{"id":"W4406824986","doi":"10.1080/17576180.2024.2439229","title":"2024 White Paper on Recent Issues in Bioanalysis: Evolution of Immunogenicity Assessment beyond ADA/NAb; Regulated Genomic/NGS Assays; Hypersensitivity Reactions; Minimum Noise Reduction; False Positive Range; Modernized Vaccine Approaches; NAb/TAb Correlation (PART 3A – Recommendations on Advanced Strategies for Molecular Assays and Immunogenicity of Gene Therapy, Cell Therapy, Vaccine; Biotherapeutics Immunogenicity Assessment & Clinical Relevance PART 3B – Regulatory Agencies’ Input on Immunogenicity/Technologies of Biotherapeutics, Gene, Cell & Vaccine Therapies)","year":2025,"lang":"en","type":"article","venue":"Bioanalysis","topic":"Biosimilars and Bioanalytical Methods","field":"Immunology and Microbiology","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Esri (Canada); Frontier Geosciences (Canada); Health Canada","funders":"","keywords":"Bioanalysis; Immunogenicity; Computational biology; Chemistry; Nanotechnology; Biology; Genetics; Chromatography; Materials science; Immune system","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.004156431,0.001154789,0.002710262,0.001497075,0.0004952898,0.00007302117,0.0007414037,0.001483055,0.0001456797],"category_scores_gemma":[0.0002081422,0.001022845,0.00136685,0.002424903,0.0005656552,0.0003206186,0.0002146023,0.001246381,0.000003696495],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009731349,"about_ca_system_score_gemma":0.0005980576,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004616005,"about_ca_topic_score_gemma":0.0002563368,"domain_scores_codex":[0.9902546,0.003048623,0.003602455,0.001807048,0.0004574387,0.0008298201],"domain_scores_gemma":[0.9921169,0.001439874,0.002752853,0.002319216,0.001292955,0.00007818159],"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":[0.01039126,0.004146381,0.004247692,0.0001103767,0.008471902,0.000001366266,0.0003631432,0.003015156,0.9414957,0.0006372434,0.0001620498,0.02695776],"study_design_scores_gemma":[0.01978222,0.003223525,0.05700835,0.0004803701,0.004441984,0.00001500284,0.005021443,0.0046326,0.8856815,0.002335988,0.0154264,0.001950643],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.905947,0.06755191,0.01830256,0.003230858,0.0008708377,0.00265849,0.0009957367,0.0001475915,0.0002950616],"genre_scores_gemma":[0.9185022,0.06047566,0.01897669,0.0002493287,0.00004637872,0.0001687329,0.0007198671,0.00009746207,0.0007637535],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05581421,"threshold_uncertainty_score":0.9998133,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0365078794490103,"score_gpt":0.3236550125943445,"score_spread":0.2871471331453342,"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."}}