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- Current Indian drug/manufacturing laws are outdated and not tailored for AI systems.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
- Regulatory reform:
- Introduce risk-based, adaptive laws for AI in biomanufacturing.
- Define AI tool context and validation norms.
- Nationwide investment:
- Infrastructure and talent development beyond metro cities.
- Collaborative ecosystem:
- Involve industry, regulators, academia, and international partners.
- Promote innovation over imitation:
- Transition from “copying generics” to AI-driven creation of novel drugs and processes.