From skin scar to systemic fibrosis: Open Targets evidence and the limits of canonical anti-fibrotic axis-based cross-disease hypothesis (an EMB-3 in silico case)

Pathological fibrosis across organs — skin scarring, idiopathic pulmonary fibrosis (IPF), systemic sclerosis, renal fibrosis, hepatic fibrosis — is widely framed in medicinal-chemistry literature as sharing a converging TGF-β / Smad / MMP / CTGF / collagen-deposition master-switch network. We investigated whether this conceptual axis-sharing translates to evidence-based cross-disease applicability for an AI-derived multi-target candidate (EMB-3, an Embelia ribes embelin scaffold-hop, described in companion preprint 3) by querying the Open Targets Platform v4 GraphQL API for both directions: (a) for each fibrotic indication, what targets are associated above OT score ≥ 0.4? (b) for each canonical anti-fibrotic target, what fibrotic-spectrum diseases are associated? The results substantially temper the cross-disease hypothesis as initially framed: only PDGFRB among 9 canonical anti-fibrotic targets shows consistent OT association (≥ 0.4) across fibrotic-spectrum diseases (IPF 0.59, systemic sclerosis 0.55, ILD 0.57, pulmonary fibrosis 0.56, dermatofibrosarcoma protuberans 0.57). Other canonical targets (TGFB1, MMP1, CTGF, SMAD3, MMP3/9, LOX, COL1A1) show ≤ 1 fibrotic disease above the threshold. The "TGF-β master-switch" axis is supported in review literature but is not mirrored in Open Targets genetic-evidence-weighted association scoring. The Rentosertib (TNIK inhibitor, IPF Phase 2) precedent demonstrates that AI-discovered anti-fibrotic candidates can reach clinical efficacy, but cross-disease translation requires evidence-grounded, not framework-asserted, target prioritization. All EMB-3 affinity values are in silico; cross-disease translation requires substantial wet-lab validation. Keywords: cross-disease, fibrosis, IPF, Open Targets, EMB-3, evidence audit, PDGFRB, TGF-β master-switch.