Clinical registry metadata as a hidden bottleneck in AI-driven drug discovery: a computational audit of translational phase data in glioma research

Clinical translation in glioma and glioblastoma remains inefficient despite advances in computational drug discovery. An underrecognized contributor to this gap is structural degradation of clinical registry metadata. When clinical descriptors are not machine-readable or semantically consistent, even robust algorithms generate biologically sound yet clinically non-translatable outputs. We analyzed the complete set of 2.357 glioma-related clinical trial records available in the WHO ICTRP registry at the time of extraction (3 January 2026). A deterministic Python-based validation pipeline was developed to normalize phase and study-type annotations while distinguishing technical voids, methodological non-applicability, and structurally inconsistent entries. Two quantitative indices were introduced: the Reporting Gap (\(\:{G}_{q}\)), reflecting phase metadata completeness, and the Maturity Ratio (\(\:{M}_{r}\)), describing the balance between early and late translational stages. Phase annotation showed substantial structural degradation, with large fractions of records lacking machine-interpretable phase labels, whereas study-type fields demonstrated high completeness but severe terminological fragmentation. This asymmetry indicates a systemic mismatch between real-world research designs and the classical phase ontology. Consequently, clinically relevant evidence becomes algorithmically invisible, biasing AI-driven translational assessment. These findings demonstrate that semantic validation of registry metadata is a prerequisite for reliable integration of clinical data into computational drug discovery pipelines.