From Doing to Reviewing: How a Connected Ecosystem Streamlines Clinical Trial Finance

How a phased CRO partnership built on ICH E6(R2) and E6(R3) principles supported a multi-indication biotech portfolio.

Site training effectiveness depends on readiness at first-patient-in, requiring learning-science design principles around performance focus, cognitive load reduction, progressive sequencing, and spaced retrieval practice rather than training completion metrics or content volume.

Radiopharmaceutical therapy is exposing a broader challenge for clinical development: scientific innovation can advance only as fast as the infrastructure, data, and cross-functional decisions required to deliver it.

Mixed FSP and FSO models are gaining ground as sponsors seek agility and resource flexibility, but the oversight infrastructure, audit trails, and accountability structures needed to run them well and survive regulatory inspection are consistently lagging behind the model itself.

In this video interview, Claire Riches, VP of clinical solutions at Citeline, offers practical guidance for clinical operations teams beginning their AI journey—making the case that the tools are more accessible than many assume and that waiting to start is a competitive risk in itself.

AI agents in clinical operations acquire broader autonomous capability through expanded permissions, tools, memory, and delegated authority, requiring governance focused on whether effective capability has shifted outside approved boundaries rather than whether software has changed.

The partnership gives RealTime's network of more than 3,000 research sites access to the Archer platform for direct, automated data transfer from eSource to any sponsor electronic data capture system or data destination.

In this video interview, Saamir Pasha and Carole Berini, PhD, of Ontada, identify three persistent obstacles to consistent real-world evidence generation in oncology—inconsistent data capture, structural data limitations that modeling alone can't fix, and the need for earned generalizability through deliberate multi-site validation.