{"id":"W7128773366","doi":"10.1080/17576180.2026.2617084","title":"2025 White Paper on Recent Issues in Bioanalysis: What is the Future of Bioanalytical LIMS? AI/ML Integration in Bioanalysis; Tear Sample Collection; Radiolabeled Mass Balance Studies; Chiral Assays; Bioanalysis of Antibody-Oligonucleotide &amp; Bicycle Drug Conjugates ( <u>PART 1A</u> – Recommendations on Mass Spectrometry Assays, Chromatography, Sample Preparation and Regulated Bioanalysis Sampling, Validating, Analyzing &amp; Reporting <u>PART 1B</u> – Regulatory Agencies’ Input on Regulated Bioanalysis/BMV)","year":2025,"lang":"en","type":"article","venue":"Bioanalysis","topic":"Biosimilars and Bioanalytical Methods","field":"Immunology and Microbiology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Health Canada","funders":"Agência Nacional de Vigilância Sanitária","keywords":"Bioanalysis; White paper; Excellence; Harmonization; Agency (philosophy); Sample (material); Best practice; Regulatory science","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","bibliometrics","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.01004479,0.001911717,0.00568453,0.01028064,0.00125812,0.0005572666,0.001358744,0.001375402,0.001474592],"category_scores_gemma":[0.005412085,0.001509856,0.002687762,0.03119527,0.001860829,0.000894031,0.00039811,0.001901617,0.00002827439],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001241739,"about_ca_system_score_gemma":0.0004103453,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002780445,"about_ca_topic_score_gemma":0.01073673,"domain_scores_codex":[0.9806985,0.004762881,0.008056151,0.003572127,0.001193766,0.001716602],"domain_scores_gemma":[0.984283,0.003655746,0.006040845,0.003753848,0.001978935,0.0002876315],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.005594717,0.007000209,0.6599084,0.0009827607,0.1158407,0.00004191913,0.004731752,0.008670734,0.135983,0.004663901,0.01355836,0.04302363],"study_design_scores_gemma":[0.02271453,0.004357798,0.1079965,0.0167421,0.1198226,0.00006504367,0.06174859,0.08853851,0.3141316,0.01677717,0.2331972,0.01390829],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7544989,0.15922,0.01008463,0.06540599,0.002422473,0.004326586,0.00231546,0.0007082419,0.001017683],"genre_scores_gemma":[0.8913988,0.07570135,0.02108935,0.001936574,0.0002957476,0.0002128664,0.004220182,0.0001860917,0.004959016],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5519118,"threshold_uncertainty_score":0.999921,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02992762262156837,"score_gpt":0.3526246322477707,"score_spread":0.3226970096262023,"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."}}