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The second of a two-part perspective examining why clinical trial enrollment and representation outcomes are shaped long before the first patient is screened.

In this video interview, Saamir Pasha and Carole Berini, PhD, of Ontada, describe what operationalizing regulatory-grade quality frameworks requires—and why fit-for-purpose evidence generation is a transparency and documentation discipline, not just a data cleanliness exercise.

AI can turn complex protocols into first-pass budget inputs, letting sponsors start budgeting sooner while budget builders shift from data entry to review.

Trialbee CEO Matt Walz explores the current impact of AI in delivering practical gains in clinical development and reflects on its future potential, where enrollment predictability may be the biggest prize of all.

In this video interview, Carole Berini, PhD, of Ontada, explains how validated proxies for variables like ECOG performance status can recover not just sample size but the representativeness of hard-to-reach patient populations in real-world oncology studies.

A biomarker test now decides who enters a precision medicine trial, yet results often go unused and assays are not ready when screening opens.

In today's ACT Brief, we examine proportionate analysis frequency in RBQM, how to build trust into real-world evidence foundations for cancer trials, and how rare disease developers monetize regulatory success.

Real-time RBQM requires proportionate analysis frequency matched to data arrival and issue criticality, not continuous reanalysis, because signal churn from over-frequent runs creates alert fatigue and operational noise that obscures meaningful deterioration.

In this video interview, Saamir Pasha and Carole Berini, PhD, of Ontada, explain how building trust into the data foundation—through source lineage, standardization, and reconciliation—is what makes reliable real-world evidence generation in oncology possible.

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.

In this Q&A, Claire Riches, VP of clinical solutions at Citeline, discusses how AI is transforming endpoint selection and protocol pressure-testing, what data inputs matter most for realistic enrollment planning, and why the tools are more accessible than most sponsors assume.

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.