Isn’t it interesting to see Artificial Intelligence (AI) sculpting out to have a thriving position in the Regulatory landscape? One of the most important reasons for this advancement is the ability of AI and Machine Learning (ML) applications to tackle everyday procedural challenges and logical issues that compliance officers put up with.

AI proficiently manage huge quantities of information with speed and precision, which can theoretically renovate Regulatory compliance. It is safe to say that in the near future technology can help manufacturers easily comprehend compliance obligations and take pertinent actions. And, ultimately, the continuous usage of AI in the Regulatory landscape can largely reduce the need for people required in the procedures. 

What is driving the growing interest in AI within the regulatory function?

When you look at the broader context of how AI is being used across the pharmaceutical industry you can see what is really driving interest.

In the drug discovery process, we’ve seen AI have tremendous potential and impact on the efficient analysis of complex data sets to drive drug development. Comparatively, human identification of new molecules has been slow and far less effective. We’ve seen digital health technology and AI change the operation of clinical trials, from patient recruitment to digital endpoints and decentralized trials. On the business development and licensing side, we’ve seen companies use predictive models to streamline their business development spending. And on the commercial side, AI is being used to guide both sales and marketing efforts. 

Within regulatory affairs, you also have many of the same influencing factors which have driven interest in other parts of the industry. There is a substantial increase in the volume of datasets available.  There are also opportunities for cheaper and faster processing capabilities and there is a drastic improvement in the ability of AI algorithms.

This is coupled with the increasing complexity of the regulatory role. The number of drugs under clinical investigation is on the rise, along with the number of publications from regulatory agencies. The number of new technologies in development, both drugs and digital, that regulations must actively monitor is also increasing.

So there is a combination of headway being made in AI across adjacent spaces and a growing need from regulations to automate, manage and keep up with the influx of data. When you consider the capabilities of AI technology, it’s a perfect match to address this problem.

The potential future outcomes of artificial intelligence in regulatory affairs

Given the way AI is currently being used, in the future, it’s likely to be used to understand data inputs and construct full study reports. The development of AI could help create more jobs, especially in contract roles.

Due to the nature of the industry, with regulatory affairs being responsible as one of the first processes in a drug lifecycle, something that may be approved at the initial stage could change years later due to the effects of AI. This could create jobs for contractors to work within audits or to manage the data collated by AI. Alternatively, some Regulatory Affairs professionals believe that the industry might have to become more fluid to adapt to the potential changes in AI.

How do you see AI being used on the ground in regulatory affairs?

On its best day, AI allows the regulatory function to increase the speed, accuracy, and quality of how they execute certain functions.

For example, quality assurance professionals are tasked with continuously monitoring and updating their internal standard operating practices (SOPs) in reaction to regulatory changes across dozens of markets. AI can automate this process by ingesting regulatory changes and updating relevant SOPs. This is cost-effective, quicker, and less prone to human error. This also allows the quality function to then focus on high-value activities, such as addressing the corrective and preventative action (CAPAs) backlogs.

Another example is extracting data from unstructured documents. Valuable information can be found once data is extracted and transferred. For example, using a text mining tool you could look in detail at chemistry manufacturing and control documents to find products with the same chemical impurities in their manufacturing process.

AI tools are effectively being used to give regulatory affairs new insights into their own decision-making ability, to help to improve their operating processes, and spot recurring patterns.

In a life sciences organization, software applications that integrate with AI, enhance the efficiency of many functions in Regulatory compliance procedures. Although the probable aids of technological innovations in AI and ML are limitless, present applications of AI in compliance schemes have already demonstrated at least three benefits for Regulatory compliance. They include dropping incorrect positives, lessening prices, and tackling human faults.

Additionally, during the process of drug discovery, it is observed that AI has enormous capability and influence on the assessment of challenging data sets to run drug development. On the other hand, human identification of new molecules has been relatively sluggish and less efficient. Digital well-being expertise and AI transformed clinical trial operations. Apart from the Regulatory aspect, AI is also useful for the commercial side of the life sciences business, i.e., to perform sales and marketing, efficiently.

How will AI change the way the regulatory function operates?

There’s a great debate about whether AI will replace the human element of various tasks and responsibilities. I’m of the other school of thought that AI will actually empower employees and give them tools to improve the execution of their responsibilities.

It will allow them to automate the more mundane, manual tasks. But, they will also be able to act with greater certainty and at a greater pace on the data, they have available to them. This will then allow them to make decisions more effectively and respond quickly to market changes.

These tools aren’t replacements, they are enablers. It certainly involves change, but if a company takes the right implementation approach, they’ll find their employees are far more interested in AI adoption than they may expect.

In the coming years, regulatory intelligence will be a must-have to give companies the competitive advantage needed. These tools will help them track and compare regulations, learn from issues affecting their competition, and predict key approval timelines.

Data mining, synthesis, and analysis are all growing priorities for companies within the pharmaceutical industry. If companies want to succeed and not fall behind, they need to look into how AI can allow them to work quickly, effectively, and with greater accuracy.

Importance of AI in Regulatory Operations

Regulatory technology, commonly referred to as RegTech, is a rapidly expanding area of digital technology. Its agenda is to simplify Regulatory compliance and introduce automation.

Across the globe, Regulatory bodies face the obstacle of scrutinizing methods and procedures within their own industries. Often, companies end up experiencing substantial investments to align with the regulations, thus, increasing their compliance expenses. By introducing Big Data and AI, both organizations and officials can ensure streamlined Regulatory compliance.

What are the critical success factors for using AI within the regulatory function?

First, you need the right set of data. Focus is needed on the data foundation, in particular how to; build the right data, integrate different data sources, and combine structured data with raw, unstructured data. Realistically, AI is only as powerful as the data that goes into it.

Secondly, companies need to look at their operating models. It would be naïve to just implement an AI tool without considering its full impact on operations. AI has the potential to break down functional silos and allow teams to collaborate and communicate more effectively, but there must be a deep dive into how the operating model needs to change to achieve this.

Finally, you have to focus on culture. There must be an analysis of the challenges AI is going to present and how organizations can evolve their culture to reap the benefits of new technology and datasets. AI is a disruptive technology, but when companies focus on culture, change management, and training they are more likely to be successful in the introduction and adoption of new tools.

Benefits of AI

AI and Big Data in Regulatory Compliance

Companies can influence AI and Big Data as a component of their Regulatory technology plan to ensure compliance in today’s information-centric world. Businesses can streamline their end-to-end processes by introducing AI and Big Data to make their enterprise entity compatible.

Simplification of the Compliance Process

Often, companies fall short to fulfil various requirements simply because they rely on tedious manual tasks. The Regulatory compliance process mainly involves information gathering through different sources, verifying the information, and then presenting it to regulators. Without automation, this whole process proves to be cumbersome, costly, and labor-intensive. However, using Big Data and AI can significantly decrease the required funds and timeline, while diminishing mistakes.

Tracking Regulatory Changes

Regulatory guidelines change every now and then due to several market forces. If an organization fails to align with the new regulations, it can pose a risk to its reputation, and lead to financial penalties, or even legal action. AI and Big Data products can help companies stay compliant by keeping them up-to-date with the latest Regulatory changes. AI and Big Data tools use native linguistic processing and deep learning to understand compliance requirements and notify companies regarding the same.

Improving Decision-Making

Companies can ensure smart Regulatory compliance processes by using AI and Big Data. Perhaps, the most important value that AI and Big Data extend to companies is empowering them to comprehend and foresee the complicated patterns in risk and data management. AI and Big Data can be the backbones of Regulatory agencies.

AI Makes Regulatory Compliance Efficient

Technologies, like Deep Learning and Machine Learning, also facilitate Regulatory organizations to monitor units. Instead of executing periodic audits, which are often troublesome and time-consuming, companies can easily monitor their compliance limitations. The acceptance of AI and Big Data can streamline the compliance process.

In a nutshell, the combination of AI and technology can shape the Regulatory industry and bring advancement in compliance procedures across the globe. To align with the digital transformation, companies are advised to keep track of ever-evolving regulations and go ahead with the procedures compliantly. Stay safe. Stay compliant.

Pharma 4.0 – The New age Pharma Realm With AI tools

The industry as we know it is changing. Pharmaceutical and life sciences companies across the globe are experiencing more pressure than ever to keep up with increased regulatory standards while moving at a pace that requires them to innovate in order to remain competitive.

With more real-time automation and the steady increase in AI and Big Data sweeping the landscape, what used to be a slow-to-change and risk-averse industry is now expected to see a significant shift towards newer technology that focuses on heightened regulatory standards. Here’s how your company can get ahead of what industry experts are calling, Pharma 4.0:

What’s the cause of this shift?

Professional experts at PharmOut explain that Pharma 4.0 plays on the “convergence of people, physical systems, and data within an industrial process to increase quality, productivity, and profit by using the power of advanced data analytics”. How does this play into compliance for life sciences?

Continuous, real-time compliance monitoring

Take product quality reviews as an example. Regulators used to acknowledge annual reviews as the acceptable standard, however, with the introduction of automation and real-time monitoring, we can expect to see product quality reviews happen much more frequently than on an annual basis. Experts suggest, “Pharma 4.0 technology allows for continuous, real-time monitoring of manufacturing processes”. The goal in mind? To find and predict problems before they occur (which will help you avoid downtime and loss of product in the manufacturing facility).

AI-powered claim referencing to trial reports

Promotional and Medical, Legal, and Regulatory (MLR) reviews are yet another area in Big Data that is advancing in life sciences. During the review process, auto-linking the safety and efficacy claims back to the originating clinical trials that support them is a new application made possible by machine learning.  Currently, referencing claims to trials and clinical study reports is a tedious and time-consuming effort for marketing and medical affairs review teams. To compound the problem, the task requires some scientific literacy and familiarity with clinical research. This is where machine learning comes in. Once the dataset is large enough, you can train the AI to recognize safety and efficacy claims and suggest links to the appropriate section of the related clinical trial report—all within the same platform.

The bottom line

Advanced technologies like big data, artificial intelligence, machine learning, deep learning, and natural language processing are bringing forward a new wave of opportunities to disrupt and change the industry. From innovation to operations, this will have the power to significantly impact the processes within the pharmaceutical and life sciences industries. Although more time-consuming for companies to adhere to are these changes all that daunting? Yes. But the important distinction to note is the value it provides back to you—the ability to help you find, predict, and fix the issues related to the quality of your product and operational processes.