AI and biomanufacturing: can the policies match our ambitions?

India’s Biomanufacturing Context

• India is already a global leader in generic drugs and vaccines.
• The next leap: combining AI with biotechnology for biomanufacturing, drug discovery, and healthcare delivery.
• Modern Indian biomanufacturing uses robots, biosensors, and AI to improve precision and efficiency.

Relevance : GS 3 (Science and Technology)

AI in Biomanufacturing: Transformative Potential

• Biocon: Using AI for fermentation optimisation, drug screening, and cost-effective biologics.
• Strand Life Sciences: Employs AI for genomics and personalised medicine.
• Wipro & TCS: Developing AI tools for drug discovery, clinical trials, and treatment outcome prediction.
• AI-driven tools enable:
  • Predictive monitoring (e.g., pH, temperature shifts)
  • Reduced batch failures and waste
  • Digital twins for simulating and improving manufacturing processes
  • Faster, more efficient drug development pipelines

Policy Push: India’s Bold Initiatives

• BioE3 Policy (2024):
  • Envisions state-of-the-art biofoundries, AI-biotech hubs, and manufacturing infrastructure.
  • Significant funding support for startups and companies.
• IndiaAI Mission:
  • Focuses on ethical, explainable, and responsible AI.
  • Encourages standards for bias reduction, algorithm transparency, and AI safety in biotech applications.

Regulatory and Safety Challenges

• Current Indian drug/manufacturing laws are outdated and not tailored for AI systems.
• No clear process to ensure:
  • Data representativeness for India’s diverse conditions
  • AI model reliability under real-world disruptions
• Example risk: AI trained in urban labs may fail in rural setups due to infrastructure or environmental variability.

Global Best Practices

• EU AI Act (2024): Classifies AI tools into four risk categories, strict audits for high-risk tools.
• US FDA (2025):
  • Seven-step AI credibility framework
  • Allows predetermined model updates for evolving healthcare tech
• India currently lacks:
  • Risk-based evaluation
  • Context-aware regulation
  • Dynamic oversight mechanisms

Emerging Legal and Ethical Issues

• Data governance: Digital Personal Data Protection Act (2023) is insufficient for biotech-specific data needs.
• Bias and dataset quality: Clean, diverse, and unbiased datasets are essential — yet not mandated.
• Intellectual property:
  • Ambiguity over AI-invented molecules and processes
  • Risk of legal conflicts and stifled innovation

Path Ahead: Recommendations

1. Regulatory reform:
   1. Introduce risk-based, adaptive laws for AI in biomanufacturing.
   2. Define AI tool context and validation norms.
2. Nationwide investment:
   1. Infrastructure and talent development beyond metro cities.
3. Collaborative ecosystem:
   1. Involve industry, regulators, academia, and international partners.
4. Promote innovation over imitation:
   1. Transition from “copying generics” to AI-driven creation of novel drugs and processes.