{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01730344,0.001398135,0.001012411,0.001560388,0.002806883,0.01217162,0.0039417,0.01451123,0.056222],"category_scores_gemma":[0.01315609,0.0009164085,0.001208564,0.001646202,0.002940722,0.007128135,0.002713046,0.009495951,0.04639795],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005181414,"about_ca_system_score_gemma":0.01553619,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007237586,"about_ca_topic_score_gemma":0.01353547,"domain_scores_codex":[0.9901187,0.001362596,0.0006805488,0.001208353,0.005652425,0.0009772911],"domain_scores_gemma":[0.9789597,0.003151876,0.001304383,0.000721461,0.01183556,0.004026946],"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":[0.00004187257,0.0000294199,0.00006487497,0.0001980095,0.000004582806,0.00006607633,0.00004391687,0.00004620594,0.0008160042,0.003656673,0.9577118,0.03732064],"study_design_scores_gemma":[0.000003668452,0.0000154055,0.0001036198,0.0001502789,0.000002097438,0.0000246434,0.00004020863,0.00003825868,0.0002877087,0.0007912296,0.9985369,0.000006020569],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"other","genre_scores_codex":[0.001022108,0.07247581,0.005441159,0.4796178,0.2823243,0.0003992846,0.001369538,0.0006352554,0.1567148],"genre_scores_gemma":[0.005814916,0.04364451,0.006911217,0.1891135,0.07779299,0.0002734391,0.001966748,0.0008916926,0.673591],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.056222,"threshold_uncertainty_score":0.1880813,"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."}}