Applied Clinical Trials, July/August 2019 Issue (PDF)

In today's ACT Brief, we examine what AI simulations surface in trial design, how RBQM delivers measurable financial returns, and three new FDA-approved treatments across hair loss, movement disorder, and bone disease.

In this Q&A, Sylviane de Viron of CluePoints and Abigail Dirks, MS, of Tufts CSDD discuss the findings of a collaborative study quantifying the financial value of RBQM, why time savings emerged as the largest driver, and what sponsors struggling to justify adoption now have that they didn't before.

In this video interview, Claire Riches, VP of clinical solutions at Citeline, walks through the categories of hidden protocol risk that AI-driven simulations can identify—from overly restrictive eligibility criteria to patient dropout patterns and structural trial assumptions.

In today's ACT Brief, we examine how AI is surfacing endpoint options beyond traditional design thinking, why sponsor oversight of outsourced work often fails on inspection despite being robust in practice, and a new combination drug for advanced kidney cancer.

Sponsor oversight of outsourced CRO work is often robust in practice but fails inspection because oversight decisions are fragmented across systems and lack an audit trail, requiring sponsors to document oversight as a connected operating system with clear decision records, escalation pathways, and issue resolution from start to finish.

In this video interview, Claire Riches, VP of clinical solutions at Citeline, explains how AI is expanding endpoint selection beyond the bounded experience of a single sponsor team—and surfacing options that traditional design thinking might never have considered.

In today's ACT Brief, we examine what patient-site relationships need beyond easier technology, how to build defensible rare disease evidence from scratch, and how real-world data and AI improve enrollment screening accuracy.

In rare disease drug development where no registry or natural history dataset exists, real-world evidence quality depends on treating evidence engineering as a design-stage decision, defining intended regulatory use and standardizing endpoints, harmonization, and data provenance upfront rather than reconciling gaps after collection.