Real-World Evidence generation becoming increasingly important but also very complicated
Based on the EMA’s horizon scanning activities, it has been estimated that the ongoing trend toward complex biologicals, Advanced Therapy Medicinal Products (ATMPs), and drugs for orphan diseases will accelerate over the coming decade. This shift in nature of products causes issues with pre-regulatory evidence generation, as the once golden standard of randomized clinical trials (RCTs) often have limited relevance in these small and heterogenous treatment eligible patient populations [Eichler HG 2021]. The increasingly occurring situations of unavoidable scarce evidence, are further complicated by the expedited regulatory pathways because of the high unmet need among these patient populations.
Expedited regulatory pathways transfer uncertainty on clinical benefit into post launch evidence gap
For example, the FDA allows approval based on surrogate endpoints to stimulate the development of gene therapy products for rare disease populations. However, the American Society of Gene and Cell Therapy (ASGCT) points out, that even though surrogate endpoints may be markers that can predict clinical benefit, they are not accepted by the FDA as actual measurements of clinical benefit [Synthego 2022]. To allow faster availability the EMA is increasing its approvals based on data from registries and hospital data to support the assessment of safety and efficacy [Flynn et al 2022] however, this simply transfers the evidence gap on the clinical benefit to the post-regulatory stage. And this is further complicated by the wide variety in real-world data (RWD) policies that exists among EU health technology assessment (HTA) agencies [Hogervorst et al 2022]. It means that to be able to assess the long-term benefits of the drug / treatment, for the purpose of reimbursement and coverage decisions, Real-World Evidence(RWE) must be established during real world practice. And as even the slightest improvement that could not be captured during the pre-regulatory studies may have significant relevance for especially chronically treated patients, several authors suggest that in the case of orphan drug and highly personalized treatments routinely collected Real-World Evidence including patient reported outcomes is already part of the clinical treatment- and decision-making protocols [Eichler et al 2021, Flynn et al 2022]. For example, in the clinical oncology practice, cases have been described where Real-World Evidence has been successfully utilized to support efficacy of a drug, following the identification of subgroups within the same cancer patient population that benefitted more from one treatment, while others were more receptive to the other treatment option [Schad and Thronicke, 2022]. It means that new approaches to evaluate safety, efficacy, and effectiveness are needed, not only within the regulatory frameworks, but maybe even more so in coverage decision frameworks, where timely post-approval study completion and validity of surrogate measures to support accelerated approvals is key [Vokinger et al 2022].
Need for continuous longitudinal post launch evidence generation
This will become even more relevant according to Facey et al , who underscored that although less than twenty cell and gene therapies had received regulatory approval in the EU by the end of 2019, in early 2020 there were already 1,000 clinical trials underway in over 400 companies. Similarly, it is predicted that by 2025, the FDA will be approving 10 to 20 cell and gene therapy products a year [Statement from FDA Commissioner Scott Gottlieb 2019]. This urges payers and HTA agencies to quickly find ways to evolve their decision-making processes to help resolve uncertainties and mitigate risks. Considering a longitudinal approach to evidence generation with collection of RWD over the life cycle of the technology becomes a serious option to consider [Facey et al, 2020].
Still, there are many hurdles to use Real-World Evidence to support added benefit claims and secure patient access
Even though there is an increased call for Real-World Evidence to support clinical benefit assessment for the purpose of coverage decisions, definitions of Real-World Evidence are diverse. In general Real-World Evidence will be derived from routinely collected RWD, that may be sourced from electronic health records (EHRs), claims and billing activities, laboratory data, hospital data, product and disease registries, patient-generated data including in home-use settings, data gathered from other sources that can inform on health status (e.g., mobile devices), and data linkage approaches [FDA-site: Real-World Evidence, 2022]. This highly fluid status in the field requires more guidance and a consented definition to increase the implementation and robustness of data to be used, which will be especially relevant when coverage decisions become to rely on Real-World Evidence [Schad & Thronicke, 2022].
A review by the EMA of applications for marketing authorization in 2018 and 2019, revealed that RWD was utilized in 40% of these applications, mainly at the post-approval stage. Furthermore, it was observed that disease registries and hospital data were the two most frequently used data sources [Flyn et al, 2022]. And similarly, despite acknowledging the need to systematically use and accept RWD sources in HTA decisions this is usually limited to patient registries [Hogervorst et al, 2022]. Both these observations suggest that there are still many obstacles to overcome, to be able to use several of the above-mentioned sources of RWD to support regulatory approval, and even more so in the case of coverage decisions. But the real question is if it will be safe to rely on these external data sources, as major limitations exist, that make it most unlikely that these data are sufficiently detailed with respect to efficacy outcomes. This could significantly impact the ability to satisfactory establish the benefit/risk ratio and quantification of the improvement in the condition of the patient, as was suggested by an analysis the use of external data sources in marketing authorization applications to the EMA between 2019 and 2021 [Naumann-Winter et al 2022].
Conflicting interests on the use of Real-World Evidence
Often at the time of the relative effectiveness assessments (REAs), HTA agencies identify evidence gaps that are required to be filled by the product owner to substantiate their claim of the product’s added benefit or substantial clinical benefit in real world practice and to enable reimbursement decision-making by payers. Marketing authorization holders are therefore increasingly challenged to provide post launch evidence generation plans to address the identified uncertainties [Puñal-Riobóo et al 2022]. In addition, these often highly individualized treatments, cause a shift towards increasing healthcare delivery in specialized tertiary care facilities. To be able to make robust assessments of the effectiveness of the treatments with these technologies over time, these tertiary centers will also be held accountable to implement high levels of patient documentation to generate high-quality real-world data [Eichler HG 2021]. In addition, multicenter RWD generating trials will be necessary to acquire sufficiently large sample size for a relative effectiveness assessment in these small patient populations with wide geographical spread. Barriers and challenges that get in the way of utilizing RWD to provide Real-World Evidence thus relate to technical, regulatory, clinical & scientific, or perceptional considerations and may be true for biopharma companies, healthcare systems as well as HTA agencies. However, where biopharma experiences this as a capability gap, the healthcare system and HTA agencies have concerns about the integrity and trustworthiness of the data.
Trust in Real-World Evidence and Real-World Evidence study reproducibility
During the peak of the COVID pandemic an influx of high-profile published Real-World Evidence studies had to be retracted because of methodological shortcomings. Together with the often-mentioned lack of clarity in reporting on study implementation by healthcare – decision makers, this has reduced stakeholders’ confidence in Real-World Evidence study findings, calling for valid and robust methodology for collecting and analyzing Real-World Evidence to increase reliability of the data and confidence in its findings. Especially when it involves prognostic observational studies with limited sample size in the case of highly personalized treatments [Wang et al 2021, Wang et al 2022]. In addition to the limited sample size, the fact that the use of these new drugs will be often embedded in completely new patient care pathways that before did not exist, an additional complexity in achieving independent reproducibility is created. As such, to be able to assess the relative effectiveness of a drug or treatment, it must be considered that this will be the result of a combination of factors (e.g., drug-drug interactions, drug-diagnose or drug–MedTech combinations, definition of start & stop criteria, etc.) that are embedded in the clinical practice and care pathway which may differ between healthcare facilities.
The way forward: To enable coverage decisions of innovative treatments Real-World Evidence generation must be embedded in daily clinical practice
For companies that must commit to post launch evidence generation as part of a coverage decision, an imminent risk of ending up with insufficient or low-quality Real-World Evidence data to convince payers of substantial benefit exists. This could lead to having to withdraw the product of the market and commercial failure, not because the added value isn’t there, but because it couldn’t be concluded from the presented data. Reliance on routinely collected RWD (such as from electronic health records, claims databases, wearables, and such) as a source of Real-World Evidence is simply too big of a risk. This is not only a loss for the company but also for the healthcare system, because it may cause the dismissal of a treatment that would potentially provide substantial benefit if further refinement of the eligible patient population in real world practice could be achieved. Ultimately a “learning health care system” must be developed with the ability to provide increasingly robust assessments of drug effects over time which are embedded in daily clinical practice, preferably with cross-country standardization [Puñal-Riobóo et al 2022, Julian et al 2022]. Patient registries that include patient reported outcomes measures may not only provide meaningful data to help quantify long-term benefits but may also contribute to an improvement of healthcare delivery by their healthcare providers as well as saving costs from abolishing treatments that do not provide meaningful outcomes. To get there, early engagement, a priory protocol development and robust research design, that can be seamlessly incorporated into the healthcare delivery system will be the key attributes of success. Explicit policies that ensure data is processed for specific purposes and with patient consent should be in place, as well as strong efforts to perform data minimization and releasing administrative burden for healthcare providers and researchers as well as patients [Naidoo et al 2021]. Using technology in a smart way may contribute to this, but most of all it will require collaboration of different stakeholders, who may need to step aside from existing historically perceived conflicts of interests to be able to contribute to a learning network that ultimately improves patient outcomes.
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