An Introduction to QbD (Quality by Design) and Implications for

Technical Professionals

Biography and Disclaimer Director, Training and Continuous Improvement,

Pfizer Global Manufacturing, Richmond, VA, previously Associate Director, Engineering, Wyeth Pharmaceuticals

Ph.D. Candidate, Systems Engineering, the George Washington University (dissertation topic: “QbD and Pharmaceutical Production System Fundamental Sigma Limits”). GWU Ph.D. Committee Advisors: Dr. Thomas Mazzuchi Dr. Shahram Sarkani, P.E.

This is the outcome of the author’s research and does not necessarily represent the views of Pfizer or GWU

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Session AbstractCurrently, most pharmaceutical development and manufacturing systems rely on a Quality by Testing (QbT) model to ensure product quality. Industry at large and regulators recognize many of the limitations of the current QbT approach. As a response, Quality by Design (QbD) is emerging to enhance the assurance of safe, effective drug supply to the consumer, and also offers promise to significantly improve manufacturing quality performance. This session provides an overview of QbD, and implications for pharmaceutical technical professionals.

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Learning ObjectivesUpon completion of this session, the learner should be able to Provide a rationale for moving to QbD based on

the inherent inability of a system to achieve desired performance based on quality by testing

Describe the key elements of QbD Describe implications of QbD on technical

professionals

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The Migliaccio Conjecture . . .

The pharmaceutical industry produces six-sigma products with

three-sigma processesAdapted from Migliaccio, G., SVP Network Performance, Pfizer Global Manufacturing, in: FDA will seek Consultant Help in Implementing Quality Initiative, The Goldsheet, vol. 36, no. 9, September

2002 and a presentation by Doug Dean, Ph.D. and Francis Bruttin, 2004

The problem with this: High quality shipped product, but . . . Gap between shipped quality and production sigma High cost of quality, inherent risks Plus, low return on investment for quality improvement

initiatives

Why the Gap? The Hypotheses . . .

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1) The conjecture is true in principle

2) Without designing for quality (DFSS or QbD), production systems hit a sigma limit

+/- 6 s ProductSupply

+/- 3 s ProductionSystem

3) Prod. Systems follow S-CurveTechnological Evolution

4) Moving to higher S-Curve(s) will require QbD to resolve system contradictions

Results of the Research (summarized) 1. There is a gap between pharmaceutical

production systems and supply sigma2. Systems essentially reach a fundamental

limit3. Production systems follow an S-Curve

technological evolution profile4. Rising to higher S-Curves requires elimination

of system conflicts (contradictions, trade-offs)5. QbD offers significant opportunities over QbT

to eliminate conflicts, but additional opportunity for development within the QbD scope exists (see “Additional Material” at the end)

An intro to QbD – what is it? ICH: “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.” [1,2]

Berridge: A “holistic, systems-based approach to the design, development, and delivery of any product or service to a consumer.” [2]

My view : An approach to designing product and processes to supply product to meet patient needs at desired quality levels without reliance on release testing.

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System Comparison: Traditional vs. QbDProduct

DistributionProduct

Quarantine

FixedPackagingProcess

Fixed, BatchManufacturing

Process

ProductDevelopment

Release Testing,DocumentIntegrity

In-processTesting,

Documentation

In-processTesting,

Documentation

Fixed Parameters,

Ranges

PatientProductionSystem

Quality System

As-Is: Traditional Pharmaceutical Product Supply System

ProductDistribution

Variable Pkg Process

Variable Batch or Continuous Mfg Process

ProductDevelopment

Real-time Release

Maintain in Design Space (PAT, etc.)

Design Space, Variable

Parameters

PatientProductionSystem

Quality System

To-Be: QbD Pharmaceutical Product Supply System

ProductDistribution

ProductQuarantine

FixedPackagingProcess

Fixed, BatchManufacturing

Process

ProductDevelopment

Release Testing,DocumentIntegrity

In-processTesting,

Documentation

In-processTesting,

Documentation

Fixed Parameters,

Ranges

PatientProductionSystem

Quality System

As-Is: QbT Pharmaceutical Product Supply System

ProductDistribution

Responsive Pkg Process

Responsive Batch or

Continuous Mfg Process

ProductDevelopment

Real-time Release

Control Strategy: Maintain in Design Space (PAT, etc.)

Design Space

PatientProductionSystem

Quality System

To-Be: QbD Pharmaceutical Product Supply System

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QbD Key Concepts Build in quality versus test in quality Scientific-based knowledge of the products and

processes Identify, understand, and control CQA’s (Critical

Quality Attributes) and CPP’s (Critical Process Parameters)

QrM (Quality Risk Management) approach (risk assessment, risk control, and risk review) [10]

Design Space (DS) to identify acceptable limits of operation via DOE (Design of Experiments)

Control Strategy to ensure production is maintained within the DS

Use advanced statistical tools and technology such as PAT (Process Analytical Technology). This can extend to real-time release (RTR) 10

Advantages of QbD [2,10]

Better innovation due to the ability to improve processes without resubmission to the FDA when remaining in the Design Space

More efficient technology transfer to manufacturing

Less batch failures Greater regulator confidence of robust products Risk-based approach and identification Innovative process validation approaches Less intense regulatory oversight and less post-

approval submissions For the consumer, greater drug consistency More drug availability and less recall Improved yields, lower cost, less investigations,

reduced testing, etc.11

Better Quality . . .

The flow of QbD

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TPP (Target Product Profile) The TPP “identifies the desired performance

characteristics of the product” related to the patient’s needs. [35]

Linkage from patient to product to process is described as follows: [34]

Patient: Clinical outcome Product: CQAs (Critical Quality Attributes) Process: Material attributes and process

parameters

Attributes/Parameters Critical Quality Attribute (CQA): “A

physical, chemical, biological or microbiological property or characteristic that should be within an appropriate limit, range, or distribution to ensure the desired product quality.” [26]

Critical Process Parameter (CPP): “A process parameter whose variability has an impact on a critical quality attribute and therefore should be monitored or controlled to ensure the process produces the desired quality.” [26]

Material Attributes: Raw material or component factors that impact CQAs

DOE (Design of Experiment) DOE is defined as “a structured analysis

wherein inputs are changed and differences or variations in outputs are measured to determine the magnitude of the effect of each of the inputs or combination of inputs.” [25]

Full factorial example:

Dependent Variable

(Response)

Run Factor X1 Factor X2 Factor Y1

1 High High Output12 Low High Output23 High Low Output34 Low Low Output4

Independent Variable (Controlling Factors)

A Design Space [11b]

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Knowledge Space

Design Space

NOR

CQA

Design Space: “Multidimensional combination and interaction of input variables (e.g., material attributes) and process parameters that have been demonstrated to provide assurance of quality.” [1]

Knowledge Space: “A summary of all process knowledge obtained during product development.” [33]

Control Strategy Control Strategy: “A planned set of controls, derived

from current product and process understanding, that assures process performance and product quality. The controls can include parameters and attributes related to drug substance and drug product materials and components, facility and equipment operating conditions, in-process controls, finished product specifications, and the associated methods and frequency of monitoring and control.” [36]

When the Control Strategy demonstrates CPPs (Critical Process Parameters) are controlled within the DS (Design Space), product can be released in real-time. [1]

Control Strategy Elements Input material attribute control Product specifications Unit operation controls that have a

downstream impact In-process testing or real time release in lieu

of end-product testing Monitoring program that verifies multivariate

prediction models

PAT (Process Analytical Technology) “A system for designing, analyzing, and

controlling manufacturing through timely measurements (i.e., during processing) of critical and performance attributes of raw and in-process materials and processes with the goal of ensuring final product quality.” [38]

PAT is considered a subset or enabling aspect of QbD [8] and is needed to ensure a process remains inside the DS (Design Space). [39]

PAT Example Liquid product, used to determine mix time CQA related to mix uniformity CPP’s (Critical Process Parameters) included agitator speed,

time after addition of one ingredient until the addition of another, solution temperature, and recirculation flow rate.

Process analyzer used was a refractometer Resulted in cost savings and quality enhancement

Mix Tank

Pump

RI Sensor

SCADA, User

Interface

Control System

Data Historian

Envisioning the future . . .

Implications for R&D/Development Adapt to emerging QbD Better mechanistic understanding of factor interactions Emphasis on statistics Must involve manufacturing professionals early Better understanding of raw materials and components

(e.g. leachables and extractables) QbD-based filings Need to develop cost effective and timely methods to

compensate for additional QbD activities (e.g. DoE) Technology acumen (continuous processing, PAT, etc.)

Implications for Manufacturing More predictable quality Flexible processes QbD concepts (Design Space) Higher technologies (PAT, dynamic systems,

data-centric) Skillsets (QbD, DOE, multivariate statistics,

etc.) Early alignment with development needed Continuous-like manufacturing and continuous

quality verification Revitalized continuous improvement

opportunities

Implications for Quality More predictable quality Less post-production testing Real-time-release (RTR) Shift focus to remaining in Design Space and

ensuring robust Control Strategy Multivariate statistics – skillsets Different technologies and systems Flexible processes Reduced laboratory work Data historian emphasis for investigations

Implications for Validation The costly approach to produce three batches

is expected to be replaced by “demonstrated scientific process and product knowledge.” [40]

Continued Process Verification (CPV) defined as “an alternative approach to process validation in which manufacturing process performance is continuously monitored and evaluated.” [41]

DS (Design Space) limits provide the basis for validation acceptance criteria. [39]

Skillsets (similar to previous) Intimate knowledge of QbD

Validation Implications (continued) Also see FDA’s recent “Guidance for Industry:

Process Validation: General Principles and Practices” Must understand implications of variations on

quality Control of in-process material Evaluation of all attributes and controls Goal is homogeneity in a batch and consistency

between batches PAT may warrant a different approach (e.g. “focus

on the measurement system and control loop for the measured attribute”)

Emphasis on the use of quantitative and statistical methods

Implications for Engineering Expect more robust requirements from Development Design flexible processes Smaller facilities (move from stainless steel focus to

disposables, continuous improvement, flexible factories, etc.)

Knowledge of quantitative methods (e.g. multivariate statistics)

IT integration, data acquisition and control PAT Better mechanistic understanding of product (VOC) Embed contradiction-eliminating design features Team approach more essential (e.g. will need to add

analytical chemistry expertise)

Implications for Vendors and Suppliers Knowledge of QbD Embed QbD principles Standardization, platforms Incorporate QbD elements early (e.g. DS, PAT, etc.) Robust software Continuous processes Disposables Further development of PAT needed

Improve sensor technologies or prove for pharma application

Standardized platforms would be useful Fully integrate in continuous processing

Summary QbD offers significant opportunities over

traditional approaches to improve performance

QbD will enable moving from fixed processes/variable product to variable process/consistent product

Technology is needed to enable and facilitate QbD

New skillsets and knowledge is needed for technical professionals

If QbD continues to emerge, we all will have to change

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Early QbD Promising Results [16]

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2007 study Traditional development and manufacturing resulted in

81% of the measured PpK (Process Capability) > 1 QbD developed products resulted in 92% of the

measured PpK >1 Represents a 14% improvement in PpK (Process

Capability) Assay PpK

At launch 1.2 (3-4 sigma) Six months after launch PpK = 1.8 (5-6 sigma)

Tablet production Based on % of batches right first time 15 yr + Traditionally Developed Product: 3.33 sigma First-year QbD Developed Product: 3.96 sigma

𝑃𝑝𝑘 = min൛𝑃𝑝𝑢,𝑃𝑝𝑙ൟ 𝑃𝑝𝑢 = 𝑈𝑆𝐿− 𝜇3𝜎

QbD Enablers to Overcome System Contradictions (super-system level)

Contradiction QbD EnablerQbD

Current State

Quality by Documentation Focus on Control Strategy, Design Space ?

Batch-centric Processing Continuous quality control +

Static Processes Flexible, responsive manufacturing (e.g. PAT) ++

Batch-based Quality Continuous quality control ++

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(Continuous-like processing)

QbD Elements Effective to Eliminate System Contradictions Move to continuous quality monitoring and control Design Space (DS) including CQAs (Critical Quality

Attributes), Raw Material Attributes, and CPPs (Critical Process Parameters)

Ability to divide DS into parts for various steps in the production process

Focus on what is critical to quality through a risk-based approach

Variable processes to react to changing inputs while ensuring consistent outputs

Process Analytical Technology to monitor and provide feedback to support a Control Strategy

Multivariate statistical approach

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Areas of QbD opportunity Emphasis on less quality by documentation reliance Consider optimal state during development to

minimize system conflicts during production (e.g. phase state, etc.)

Emphasize QbD extension to the supply chain Continuous verification of raw materials and

components prior to entering production stream Disposables More emphasis on continuous-like processing (or

parallel processing, quasi-continuous, mini-batch) PAT standardization, plug-and-play TRIZ principles in engineering design More robust KM Human factoring (training, development, skill-sets,

change management)