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Indian pharma industry taps AI for regulatory compliance to boost speed, accuracy and scalability
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Nandita Vijayasimha, Bengaluru
October 03 , 2025
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The Indian pharmaceutical industry is increasingly recognizing that artificial intelligence (AI)-powered regulatory intelligence engines are helping companies stay ahead of changing regulations.
Now, AI is embedded across the regulatory lifecycle. It spans from generative AI drafts, clinical study reports and labeling documents to natural language processing (NLP) engines monitoring FDA, EMA, and ICH guidance in real time and predictive analytics anticipating health authority queries and compliance risks. This shift enables regulatory teams to move from reactive to proactive compliance, improving speed, accuracy, and scalability, said Shreshta Anantha, associate director-RWE, Healthark.
In 2025, the pharmaceutical industry is undergoing a transformation on how it manages regulatory compliance. The convergence of growing data volumes, fragmented global regulations, and pressure to accelerate drug development has exposed the limitations of traditional compliance models. Regulatory affairs teams must now handle extensive documentation, monitor evolving guidance across jurisdictions, and respond to health authority queries, all while maintaining audit readiness and data integrity, she added.
AI has emerged as a strategic enabler. Real-world examples reinforce this shift. A mid-sized European pharma firm reported 60% faster submission preparation and zero first-cycle rejections, saving over $200,000 annually in labor and resubmission costs. Meanwhile, a US biotech achieved a 70% reduction in project timelines by partnering with an AI-enabled CRO, preserving critical cash runway and accelerating time-to-IND by three weeks.
Historically, regulatory affairs relied on manual compilation of dossiers, static monitoring of regulatory changes, and reactive responses to agency queries. These workflows were slow, error-prone, and resource-intensive. The complexity of global regulations - such as differing formats and expectations from the FDA, EMA, etc.-further compounded the challenge. A single phase III trial can generate over 1 million data points, spanning clinical outcomes, safety signals, and manufacturing metrics. Traditional pharmacovigilance systems struggle with under-reporting, fragmented data, and manual case processing, said Anantha.
AI is transforming submission preparation. NLP tools review draft documents to flag inconsistencies, while ML algorithms cross-check new drug applications against historical approvals. Generative AI tools are now capable of drafting clinical study reports in minutes, reducing writing time by nearly 50%. These drafts, while requiring human review, are often 80% correct, significantly speeding up the submission process, she said.
Further, NLP and data mining tools monitor FDA databases, warning letters, and global guidance documents to flag relevant updates. One biopharma firm built a centralized data lake integrating internal quality metrics with external regulatory data, enabling real-time dashboards for compliance risk. These systems have become mainstream, with 90% of top pharma and medtech firms using AI to analyze inspection trends, reportedly saving hundreds of staff hours monthly, she said.
There are pharmacovigilance automation tools to automate adverse event detection, signal analysis, and case processing. Deep learning models and NLP pipelines extract meaningful insights from unstructured safety data, improving signal detection and reducing manual workload.
Regulatory labelling which is critical for drug safety and usage is being streamlined using AI. In fact, AI platforms forecast regulatory risks by analyzing historical health authority queries and adverse event data. Dashboards alert teams to potential issues before they arise, enabling proactive compliance. Pattern-recognition algorithms detect documentation errors and deviations in manufacturing data. For instance, GSK’s AI-powered compliance system reduced audit errors and improved inspection readiness, said Anantha.
Therefore, AI is no longer experimental, but a core operational capability. Companies embedding AI into their compliance strategy are not just accelerating approvals they are redefining regulatory affairs as a driver of competitive advantage, she said.
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