An Intelligent Drug Traceability Framework for Secure and Transparent Pharmaceutical Supply Chain

Abstract

The pharmaceutical supply chain is increasingly vulnerable to counterfeit drugs, posing a significant risk to patient safety and regulatory compliance. To address this issue, we propose an intelligent drug traceability system that integrates blockchain, Internet of Things (IoT), and artificial intelligence (AI) to ensure the secure and transparent movement of pharmaceuticals from manufacturers to endusers. The framework employs RFID tags, QR codes, and smart sensors for real-time tracking, while blockchain ensures data immutability and traceability. AI algorithms are applied for anomaly detection, fraud prevention, and predictive analytics. Additionally, automated alerts and compliance reports provide actionable insights to stakeholders. This system enhances operational efficiency, reduces the risk of counterfeit drugs, and supports regulatory authorities in maintaining drug safety across the supply chain

Introduction

The pharmaceutical supply chain faces growing threats from counterfeit drugs, endangering patient safety and regulatory compliance. To address this, an intelligent drug traceability system is proposed, integrating blockchain, Internet of Things (IoT), and Artificial Intelligence (AI) technologies to secure and monitor drug movement from production to consumption.

Core Features of the Proposed System:

• IoT & Sensors: Uses RFID tags, QR codes, and smart sensors for real-time tracking of environmental conditions like temperature and humidity.

• Blockchain: Ensures tamper-proof, transparent, and immutable logging of transactions and drug lifecycle events.

• AI: Enables counterfeit detection, predictive analytics, and anomaly detection using deep learning (e.g., YOLOv5/YOLOv8) and OCR tools.

• Smart Contracts: Automates verification and regulatory compliance during manufacturing, shipping, and dispensing stages.

• Alerts & Dashboards: Provides real-time alerts and visual analytics for stakeholders via mobile and web platforms.

Literature Survey Highlights:

1. Blockchain-Based Tracking (IEEE, 2021): Enables secure, decentralized drug lifecycle tracking with smart contracts for enhanced transparency and compliance.

2. IoT + RFID Monitoring (Elsevier, 2022): Facilitates real-time condition tracking of drugs, crucial for cold chain management (e.g., vaccines).

3. AI Counterfeit Detection (Springer, 2022): Uses image analysis and OCR to identify fake packaging with over 92% accuracy.

4. ML for Supply Chain Forecasting (IEEE, 2023): Employs machine learning (e.g., SVM, Random Forest) to predict supply disruptions and optimize logistics.

5. IPFS Storage (Elsevier, 2020): Utilizes decentralized IPFS storage for large drug-related records, maintaining blockchain verifiability without overloading the chain.

6. Cold Chain with WSN (IEEE, 2020): Uses wireless sensors for continuous monitoring of transport conditions with real-time alerts.

7. Blockchain Vaccine Passport (Elsevier, 2021): Demonstrates blockchain use for secure immunization records, relevant to high-value drug tracking.

Methodology Overview:

1. Data Collection: Gathers tracking and compliance data from QR codes, RFID, barcodes, EHRs, and regulatory records.

2. Preprocessing: Cleans, standardizes, and extracts structured data using NLP and aligns it with global standards like GS1.

3. Authentication Module: Uses AI models (YOLO, OCR) to verify packaging and extract batch/expiry data.

4. Blockchain Logging: Stores verified data in immutable smart contracts using platforms like TensorFlow and PyTorch.

5. System Design: Architected to support scalability, with cloud-edge integration, mobile/web interfaces, and real-time analytics.

System Benefits:

• Prevents Counterfeit Drugs: Through AI-based detection and blockchain-backed verification.

• Improves Compliance: Automates reporting for regulatory bodies.

• Enhances Transparency & Trust: All drug transactions are visible and immutable.

• Optimizes Supply Chain: ML-based forecasting reduces delays and wastage.

• Scalable & Modular: Adaptable to various regulatory environments and technologies.

Conclusion

The proposed drug traceability system successfully integrates blockchain, IoT, and artificial intelligence to address critical challenges in pharmaceutical supply chains. By enabling real-time tracking and secure verification, the system reduces the risks associated with counterfeit drugs and non-compliant deliveries. The use of blockchain ensures data immutability and transparency, while AI models assist in anomaly detection and counterfeit identification with high accuracy. IoT sensors and GPS modules provide continuous monitoring of environmental conditions and location tracking during shipment. The system is capable of generating instant alerts to stakeholders in case of violations, ensuring timely corrective action. The modular design allows easy scalability and adaptation to various pharmaceutical environments. Test evaluations across diverse scenarios demonstrate reliable performance, strong detection precision, and regulatory compliance. This traceability framework provides a powerful solution to enhance patient safety, streamline operations, and support global healthcare goals.

References

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Copyright

Copyright © 2025 Nagarathna C, Abhay Surya, Amith M P, Arjun C Gowda, Abdul Raheem. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.