by firms)
Case Study #2
Subpotency (multivariate cause?)
 Tablet product
 Assay failure on Stability (9 months)
 Firm’s investigation concludes that product stability
needs to be improved by:
1. changing to a different API source
2. modification of formula
3. improved container/closure system
Microbiological Measurements:
Complexity of Measuring Risk to the Patient
 Type of organism
 “Opportunistic not primary pathogens cause most infection”
 Infective dose
 Very elusive based on next factor…
 Host resistance/susceptibility to infection
 Route of Administration
 “In general, the risk of infection will be much reduced for a drug given
orally or applied to intact skin compared with a formulation used for
treatment of abraded skin or mucous membrane, or damaged eye.”
[Bloomfield, “Microbial Contamination: Spoilage and Hazard.” Chapter 2 in Guide to Microbiological Control in
Pharmaceuticals, 1990.]
Interpreting Atypical Measurements:
OOS Guidance
OOS Guidance
FDA’s 2006 Guidance addresses:
Conducting/concluding the investigation
Interpretation of results
Handling of inconclusive results
Appropriate use of averaging
Appropriate use of outlier tests
OOS Guidance: General
Recommended procedures for OOS
investigations are divided into two phases to
reflect that the OOS result can be caused by
 An aberration of the measurement process
(i.e. laboratory error)
 An aberration of the production process
(i.e. the product is OOS)
OOS Guidance: General
 Too often root cause is unknown, or arbitrarily
attributed to an analyst. Two possibilities:
 Product related variability: Formulation components,
manufacturing process, operator, etc.
 Measurement system (method) related variability:
Sampling bias, apparatus setup, analytical, operator,
reference standard, etc.
Warning Letter – Data Integrity
Can You Trust the Reported Measurements?
 Failure to establish appropriate controls over computer or related systems
to assure that changes in master production and control records or other
records are instituted only by authorized personnel (21 CFR § 211.68 (b)).
The UV/Visible spectrophotometer data acquisition systems allow analysts
to modify, overwrite, and delete original raw data files.
 This equipment is used for dissolution testing of finished product,
stability samples, and process and method validation studies.
 All laboratory personnel were given roles as Managers, which allowed
them to modify, delete, and overwrite results files.
 This system also does not include an audit trail or any history of
revisions that would record any modification or deletion of raw data or
 Your laboratory computer system lacks necessary controls to ensure
that data is protected from tampering, and it also lacks audit trail
capabilities to detect data that could be potentially compromised.
OOS Guidance - Investigations
OOS results may indicate a flaw in product or process
design. For example, a lack of robustness in product
formulation, inadequate raw material characterization or
control, substantial variation introduced by one or more
unit operations of the manufacturing process, or a
combination of these factors can be the cause of
inconsistent product quality. In such cases, it is
essential that redesign of the product or process be
undertaken to ensure reproducible product quality.
Building knowledge…Opportunities for
Variability Reduction and Innovation
“Implement appropriate product
quality improvements, process
improvements, variability reduction,
innovations and pharmaceutical quality
system enhancements.
[ICH Q10 ]
 Provides NIR spectral signature (reflectance from solid or
transmission through liquid) with adequate signal/noise
 spectra contain information on chemical composition as well as physical
attributes of particulates
 Process Endpoints: information can be used to monitor progress of a
chemical or physical change, and determine unit process endpoints
 API synthesis, drier moisture endpoint, blending, solvent
evaporation, online content uniformity and tablet weights, etc.
 Increasingly used for real time release
Development Predictions
Steps in Pharmaceutical
Process Development & Scale-up
Validation of
Predictions using models
Experimentation and Data Analysis
Building knowledge…Development
“Monitoring during scale-up activities
can provide a preliminary indication of
process performance and the successful
integration into manufacturing. Knowledge
obtained during transfer and scale-up
activities can be useful in further developing
the control strategy.”
[ICH Q10 – Technology Transfer]
From Development to Commercial Scale
Process Development predictions can be affected by:
 Lack of fundamental knowledge and understanding
of scale dependent factors
 Lack of correspondence between equipment used in
lab and in factory
 Lack of full understanding of critical attributes of raw
materials, and how they affect physics and
mechanics of processing
 Finite time lines and pressures to market curtail
some process development and scale-up activities
Predictions - DOE Weaknesses
 Based on model
 Predictions are extrapolations
 inside as well as outside explored space
 Experiments done at lab scale
 Missed factors
 Missed interactions at screening
 Each factor alone has little impact
 e.g., cycle # and regeneration buffer salt
 Greater weakness for complex products and processes
Dr. S. Kozlowski, 2008
Pilot Scale and Lot Data
• Statistical Process
• Multivariate SPA
– Gap between
process experience
and DOE
Adapted from T. Kourti
[Jean-Marie Geoffroy, May, 2007]
Building Knowledge…
Monitoring and Adaptation
 Process performance and product
quality monitoring systems for each
product should include:
 Management review of process
performance and product quality
 Corrective action & preventive
action (CAPA) and Change
 Continuous Process Verification
(CPV ) provides strong scientific
basis for maintaining “state of
control” throughout commercial
“Constantly and forever improve
the system of production and
- W. Edward Deming
Every batch, Every day…
“We rely upon the manufacturing controls and
standards to ensure that time and time again, lot
after lot, year after year the same clinical profile will
be delivered because the product will be the same in
its quality… We have to think of the primary
customers as people consuming that medicine and
we have to think of the statute and what we are
guaranteeing in there, that the drug will continue to
be safe and effective and perform as described in the
- Janet Woodcock, M.D.
Summary: Paradigm Shift
 Quality Should Be:
 Proactive, rather than reactive
 Built on knowledge and understanding of product, process and
 Built in through design of facilities, equipment and processes
 Based on use of state of the art tools of measurement science to
control processes
 Utility of Better Measurements in Quality Assurance
 Improved process control
 Continuous quality monitoring in real time
 Automated, objective decision algorithms
 Data can be analyzed retrospectively (performance monitoring) to
trigger improvements in process capability
Tara Gooen
Vibhakar Shah
Lynn Torbeck
Steve Wolfgang
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