Abstract Revisions to the Common Rule and NIH policy require the use of a single Institutional Review Board (sIRB) for the review of most federally funded, multisite research, with the intent of streamlining the review process. However, since initial implementation in 2018, many IRBs and institutions continue to struggle with the logistics of implementing this requirement. In this paper, we report the findings of a workshop held in 2022 to examine why sIRB review remains problematic and propose possible solutions. Workshop participants identified several issues as major barriers, including new responsibilities for study teams, persistent duplicative review processes, the lack of harmonization of policies and practices across institutions, the absence of additional guidance from federal agencies, and the need for greater flexibility in policy requirements. Addressing these problems will require providing additional resources and training to research teams, the commitment of institutional leaders to harmonize practice, and policymakers to critically evaluate the requirement and provide flexibility in applicability.

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Implementation of single IRB review for multisite human subjects research: Persistent challenges and possible solutions

Clinical trial enrichment is the targeted recruitment of prospective individual patients with defined clinical characteristics who are likely to benefit from newly developed or repurposed drugs. This process is central to the success of clinical trials together with patient management and regulatory compliance. A main challenge in clinical trial enrichment lies in the recognition of a priori clinical parameters and information that informs drug efficacy or toxicity, particularly when intended for a broader unseen population. Although Artificial Intelligence (AI) approaches, especially large language models (LLMs), have been employed in many aspects of clinical trials, to our knowledge, there is no AI method that has been developed which offers a prospective prediction and assesses the extent to which a given therapeutic intervention benefits an unseen population. Here, we offer an outlook on how to build Artificial Clinic Intelligence (ACI), a generative AI (GAI)-powered modeling platform for modeling clinical trial enrichment. ACI generates synthetic patient data and models clinical trial enrichment to inform clinicians on key clinical parameters that are enriched in prospective patients prior to accrual. 

Artificial Clinic Intelligence (ACI): A Generative AI-Powered Modeling Platform to Optimize Patient Cohort Enrichment and Clinical Trial Optimization

Background Institutional review boards play a crucial role in initiating clinical trials. Though many multicenter clinical trials utilize an individual IRB model, where each institution uses their own local IRB, it is unknown if a shared (single IRB) model would reduce the time required to approve a standard IRB protocol. Objective The objective of this study was to compare processing times and other processing characteristics between sites using a single IRB and those using their own site IRBs in a multicenter clinical trial. Study Design This was a retrospective study of sites in an open-label, multicenter randomized control trial from 2014-2021. Participating sites in the multicenter Chronic Hypertension and Pregnancy (CHAP) trial were asked to complete a survey collecting data describing their IRB approval process. Results Forty-five sites participated in the survey (7 used a shared IRB and 38 used their own individual IRBs). Most sites (86%) using the shared IRB model did not require a full board IRB meeting prior to protocol approval, compared to one site (3%) within the individual IRB group (p < 0.001). Median total approval times (41 vs. 56 days; p = 0.42), numbers of submission rounds (1 vs. 2, p=0.09), and numbers of IRB stipulations (1 vs. 4; p=0.12) were lower for the shared than individual site IRB model, but these differences were not statistically significant. Conclusion Our findings support the hypothesis that the shared IRB model for multicenter studies may be more efficient in terms of cumulative time and effort required to obtain approval of an IRB protocol. Given that these data have important implications for multicenter clinical trials, future research should evaluate these findings using larger or multiple multicenter trials. 

Outcomes of Shared IRB Compared with Multiple Individual Site IRB Models in a Multisite Clinical Trial.

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Outcomes of Shared IRB Compared with Multiple Individual Site IRB Models in a Multisite Clinical Trial.