{"id":"W2892257705","doi":"10.1016/j.xphs.2018.09.001","title":"Application of the Tissue Composition–Based Model to Minipig for Predicting the Volume of Distribution at Steady State and Dermis-to-Plasma Partition Coefficients of Drugs Used in the Physiologically Based Pharmacokinetics Model in Dermatology","year":2018,"lang":"en","type":"article","venue":"Journal of Pharmaceutical Sciences","topic":"Advancements in Transdermal Drug Delivery","field":"Pharmacology, Toxicology and Pharmaceutics","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal; Université du Québec","funders":"","keywords":"Pharmacokinetics; Volume of distribution; Distribution (mathematics); Partition (number theory); Partition coefficient; Dermis; Volume (thermodynamics); Plasma volume; Chemistry; Chromatography; Pharmacology; Medicine; Pathology; Mathematics; Internal medicine; Thermodynamics","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":[],"consensus_categories":[],"category_scores_codex":[0.001929085,0.0001446285,0.0002951364,0.00009372973,0.0002768609,0.00001051828,0.0006262961,0.00008667586,0.00002393005],"category_scores_gemma":[0.0001283614,0.00008499232,0.0000707296,0.0005592478,0.001194598,0.000100125,0.00007921358,0.000319226,0.000001012417],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007289084,"about_ca_system_score_gemma":0.0001227586,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000055288,"about_ca_topic_score_gemma":0.00001866332,"domain_scores_codex":[0.9975777,0.0005960154,0.0008488375,0.0002085901,0.0004448543,0.0003240017],"domain_scores_gemma":[0.997926,0.001038925,0.0005043525,0.0001157453,0.0003099293,0.0001050589],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001075102,0.0004853478,0.008473475,0.00007674455,0.00001549874,7.438505e-7,0.00111657,0.7008158,0.2852033,0.00009373705,0.0001358131,0.002507839],"study_design_scores_gemma":[0.001515836,0.0002641756,0.00196058,0.00004093929,0.00007442324,0.000004707908,0.0001306603,0.7406133,0.2547351,0.0002271821,0.0003681569,0.00006492729],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8211203,0.00004259349,0.1746149,0.002940752,0.0001252246,0.0008971493,0.0002328343,0.000002782924,0.00002350135],"genre_scores_gemma":[0.9969321,0.00002129259,0.001459001,0.001494174,0.00002982651,0.00004679395,0.000006346459,0.000005657807,0.000004821845],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1758118,"threshold_uncertainty_score":0.4401546,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09620619774965006,"score_gpt":0.4395507109685294,"score_spread":0.3433445132188794,"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."}}