Use of real-world data as pivotal evidence in veterinary regulatory applications

TL;DR

This paper explores how real-world data can be used in veterinary medicine to support drug approvals, similar to its use in human medicine.

Contribution

The paper highlights the potential of real-world data in veterinary regulatory applications despite current limitations.

Findings

Real-world data can complement or replace randomized clinical trials in veterinary medicine.

Challenges include data quality, methodological rigor, and regulatory acceptance.

Reflections on RWD applications in veterinary medicine are already feasible.

Abstract

Real-world data (RWD) has the potential to complement or serve as an alternative to randomized clinical trials (RCTs) in veterinary medicine, mirroring trends observed in human medicine. Sourced from diverse platforms including digital databases and wearable devices, RWD may provide valuable insights into the effectiveness, safety, and broader societal impacts of veterinary medicinal products. Although its role as pivotal evidence in veterinary drug submissions remains limited due to challenges related to data quality, methodological rigor, and regulatory acceptance, reflections on its potential applications in the veterinary domain are already possible.

Full text

Drawing parallels to its role in human medicine development, real-world data (RWD) holds significant promise for future applications in veterinary medicine, potentially to complement or even partially replace standard randomized clinical trial (RCTs) data in regulatory submissions.

Although a common global regulatory definition of RWD is not yet available (1), both the FDA and EMA converge on the definition of RWD as patient data derived from a variety of sources such as electronic health records from clinics or laboratories, patient registries, prescription data, and information generated from wearables or collected via apps in a home setting (2, 3). Additional veterinary specific RWD sources include the data generated by animal owners, remote health sensing devices, data from robotic milking systems, or from slaughterhouses (4). These sources often involve digital technologies for data generation, collection, or availability; however, the association with digital technologies is not a prerequisite for classification as RWD. The European definition more precisely categorizes RWD based on routine data collection from sources other than traditional clinical trials (3), while the FDA definition does not exclude data from clinical trials. The real-world evidence (RWE) is the information derived from the analysis of one or more RWD sources and which can become pivotal evidence of effectiveness or safety once included within regulatory applications.

The post-authorization collection of information through pharmacovigilance systems is a well-established example of RWE providing critical safety data for both human and veterinary medicinal products, often becoming pivotal evidence to support label amendments of authorized therapies. However, the acceptance of RWD for generating evidence of clinical effectiveness and supporting regulatory approval of innovative products or new therapeutic indications remains limited. In human medicine, RWD/RWE contributed to innovation directly influencing the regulatory decision by serving as external or historical comparators for single-arm trials, comparing surrogate and clinical endpoints, and assessing the effectiveness between treatment groups; in other cases, the RWE role was more limited, providing supportive information to the standard datasets such as incidence, prevalence, or evolution of diseases (5–7). The EMA has recently qualified a primary endpoint based on data passively collected by digital and wearable devices in home settings, paving the way for new methods of evaluating treatment effectiveness in real-life conditions, particularly for indications impacting ambulatory function (8).

While examples in the veterinary field are more limited, regulatory authorities have previously accepted applications based on data generated under real-life conditions that, according to current classification criteria, could be defined as RWD/RWE. For instance, the bibliographic applications, where applicants may substitute original safety and efficacy studies with published scientific literature, represent a registration route supported by data collected from a variety of sources other than standard clinical trials, consistent with the definition of RWD (9–11); however, this option has always been limited to well-established active substances with an already recognized level of effectiveness and safety. Another example is the effectiveness of new medicinal products assessed for regulatory purposes via wearable devices in the home setting, but as part of standard clinical controlled studies (12, 13).

The growing interest in RWD is driven by advancements in digital technologies, which provide novel solutions for data generation at the individual and patient level, as well as for the storage and accessibility of large datasets. This interest extends to companies developing veterinary medicines, which are eager to leverage these emerging data sources, similar to their counterparts in human medicine. However, the challenges and limitations associated with accepting RWE as pivotal evidence in regulatory submissions for human medicines are equally relevant in the veterinary sector. These challenges include issues related to data quality and heterogeneity, variations in data collection practices across regions, the design and methodology of data collection, the statistical analysis plan underpinning data interpretation and the potential for bias and measurement errors (3, 14–16). Given the unique characteristics of the animal health sector, some of these challenges are likely to be more pronounced in the veterinary field, where the typical technology-driven sources of RWD—such as electronic health records, e-health services, insurance claims and billing data—are less widely adopted for animal patients. For example, the SAVSNET network—which integrates a centralized database of anonymized electronic health records from UK veterinary practices and diagnostic laboratories (17)—has no comparable system in other EU Member States. Addressing concerns about RWD relevance and reliability, the representativeness of the fewer available databases and the generalizability of the information obtained will be critical (4). Furthermore, the approval of alternative data collection methods may necessitate rigorous review before being approved for use (8) and such demanding qualification processes could lead to prohibitive costs within the veterinary sector.

However, despite the aforementioned challenges, RWD may offer several potential advantages for innovation in veterinary medicine, some of which are specific to the veterinary sector:

In conclusion, the use of real-world data (RWD) in veterinary regulatory applications holds great potential to complement or, in certain cases, replace traditional randomized clinical trials (RCTs). While challenges related to data quality, collection methodologies, and region-specific practices remain, the growing availability of digital technologies offers new ways to harness data from diverse sources. Real-world evidence (RWE) derived from these data can support regulatory decisions, particularly for post-authorization monitoring and the addition of new claims to existing products. As the veterinary sector evolves, leveraging RWD could provide a more comprehensive understanding of product safety and effectiveness under actual usage conditions, while also addressing broader societal impacts such as antimicrobial resistance and environmental sustainability.