{"id":"W4390405127","doi":"10.1002/psp4.13102","title":"Maximum likelihood estimation of renal transporter ontogeny profiles for pediatric PBPK modeling","year":2023,"lang":"en","type":"article","venue":"CPT Pharmacometrics & Systems Pharmacology","topic":"Drug Transport and Resistance Mechanisms","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Institute of Child Health and Human Development; Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Institute of Diabetes and Digestive and Kidney Diseases; University of Utah; Brigham Young University","keywords":"Physiologically based pharmacokinetic modelling; Ontogeny; Pharmacokinetics; Furosemide; Biology; Transporter; Meropenem; Pharmacology; Internal medicine; Medicine; Endocrinology; Gene","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"],"consensus_categories":[],"category_scores_codex":[0.00130593,0.0003876463,0.0009648626,0.002453467,0.0001555381,0.00001859808,0.000296943,0.0002610267,0.0001314824],"category_scores_gemma":[0.00003866544,0.0003531578,0.000392262,0.003677125,0.00005113316,0.0001676164,0.0000257451,0.0003446131,0.00005019144],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001378401,"about_ca_system_score_gemma":0.00030296,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002295823,"about_ca_topic_score_gemma":0.000001066804,"domain_scores_codex":[0.9965756,0.00008354918,0.001259179,0.0005945045,0.00064092,0.0008462359],"domain_scores_gemma":[0.9982831,0.0002374239,0.0003825056,0.0002429344,0.0004889337,0.000365067],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003865654,0.001951689,0.01079419,0.01892578,0.001918231,0.0005610146,0.001550996,0.141029,0.7762089,0.0004796105,0.02522737,0.01748755],"study_design_scores_gemma":[0.01535159,0.0007186974,0.0009603614,0.0001753611,0.004358327,0.00007591102,0.0002798073,0.8527172,0.119445,0.0004346534,0.004718035,0.000765083],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8025568,0.003288954,0.1845984,0.0003571144,0.004734009,0.003451313,0.0003405027,0.0004477181,0.0002252926],"genre_scores_gemma":[0.9943193,0.001224988,0.001438172,0.0001528899,0.001130384,0.0006627017,0.0004663802,0.0001042859,0.0005009578],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7116882,"threshold_uncertainty_score":0.9998921,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04037110607239169,"score_gpt":0.3314192358834291,"score_spread":0.2910481298110374,"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."}}