Solidifying Oncology Drug Effectiveness After Approval with Real-World Evidence

The cancer treatment landscape is a crowded and complex one. Though not perfect, it has been able to prolong the median survival of patients drastically over the past decades. For instance, in the case of breast cancer, around 75% of patients will survive their cancer for 10 years or more after diagnosis (Cancer Research UK, 2024). This is a significant achievement that is rightfully celebrated. It does pose a new challenge: it complicates studying the effectiveness of new treatments that want to enter the market. Imagine a new drug with an innovative mechanism of action with a potential to significantly improve patient outcomes. It would take at least 10 years to determine its superiority over current treatments in terms of overall survival (OS), because the existing treatments have already succeeded in stretching survival that long. This indicates that OS is a challenging criterion for new medicine developers to prove if they want their new medicine to be approved by a drug approval body, such as the European Medicines Agency (EMA) or the US Food and Drug Administration (FDA).

To address this issue, surrogate endpoints like progression-free survival (PFS) are often used to indicate the effectiveness of drugs during the approval process. PFS can generally be proven in a much shorter time span, making it a practical alternative. Regulatory agencies such as the FDA and the EMA rely heavily on PFS when considering medicines for approval. This shift is logical and beneficial for patients needing timely access to new treatments without having to wait for over a decade.

However, there are significant problems with how accurately PFS predicts overall survival, as highlighted by a recent retrospective analysis by Josh Elbaz, published in Cancer Medicine (Elbaz, 2024). This study is pivotal, as it investigates the overall survival of patients that were treated with medicines that have been approved based on surrogate criteria.

Key Findings of the Study

The study looked at 392 drugs that were approved by the FDA in the period of 2006-2023. The most important conclusion:

Subsequently, the effect on overall survival of 147 of the drugs that had been approved based on surrogate markers was studied in real-world patients:

These results leave us with a stark reality: the approval of most of the new cancer treatments are based on criteria that possess very little predictive power for overall survival.

Implications for Regulatory Practices

The reliance on surrogate endpoints for accelerated drug approval processes is a double-edged sword. On the one hand, it allows for faster access to potentially life-saving treatments. On the other, it introduces significant uncertainty regarding the actual benefit these drugs provide in terms of extending patient lives.

This challenge is not easily solved. Clinical trials designed to measure OS directly would require so many years, that it would be virtually impossible to keep subjects involved. The dynamic nature of oncology treatment further complicates the issue. As new therapies are continuously developed, patients’ treatment plans often change, making it difficult to maintain controlled conditions over extended periods. The number of variables and treatment pathways make for a near-infinite number of potential care paths, and it would be impossible to replicate or even remotely approach reality in a clinical trial.

Moreover, the need to adapt to new drugs and treatment strategies as they become available can render long-term trials obsolete. Following a patient for 12 years might yield outdated results as new, more effective treatments emerge. This fluidity in cancer treatment necessitates a flexible approach that simply does not allow for long-term clinical trials that study overall survival.

Seen in this light, it is good that alternative criteria, that can realistically be studied in clinical trials, have been created. It gives the regulatory agencies something to grab onto while judging new treatments. However, in the end, patients are not benefited by treatments that have been approved, but that don’t bring any actual benefits to them.

The Role of Real-World Evidence

Given this conundrum, it might be time for an alternative way to look at drug approvals for cancer. And real-world evidence (RWE) should play a role of major significance in this shift. The study referenced earlier in this piece utilised real-world data (RWD) in a way that is extremely useful. It allowed RWE to complement the information that came out of the clinical trials of the approved medicine. By analysing data from actual clinical practice, RWE provides comprehensive insights into how these drugs perform outside controlled clinical trial environments. Unlike trials, RWE captures diverse patient populations, treatment settings, and outcomes.

RWE’s flexibility allows for real-time adaptation to evolving treatment guidelines and emerging therapies, which is crucial in the rapidly changing landscape of oncology care. It enables nuanced assessments of treatment outcomes across various patient subgroups, addressing disparities often overlooked in traditional trials. Moreover, RWE contributes to ongoing pharmacovigilance efforts by monitoring drug safety and identifying adverse events in real-world settings.

Given the significant challenges with using progression-free survival (PFS) as an approval criterion, follow-up with RWD research should be a requirement for medicines to keep their approved status over time. This approach would combine the best of both worlds: it would still allow innovative, promising therapies swift approval based on surrogate criteria. But over time, the drug will need to prove its effectiveness in real-world patients.  

Conclusion

Given the rapid changes in Standard of Care and the generally approved overall survival in many cancers, relying solely on overall survival (OS) as a benchmark is becoming increasingly impractical within an acceptable timeframe. In the face of this reality, using surrogate criteria is the best option for making sure potentially life-saving medicines become available to the patients that need them sooner. However, this should not mark the end of the evaluation process. Now that the evidence shows that PFS are imprecise when it comes to predicting overall survival, continuing to monitor the effectiveness of new cancer treatments in the real world with the help of real-world data analysis should become an integral part of a prolonged approval evaluation process. This way, patients will end up with medicines that perform well, allowing for the best possible clinical outcomes.

This dual approach—swift initial approval based on surrogate criteria, followed by rigorous real-world validation—ensures that patients receive treatments that not only reach them quickly, but also demonstrate proven efficacy in real-world applications, ultimately leading to the best possible clinical outcomes.