{"id":"W4388971725","doi":"10.3390/pharmaceutics15122667","title":"Enhancement of Skin Permeability Prediction through PBPK Modeling, Bayesian Inference, and Experiment Design","year":2023,"lang":"en","type":"article","venue":"Pharmaceutics","topic":"Advancements in Transdermal Drug Delivery","field":"Pharmacology, Toxicology and Pharmaceutics","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Beiersdorf; Procter and Gamble","keywords":"Physiologically based pharmacokinetic modelling; Markov chain Monte Carlo; Bayesian inference; Computer science; Bayesian probability; Inference; Permeability (electromagnetism); Workflow; Biological system; Markov chain; Chemistry; Machine learning; Pharmacokinetics; Artificial intelligence; Bioinformatics; Database","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":[0.001049711,0.0003542437,0.0003801897,0.0001474125,0.0002784793,0.00001632553,0.0002882246,0.0002525448,0.0009076076],"category_scores_gemma":[0.00007478019,0.0003689615,0.00009399024,0.0004216979,0.0003676231,0.0002980764,0.0001909074,0.0006479862,0.0000688095],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001307265,"about_ca_system_score_gemma":0.00009949889,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001685352,"about_ca_topic_score_gemma":0.000001779844,"domain_scores_codex":[0.9971396,0.0005262219,0.0007456481,0.0005498256,0.0003947142,0.0006439573],"domain_scores_gemma":[0.9987134,0.0004174701,0.0001680872,0.0003072877,0.000165118,0.0002286354],"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":[0.002549311,0.002890683,0.01410015,0.00125041,0.001418036,0.00007047101,0.02682489,0.4219089,0.4423313,0.003272928,0.00436849,0.07901446],"study_design_scores_gemma":[0.00175934,0.0001659596,0.00008087114,0.00002718309,0.0001921217,0.000004162483,0.0004504095,0.6192955,0.3617716,0.002098177,0.01388911,0.0002655055],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5101291,0.00168024,0.4820527,0.0004508482,0.001668126,0.001507965,0.0001880421,0.0003294953,0.001993424],"genre_scores_gemma":[0.9869279,0.007398288,0.004309019,0.0006119332,0.0001419397,0.000201727,0.00005929447,0.00004121367,0.0003087366],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4777437,"threshold_uncertainty_score":0.9998763,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2441837813404232,"score_gpt":0.4833379894187339,"score_spread":0.2391542080783107,"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."}}