Module 4 Part 7: Quality by Design (QbD) Applications

🔬 QbD Framework Fundamentals

Step-by-Step Understanding: What is Quality by Design?

Step 1 - Traditional Approach Problem: "Let's think about the old way - we developed a formulation, tested it, and if something failed, we fixed it. This reactive approach was costly and time-consuming."

Step 2 - QbD Philosophy: "QbD says: Let's understand the science FIRST, then design quality into the product from the beginning. Use statistics and DoE to build knowledge."

Step 3 - Regulatory Benefits: "FDA/EMA reward this approach with more flexible manufacturing and faster approvals. Why? Because you've proven you understand your process."

🎯 The Four Pillars of QbD

1. QTPP (Quality Target Product Profile)

Definition: Summary of the quality characteristics that ideally will be achieved to ensure the desired quality, taking into account safety and efficacy.

💊 Example: Immediate Release Tablet

Dosage Form: Oral tablet, immediate release
Strength: 250 mg active ingredient
Route of Administration: Oral
Dissolution: ≥85% in 30 minutes (USP <711>)
Shelf Life: 24 months at 25°C/60% RH

2. CQA (Critical Quality Attributes)

Definition: Physical, chemical, biological, or microbiological properties that should be within an appropriate limit to ensure the desired product quality.

Quality Attribute	Target	Specification Range	Critical?	Impact on QTPP
Assay (% label claim)	100%	95.0 - 105.0%	✅ Yes	Efficacy/Safety
Dissolution Rate	≥85% at 30 min	≥80% at 30 min	✅ Yes	Bioavailability
Content Uniformity	AV ≤ 15	AV ≤ 15	✅ Yes	Dose consistency
Hardness	8-12 kp	6-15 kp	⚪ No	Handling
Friability	≤0.8%	≤1.0%	⚪ No	Handling

3. CPP/CMA (Critical Process Parameters / Critical Material Attributes)

Definition: Input variables that, when varied within their ranges, have a significant impact on CQAs.

🔧 Critical Process Parameters

Compression Force (5-15 kN)
Granulation Time (3-7 minutes)
Drying Temperature (40-60°C)
Blend Time (5-15 minutes)

📦 Critical Material Attributes

API Particle Size (10-50 μm)
Binder Concentration (2-6%)
Lubricant Level (0.5-1.5%)
Moisture Content (≤3%)

📊 Design Space Definition and Visualization

Step-by-Step Reasoning: Building Design Space

Step 1 - Mathematical Definition: "Design Space is the multidimensional combination of input variables (CPPs) and process parameters that provide assurance of quality."

Step 2 - Statistical Basis: "We use DoE models to predict CQA responses. Where all CQAs meet specifications = Design Space."

Step 3 - Regulatory Significance: "Working within Design Space is not considered a change requiring prior approval. This is regulatory flexibility!"

🎯 Design Space Construction Process

📈 Method 1: Contour Overlay Approach

Step 1: Individual Response Contours

Dissolution ≥ 85% (Green Region)
Assay: 95-105% (Blue Region)
Content Uniformity AV ≤ 15 (Yellow Region)

Step 2: Overlay Analysis

Find intersection of all acceptable regions
Common area = Proven Acceptable Range
Edge of failure = Process boundaries

💊 Worked Example: Tablet Compression Design Space

Scenario: We have DoE models for Compression Force (X₁: 5-15 kN) and Binder % (X₂: 2-6%)

Step 1 - Model Equations from DoE:
• Dissolution % = 87.5 + 3.2×X₁ - 2.1×X₂ - 0.8×X₁×X₂
• Hardness (kp) = 9.2 + 2.5×X₁ + 1.3×X₂ + 0.4×X₁×X₂

Step 2 - Constraint Equations:
• For Dissolution ≥ 85%: 87.5 + 3.2×X₁ - 2.1×X₂ - 0.8×X₁×X₂ ≥ 85
• For Hardness 6-15 kp: 6 ≤ 9.2 + 2.5×X₁ + 1.3×X₂ + 0.4×X₁×X₂ ≤ 15

Step 3 - Design Space Boundaries:
• Safe Operating Region: Compression Force 7-13 kN, Binder 2.5-5.5%
• Edge of Failure: Outside these ranges, quality specifications fail

📊 Edge of Failure Analysis

Safe Zone

All CQAs meet specifications with high confidence

Risk Level: Low

Edge Zone

Some CQAs approach specification limits

Risk Level: Medium

Failure Zone

One or more CQAs fail specifications

Risk Level: High

💻 Excel Implementation for Design Space

=IF(AND(Dissolution_Model>=85, Hardness_Model>=6, Hardness_Model<=15), "Acceptable", "Outside Design Space")

Where:

Dissolution_Model = 87.5 + 3.2*Force - 2.1*Binder - 0.8*Force*Binder
Hardness_Model = 9.2 + 2.5*Force + 1.3*Binder + 0.4*Force*Binder

⚠️ Risk Assessment and FMEA Integration

Step-by-Step Reasoning: Why Risk Assessment in QbD?

Step 1 - Proactive vs Reactive: "Instead of waiting for problems, we identify potential failure modes before they happen."

Step 2 - Statistical Evidence: "DoE provides data on how likely failures are and which factors cause them."

Step 3 - Prioritized Actions: "FMEA scores help us focus resources on the highest-risk areas first."

🔍 FMEA (Failure Mode and Effects Analysis) in Formulation

Risk Priority Number (RPN) Calculation

Formula: RPN = Severity × Occurrence × Detection

Severity (1-10)

1-3: Minor impact (cosmetic defects)
4-6: Moderate impact (out of spec, rework)
7-8: High impact (batch failure)
9-10: Critical (safety risk)

Occurrence (1-10)

1-2: Very rare (DoE shows robust)
3-5: Occasional (moderate sensitivity)
6-8: Frequent (high variability)
9-10: Almost certain (edge of failure)

Detection (1-10)

1-2: Almost certain detection
3-5: High detection probability
6-8: Moderate detection
9-10: Poor detection capability

Action Criteria

RPN 1-50: Monitor
RPN 51-150: Action recommended
RPN 151-300: Action required
RPN >300: Immediate action

💊 FMEA Example: Dissolution Failure Analysis

Failure Mode	Potential Cause	Severity	Occurrence	Detection	RPN	Action Required
Low dissolution rate	Excessive lubricant	8	4	3	96	Tighten lubricant control
Tablet too hard	High compression force	6	6	2	72	Monitor force closely
Content non-uniformity	Poor blending	9	3	4	108	Validate blend process

DoE-Informed Occurrence Scoring: "DoE tells us how sensitive each response is to factor changes. High sensitivity = higher occurrence score."

🎛️ Control Strategy Development

Step-by-Step Reasoning: Building Robust Control

Step 1 - Three Levels of Control: "Control strategy operates at material level, process level, and final product level. Each builds on DoE knowledge."

Step 2 - Risk-Based Approach: "Highest risk parameters (high RPN) get the tightest controls and most frequent monitoring."

Step 3 - Real-Time Release Testing (RTRT): "When process understanding is strong enough, we can release based on process data instead of waiting for end-product testing."

🔧 Control Strategy Framework

📦 Material Controls

API Particle Size: 20-40 μm (±5 μm tolerance)
Excipient Moisture: ≤2.5% (critical for flow)
Binder Viscosity: 15-25 cP (affects granulation)
Release Strategy: Certificate of analysis + incoming inspection

⚙️ Process Controls

Granulation Endpoint: Power consumption method
Compression Force: Real-time monitoring ±5%
Blend Uniformity: NIR spectroscopy validation
Environmental: Temperature 20-25°C, RH 40-60%

🔬 Product Controls

Weight Variation: Every tablet weighed
Hardness: 10 tablets per hour
Dissolution: 6 tablets per batch (or RTRT)
Assay/CU: Validated sampling plan

📊 Continuous Monitoring

Control Charts: X-bar and R charts for CPPs
Trend Analysis: Weekly review of capability indices
Alert Limits: 95% confidence intervals from DoE
Action Limits: Specification boundaries

📈 Real-Time Release Testing (RTRT) Qualification

💊 RTRT Example: Dissolution Prediction Model

Scenario: Can we predict dissolution from in-process measurements instead of waiting 45 minutes for dissolution test?

Step 1 - Model Development:
Dissolution % = 85.2 + 1.8×(Hardness) - 0.3×(Hardness)² + 4.2×(Disintegration Time)

Step 2 - Model Validation:
• R² = 0.94 (excellent fit)
• Validation batches: 15 batches, prediction error <3%
• 95% Prediction Interval: ±4.2%

Step 3 - RTRT Decision Criteria:
• IF predicted dissolution ≥ 89% (85% + 4% buffer), THEN release
• IF predicted 81-89%, THEN test dissolution
• IF predicted <81%, THEN investigate immediately

💻 Excel RTRT Calculator

=85.2 + 1.8*B2 - 0.3*B2^2 + 4.2*C2

Where B2 = Hardness (kp), C2 = Disintegration Time (sec)

=IF(D2>=89,"RELEASE",IF(D2>=81,"TEST","INVESTIGATE"))

Where D2 = Predicted dissolution %

📋 QbD Regulatory Documentation

Step-by-Step Reasoning: Regulatory Filing Strategy

Step 1 - Tell the Story: "Regulators want to see the journey from QTPP to final product. Show how DoE built your understanding."

Step 2 - Evidence-Based Justification: "Every control strategy decision should be backed by statistical evidence from your DoE studies."

Step 3 - Design Space Benefits: "Clearly articulate the regulatory flexibility you're requesting and why it's justified."

📄 QbD Submission Checklist

✅ Section 1: Quality Target Product Profile (QTPP)

Intended use and patient population
Route of administration and dosage form
Strength and dose frequency
Pharmacokinetic characteristics
Stability and shelf life targets

✅ Section 2: Critical Quality Attributes (CQAs)

Risk assessment linking QTPP to CQAs
Justification for criticality decisions
Specification limits with statistical rationale
Test methods and their validation status

✅ Section 3: Design of Experiments Summary

DoE strategy and design justification
All factors studied (CPPs and CMAs)
Statistical models and their adequacy
Model validation and confirmation studies
Raw data in appendices

✅ Section 4: Design Space Definition

Mathematical/graphical representation
Edge of failure analysis
Proven acceptable ranges for all CPPs
Robustness demonstration
Scale-up considerations

✅ Section 5: Control Strategy

Risk-based control approach
Material, process, and product controls
RTRT qualification (if applicable)
Continuous monitoring plan
Change management procedures

📝 Sample Regulatory Language

"Design Space Justification" Section Example:

"The Design Space was established through a systematic DoE approach using a Central Composite Design (CCD) with 3 factors and 17 experimental runs. Statistical models were developed for three CQAs: dissolution rate (R² = 0.96), hardness (R² = 0.91), and assay (R² = 0.94). The Design Space represents the region where all CQAs simultaneously meet their specifications with 95% confidence. Operating within this Design Space provides assurance of quality and is not considered a change requiring prior regulatory approval per ICH Q8(R2) guidance."

🎮 Interactive QbD Workshop

Workshop Progress

Ready to start! Click the activities below to practice QbD concepts.

🎯 Activity 1: QTPP Builder

Define QTPP for a new sustained-release formulation

📊 Activity 2: Design Space Mapper

Use DoE results to visualize and define Design Space

⚠️ Activity 3: FMEA Calculator

Conduct risk assessment and calculate RPN scores

🎛️ Activity 4: Control Strategy Designer

Build comprehensive control strategy framework

🎯 Key Takeaways

🔑 Essential QbD Principles

1. Science-Based Development

Use DoE to understand cause-and-effect relationships. Build statistical models that predict quality attributes.

2. Risk-Based Decision Making

Focus resources on high-risk areas. Use FMEA to prioritize controls and monitoring.

3. Regulatory Flexibility

Design Space provides manufacturing flexibility. Changes within Design Space don't require prior approval.

4. Continuous Improvement

Use ongoing data to refine understanding. Update Design Space as knowledge grows.

🏆 QbD Success Story

Case Study: Major pharmaceutical company implemented QbD for tablet manufacturing:

Development Time: Reduced from 18 to 12 months
Batch Failures: Decreased from 8% to 0.5%
Regulatory Interactions: 60% fewer information requests
Manufacturing Flexibility: Ability to adjust 5 process parameters without prior approval
Cost Savings: $2.3M annually through reduced testing and failures

📚 Learning Objectives