The evolving landscape of inflammation-related clinical trials: a 25-year registry-based network analysis from a clinical pharmacology perspective

The longitudinal evolution of inflammation-related clinical research has not been systematically characterized at the registry level, and the differential impact of the coronavirus disease 2019 (COVID-19) pandemic across inflammatory disease domains remains incompletely resolved.We analyzed 15,051 inflammation-related interventional trials registered on ClinicalTrials.gov, stratified into pre-pandemic (≤ 2019; n = 10,366), pandemic (2020-2022; n = 2,356), and post-pandemic (2023-2025; n = 2,329) epochs.We quantified volumetric, phase, and disease-category metrics; assessed thematic evolution using temporally stratified keyword co-occurrence networks; and characterized the static disease-mechanism architecture through bipartite network analysis using the H2′ specialization and Nestedness based on Overlap and Decreasing Fill (NODF) nestedness indices.Annual registration sustained near pandemic-era rates through the post-pandemic window, peaking in 2025 (871 trials), alongside a sustained shift toward earlier-stage development.Disease-category composition reorganized into two distinct trajectories: a transient pulmonary surge (13.5%→21.4%→15.3%)and a sustained oncology expansion (12.3%→16.9%→21.3%;73.2% relative increase), with oncology overtaking rheumatology as the largest category by 2023-2025.Co-occurrence networks underwent structural simplification (density 0.426→0.247;modularity 0.527→0.393),while the betweenness centrality of immune increased monotonically (0.171→0.333), identifying it as a progressively load-bearing bridging concept.Bipartite analysis showed moderate specialization (H2′ = 0.415) and substantial nestedness (NODF = 51.07), with cross-domain coupling of immunologic and oncologic vocabularies.Formal artificial intelligence and machine learning (AI/ML) incorporation remained negligible (0.14%).COVID-19 functioned as a discrete inflection point amplifying a longer-term reorientation of inflammation research toward oncology-anchored, immune-centered investigation; AI/ML methodologies have not yet translated into prospective trial design.