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 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.

The pilot pairs drug sponsors with qualified research institutions to compress the path from drug identification to first-in-human study through rolling submission review and earlier coordination of institutional review board and site activation activities.

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.

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.

RWE is finding a role earlier in drug development than ever before, but the data quality, integration, and organizational alignment required to make it regulatory-grade are still catching up to the ambition.

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.

Cell and gene therapy access expands into community care settings only when operational coordination, site readiness, and supply chain standardization become first-order priorities equivalent to manufacturing capacity, requiring standardized processes, digital integration, and distributed logistics networks.