1. Introduction
1.1 The challenges of pharma compliance
Managing compliance requires enormous resources, from constant updates to legal and data practices to ensuring that marketing materials and communications meet stringent regulatory standards. For instance, in 2020, in the U.S., the Food and Drug Administration (FDA) issued 304 warning letters for issues such as non-compliance in marketing claims, misbranding, and unapproved product claims during the COVID-19 pandemic.
Not only do pharma companies need to obey domestic and international rules and regulations in drug discovery and development, but now they must also consider artificial intelligence (AI). AI in pharma represents great progress, speeding up product-to-market processes and improving customer engagement.
However, sensitive subjects such as personal data management and privacy protection raise the stakes for complying with increasingly complex regulatory frameworks. These regulatory demands also extend to AI applications, digital marketing, and engagement channels, where companies must remain compliant in all interactions with healthcare professionals (HCPs) and patients.
1.2 How AI can help address these challenges
AI in pharmaceutical companies opens opportunities not only for the drug discovery process or to improve patient outcomes but can also present an innovative path for addressing compliance challenges. By automating repetitive tasks, interpreting vast data sets, and enabling predictive compliance measures, AI can support pharmaceutical companies in maintaining compliance across marketing channels, HCP portals, and patient engagement tools.
For instance, AI in pharma compliance can help with real-time content scanning, automatically ensuring that all digital communications adhere to the latest regulations or managing marketing materials to prevent non-compliant messaging. These tools not only make compliance processes faster but also make them scalable across digital channels.
2. Key applications of AI in ensuring compliance
2.1 Automating compliance monitoring and reporting
AI-powered compliance monitoring tools are already transforming how pharma companies track and report compliance. For example, AI can continuously monitor various marketing assets and HCP portals, flagging content that may be considered improper by promotional regulations.
Let’s consider this example: a pharma company uses GenAI for personalized HCP engagement in its customer strategy. Therefore, the company must ensure that informational and promotional materials on HCP portals remain compliant in different regions.
AI-driven monitoring systems can scan these materials, detect non-compliant language (like unapproved indications), and prompt immediate correction. This level of automated compliance insights allows companies to reduce the workloads of their compliance teams and maintain real-time accuracy across all touchpoints.
2.2 AI for regulatory documentation and audits
From drug design to clinical trials to data management or patient outcomes, regulatory documentation and audit readiness are critical. At the same time, they are also time-consuming, labor-intensive activities. Regulatory intelligence assisted by an AI tool can deliver structure and validation for vast amounts of documentation.
Such an AI solution can ensure that each submission adheres to the required standards. Roche is one of the pharma companies publicly engaged in harnessing the power of AI technologies across its entire pharma value chain. Regarding compliance, it implemented a Risk Based Quality Management (RBQM )that leverages AI in clinical trials.
Their AI-enhanced quality control system uses Large Language Models (LLM) to sift through clinical trial data and identify key compliance elements, significantly reducing the time it takes to prepare audit reports. With AI’s ability to analyze documents and categorize information automatically, Roche was able to streamline the audit process, ensuring all relevant documentation was ready for review.
2.3 Real-time data processing for regulatory updates
Regulations in the pharmaceutical industry are continually changing, particularly in response to innovations in drug development and marketing channels. AI in pharma compliance can track regulatory updates from multiple countries, automatically analyzing changes and updating compliance criteria in real-time.
For example, AI algorithms can pull new regulatory guidelines from the FDA, EMA, or local authorities and match these with existing compliance protocols.
As a matter of fact, the FDA issued a comprehensive discussion paper on how AI and Machine Learning can enhance the development of drugs and biological products to protect, promote, and advance public health.
This real-time processing also helps ensure that compliance teams are instantly aware of relevant updates. AI in nurse education platforms can enable faster adjustments to education materials.
Pfizer leverages AI tools to stay ahead of regulatory shifts. With automated data processing systems, Pfizer can quickly analyze and apply new guidelines, facilitating a streamlined approach to international compliance. For instance, during the COVID-19 pandemic, the use of AI and ML enabled the research and development team to check and analyze an enormous volume of patient data 50% faster than before. This resulted in the delivery of a new drug to the market that helped fight the pandemic.
2.4 Natural Language Processing (NLP) in regulatory submissions
NLP technologies have transformed regulatory submissions by enabling AI to interpret and analyze large volumes of unstructured data. NLP algorithms can structure and extract critical information from dense documents, such as clinical trial reports, safety records, healthcare providers, and patient data, to structure it according to submission standards.
Johnson&Johnson uses NLP to process Electronic Health Records (HER) data and retrieve valuable information that allows them to accelerate and focus on product development programs. For a human team, the same work would take thousands of labor hours, and the delays could effectively cost the patients’ lives.
3. Benefits of using AI for pharma compliance
3.1 Reducing human error and enhancing accuracy
Human error is a significant risk in regulatory compliance, especially when managing data across multiple systems and regions. When using an AI tool, pharmaceutical researchers can minimize these risks by standardizing processes, automatically flagging inconsistencies, and cross-referencing data for accuracy.
Novartis implemented AE Brain, an AI-driven tool that automates repetitive processes. While it takes out much of the burden of repetitive work for humans, the system also improves the quality of the safety information. With the help of Natural Language Processing (NLP) technology, it identifies possible adverse events in the messages that it processes. AE Brain acts as a decision support system for human experts.
3.2 Speeding up compliance workflows
AI accelerates compliance workflows by automating repetitive tasks like documentation and reporting. By cutting down on time-intensive work, compliance teams can focus more on strategic activities and risk management. Alex Devereson, McKinsey partner, estimated that thanks to AI technology, access to new drugs could happen ”in one-tenth of the time, from being discovered to being able to treat patients.”
At the same time, recruiting patients for clinical trials is also a lengthy process. According to Deloitte, 80% of studies fail to meet the recruitment targets due to overestimations of patient availability. Enrolling patients in a mid-stage trial, in the ”traditional way,” can take about 18 months. Amgen developed an AI tool named ATOMIC, which reduces that time to half thanks to its ability to scan immense volumes of internal and public data.
3.3 Improved risk management and predictive analytics
Predictive analytics allows pharma companies to forecast compliance risks by analyzing historical data. AI-driven risk models can help companies proactively address potential issues and improve decision-making.
Merck uses machine learning to analyze patterns in adverse event reporting, predicting potential compliance issues related to post-market surveillance. By identifying trends early, Merck can address safety concerns proactively, mitigating compliance risks. GSK researchers use advanced medical technology to build response predictions to a potential therapy that would treat chronic hepatitis B. This viral infection affects over 250 million people worldwide.
3.4 Reducing costs associated with compliance
Drug development proves extremely burdensome for a pharma company because it can take up to a decade and $1.4 billion. Human studies are known to lead to costs of over a billion dollars, from drug discovery to marketing. The compliance process is an important part of that cost, especially with multiple global requirements.
AI reduces these costs by automating manual tasks and minimizing the need for extensive compliance teams, allowing resources to be reallocated to other strategic areas. According to McKinsey, leveraging the power of AI can reduce costs by up to 50 %, thanks to auto-drafting trial documents and streamlining clinical trials.
4. Future trends in AI and pharma compliance
4.1 Evolving regulations and the need for adaptive AI systems
The pharmaceutical industry’s regulatory landscape is rapidly changing, with AI systems increasingly designed to adapt autonomously to new guidelines. Future AI systems will be equipped with machine learning capabilities to adjust compliance criteria in response to updates, ensuring companies stay compliant across jurisdictions without requiring constant manual intervention.
Between 2016 and 2022, the U.S. FDA received around 300 applications that referred to the use of AI or machine learning in the drug discovery and development process. The vast majority, 90%, dated from 2020 to 2022. The FDA’s Center for Drug Evaluation and Research has begun exploring the role of adaptive AI in regulatory compliance, encouraging the development of AI solutions capable of evolving alongside regulations.
4.2 The rise of Machine Learning for predictive compliance
Machine learning will continue to grow in importance for predictive compliance, with systems analyzing trends and historical data to forecast compliance issues before they become costly problems.
For example, the almost 150-year-old pharma company Eli Lilly keeps pace with the modern times. It has deployed machine learning models that predict compliance risks based on previous patterns of non-compliance, allowing the company to prioritize monitoring efforts and proactively address concerns.
4.3 Other applications of AI in regulatory compliance
AI’s potential in pharma goes beyond traditional compliance, extending into pharmacovigilance, adverse event monitoring, and patient safety management. By cross-referencing data across areas and automatically applying regulatory changes, AI in pharma compliance is set to become an essential tool for pharma companies in multiple compliance aspects.
Pharma companies can employ Generative AI to understand regulations, assess impact, and implement changes. This technology can support training on the new regulations and streamline the communication process about regulatory updates in all the locations where the company is present.
GSK leverages AI and machine learning in a process known as “lab-in-the-loop.” It establishes a feedback loop that aids in research decision-making. In this setup, tech engineers work alongside geneticists and biologists to build active learning pipelines. This approach enables scientists to prioritize experiments from a wide array of possible hypotheses by focusing on targets identified by the algorithms. The outcomes of these experiments then inform further research directions—such as identifying which patients are most likely to respond positively to investigational treatments in clinical trials.
In conclusion, AI offers transformative capabilities for pharma compliance, from reducing human error and accelerating workflows to predicting regulatory risks and enabling adaptive compliance strategies.
By embracing AI, pharmaceutical companies can improve the efficiency and accuracy of their compliance processes, reduce costs, and enhance their ability to respond proactively to an ever-changing regulatory landscape. As regulations evolve and AI technology continues to advance, the role of AI in pharma compliance will likely become even more integral, helping companies navigate this complex landscape with confidence and precision.
To sum up, the integration of AI into the compliance processes helps pharma companies to manage risk better, improve accuracy, and drive efficiency in response to regulatory demands. As AI technology advances, it will further empower companies to proactively meet complex regulatory standards, building a solid foundation for compliant, efficient, and innovative engagement in the pharma industry.
