InsightRP2: An Interdisciplinary Framework for Therapy Development in RP2-Associated Retinopathy

It proposes InsightRP2, an integrated translational research framework that brings clinical, molecular, mechanistic, and regulatory expertise together within a single highly interactive effort. The text notes these aspects are "often addressed in isolation"; the framework's novelty lies in combining them into a complementary, collaborative whole rather than pursuing them separately. A Perspective piece: the argument rests on the authors' proposed framework and rationale, with no experimental data or clinical results reported.

The framework explicitly incorporates four interlinked components: clinical data, artificial intelligence-supported imaging analysis, experimental disease modeling, and adeno-associated virus design. It places AI-based imaging analysis and AAV vector design alongside clinical observation and disease modeling as parts of one translational chain for the same rare inherited eye disorder. A framework-level methodological description; the text provides no technical details, sample sizes, or validation results for the individual components.

It emphasizes early-stage patient involvement and targeted clinical observation alongside molecular and therapeutic research, which may help enable clinical trial readiness and support translating preclinical therapeutic innovations into clinical care. It moves patient involvement and clinical observation forward to run in parallel with basic research rather than leaving them to a later stage, as a route linking preclinical work to clinical care. The text uses "potentially enable," framing this as an expectation; no realized translation cases or trial data are provided.

The framework addresses targeted gene therapy development for RP2-associated retinitis pigmentosa, a specific rare inherited eye disorder, and is intended for research and translational teams seeking to coordinate clinical observation, AI imaging analysis, disease modeling, and AAV design; its vision presupposes integrated collaboration and early patient involvement.

The text does not provide concrete implementation methods, timelines, or validation results for the framework's components, nor specific data on AI imaging analysis, disease modeling, or AAV design; readers may watch whether the framework later yields an actionable collaborative workflow and preclinical or clinical validation evidence.