Broadening Eligibility Criteria for Oncology Clinical Trials: Current Advances and Future Directions

The use of real-world evidence to support FDA post-approval study requirements for oncology drugs

BACKGROUND

: The 21^st Century Cures Act of 2016 included provisions for the Food and Drug Administration (FDA) to evaluate the potential for real-world evidence (RWE) to support or fulfill post-approval study requirements. This study reviewed post-marketing requirement (PMR) and post-marketing commitment (PMC) obligations for oncology drugs approved by the FDA post-Cures Act to identify those with RWE components.

METHODS

: Approval letters issued by the FDA between 2017-2020 for oncology drugs were systematically analyzed for PMRs or PMCs with requests for RWE. For each PMR/PMC identified, the characteristics of the approvals, the PMRs/PMCs, and the RWE requested were reviewed.

RESULTS

: Of 189 oncology drug approvals with 456 associated PMRs/PMCs, a total of 15 PMRs/PMCs specified RWE. Compared with all oncology drug approvals, the 14 approvals with PMRs/PMCs requesting RWE were more frequently accelerated approvals, for new therapies, with orphan indications. All 15 PMRs/PMCs requested real-world safety data, with 3 also requesting real-world effectiveness data. RWE requested included post-marketing safety reports, prospective observational studies, expanded access study data, and registry data.

CONCLUSION

: As a greater proportion of safety and efficacy data generation for oncology drugs shifts to the post-marketing setting, RWE has the potential to become an integral component of PMR/PMC fulfillment.

Leveraging electronic health record data for clinical trial planning by assessing eligibility criteria's impact on patient count and safety

OBJECTIVE

To present an approach on using electronic health record (EHR) data that assesses how different eligibility criteria, either individually or in combination, can impact patient count and safety (exemplified by all-cause hospitalization risk) and further assist with criteria selection for prospective clinical trials.

MATERIALS AND METHODS

Trials in three disease domains - relapsed/refractory (r/r) lymphoma/leukemia; hepatitis C virus (HCV); stages 3 and 4 chronic kidney disease (CKD) - were analyzed as case studies for this approach. For each disease domain, criteria were identified and all criteria combinations were used to create EHR cohorts. Per combination, two values were derived: (1) number of eligible patients meeting the selected criteria; (2) hospitalization risk, measured as the hazard ratio between those that qualified and those that did not. From these values, k-means clustering was applied to derive which criteria combinations maximized patient counts but minimized hospitalization risk.

RESULTS

Criteria combinations that reduced hospitalization risk without substantial reductions on patient counts were as follows: for r/r lymphoma/leukemia (23 trials; 9 criteria; 623 patients), applying no infection and adequate absolute neutrophil count while forgoing no prior malignancy; for HCV (15; 7; 751), applying no human immunodeficiency virus and no hepatocellular carcinoma while forgoing no decompensated liver disease/cirrhosis; for CKD (10; 9; 23893), applying no congestive heart failure.

CONCLUSIONS

Within each disease domain, the more drastic effects were generally driven by a few criteria. Similar criteria across different disease domains introduce different changes. Although results are contingent on the trial sample and the EHR data used, this approach demonstrates how EHR data can inform the impact on safety and available patients when exploring different criteria combinations for designing clinical trials.