AI and Biomanufacturing Issues

Context

India stands at a critical juncture in its quest to use artificial intelligence (AI) for biotechnology innovation. On the one hand, initiatives like the BioE3 policy and IndiaAI Mission position the country as a global leader in AI-driven biomanufacturing and ethical AI development, on the other, weak regulations and safeguards slow down this progress.

AI-Driven Biomanufacturing in India

• India has been a global supplier of generic drugs and vaccines for decades.
• The use of AI has transformed biomanufacturing. Robots, biosensors, and AI models are optimizing production processes.
• Examples
  • Biocon: AI-based predictive analytics improves drug screening and biologics production
  • Strand Life Sciences: AI used in genomics and personalized medicine, accelerating drug discovery and diagnostics
  • IT companies like Wipro and TCS are also using AI in drug research and clinical trials.

Impact of AI

• Production process: Using data from sensors, AI detects changes in temperature, pH or cell growth and automatically adjusts the process.
• Digital twins: These are virtual models that mimic real plants and predict and solve problems.
• Result: Fewer failed batches, less waste and higher quality products.

Key Government Initiatives

BioE3 Policy (2024)

• Objective: Establishment of Biomanufacturing hubs, Biofoundries and Bio-AI hubs
• Provision of grants and funding to encourage innovation and research

IndiaAI Mission

• Goal: Development of ethical, transparent and responsible AI
• Support research areas such as machine unlearning and reducing algorithm bias

Policy Challenges

• Antiquated legal and regulatory framework: Existing regulations are designed for traditional drug production, while AI-based systems do not match them.
• Data diversity and reliability: AI models may fail in rural or semi-urban areas if they are based only on urban data.
  • Example: A model trained in Bengaluru cannot understand the climate or water quality of Shimla.
  • Consequences: Revenue loss, resource waste and damage to India’s reputation for quality.

Global perspective

• EU AI Act (2024): Classifies AI tools into four risk levels.
• US FDA guidelines (2025): Provisions such as ‘pre-defined change control plans’ allow safe AI innovation.
• India also needs such a relevant, risk-based and flexible regulatory system.

Other applications

• Drug discovery: AI screens millions of compounds, reducing time and cost.
• Clinical trials: AI improves patient recruitment and trial design.
• Supply chain: AI-based maintenance and demand forecasting reduce drug supply shortages and waste.
• Examples:
  • Wipro: AI-powered solutions in drug discovery
  • TCS: Improving clinical trials with ‘Advanced Drug Development’ platform

Challenges

• Data governance: Datasets need to be diverse and bias-free.
• Intellectual property: Ownership and licensing issues of molecules and processes developed by AI.
• Digital Personal Data Protection Act, 2023: Does not address specific requirements for biomanufacturing.

Way forward

• Develop a risk-based, adaptive regulatory framework
• Clear standards for data quality and model validation
• Investment in infrastructure and talent, especially in rural areas
• Collaboration between regulators, industry, academia and international partners

Conclusion

India has a strong legacy of global leadership in generic medicines, but the future belongs to countries that can innovate, not just copy, using the power of AI. The confluence of technology and bio-manufacturing can open a new era for India on the global stage, provided our policies keep pace with science.