ADC and bispecific antibody supplement pipeline with FIH landscape intelligence
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obs· Harvested: 2026-05-03 · Original date: 2026-05-03T13:39:50.148Z Metadata:{"project":"lunhsiangyuan","type":"feature","obs_id":65070}
obs/65070 · feature · 2026-05-03T13:39:50.148Z
ADC and bispecific antibody supplement pipeline with FIH landscape intelligence
Built comprehensive supplement pipeline for oncology FIH research database targeting ADC and bispecific antibody trials. The pipeline executes 12 targeted queries to fetch trials not captured by baseline ETL, achieving 96.4% coverage of validation checklist drugs. Inserted 470 net-new trials and retroactively tagged 326 existing trials with mechanism classifications using rule-based logic. Database now contains 852 mechanism-tagged trials (449 ADC + 403 bispecific), up 66% from baseline. Generated strategic intelligence report through 15-round iterative analysis, surfacing actionable insights including geographic distribution patterns (US vs Europe dominance by mechanism), elite site identification (Korean trio), white space opportunities (Mayo Clinic), and competitive moat characteristics (dual-modality site capabilities).
Concepts: [“what-changed”,“how-it-works”,“problem-solution”]
Facts: [“Supplement pipeline etl/supplement_adc_bispecific.py executed 12 queries (7 bispecific + 5 ADC) fetching 706 unique trials”,“470 new trials inserted incrementally without database rebuild”,“Rule-based mechanism tagging added 198 bispecific and 128 ADC tags to existing records”,“Coverage verification achieved 96.4% (54/56 GPT-5.5 checklist drugs)”,“Database expanded from 321 ADC + 205 bispecific to 449 ADC + 403 bispecific trials”,“15-round loop FIH analysis generated 900-line report at output/adc-bispecific-landscape.md”,“Key strategic findings: ADC is US-dominated game, bispecific is Europe-dominated, Korean trio (Asan/Seoul/Samsung) identified as universal elite sites”,“Mayo Clinic identified as largest white space opportunity: 176 trials with 157-day lag but only 2 FIH”,“170 dual-modality sites identified as product moat differentiator”]
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