The rise in direct-to-patient therapeutics access via online compounding pharmacies and other direct-to-patient sales models has increased the likelihood of patients utilizing medications alongside other therapeutics and with various complex co-morbidities.
With the growth of GLP-1 drugs and the associated direct-to-patient prescribing options, this category of drug is well-positioned to leverage real-world data to identify and demonstrate benefit for patients in other therapeutic areas – beyond current approvals in obesity and diabetes. While GLP-1 drugs are at the forefront of a renewed opportunity for real-world study and surveillance, every new drug and pharmaceutical category can benefit from ongoing post-market surveillance and observational research to identify opportunities for label extension and new patient cohorts that are not initially studied in clinical trials.
As real-world data has become more available, most new drug applications to the U.S. Food and Drug Administration (FDA) now include real-world data. This is due, in part, to the 21st Century Cures Act, which requires the FDA to assess real-world data in support of new drug indications and label extensions.
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Policymakers, regulatory leaders, and clinical development executives should consider new ways that healthcare stakeholders can gain insight into new drug classes to discern their risks and efficacy when used in real-world contexts. Engaging in data sharing, observational cohort research, and deeper analysis of available data to yield further insights can drive improved results for patients, making label and cohort extension faster and more robust.
More robust data sharing among providers will ensure that all prescribers have the most up-to-date information on a patient’s health status.
The disparate nature of patient data sharing in the American healthcare system poses a major challenge to prescribers, who often lack line of sight into additional medications used by a patient. When prescribed by another physician, or in cases where the patient does not specifically disclose their full prescription history and lineup, prescribers will not have access to the full picture. Especially in the online compounding model of direct-to-patient access, patients may not be required to provide their medical record to the prescriber, who interacts with the patient in a one-time transaction online.
Healthcare provider capabilities – such as medicine reconciliation and health information exchanges – can increase the transparency of data among prescribers. This will facilitate real-world insight into off-label use of therapeutics, any adverse events, and the evolving safety and efficacy profile of a specific medication. These capabilities, which are supported by AI and other innovative technologies, are intended to reduce the risk of contraindications in patients and generate new evidence in support of label and cohort extension. While these tools are effective in cases with extensive safety profiling and risk information, they are not adequately equipped for the large-scale and real-time data sharing requirements of the direct-to-patient prescribing scenario.
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Direct-to-patient access channels should leverage these data sharing technologies to access more health information about the patients who use them. Another option is to pursue collaborations with electronic health record companies or healthcare provider organizations to gain a more comprehensive understanding of a patient’s health status and prescription history.
This year, the Drug Enforcement Agency (DEA) published proposed rules that recommend telehealth providers participate in a special registry that tracks the prescribing of controlled substances. In the proposed rules, the DEA states, “The rise of DTC online telemedicine platforms in recent years has further transformed healthcare delivery, but it has also introduced new challenges and heightened risks of diversion due to the remote nature of care delivery,” demonstrating the need for improved data sharing and transparency across the industry.
Observational research can shed new light on drug interaction risks, and benefits not previously understood in real-world use.
Observational studies of co-therapeutic drug use can shed light on the potential risks of usage under conditions that might be unethical for randomization. For example, it would be unethical to randomize a pregnant patient into an interventional trial to test the safety of GLP-1 drugs on maternal and fetal health outcomes. Alternatively, retrospective self-report cohort studies can be used to better understand the outcomes associated with GLP-1 use during pregnancy.
Observational study design involves researchers collecting data reported by the study participants on their behavior or exposures. While causation cannot generally be proven through observational research methods, statistical methods can be used to normalize the data and identify associations between behaviors and outcomes that can yield insights that inform clinical recommendations and guidelines.
Furthermore, a January 2025 publication in Nature Medicine used a cohort of patients with diabetes generated from the US Department of Veterans Affairs databases (n=215,970) to understand the health effects of GLP-1 drugs, compared to an alternative treatment modality. The observational study found reduced and increased health risks in relation to 175 health outcomes assessed, and the authors conclude that “the results provide insights into the benefits and risks of GLP-1 [drugs] and may be useful for informing clinical care and guiding research agendas.” These insights at the scale observed would have taken much longer to be delivered were it not for observational research.
The sheer quantity of real-world data offers new opportunities for clinical development executives and regulators to conduct deeper, more novel analyses
As consumer-and patient-reported data platforms proliferate, such as Whoop and Oura, these tools can be used to encourage consumers to report their health behaviors and digitally track their health outcomes. For example, Whoop has published several observational studies based on data gathered by their wearable device. These patient-or user-reported datasets can supplement healthcare records and lab data to facilitate multi-omic analyses to yield further insights and more rigorous cohort stratification.
In addition to expanding access to effective and safe medicines to treat novel indications in new patient populations, real world data can improve diagnostics to ensure that patients are treated for the right condition, as evidenced by their healthcare data. For example, real-world data can facilitate early detection and diagnosis by analyzing large datasets from electronic health records, wearable devices, and other sources to identify patterns and anomalies that may indicate the early stages of diseases.
Real-world data can also be used to facilitate personalized medicine by developing bespoke diagnostic tools that collect patient health histories, genetic information, and lifestyle data. These insights are gathered through patient-reported channels, electronic medical record data, and other databases. In combination with machine learning and artificial intelligence, diagnostic algorithms can be refined to support patients and healthcare providers to make earlier and more accurate diagnoses, generating essential data to expand use of approved therapeutics into new patient cohorts or health indications.
Clinical and observational research can be a valuable tool for proving the risks and benefits associated with prescription use in diverse patient groups and for identifying label extension opportunities. By implementing robust data-sharing, healthcare stakeholders can better understand how emerging products are used in the real world. Improving patient access to therapeutics through direct-to-patient channels will increase the availability of treatment and can offer new opportunities to gather data on real-world use of novel therapeutics. As GLP-1 usage expands, real-world data can support label extension into cardiovascular disease and other indications, including neurodegenerative diseases, PCOS, chronic kidney disease, and others. With more data, pharmaceutical organizations can improve diagnostic protocols, enhance personalization of treatment plans, and better understand potential risks and safety indicators. Rigorous observational research and data-sharing among healthcare stakeholders can support the safe and effective use of all new categories of drugs.
Editor’s note: The author has no relationship to any of the companies/products mentioned.