{"id":"W4409689959","doi":"10.1158/1538-7445.am2025-1230","title":"Abstract 1230: High throughput quantitative molecular characterization of cytotoxic antibody-drug conjugates in spheroid models for improved functional characterization, screening and candidate selection","year":2025,"lang":"en","type":"article","venue":"Cancer Research","topic":"HER2/EGFR in Cancer Research","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Zymeworks (Canada)","funders":"","keywords":"Antibody-drug conjugate; Characterization (materials science); Cytotoxic T cell; Selection (genetic algorithm); High-throughput screening; Drug; Computational biology; Spheroid; Antibody; Chemistry; Biology; Pharmacology; Immunology; In vitro; Bioinformatics; Materials science; Nanotechnology; Monoclonal antibody; Computer science; Biochemistry","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007230031,0.0007022032,0.0005334378,0.0005641701,0.0003196077,0.0004697924,0.0002602492,0.0003822915,0.002413935],"category_scores_gemma":[0.0003499625,0.0002227126,0.0003222764,0.0006557307,0.0003060741,0.0002522769,0.0002075169,0.0005675593,0.001395505],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006031276,"about_ca_system_score_gemma":0.0005663478,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001388318,"about_ca_topic_score_gemma":0.002439217,"domain_scores_codex":[0.9994216,0.00009358301,0.00004004052,0.0001163398,0.0002905786,0.00003785021],"domain_scores_gemma":[0.9997278,0.00007697174,0.00004171115,0.0000378307,0.00009308999,0.00002262695],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003550823,0.00001787904,0.0001213781,0.00002940947,0.000002800342,0.00001768327,0.00001419542,0.00023129,0.9975051,0.00008070356,0.0001668084,0.001777093],"study_design_scores_gemma":[0.00000748235,0.0001251256,0.001083838,0.000003237709,0.000006751023,0.00003367624,0.00000752543,0.003068277,0.993331,0.00003224126,0.002294974,0.000005951516],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6506274,0.001487185,0.3234016,0.0002280194,0.0001090857,0.001005059,0.009094281,0.003996953,0.01005045],"genre_scores_gemma":[0.6820072,0.00173124,0.2802643,0.000233259,0.00002847077,0.002196077,0.01429587,0.0007702852,0.01847334],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002413935,"threshold_uncertainty_score":0.008075416,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06537420895556471,"score_gpt":0.4193367668752878,"score_spread":0.3539625579197231,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}