📋 Case Study

Pharmaceutical Batch Reactor Deviation Mitigation (FDA-Approved Twin)

Batch-to-batch variability causing 12% reject rate and regulatory scrutiny

🏗️ Project Overview

End-to-end digital twin for API crystallization suite at a GMP facility

🎯 Challenge

Batch-to-batch variability causing 12% reject rate and regulatory scrutiny

🔧 Design Approach

Physics-informed neural network (PINN) twin trained on historical PAT data and validated per ICH Q5C & FDA Digital Health Center guidance

📐 Key Calculations

Supersaturation Ratio (σ)

σ = C/C*

Result: 1.82

Critical driver of nucleation kinetics

Crystal Size Distribution CV

CV = (σ_D / μ_D) × 100

Result: 24.6%

Target: ≤22% for tablet compressibility

📊 Results

Reject rate reduced to 3.1%, 100% batch release on first attempt, FDA pre-submission approval of twin validation package

💡 Lessons Learned

• •PAT sensor placement must cover all geometric zones of mixing
• •Twin must log all inputs/outputs in ALCOA+ compliant audit trail

✅ Key Takeaways

• 1PAT sensor placement must cover all geometric zones of mixing
• 2Twin must log all inputs/outputs in ALCOA+ compliant audit trail