{"id":"W284264172","doi":"10.1016/j.ejpb.2015.04.028","title":"Optimization of controlled release nanoparticle formulation of verapamil hydrochloride using artificial neural networks with genetic algorithm and response surface methodology","year":2015,"lang":"en","type":"article","venue":"European Journal of Pharmaceutics and Biopharmaceutics","topic":"Advancements in Transdermal Drug Delivery","field":"Pharmacology, Toxicology and Pharmaceutics","cited_by":99,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Response surface methodology; Artificial neural network; Central composite design; Biological system; Materials science; Particle size; Nanoparticle; Particle swarm optimization; Mathematics; Computer science; Algorithm; Chemistry; Chromatography; Chemical engineering; Nanotechnology; Machine learning; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003047457,0.0003858221,0.0004303575,0.0002028092,0.0001350001,0.0004426932,0.0002799976,0.0004372525,0.0003860428],"category_scores_gemma":[0.0003804274,0.0002175366,0.0003851626,0.0001793227,0.0001385217,0.0003173599,0.0001683663,0.000294053,0.00008835195],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000359595,"about_ca_system_score_gemma":0.0002976249,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001223723,"about_ca_topic_score_gemma":0.001725016,"domain_scores_codex":[0.9998571,0.00002491094,0.00001079892,0.00003566364,0.00005318511,0.00001827137],"domain_scores_gemma":[0.9999073,0.00003222515,0.00002003827,0.000003657044,0.00003230226,0.000004497773],"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":[0.0004718653,0.0004208438,0.0009054787,0.0004935031,0.00007842504,0.0002115702,0.0000616447,0.2868581,0.649187,0.001313893,0.0004008213,0.059597],"study_design_scores_gemma":[0.00004220604,0.0008552239,0.001072199,0.00001187431,0.00006628015,0.0000582875,0.00003035991,0.7644612,0.2318174,0.0001819687,0.001377783,0.00002517466],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.8535444,0.00167846,0.1381798,0.0001791213,0.0001308626,0.0001599799,0.00007295586,0.0002268737,0.005827696],"genre_scores_gemma":[0.9429039,0.0005580788,0.05405751,0.00003822245,0.00001075887,0.000105471,0.00005556329,0.00003180302,0.002238566],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.001223723,"threshold_uncertainty_score":0.002609074,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2087789203769396,"score_gpt":0.4196622517784016,"score_spread":0.2108833314014619,"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."}}