De-Risking in Rare Kidney Disease Development: From Gene to Global Trial Execution

Rare kidney disease programs fail less often because of biology than because of execution risk. For emerging renal sponsors, success hinges on preserving capital, accelerating timelines, and establishing regulatory credibility, long before the first patient is dosed.

This webinar focused on how to systematically remove avoidable risk across early and mid-stage nephrology development by integrating operational rigor with embedded medical scientific leadership from Day One. This session moves beyond disease overviews to address critical factors for sponsors: access, speed, credibility, and control of spend.

We explored why access means more than site lists – covering the optimal mix of renal centers of excellence and community nephrology, genetically enriched patient identification, registry-based pre-screening, and advocacy alignment without sacrificing scientific integrity. Viewers will gain practical insight into adaptive execution models, including modular CRO engagement, phase-appropriate staffing, contingency-based resourcing, and data-driven triggers for global expansion.

Critically, the webinar demonstrated how to ensure that medical leadership is not only an escalation pathway, but how drug developers can embed it from the planning stage of a trial, enabling early safety signal interpretation, renal adjudication support, DMC-ready narratives, and regulatory-grade risk framing. Using real-world examples, we showed how sponsors can “de-risk before they spend” through pre-IND and protocol stress-testing, team reviews, and realistic enrollment modeling with the goal of aiming to kill bad assumptions before regulators or patients do.

This session demonstrated how you can optimize your nephrology development with the right mix of medical and operational expertise functioning as one system to deliver credible, scalable global trials.

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American Academy of Dermatology

27th March 2026 to 29th March 2026
Colorado Convention Center
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