CAD quality by design mpharm 2nd sem 1st year

1. Quality by Design

2. Quality by Design
⦁ 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

3. New Quality Paradigm

6. The new paradigm emphasize:
⦁ Quality must be mainly built-in andit will not improve by additional testing
and inspection
⦁ Better utilization of modern science throughout the product lifecycle
⦁ QRM is a key enabler throughout the product lifecycle
⦁ Robust Pharmaceutical Quality System (PQS),with appropriate knowledge
management,assuresquality throughout the product life cycle
⦁ An integrated approach to development, manufacturing, andquality for both
industry and regulators

7. Elements of QbD
⦁ QualityTarget Product profile (QTTP)
Product
profile
CQA
Risk
Assessment
Design
space
Control
strategy
Continual
impovement
⦁ Determine potential critical quality attributes
(CQA)
⦁ Link raw material attributes and process
parameters to CQA and perform risk
assessment (ICH Q9)
⦁ Develop adesign space
⦁ Design and implement acontrol strategy
⦁ Manage product lifecycle, including continual
improvement,(ICH Q10)

8. Quality target product profile

9. ⦁ Forms the basis of design for the development of the
product.
⦁ Intended use in clinical setting, route of administration, dosage form,
delivery systems;
⦁ Dosage strength(s);
⦁ Container closure system;
⦁ Therapeutic moiety release or delivery and attributes affecting pharmacokinetic
characteristics (e.g.,dissolution, aerodynamic performance)
⦁ Drug product quality criteria (e.g.,sterility, purity, stability anddrug release)
Quality target product profile

10. Critical Quality Attributes
⦁ A CQA is 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.
⦁ CQAs are generally associated with the
⦁ Drug substance,
⦁ Excipients,
⦁ Intermediates (in-process materials) and
⦁ Drug product.

11. QTPP
Others
Purity
Potency
Identity
Potential Quality
Attributes
•Appearance
•Assay
•Crystallinity
•Degradation
•Disintegration
•Dissolution
•IR/MR
•Microbiology
•Sterility
Safety
and
efficacy
Criticality
assessment
CQAs
QTPP to CQA

12. Risk Assessment: Link raw material
attributes and process parameters to CQA
⦁ Material:
Raw materials, starting materials,reagents,solvents,process aids,intermediates,
APIs,and packaging and labeling materials, ICH Q7A
⦁ Attribute:
A physical,chemical,biological, or microbiological property or characteristic

13. Material Attribute:
⦁ Can be an excipient CQA,
⦁ Raw material CQA,
⦁ Starting material CQA,
⦁ Drug substance CQA etc
A Material Attribute can be quantified
⦁ Typically fixed
⦁ Can sometimes be changed during further processing (e.g.PSD– milling)
⦁ Examples of material attributes: PSD,Impurity profile, porosity, specific volume,
moisture level,sterility

14. Mapping material attributes and CPP to
CQAs
⦁ Understand & control the variability of Material attributes
and critical process parameters to meet Product CQA‘s
Critical Material
Attributes
MA1
MA2
Critical Quality
Attributes
QCA1
QCA2
QCA3
Critical Process
parameters
CPP1
CPP2

15. Process parameters
⦁ A process parameter whose variability has an impact on acritical quality attribute and
therefore should be monitored or controlled to ensure the process produces
the desired quality (Q8R2)
⦁ CPPs have adirect impact on the C Q A s
⦁ A process parameter (PP) can be measured and controlled (adjusted)
⦁ Examples of CPPs for small molecule: Temperature, addition rate,cooling
rate,rotation speed
⦁ Examples of CPPs for large molecule: Temperature, pH, Agitation, Dissolved
oxygen,Medium constituents,Feed type and rate

16. Material attributes and process
parameters
Process
(or process step)
Input
materials
Input
Process
parameters
Product or
intermediate
Process
variability

17. Facilities
Material
Design
Process
Distribution
Manufacturing
Patient
Opportunities to impact
risk using quality risk
management
Risk management methodology

18. Q10
Q8
Facilities
Material
Design
Process
Distribution
Manufacturing
Patient
Opportunities to impact
risk using quality risk
management Q9
Risk management methodology

19. Risk Management
⦁ Describes systematic processes for the assessment, control, communication,
and review of quality risks
⦁ Applies over product lifecycle: development,manufacturing, and distribution
⦁ Includes principles, methodologies, and examples of tools for quality
risk management
⦁ Assessment of risk to quality should:
⦁ Be based on scientific knowledge
⦁ Link to the protection of the patient
⦁ Extend over the lifecycle of the product

20. Overview: some tools and their key
words
⦁ Failure Mode EffectsAnalysis (FMEA)
⦁ Break down large complex processes into manageable steps
⦁ Failure Mode,Effects and Criticality Analysis (FMECA)
⦁ FMEA & links severity,probability & detectability to criticality
⦁ FaultTreeAnalysis (FTA)
⦁ Tree of failure modes combinations with logical operators
⦁ HazardAnalysis and Critical Control Points (HACCP)
⦁ Systematic,proactive, and preventive method on criticality

21. Contd..
⦁ Hazard Operability Analysis (HAZOP)
⦁ Brainstorming technique
⦁ Preliminary HazardAnalysis (PHA)
⦁ Possibilities that the risk event happens
⦁ Risk ranking and filtering
⦁ Compare and prioritize risks with factors for each risk
considerations

23. Design space
⦁ The multidimensional combination and interaction of input
variables (e.g.,material attributes) and process parameters that
have been demonstrated to provide assurance of quality
⦁ Regulatory flexibility
⦁ Working within the design space is not considered achange
⦁ Important to note
⦁ Design space is proposed by the applicant and is subject to regulatory
assessment and approval

24. Design space determination
⦁ First-principles approach
Combination of experimental data and mechanistic knowledge of
chemistry, physics,andengineering to model andpredict performance
⦁ Non-mechanistic/ empirical approach
Statistically designedexperiments (DOEs)
Linear and multiple-linear regression

25. ⦁ Scale-up correlations
translate operating conditions between different scales or pieces of
equipment
⦁ RiskAnalysis
determine the significance of effects
⦁ Any combination of the above

26. Example

28. Design space and quality
control strategy
Process
(or process step)
Input
materials
Input
Process
parameters
Product or
intermediate
Process
variability
Design space

29. 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,
⦁ drug product materials ,
⦁ components,facility ,
⦁ equipment operating conditions,
⦁ in-process controls,
⦁ finished product specifications,and
⦁ the associated methods and frequency of monitoring and control

30. Process
(or process step)
Input
materials
Input
Process
parameters
Product or
intermediate
Process
variability
Design space
Process Controls/PAT
Monitoring of
parameters or
attributes
Reduced product
variability

31. Continual Improvement

32. Traditional vs QbD approaches

37. Why QbD?
⦁ Higher level of assurance of product quality for patient
• Improved product and process design and
• Understanding quality risk management in manufacturing
• Monitoring, tracking and trending of product and process
• Continual improvement

38. ⦁ Cost saving and efficiency for industry
• Increase efficiency of manufacturing process
• Minimize/eliminate potential compliance actions
• Provide opportunities for continual improvement
• Facilitate innovation
• More efficient regulatory oversight
• Streamline post approval manufacturing changes andregulatory
processes

39. • Depending on the level of development (scientific understanding)
achieved and an adapted quality system in place, opportunities exist to
develop more flexible regulatory approaches,for example,to facilitate:
• Risk-based regulatory decisions (reviews and inspections);
• Manufacturing process improvements, within the approved design space described
in the dossier, without further regulatory review;
• Reduction of post-approval submissions;
• Real-time release testing, leading to a reduction of endproduct release
testing.

40. Barrier to QbD
⦁ Culture challenges
⦁ Move from prescriptive approach
⦁ More sharing of scientific and risk information
⦁ Business Challenges
⦁ Business justification
⦁ Management Support
⦁ Budgeting silos across business units
⦁ Implementation Challenges
⦁ Collaboration between functions
⦁ Experience with new concepts
🞂 Workload and resource limitations

41. Tools of QbD
⦁ Prior Knowledge
⦁ RiskAssessment
⦁ Mechanistic Model,Design of Experiments,and DataAnalysis
⦁ ProcessAnalyticalTechnology
⦁ Application of PAT involves four key components as follows
⦁ Multivariate data acquisition and analysis
⦁ Process analytical chemistry tools
⦁ Process monitoring and control
⦁ Continuous process optimization and knowledge
management

42. Prior Knowledge
• The word “prior” in the term “prior knowledge” not only means “previous,” but also
associates with ownership and confidentiality, not available to the public.
• Knowledge gained through education or public literature may be termed public
knowledge.
• Prior knowledge in the QbD framework generally refers to knowledge that stems
from previous experience that is not in publically available literature.
• Prior knowledge may be the proprietary information, understanding, or skill that
applicants acquire through previous studies.

43. Risk Assessment
• ICH Q9 (4) provides a nonexhaustive list of common risk assessment tools as follows:
• Basic risk management facilitation methods (flowcharts, check sheets, etc.)
• Fault tree analysis
• Risk ranking and filtering
• Preliminary hazard analysis
• Hazard analysis and critical control points
• Failure mode effects analysis
• Failure mode, effects, and criticality analysis
• Hazard operability analysis

44. Mechanistic Model, Design of Experiments,
and Data Analysis
• When DoE is applied to the formulation or process development,
• input variables include the material attributes (e.g., particle size) of raw material or
excipients and process parameters (e.g., press speed or spray rate), while
• outputs are the critical quality attributes of the in-process materials or final drug
product (e.g., blend uniformity, particle size or particle size distribution of the
granules, tablet assay, content uniformity, or drug release).
• DoE can help identify optimal conditions, CMAs, CPPs, and, ultimately, the design
space.

45. Process Analytical Technology
• The application of PAT may be part of the control strategy. ICH Q8 (R2) identifies the
use of PAT to ensure that the process remains within an established design space.
Application of PAT involves four key components as follows:
• Multivariate data acquisition and analysis
• Process analytical chemistry tools
• Process monitoring and control
• Continuous process optimization and knowledge management

47. Example

48. Thank you