{"id":"W4280547561","doi":"10.4155/bio-2022-0078","title":"2021 White Paper on Recent Issues in Bioanalysis: Mass Spec of Proteins, Extracellular Vesicles, CRISPR, Chiral Assays, Oligos; Nanomedicines Bioanalysis; ICH M10 Section 7.1; Non-Liquid & Rare Matrices; Regulatory Inputs ( Part 1A – Recommendations on Endogenous Compounds, Small Molecules, Complex Methods, Regulated Mass Spec of Large Molecules, Small Molecule, PoC & Part 1B - Regulatory Agencies' Inputs on Bioanalysis, Biomarkers, Immunogenicity, Gene & Cell Therapy and Vaccine)","year":2022,"lang":"en","type":"article","venue":"Bioanalysis","topic":"Biosimilars and Bioanalytical Methods","field":"Immunology and Microbiology","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"Health Canada; Seagen (Canada)","funders":"World Health Organization","keywords":"Bioanalysis; White paper; Excellence; Computer science; Computational biology; Political science; Nanotechnology; Biology","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":[0.008338195,0.00222678,0.001202484,0.00267133,0.002682809,0.008541895,0.003496929,0.01071829,0.1209711],"category_scores_gemma":[0.006185998,0.001130721,0.001220589,0.002182799,0.002015896,0.004042226,0.002676784,0.005785568,0.1401518],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003798369,"about_ca_system_score_gemma":0.009499716,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004879653,"about_ca_topic_score_gemma":0.0117854,"domain_scores_codex":[0.9927593,0.0006990954,0.0003708826,0.0008317056,0.004685895,0.0006530856],"domain_scores_gemma":[0.9895189,0.001568053,0.0009914576,0.0004764179,0.005498879,0.001946194],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005719507,0.00006384558,0.00005595381,0.0002701862,0.000005032031,0.00007722303,0.00004019976,0.00008588667,0.0028567,0.002526169,0.9463109,0.04765076],"study_design_scores_gemma":[0.000004717516,0.00002800373,0.000163811,0.00008593908,0.00000241908,0.00003639091,0.00001709317,0.00005377627,0.001219859,0.0006050379,0.9977745,0.000008297403],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.001707097,0.04382347,0.01462643,0.09627496,0.2201062,0.001413821,0.004398855,0.002255071,0.6153941],"genre_scores_gemma":[0.00276508,0.009192523,0.003200477,0.01812746,0.01754723,0.0003003173,0.002305125,0.0009204504,0.9456413],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.1209711,"threshold_uncertainty_score":0.4046888,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04661098129259345,"score_gpt":0.2895291604763159,"score_spread":0.2429181791837224,"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."}}