Manufacturing, Measurements & Quality

Systems: Seeking Order in Complexity

Richard L. Friedman

Director,

Division of Manufacturing & Product Quality

Center for Drug Evaluation & Research

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ICH Q9

Risk

Control

Risk Review

Risk Assessment

Risk Evaluationunacceptable

Risk Control

Risk Analysis

Risk Reduction

Risk Identification

Review Events

Risk Acceptance

Initiate

Quality Risk Management Process

Output / Result of the

Quality Risk Management Process

Risk

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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

Includes:

Materials System

Equipment & Facilities

Production

Laboratory

Packaging & Labeling

Quality System

Adaptation: Process Control and Improvement

FEEDBACK SYSTEMS:

“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)

IMPROVEMENT:

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

process”

- W. Edwards 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

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.

Measure Pharma Automotive Aerospace Computer

Consumer

Packaged

Goods

Overall equipment

effectiveness 10% to 60% 70% to 85% 50% to 70% 80% to 90% 70% to 90%

Annual productivity

improvement 1% to 3% 5% to 15% 5% to 10% 1% to 3% 5% to 15%

First-pass yield - zero defects 60% 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

days 60 to 90 3 to 30 3 to 30 5 to 50 10 to 40

Labor value-add time 20% 60% to 70% 60% to 70% 60% to 70% 60% to 90%

Direct/indirect labor ratio 1:1 10:1 10:1 10:1 10:1

The Gold Sheet, January 2009

Current state of Pharmaceutical Manufacturing

Why Measure?

“The basis of all scientific work is

the conviction that the world is an

ordered and comprehensible

entity”

– Einstein

For a Measurement to provide meaningful evaluation

of quality, it should:

1. 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

4. 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?

Tests 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

assessment

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

quality?

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

Measurements?

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.

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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%.

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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)

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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:

1. Conducting/concluding the investigation

2. Interpretation of results

3. Handling of inconclusive results

4. Retesting

5. Appropriate use of averaging

6. 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

either:

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 IntegrityCan 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

files.

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.

[Paraphrased]

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 ]

NIR in PAT

Provides NIR spectral signature (reflectance from solid or transmission through liquid) with adequate signal/noise ratio

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 (e.g. API synthesis, drying, blending).

NIR is increasingly used as PAT tool to determine process endpoints

Blend uniformity, solvent evaporation (drier moisture endpoint)

Online content uniformity and tablet weights

Increasingly used for real time release

Development Predictions

Steps in Pharmaceutical

Process Development & Scale-up

Validation of

Predictions

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]

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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

• Multivariate SPA

Adapted from T. Kourti

• Statistical Process Analysis

– Gap between

process experience

and DOE

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[Jean-Marie Geoffroy, May, 2007]

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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 Management

Continuous Process Verification (CPV ) provides strong scientific basis for maintaining “state of control” throughout commercial lifecycle.

Pre-approval

Post-approval

“Constantly and forever improve

the system of production and

service”

- W. Edwards Deming

CGMP:

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 label.”

- Janet Woodcock, M.D.

Summary: Paradigm Shift

Quality Should Be:

Proactive, rather than reactive

Built on knowledge and understanding of product, process and

materials

Built in through design of facilities, equipment and processes

(CGMP)

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

Acknowledgments:

Tara Gooen

Vibhakar Shah

Lynn Torbeck

Steve Wolfgang

For More Information:www.fda.gov/aboutfda/centersoffices/cder/ucm096102.ht