AI and Biomanufacturing: Can the Policies Match Our Ambitions?, Pg7

Key Highlights:

• India is leveraging AI to modernise biomanufacturing in drug discovery, fermentation, and diagnostics.
• Policies like BioE3 (2024) and the IndiaAI Mission aim to foster innovation through Bio-AI hubs and ethical AI practices.
• Companies such as Biocon and Wipro are applying AI in biologics manufacturing and drug development.
• India currently lacks a unified, risk-based regulatory framework for AI in biomanufacturing.
• The EU AI Act and US FDA’s AI Framework provide context-aware models for safe AI deployment.
• Data quality, regional diversity, and safety validation remain weak spots in India’s AI policy ecosystem.
• Intellectual property issues and bias in AI training data pose potential challenges to equitable innovation.

Detailed Insights:

• India’s traditional strength in generic drug manufacturing is evolving into AI-powered bioproduction systems.
• AI-driven predictive systems optimise fermentation and reduce waste, improving quality and efficiency.
• Digital twins and machine learning models are now core to plant monitoring, simulation, and real-time decision-making.
• While visionary policies like BioE3 are in place, they are not matched by updated regulatory mechanisms.
• Existing drug regulatory systems do not account for dynamic AI tools that evolve over time.
• The lack of context-specific validation could lead to model failures in semi-urban/rural settings.
• Global best practices, like the FDA’s “Predetermined Change Control Plans”, provide guidance on adaptive oversight.
• Regulatory reforms must ensure datasets are diverse, training is representative, and deployment is risk-tiered.
• Effective policy must balance speed of innovation with safeguards for public safety and data governance.
• Collaboration between government, industry, and academia is essential for standard-setting and implementation.

Scientific/Technical Concepts Involved:

• Biomanufacturing: Use of living cells and systems to produce biological products like vaccines, enzymes, and drugs.
• Digital Twin: Virtual replica of a physical system for simulation, optimisation, and monitoring.
• Risk-Based Regulation: Regulatory approach that applies oversight proportional to the level of risk posed by an AI system.
• Explainable AI (XAI): AI models that offer transparent, understandable decisions, crucial in sensitive fields like health.
• Machine Unlearning: Process of removing specific training data from an AI model to correct bias or meet privacy requirements.

Mains Mock Question:

Q. "With reference to AI-driven biomanufacturing, critically examine the role of policy in ensuring both innovation and accountability. What lessons can India draw from global regulatory practices?"