Artificial Intelligence in Biomanufacturing

GS3 – Science & Technology

Context

India is strategically positioned to leverage artificial intelligence (AI) in advancing biotechnology, guided by forward-looking initiatives such as the BioE3 Policy and the India AI Mission.

BioE3 Policy – 2024

The BioE3 (Biotechnology for Economy, Environment, and Employment) Policy, approved by the Union Cabinet in 2024, represents a significant stride in India’s biotech journey.

• Objective: To establish India as a global leader in biotechnology by promoting advanced biomanufacturing while addressing economic growth, environmental sustainability, and employment generation.
• It encourages a transition from traditional chemical industries to eco-friendly, bio-based systems, aligning with India’s long-term sustainability and net-zero emission goals.
• Key initiatives include:
  • Establishment of biofoundry clusters
  • Creation of bio-AI hubs
  • Promotion of regenerative biomanufacturing to support a circular bioeconomy

India AI Mission

• Focuses on building ethical and responsible AI systems.
• Prioritizes areas like explainable AI, reducing algorithmic biases, and developing machine unlearning models.
• Special emphasis is given to healthcare and biotech sectors, ensuring transparency and accountability in AI deployment.

Status of India’s Bioeconomy

• Grew from $10 billion in 2014 to $165.7 billion by 2024, with a target of $300 billion by 2030.
• Accounts for 4.25% of India’s GDP, showing a 17.9% CAGR over the last four years.
• The government envisions India as a global bio-manufacturing hub, driven by innovation, sustainability, and inclusive growth.

Role of AI in the Bioeconomy

Applications:

1. Real-time Monitoring:
   AI detects minute variations in process conditions such as temperature, pH, and cell development by analyzing large data sets instantly.
2. Digital Twins:
   Virtual replicas of manufacturing setups allow for process simulations, testing, and troubleshooting without impacting actual production.
3. Predictive Maintenance:
   AI can forecast equipment issues before failure, reducing waste and enhancing batch consistency.
4. Process Optimization:
   Automated adjustments ensure consistent quality with minimal human intervention.

Regulatory Challenges

1. Outdated Norms:
   Current frameworks like Schedule M are inadequate to assess dynamic AI tools — for instance, Biocon’s AI-based insulin purification tool which adapts in real-time.
2. Validation Standards Lacking:
   No established protocols exist to certify AI used in bioreactors or for vaccine production — e.g., Serum Institute’s AI system lacks standards to cope with unpredictable power issues during Mumbai’s monsoon.
3. Data Representation Issues:
   Training data often doesn’t reflect India’s climate diversity — e.g., Dr. Reddy’s AI models, designed for Western climates, fail to predict drug stability under Rajasthan’s heat or Kerala’s humidity.
4. Inadequate Risk Assessment Models:
   AI tools are not tailored to address local public health threats — e.g., AI recommending antibiotics for XDR-TB in Mumbai slums without India-specific risk models may lead to harmful misprescriptions.

Suggested Way Forward

1. Regulatory Overhaul:
   Develop adaptive, risk-based regulatory systems. Example: CDSCO could introduce fast-track approvals for AI tools that meet predefined accuracy metrics, cutting wait times from 18 months to 6.
2. Infrastructure Expansion:
   Invest in AI-ready biotech manufacturing beyond metros — in cities like Vizag and Nashik, ensuring high-speed connectivity and automated quality systems.
3. Collaborative Innovation:
   Encourage cooperation among regulators, industry, academia, and global partners to create a unified AI-biotech ecosystem.
4. Skilling and Talent Development:
   Launch dedicated Centres of Excellence for AI in Life Sciences at institutions like JNU, BHU, and regional medical universities to build national capacity.