Presentation (CTI)

OBJECTIVES: Static Target Product Profiles (TPPs) frequently fail to incorporate evolving Health Technology Assessment (HTA) and payer requirements, exposing development pipelines to late-stage market access risk. While pharmaceutical companies recognize the need for dynamic TPPs responsive to shifting comparator landscapes and HTA evidentiary standards, no integrated framework has been described to operationalize this process at scale. This study proposes a conceptual architecture linking AI-assisted living systematic literature reviews (SLRs) to automated TPP deviation alerting.
METHODS: A conceptual framework was developed through structured analysis of two operational gaps: (1) evidence latency: the lag between published HTA decisions, competitor trial readouts, and clinical development assumptions; and (2) risk visibility: the absence of automated mechanisms to flag evidentiary misalignment against TPP parameters with sufficient lead time for protocol modification. The framework was informed by EU Joint Clinical Assessment requirements, living SLR methodology, and HTA and clinical trial intelligence sources.
RESULTS: The framework comprises two operational layers. Layer 1: AI-Assisted Living SLR with Human-in-the-Loop. Continuous AI-assisted screening and extraction of literature, HTA decisions, and competitor trial intelligence produce structured evidence outputs. Human review is embedded within the workflow to maintain methodological rigor and transparency. Layer 2: Automated TPP Deviation Alerting. Validated intelligence is compared against TPP parameters. Where deviations are detected, including comparator shifts, emerging subgroup evidence requirements, or evolving endpoint standards, structured alerts are generated and routed to clinical, market access, and evidence generation teams for review and decision-making.
CONCLUSIONS: This framework demonstrates how AI-assisted living SLRs with embedded human validation and automated TPP deviation alerting may support a more proactive approach to evidence monitoring. By routing validated intelligence against existing TPP assumptions, development teams gain earlier visibility of potential evidentiary misalignment while retaining governance over protocol decisions. Future work should validate the framework across therapy areas and assess its impact on market access risk reduction.