Why are oncology trials not successful?

The probability that a novel cancer drug will successfully transition from phase I to final approval is around 3-5 percent, which is 5 to 10 times lower compared to other therapeutic areas1,2,3. Oncology is currently the largest therapeutic area with 24% of all trials being oncology trials and 85,000 oncology trials conducted globally4. There are currently 20,000 ongoing phase I oncology trials, these statistics imply that only 700 of those will result in eventual cancer treatments. Why is this number so low? How can these numbers be improved?

One of the reasons why oncology trials have a higher failure rate is because these studies often take longer to complete when compared to other therapeutic areas. Each of the three phases in oncology trials take, on average, up to 18 months longer, compared to trials for other drugs, resulting in almost 12 years of clinical research compared to eight years for non oncology trials5. As a result, studies are burdensome and drop out rates are high, up to 19%6. This underlines the importance of measures to improve patient adherence, by means of engaging the patient early on. Studying cancer drugs in decentralized clinical trials (DCTs) and collecting electronic patient reported outcomes (ePRO) are areas that have seen increased interest.

It is important to keep the impact of cancer treatment on patients in mind when designing strategies for data collection in oncology DCTs. For instance, as cancer treatment greatly impacts patients' health especially at the early stages of treatment, researchers may enquire more frequently about clinical symptoms in the first few months of the study. Meanwhile, the quality of life of cancer patients usually changes less rapidly, as physical and emotional functioning plays a major role here making it wise to enquire about these more frequently later on. Traditional data collection schedules ask patients to fill in their data in a regular fashion (e.g. daily or weekly), but this does not align with the length and variations of treatment. Thus, while data collection has changed by technology, the organization of data collection is still very much based on old-fashioned regimes. Properly adjusted data collection schemes will not only reduce the burden on patients, but also improve data quality in real-time. This in turn may have major implications as it will prompt swift clinical intervention, subsequently directly impacting patient outcomes. Therefore, it is imperative that data collection is synchronized with the treatment schedule and at the same time is flexible enough to allow the capture of adverse events and occurrence of symptoms. This will serve to demonstrate the benefit of the treatment7.

To facilitate this flexible yet accurate data collection, creative electronic patient reported outcome (ePRO) strategies need to be developed. Such action will improve the quality of the data being collected and reduce the number of missing data points. To exemplify, ePRO solutions should be flexible when it comes to missed visits and not lock patients out of their ePRO assessments. This is challenging as it means that the flexibility of the ePRO collection should not interfere with the timing of PRO collection relative to the time of medication intake. The same is true for the reporting of patient compliance. All too often, one missed visit can thwart an entire assessment schedule if it's not adapted to flexible visit dates8.

In addition, there may be a need for extending the window of visits, which would allow patients to complete their visits on different dates. Such a visit reactivation component is very important in ePRO collection platforms for oncology trials as it avoids unnecessary visits for patients to clinical sites9.

Other important aspects of ePRO solutions are the user-interface (UI) and user-experience (UX). Effective UX and UI have been shown to enhance protocol compliance10. Therefore, it is important to engage with patients early on and verify the use and flow of information within mobile apps. This is challenging as there are many stakeholders involved in clinical trials and researchers can overlook the fact that the end-users (e.g. patients) need to be able to work well with the app as they eventually need to ensure easy and high quality data collection. This is what truly user-centric design entails. 

Thus, for improvement of future oncology trials it is vital to have a flexible ePRO collection schedule inside a patient-centric app which facilitates first-rate data quality. At Patchai we support this transition by providing conversational patient reported outcomes (Co-PRO). Want to learn more? Click here.

References

  1. https://www.statista.com/statistics/1201162/clinical-trial-success-rates-by-therapeutic-area/
  2. https://globalforum.diaglobal.org/issue/may-2019/what-are-the-chances-of-getting-a-cancer-drug-approved/
  3. Beinse et al., Prediction of Drug Approval After Phase I Clinical Trials in Oncology: RESOLVED2, 2019, JCO Clinical Cancer Informatics, DOI: 10.1200/CCI.19.00023 
  4. Global Data, Clinical Trial Database
  5. Passut, Oncology Trials Outpacing Rest of the Field in Complexity and Duration, Study Shows, 2021 https://www.centerwatch.com/articles/25599-oncology-trials-outpacing-rest-of-the-field-in-complexity-and-duration-study-shows#:~:text=The%20three%20phases%20of%20oncology,of%20Drug%20Development%20(CSDD).
  6. Ramsey, Recruitment Rates Rising, but Retention Rates Fall, According to New Study, 2020 https://www.centerwatch.com/articles/24543-recruitment-rates-rising-but-retention-rates-fall-according-to-new-study#:~:text=CNS%20trial%20dropout%20rates%20grew,percent%20and%206.5%20percent%20respectively.
  7. Lundy et al. Collection of Post-treatment PRO Data in Oncology Clinical Trials,Ther Innov Regul Sci, 2021, DOI: 10.1007/s43441-020-00195-3 
  8. Little et al., The Prevention and Treatment of Missing Data in Clinical Trials, NEJM, 2013, DOI: 10.1056/NEJMsr1203730 
  9. Calvert, Guidelines for Inclusion of Patient-Reported Outcomes in Clinical Trial Protocols The SPIRIT-PRO, JAMA, 2018, DOI: 10.1001/jama.2017.21903
  10. Taylor et al., Use of In-Game Rewards to Motivate Daily Self-Report Compliance: Randomized Controlled Trial, J Med Internet Res, 2019, DOI: 10.2196/11683