The Drug Supply Chain Security Act (DSCSA) is a federal regulation that was passed by the FDA in 2013 in order to enhance the security and traceability of pharmaceutical drugs through the supply chain. The primary purpose of the DSCSA is to ensure that drugs being released to the public are safe, traceable, and legitimate.   

DSCSA serialization compliance is complex, with deadlines and regulations varying per trading partner (manufacturers, repackagers, wholesale distributors, and dispensers). The main factors that compliance with the DSCSA relies on include serialization and traceability, data interoperability and electronic record keeping, verification and risk-based product investigation, and Authorized Trading Partner (ATP) requirements.   

Deadline extensions, many of which have been extended to 2025, are another important aspect of compliance. These extensions place more pressure on firms, especially further down the supply chain, to implement accurate systems in order to ensure compliance with DSCSA. This is where AI can come into play, especially due to a rise in importance of data processing systems coupled with a rise in language learning models.  

What Role Can AI Play? 

AI within the pharmaceutical industry has already made lasting impacts, from optimizing clinical trials and research, to accelerating innovation and cutting down on drug development costs. A biotech company headquartered in Boston known as Insilico Medicine has dosed the first patients in Phase II clinical trial with a fully AI discovered and designed drug. Other companies like Sanofi and Novartis have begun investing heavily into AI, with Novartis focusing on funding generative AI-based clinical trials, and Sanofi on “going all in on AI.”  

With DSCSA deadlines approaching, we’re now hearing discussions around how AI can potentially help ensure serialization compliance.  

DSCSA Serialization Compliance Challenges 

Before diving into the opportunities of AI, it’s important to discuss the challenges that come with DSCSA compliance itself – challenges that can potentially be mitigated or even solved through the use of AI. 

Varying Requirements: One challenge of compliance is the fact that regulations vary depending on where a firm is in the supply chain. The DSCSA establishes varying requirements for product tracing to 4 main types of trading partners including manufacturers and repackagers (with a deadline extension that expired on May 27th, 2025), wholesale distributors (with a deadline extension till August 27th, 2025), and dispensers (dispensers with 26 or more employees being November 27th, 2025, and those with 25 or less being November 27th, 2026). They also require that any trading partners of any of those mentioned above must be meet the requirements to be recognized as “authorized trading partners.” Finding trading partners that are right for your firm can be extremely challenging and is a source of issue, especially because of different guidelines for each authorized trading partner.  

Verbose and Technical Language: Another challenge involves the hard-to-understand language of the FDA. Since the FDA operates under the U.S. Department of Health and Human Services, much of its communication to the public follows and includes a formal government format and language. Due to this, their releases tend to be verbose and technical, making interpretation a genuine issue. This complexity creates barriers for those without an expert, making it hard to follow compliance regulations. As a result, simply understanding DSCSA regulations requires a significant amount of time and/or external expertise. This hard-to-understand information coupled with varying regulations and challenges with authorized trading partners can be an extremely large non-technical problem in DSCSA serialization compliance.  

L4 Solutions: A more technical problem, specifically for manufacturers, is finding an L4 solution that fits their firm. For some context, according to ISA-95 framework, there are five levels of trace, track, and serialization (0-4). This is where DSCSA serialization comes especially into play and is extremely important in becoming DSCSA compliant. The goal of an L4 solution is to efficiently and effectively allow for data interoperability at all levels (which is also a requirement of the DSCSA). According to a study that surveyed over 100 pharmaceutical firms, 41% of the respondents stated that the manual rework process for L4 was a “major pain point,” and 31% reported data exchange errors, which is a major issue given the importance of accurate traceability within the DSCSA.  

The issues of varying requirements, hard-to-understand information, and data interoperability, especially with many of the later deadlines approaching, pose a serious problem for firms. This is where AI presents an enormous opportunity to address these challenges and aid firms in ensuring DSCSA compliance.  

Opportunities for AI and DSCSA Serialization Compliance

It’s important to first recognize the FDA’s regulations on AI use in the context of the DSCSA, or lack thereof. The FDA doesn’t actually mention AI anywhere within the DSCSA, given that it was enacted in 2013, and AI was still in its very early stages at that time. There have been requirements made by the FDA such as data aggregation, which is a foundational step for serialization to work effectively. However, due to AI not being specifically mentioned, there is a bit of a gray area that firms can take advantage of to optimize compliance with the DSCSA.   

Understanding Regulations: Given the challenges with hard-to-understand information from the DSCSA, firms can use AI to optimize their understanding of regulations that directly relate to them. A finance company known as Datarails provides software that, through AI, automates the collection and analysis of financial data, allowing firms to stay on top of regulatory changes and requirements. A similar technology could be used in the lens of DSCSA compliance, especially since the regulations are set in stone and have not changed. By implementing AI, firms can allocate fewer resources on simply understanding the regulations that apply to them, while also having an accurate understanding of what they need to do to ensure compliance.  

Evaluating Authorized Trading Partners: Another avenue where pharmaceutical companies could use AI involves the evaluation of what ATP is right for them. Evaluating authorized trading partners is a highly important and rigorous process, involving numerous attributes to consider, ranging from facility features to CMO past compliance history. Due to the complex nature of evaluation, it can end up costing a significant amount of time and resources for a firm.  

For example, one aspect of evaluating a CMO is determining if their L4 solution is a good fit. AI can directly evaluate L4 serialization systems by analyzing numerous enterprise-level functions provided by an L4 serialization system vendor and determining whether it would be a right fit for a firm. Similarly, AI can test whether a pharmaceutical company and CMO have both established similar serialization apps, allowing for an easy transfer of information. Even though having the same serialization apps is not a necessity, it makes the information transfer process much easier for both sides.  

Data Processing: Finally, using AI in data processing can allow for the reallocation of resources, and even provides an opportunity for far more accurate monitoring than always having an employee monitoring for errors. Tracelink offers a solution known as SPI, or Serialized Product Intelligence, which allows a firm to look at data, analyze it, and act on it. Through this software, an alert would be sent when there is an error, and the firm could then easily locate and resolve the error.  

This is an incredibly useful resource for firms involved in serialization, especially DSCSA serialization compliance; however, implementing AI could prove to be far more valuable. AI models continue learning, so not only would it be able to detect and alert someone when there is an error, it could analyze current and historical data and correlate common delays and failures to either the production line or trading partners, in a rapid fashion. Similar to AI used in other business processes, it can provide actionable insights on its own, without having a user analyze the data being provided to them.  

Moving Forward 

With DSCSA deadlines creeping up the supply chain, there are many challenges, ranging from understanding the regulations to DSCSA serialization compliance. Through AI, firms can begin to efficiently optimize their journey to DSCSA compliance and hurdle these challenges with ease.  

To ensure successful implementation of AI, it’s important to make sure your organization is well prepared. From training employees to ensuring compliance, an organization needs to ensure they are approaching AI with intention and preparation. For guidance in this space, reach out to our team of DSCSA and serialization experts. 

Contributions by Ishi Shekhar