Blinded by Science: The Importance of RWD and Post-market Surveillance

The blockbuster popularity of GLP-1 medications has everyone talking about them, from promotion by Senera Williams to satire on South Park. 

That heightened profile brings attention more to all aspects of the treatment, from the profound role it plays in improving people’s ability to manage obesity and cardiovascular risk, to indications of potential side effects and risks not revealed through clinical studies. 

With the latter aspect, GLP-1s are providing an important reminder of an essential and oft-underappreciated aspect of any drug or medical treatment development: ongoing post-market surveillance. 

This crucial phase of development following a drug’s release allows pharmaceutical companies (as well as the FDA and other regulatory agencies) to actively monitor the safety, effectiveness, and use of a new treatment in the “real-world.” This evaluation is essential to understand use in diverse populations, off-label and special groups experiences, and to catch issues that may have been missed in clinical trials, such as rare or long-term adverse effects. 

Recent advances in artificial intelligence are aiding these post-market surveillance efforts. 

AI-driven technologies are being used by teams of medical and technological experts to significantly expand the ability of researchers and regulators to gain meaningful access to volumes or real world data, supporting important observations and decisions that can improve drug safety and healthcare outcomes for all.

The illuminating case of GLP-1s

GLP-1 medications, a class of drugs mimicking the action of the natural glucagon-like peptide-1 (GLP-1) hormone, which is released by the gut after eating, were developed to treat type 2 diabetes. Like many drugs, they have found secondary, and in some cases off-label, usage different from their initial clinical focus. 

They are now being used for reducing cardiovascular risk and managing obesity by helping to control blood sugar levels, promoting feelings of fullness to reduce appetite, and slowing down stomach emptying. 

No doubt GLP-1s are changing lives, and fueled by a social media frenzy, the popularity of the drugs for weight loss use has soared. Recent research shows that roughly 12% of U.S. adults have used a GLP-1 drug. 

As with most relatively new treatments, ongoing use and emerging data reveals more complete information, from effectiveness to side effects. In the case of GLP1’s, evidence suggests both unanticipated benefits and risks.

Unforeseen consequences

While studies suggest a possible protective effect against glaucoma and a reduced risk of wet age-related macular degeneration (AMD), other research indicates a higher risk of neovascular AMD. Additionally, some patients have experienced vision problems, including nonarteritic anterior ischemic optic neuropathy (NAION), while taking these medications.

Lawsuits filed against the manufacturers of GLP-1 receptor agonists, medications like semaglutide (Ozempic, Wegovy) and tirzepatide (Mounjaro, Zepbound), allege they’ve caused blindness and vision loss. A central claim in these cases is that the drug companies failed to adequately warn patients and doctors about the potential ocular risks.

Recent peer-reviewed studies and global pharmacovigilance data suggest GLP-1s may significantly increase the risk of vision-threatening conditions like non-arteritic anterior ischemic optic neuropathy (NAION) and neovascular age-related macular degeneration (nAMD). 

While the U.S. FDA FAERS and the WHO VigiBase show disproportionately high rates of vision-related adverse events in GLP-1 users, findings from a recent study by the American Journal of Ophthalmology did not support an association between NAION and GLP-1s.

According to the American Academy of Ophthalmology and the North American Neuro-Ophthalmology Society (NANOS), patients experiencing the “very rare” side effect of vision loss associated with NAION while taking GLP-1s, discontinuing treatment could pose significant health risks to patients’ overall health.

Conflicting findings and blind pots

These conflicting research findings for new drugs are common, and can arise from a variety of issues related to trial design, varying patient populations, and so on. Multiple variables can confound studies investigating the link between new drugs and, say eye conditions. Because patients on GLP-1s often have severe or longstanding diabetes and obesity, underlying health issues — and their associated risk factors — are major confounders.

Lack of broad, post-market data for new drugs can lead to regulatory blind spots. With limited safety data, it becomes essential to contextualize with more comprehensive information. 

Although studies on the safety of GLP-1s have been well designed and offer meaningful insight, they aren’t necessarily the final answer. Many of these studies have limitations that may impact their generalizability and clinical significance. Without sufficient follow-up data, it will be difficult to answer a number of questions: Are there actually beneficial and/or adverse longitudinal effects? Could early worsening overshadow later stabilization?

Getting real: The importance of post-market surveillance

Real-world data (RWD) offers a window to adverse events across diverse patient populations, including groups historically underrepresented in randomized controlled trials (RCTs), e.g., older adults, racial and ethnic minorities, patients with multiple comorbidities. RWD also provides evidence of long-term effectiveness. 

Whereas RCTs typically measure outcomes over months or a few years, RWD allows analysis of durability of treatment effects, adherence patterns, and effectiveness across healthcare settings over sustained periods — with GLP-1s this is critical. Rare adverse events may be missed when the incidence of the disease is too low to be captured in the typically sized clinical trials. This is why unbiased post-market surveillance is critical following new drug approvals.

By capturing safety and effectiveness across diverse, long-term, and unselected patient populations, RWD supplements trial evidence and ensures that comparisons are fair, contextualized, and clinically meaningful.

All drugs require careful, ongoing post-market surveillance, particularly so in the case of off-label usage. The primary challenges of any off-label drug use stem from a lack of robust clinical evidence for the unapproved indication. Off-label use of drugs is associated with a significantly higher likelihood of adverse drug events (ADEs), often due to use in vulnerable populations not included in original clinical trials.

A new era in RWD

AI-driven tools bring new capabilities to processing and curating RWD, particularly unstructured data like the clinical notes in electronic health records (EHRs). 

When applied with a rigorous, subject-matter-expert guided process, AI-currated RWD is an essential resource giving life science organizations new power to monitor the diverse variety of ways drugs are actually utilized post-approval. 

The development represents an important advance in post-marketing surveillance. Understanding the longitudinal safety impact of a new treatment, and its impact combined with other medications and real world conditions, has far-reaching implications, not only for patients but life science organizations as well.

Maximizing the value of AI-driven capabilities to scour RWD for emerging safety signals and identify and assess unexpected benefits or harms as soon as possible is essential to timely actions to promote patient welfare and protect our healthcare system.

Photo: phive2015, Getty Images

Sujay Jadhav is the Chief Executive Officer at Verana Health where he is helping to accelerate the company’s growth and sustainability by advancing clinical trial capabilities, data-as-a-service offerings, medical society partnerships, and data enrichment.

Sujay joins Verana Health with more than 20 years of experience as a seasoned executive, entrepreneur, and global business leader. Most recently, Sujay was the Global Vice President, Health Sciences Business Unit at Oracle, where he ran the organization’s entire product and engineering teams. Before Oracle, Sujay was the CEO of cloud-based clinical research platform goBalto, where he oversaw the acquisition of the company by Oracle. Sujay is also a former executive for the life sciences technology company Model N, where he helped to oversee its transition to a public company.

Sujay holds an MBA from Harvard University and a bachelor’s degree in electronic engineering from the University of South Australia.

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