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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 reductionalgorithm 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  • Current Indian drug/manufacturing laws are outdated and not tailored for AI systems.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.

 

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