⭐# CMC AI Data Platform for Biopharma Manufacturing

Building AI‑ready CMC data products for predictive quality, batch intelligence, and process optimization in biopharmaceutical manufacturing.

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📌 Project Overview

Chemistry, Manufacturing and Controls (CMC) data in biopharma is typically fragmented across multiple systems:

• LIMS – analytical test results
• MES – batch manufacturing records
• ELN – experimental development data
• PAT sensors – real‑time process signals

Because these datasets are heterogeneous and siloed, it is difficult to build AI/ML models for:

• Predictive quality analytics
• Batch failure prediction
• Process anomaly detection
• Yield optimization
• Real‑time manufacturing insights

This project demonstrates how to build an AI‑ready CMC data platform** that unifies manufacturing, analytical, and sensor data into standardized data products for advanced analytics and machine learning.

The goal is to simulate a real‑world pharmaceutical CMC data science workflow and show how modern data engineering + ML + LLMs can enable next‑generation biomanufacturing intelligence.

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🎯 Key Objectives

This project focuses on building AI‑ready data products for pharmaceutical manufacturing analytics.

Core objectives:

• Integrate heterogeneous CMC datasets (MES, LIMS, PAT, ELN)
• Build a unified CMC data model
• Engineer features from process, analytical, and stability data
• Train ML models for:
  • Quality prediction (CQAs)
  • Batch failure prediction
  • Process anomaly detection
• Develop an interactive manufacturing analytics dashboard
• Implement an LLM‑powered CMC knowledge assistant using RAG

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🏗️ Project Architecture

LIMS (Analytical Data)
MES (Manufacturing Data)
PAT (Process Sensors)
ELN (Lab Experiments)
│
▼
Data Ingestion Layer
│
▼
Unified CMC Data Model
│
▼
Feature Engineering Layer
│
▼
Machine Learning Models
│
▼
AI‑Ready Data Products
│
▼
Dashboard + LLM Knowledge Assistant

This architecture demonstrates how data engineering, machine learning, and generative AI work together to enable modern CMC analytics.

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📁 Repository Structure

cmc-ai-data-platform
│
├── data
│   ├── raw
│   │    batch_process.csv
│   │    hplc_results.csv
│   │    stability_results.csv
│   │    sensor_timeseries.csv
│   │
│   └── processed
│
├── notebooks
│    ├── 01_data_exploration.ipynb
│    └── 02_feature_engineering.ipynb
│
├── src
│   ├── ingestion
│   │     ingest_lims.py
│   │     ingest_mes.py
│   │
│   ├── features
│   │     process_features.py
│   │
│   ├── models
│   │     impurity_prediction.py
│   │     batch_failure_prediction.py
│   │     anomaly_detection.py
│   │
│   ├── llm
│   │     cmc_rag_assistant.py
│   │
│   └── pipeline
│         training_pipeline.py
│
├── dashboard
│     streamlit_app.py
│
├── diagrams
│     architecture.png
│
└── README.md

📊 Data Sources

This project simulates typical CMC datasets used in biopharmaceutical manufacturing.

### **1. Manufacturing Batch Data (MES)**

Column	Description
Batch_ID	Batch identifier
Reactor_Temp	Reactor temperature
pH	Reaction pH
Pressure	Reactor pressure
Reaction_Time	Duration of reaction
Agitation_Speed	Mixing speed
Yield	Product yield
Batch_Status	Pass / Fail

### **2. Analytical Laboratory Data (LIMS – HPLC)**

Column	Description
Batch_ID	Batch identifier
Retention_Time	Chromatographic retention time
Impurity_A	Impurity level
Impurity_B	Impurity level
Purity	Product purity
Total_Impurity	Total impurity

### **3. Stability Testing Data**

Column	Description
Batch_ID	Batch identifier
Storage_Temp	Storage temperature
Time_Month	Storage duration
Potency	Drug potency
Degradation	Degradation level

### **4. Process Sensor Data (PAT)**

Column	Description
timestamp	Time stamp
Batch_ID	Batch identifier
Temperature	Process temperature
Pressure	Reactor pressure
pH	Process pH
Dissolved_Oxygen	Oxygen concentration

## 🧪 Feature Engineering

Manufacturing features are engineered from process time‑series data.

Examples:

- Mean process temperature  
- Maximum pressure  
- pH variability  
- Reaction duration  
- Sensor drift indicators  

These features enable predictive modeling of critical quality attributes (CQAs).

🤖 Machine Learning Models

1. Quality Prediction (CQAs) Predict:

• Purity
• Total impurity
• Yield

Models:

• XGBoost
• Random Forest

2. Batch Failure Prediction Binary classification: Pass vs Fail.

Models:

• Logistic Regression
• Gradient Boosting

3. Process Anomaly Detection Algorithm:

• Isolation Forest

Used for early detection of process drift.

📦 AI‑Ready Data Products Outputs include:

• batch_dataset.parquet
• analytical_dataset.parquet
• sensor_features.parquet

Designed for:

• ML model training
• Predictive manufacturing analytics
• Quality monitoring

🧠 CMC Knowledge Assistant (LLM) A Retrieval‑Augmented Generation (RAG) assistant that answers CMC questions using:

• ISA-88,95
• CDISC
• eCTD
• ALCOA+
• 21 CFR Part 11

📊 Manufacturing Analytics Dashboard A Streamlit dashboard provides:

• Batch yield distribution
• Impurity prediction
• Process trend monitoring
• Anomaly detection alerts

🛠️ Technologies Used

• Python
• Pandas
• NumPy
• Scikit‑learn
• XGBoost
• Streamlit
• Matplotlib
• LLM / Generative AI
• LangChain
• Retrieval‑Augmented Generation (RAG)

📌 Summary This project demonstrates how to build an AI‑ready CMC data platform that unifies manufacturing, analytical, and sensor data to enable:

• Predictive quality

• Batch intelligence

• Process monitoring

• LLM‑powered CMC insights

  📊 Example Output:

Batch Status

Pressure vs Yield

Yield distribution

It simulates a realistic biopharma manufacturing analytics workflow and showcases how modern data engineering and AI can transform CMC operations.