📖 Series Overview
This series teaches practical applications of AI technology to pharmaceutical manufacturing processes. You will master implementation-level AI solutions for pharmaceutical industry-specific challenges including GMP (Good Manufacturing Practice) compliant quality control, batch record analysis, Process Analytical Technology (PAT), and continuous manufacturing optimization.
From quality assurance, batch production, deviation management, transition to continuous manufacturing, to regulatory compliance, you will systematically learn practical knowledge immediately applicable to pharmaceutical manufacturing sites.
Each chapter provides abundant GMP-compliant implementation examples, enabling you to master AI technology application methods to pharmaceutical manufacturing through Python implementation.
🎯 Learning Objectives
- GMP-Compliant Quality Control: Statistical quality control, CPV, APR automation implementation
- Batch Record Analysis: Automated analysis of electronic batch records, anomaly detection, root cause analysis
- PAT (Process Analytical Technology): Real-time spectroscopic analysis, multivariate analysis
- Continuous Manufacturing Optimization: Batch to continuous transition, real-time control, QbD implementation
- Regulatory Compliance and CSV: Computer System Validation, 21 CFR Part 11 compliant implementation strategies
📚 Prerequisites
- Basic Python programming (NumPy, Pandas)
- Fundamental machine learning concepts (regression, classification, anomaly detection)
- Basics of pharmaceutical processes (synthesis, crystallization, granulation, tableting)
- Basic understanding of GMP/GMPDP
- Fundamentals of statistics (control charts, process capability indices)
📚 Chapter Structure
GMP-Compliant Statistical Quality Control
Learn statistical quality control that meets GMP requirements in pharmaceutical manufacturing. Implement automation of control charts, process capability indices, CPV, and APR.
Electronic Batch Record Analysis and Deviation Management
Learn automated analysis of Electronic Batch Records (EBR) and deviation management. Implement automation of anomaly detection, root cause analysis, and CAPA proposals.
PAT and Real-Time Quality Control
Learn real-time quality control through Process Analytical Technology (PAT). Practice implementation of NIR/Raman spectroscopic analysis, multivariate analysis, and RTRT.
Continuous Manufacturing and QbD Implementation
Learn transition strategies from batch to continuous manufacturing. Practice implementation of QbD (Quality by Design), DoE, and design space.
Regulatory Compliance and CSV Implementation Strategy
Learn Computer System Validation (CSV) and 21 CFR Part 11 compliance for AI systems. Practice implementation of audit trails, electronic signatures, and data integrity.
❓ Frequently Asked Questions
Q1: Who is the target audience for this series?
Quality control personnel in pharmaceutical manufacturing, manufacturing engineers, data scientists, and graduate students majoring in pharmacy or chemical engineering. Understanding is possible with basic knowledge of GMP/GMPDP.
Q2: Is knowledge of GMP and CSV mandatory?
Basic understanding of GMP requirements is recommended, but this series explains necessary regulatory requirements together with implementation examples. CSV practice is covered in detail in Chapter 5.
Q3: Is application to actual manufacturing lines possible?
Code examples in this series are designed assuming GMP compliance. However, actual manufacturing application requires individual qualification assessments including CSV, audit trails, and change management. Chapter 5 explains implementation strategies in detail.
Q4: Does this cover both batch and continuous manufacturing?
Yes. Chapters 1-2 cover quality control in batch manufacturing, and Chapters 3-4 cover PAT and continuous manufacturing. Transition strategies from batch to continuous are also explained in detail in Chapter 4.
Q5: What is the relationship with other Process Informatics Dojo series?
This is positioned as an advanced edition of "AI Applications to Chemical Plants". While the fundamental technologies are common, this series focuses on pharmaceutical-specific GMP requirements, batch management, and regulatory compliance.
References
- Montgomery, D. C. (2019). Design and Analysis of Experiments (9th ed.). Wiley.
- Box, G. E. P., Hunter, J. S., & Hunter, W. G. (2005). Statistics for Experimenters: Design, Innovation, and Discovery (2nd ed.). Wiley.
- Seborg, D. E., Edgar, T. F., Mellichamp, D. A., & Doyle III, F. J. (2016). Process Dynamics and Control (4th ed.). Wiley.
- McKay, M. D., Beckman, R. J., & Conover, W. J. (2000). "A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code." Technometrics, 42(1), 55-61.
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