{"id":"W2774347927","doi":"10.4155/bio-2017-4973","title":"2017 White Paper on Recent Issues in Bioanalysis: Rise of Hybrid LBA/LCMS Immunogenicity Assays (Part 2: Hybrid LBA/LCMS Biotherapeutics, Biomarkers & Immunogenicity Assays and Regulatory Agencies’ Inputs)","year":2017,"lang":"en","type":"article","venue":"Bioanalysis","topic":"Biosimilars and Bioanalytical Methods","field":"Immunology and Microbiology","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"Caprion (Canada); Health Canada","funders":"Health Canada; Genentech; Angelini Pharma; Sanofi; Amgen; Pfizer; U.S. Food and Drug Administration; Ministry of Health, Labour and Welfare; Bristol-Myers Squibb; Agence Nationale de Sécurité du Médicament et des Produits de Santé; GlaxoSmithKline","keywords":"Immunogenicity; Bioanalysis; Biopharmaceutical; Regulatory science; Computer science; Chemistry; Biotechnology; Medicine; Biology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.004220852,0.001021071,0.002306107,0.001056306,0.001002772,0.0002393122,0.001706202,0.0007190776,0.002324577],"category_scores_gemma":[0.0008003172,0.0008572346,0.001282881,0.0007965007,0.00278073,0.0003955015,0.0007128304,0.000888583,0.0001576816],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003654033,"about_ca_system_score_gemma":0.0001960128,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001649935,"about_ca_topic_score_gemma":0.0009564318,"domain_scores_codex":[0.9929666,0.00167196,0.001986467,0.001711244,0.0004231469,0.001240585],"domain_scores_gemma":[0.9936788,0.0003879298,0.00160724,0.003755528,0.0003676152,0.0002028788],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.004375162,0.003253986,0.1860329,0.0003112287,0.0432682,0.0001687728,0.0007718814,0.00005055799,0.6333091,0.0007172697,0.01523109,0.1125099],"study_design_scores_gemma":[0.006636119,0.0008707651,0.2127037,0.0005789189,0.008977982,0.0001341705,0.0006455976,0.0005887285,0.343832,0.001254877,0.4203936,0.003383432],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8402334,0.1376509,0.0005092759,0.01216784,0.001851827,0.001163633,0.00174582,0.0002189634,0.004458257],"genre_scores_gemma":[0.9636343,0.02987384,0.001841698,0.0007814819,0.00008388703,0.00003599239,0.0002159445,0.00008055213,0.00345236],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4051626,"threshold_uncertainty_score":0.9999331,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04165581480862591,"score_gpt":0.3114225046959653,"score_spread":0.2697666898873394,"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."}}