In News:

The integration of Artificial Intelligence into India's biomanufacturing sector is gaining momentum with the launch of the BioE3 Policy and the IndiaAI Mission.

What is Biomanufacturing?

• Biomanufacturing involves the use of living cells, enzymes, or biological systems to produce commercial goods such as vaccines, biologics, biofuels, specialty chemicals, biodegradable plastics, and advanced materials.
• The convergence of synthetic biology, industrial biotechnology, and artificial intelligence (AI) has expanded its scope across sectors like healthcare, agriculture, energy, and materials science.
• India, often called the “Pharmacy of the World”, produces over 60% of global vaccines, underlining its industrial strength in biomanufacturing.

Role of Artificial Intelligence in Biomanufacturing

AI is revolutionizing biomanufacturing by making it predictive, efficient, and scalable:

• AI-Powered Process Optimization: Machine learning tools adjust variables like temperature, pH, and nutrient supply in real time to enhance fermentation and reduce batch failure.
• Digital Twins: Virtual replicas of biomanufacturing plants allow engineers to simulate operations, test changes, and foresee potential disruptions without real-world risks.
• Accelerated Drug Discovery: AI expedites molecular modeling and screening of drug candidates, reducing time and cost of development.
• Predictive Maintenance: AI forecasts machinery failures, improving equipment reliability and reducing downtime.
• Smart Supply Chains: AI-driven logistics optimize cold-chain storage and forecast medicine demand, ensuring timely distribution.

Indian Examples and Industrial Applications

• Biocon uses AI to enhance drug screening and fermentation quality.
• Strand Life Sciences applies machine learning in genomics for faster diagnostics.
• Wipro and TCS are developing AI platforms for clinical trials, molecule screening, and treatment prediction.
• AI is also being explored in rural healthcare, using region-specific data for localized diagnostics and advisories.

Key Government Initiatives

• BioE3 Policy (2024):
  • Envisions Bio-AI hubs, biofoundries, and next-gen biomanufacturing infrastructure.
  • Supports startups with funding and incentives.
• IndiaAI Mission:
  • Promotes ethical, explainable AI in sectors like health and biotech.
  • Supports bias reduction, machine unlearning, and transparency in AI models.
• Biomanufacturing Mission (2023): Aims to promote R&D and domestic production in bio-based sectors.
• PLI Scheme for Biotech: Incentivizes local production of enzymes, fermentation inputs, and biologics.
• Digital Personal Data Protection Act (2023): Lays down principles for lawful data processing, though not tailored for AI-biotech intersection yet.

Challenges in Policy and Regulation

Regulatory Gaps:

• India’s existing drug and biotech laws were designed before the AI era.
• No clear mechanism exists to audit, certify, or govern AI-operated bioreactors or predictive drug systems.

Data and Model Risks:

• AI systems trained on urban datasets may fail in rural or semi-urban manufacturing due to variable water quality, temperature, or power conditions.
• Lack of norms on dataset diversity and model validation raises risk of system failure and reputational damage.
• Intellectual Property Issues: Traditional IP laws do not clarify ownership of AI-generated inventions, molecules, or production protocols.

Workforce and Infrastructure:

• Biomanufacturing needs a workforce skilled in both computational biology and automation.
• India’s AI-bio talent gap and limited high-tech infrastructure outside metro cities hinders inclusive growth.

Ethical & Safety Concerns:

• Without context-specific oversight, AI errors can threaten public safety and product integrity.
• Trust in AI systems requires clear guidelines on explainability, accountability, and redress mechanisms.

Global Best Practices

• EU’s AI Act (2024): Classifies AI applications based on risk levels. High-risk applications (e.g., genetic editing) are subject to strict audits.
• US FDA Guidance (2025):
  • Introduces seven-step credibility frameworks for AI in healthcare.
  • Predetermined Change Control Plans (PCCPs) allow iterative AI updates while ensuring safety.

India lacks similar risk-based, adaptive oversight.

Policy Recommendations

• Establish AI-Biomanufacturing Regulatory Framework:
  • Introduce tiered regulation based on context and risk.
  • Define use-cases, audit mechanisms, and model validation standards.
• Mandate Dataset Diversity & Safety Audits:
  • Ensure AI tools are trained on representative, unbiased, clean data.
  • Create regulatory sandboxes to test AI systems in controlled environments.
• Strengthen Public–Private Partnerships:
  • Boost industry-academia collaborations.
  • Incentivize private investment through R&D credits and de-risking instruments.
• Modernize IP and Licensing Laws:
  • Establish clarity on ownership of AI-generated discoveries.
  • Develop licensing frameworks for bio-AI algorithms and training data.
• Upskill the Workforce: Promote interdisciplinary training across life sciences, data science, and industrial robotics.