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Real-Time Data, Right-Time Decisions: Rethinking Analysis Frequency in RBQM

What It Takes to Harmonize AI Across Fragmented Oncology Health Systems

ACT Brief: AI and Protocol Design, Site Training Readiness, and GLP-1 Unmet Needs

Better Site Training Starts With Five Simple Questions

Using AI to Design Better Trials Before the First Patient Enrolls: Q&A with Claire Riches, Citeline

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In today's ACT Brief, we examine the FDA's multi-domain transparency priorities, why radiopharmaceutical trials need redesigned infrastructure, and how AI can make enrollment more predictable.

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.

A new FDA Voices post from the agency's acting chief of staff details completed milestones and upcoming priorities across complete response letter disclosure, application lifecycle transparency, new approach methodologies, labeling, and inspectional records.

In today's ACT Brief, we examine FDA input sought on early-phase ibogaine protocols, why mixed outsourcing models spread faster than oversight infrastructure, and a major partnership for a rare kidney disease treatment.

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.

A new request for information asks for comments on dose selection, safety monitoring, eligibility criteria, and oversight approaches to support the responsible development of ibogaine drug products.

In today's ACT Brief, we examine practical guidance for clinical operations teams beginning AI adoption, how hierarchical endpoints better reflect treatment benefit, and why early-stage program failures signal smarter pipeline management.

Hierarchical composite endpoints analyzed through pairwise comparisons more accurately reflect multifaceted treatment benefit than time-to-first-event composites, but transparent outcome prioritization, patient involvement in ranking, and reporting of Net Treatment Benefit remain underutilized despite their importance to interpretation.

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.

In today's ACT Brief, we examine how AI enables sponsors to pressure-test protocols before enrollment, HHS's multi-initiative effort to accelerate trial design and execution, and FDA approval expanding heart disease therapy to adolescents.

The new effort combines adaptive platform trial design, AI-enabled site activation, nationwide data infrastructure, and patient data contribution tools to reduce timelines, costs, and patient burden across clinical development.

In this video interview, Claire Riches, VP of clinical solutions at Citeline, explains how AI is shifting trial risk management from reactive to proactive—enabling sponsors to pressure-test protocols and anticipate pivots before a single patient is enrolled.

In today's ACT Brief, we examine AI's role as trial design advisor with human leadership, how to govern autonomous agents in regulated operations, and real-world outcomes from switching to oral weight-loss therapy.

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.

In this video interview, Claire Riches, VP of clinical solutions at Citeline, makes the case for AI as a sophisticated strategic advisor in trial design while arguing that humans must remain in the lead—especially when factors the model can't fully account for are at stake.






















