Precision, adherence to strict standards, and speed are necessary in pharmaceutical regulatory affairs. It can take more than ten years and up to $2.6 billion to bring a new drug from the research bench to the market. Any delays or mistakes in regulatory documentation risk not only substantial financial setbacks but also patient wellbeing.
Recent analyses indicate that generative AI can help streamline clinical trial processes, possibly cutting related costs in half through automated drafting and data management. Given the immense investment involved, these efficiency gains are becoming truly essential for pharmaceutical organizations.
Think of generative AI as advanced algorithms that can produce human-quality text, analyze complex data, and even create entire documents from basic instructions.
This involves automating the creation of important regulatory affairs papers, including submissions for Investigational New Drugs (INDs), Clinical Trial Applications (CTAs), and New Drug Applications (NDAs).
Generative AI does more than only execute preprogrammed instructions. Big datasets provide it with expertise. It can produce reports, summarize research, and even suggest wording that conforms to FDA, EMA, and other regulatory requirements.
By handling much of the manual labor required to prepare regulatory submissions, this technology reduces the likelihood of human error. It also allows regulatory professionals to focus on strategy instead of just administration.
According to the Indegene study, generative AI can speed up repetitive writing in innovative medical applications. These systems can pull data from clinical databases, electronic trial master files, and text libraries to produce draft versions of crucial components, such as study design or adverse event reports.
When sections are automatically filled in using the most recent templates and references, human reviewers may focus on quality control instead of tedious data entry.
Generative AI is able to rapidly check established regulatory standards. For instance, an AI engine using machine learning rules can identify out-of-date areas and recommend revisions if the FDA modifies the format requirements for clinical data. This saves regulatory teams a lot of time.
The annual cost of non-compliance per company is between $2.2 million and $39.2 million—a 45% increase since 2011. The cost of compliance itself has also risen by 43%, averaging $5.5 million per company.
Creating labels is often a slow process because of the need for precise wording about indications, dosages, and safety warnings. Generative AI can compare labeling guidelines with existing safety data to automatically suggest label language.
By reducing repetitive drafting, teams can focus on detailed clinical or safety validations instead of spending time on standard labeling text.
Generative AI can identify recurring problems like incomplete cross-references or inconsistencies in adverse event reporting across multiple documents.
By proactively highlighting errors, regulatory professionals avoid time-consuming revisions or rejections later.
Regulatory affairs teams are the crucial link between pharmaceutical companies and governing bodies, ensuring that new treatments meet safety and efficacy standards. But they face major challenges, including:
An NDA can be over 100,000 pages, according to the FDA NDA approval process. Manually reviewing and compiling this material is slow and prone to errors.
Requirements vary by region—what the FDA requires might not perfectly match EMA guidelines, making it difficult to maintain consistent quality globally.
Regulatory standards change, requiring frequent updates to product labels, safety reports, and more. This can consume significant time and resources.
Even small mistakes could lead to delays in clearances, audit results, or even product recalls. Teams must be entirely accurate and honest.
These issues are addressed by AI using predictive modeling, automated writing, and large-scale text analysis to help businesses meet their documentation obligations accurately and on schedule.
Several companies are already using AI-based automation in their regulatory workflows, demonstrating its real-world impact.
Insilico Medicine, a company specializing in AI-driven drug discovery, has shown how generative AI can reduce the time needed to find promising drug candidates. In 2019, the business created 30,000 new molecules that met predetermined standards in 21 days using generative adversarial networks. This is much faster than the usual identification procedure, which could take months. Some of these substances demonstrated potential for future development after laboratory testing. This demonstrates how the initial study can be reduced from months to weeks through automated molecular design.
Saama Technologies used an AI-driven system to improve the clinical data query process. Traditionally, creating queries to address inconsistencies in clinical trial data took an average of 25 days. With their AI solution, this time was reduced to just 1.7 days. This faster process speeds up drug development, allowing new treatments to reach patients sooner.
These examples show how generative AI can transform drug development. Our article on AI and its impact on drug development explores this topic further with benefits, challenges, and use cases.
Introducing generative AI into regulatory affairs requires a careful approach, guided by thoughtful execution and the right expertise. Below are a few practical steps:
To identify the most time-consuming processes, map out your current documentation workflow. Pay attention to places where automation could have an instant impact.
Look for systems or products that satisfy industry standards and regulatory documents. Verify that any solution selected complies with the FDA’s eCTD regulations.
Integrating extensive industry expertise with AI capabilities is key to introducing generative AI into a complicated regulatory environment. External experts who are knowledgeable with AI and regulatory standards can offer vital advice.
Provide regulatory staff with thorough training after tool introduction. Regularly evaluate and improve your AI systems to accommodate for regulatory changes and preserve accuracy.
We recognize the special challenges and possibilities that generative AI creates for the pharmaceutical sector, ensuring the following:
Custom AI Solutions: We craft AI platforms tailored to your regulatory needs, from automating report generation to confirming compliance.
Data Expertise: AI models need reliable, consistent data. The expert data management background of our team ensures reliable results for your systems.
Strict Regulatory Compliance Adherence: We put compliance first by implementing robust security measures and following relevant FDA, EMA, and other international standards.
Digital Health Product Expertise: In addition to coding, we can help you navigate the regulatory environment and provide end-to-end support for solutions that are compliant and scalable.
Talk to an expert and discover how generative AI can be applied to your workflow.
In addition to its theoretical promise, generative AI is transforming regulatory reporting in the pharmaceuticals industry. Advanced text generation and pattern recognition algorithms speed up documentation, reduce costly errors, and ultimately save lives.
While handling massive amounts of sensitive data and changing international standards is tough, the benefits are too great to ignore. Generative AI will, and already has, impacted patients globally. Join Kanda to be a part of the future of pharmaceuticals.
