{"id":"https://openalex.org/W4410628604","doi":"https://doi.org/10.3390/make7020047","title":"Artificial Intelligence Meets Bioequivalence: Using Generative Adversarial Networks for Smarter, Smaller Trials","display_name":"Artificial Intelligence Meets Bioequivalence: Using Generative Adversarial Networks for Smarter, Smaller Trials","publication_year":2025,"publication_date":"2025-05-23","ids":{"openalex":"https://openalex.org/W4410628604","doi":"https://doi.org/10.3390/make7020047"},"language":"en","primary_location":{"id":"doi:10.3390/make7020047","is_oa":true,"landing_page_url":"https://doi.org/10.3390/make7020047","pdf_url":"https://www.mdpi.com/2504-4990/7/2/47/pdf?version=1748006990","source":{"id":"https://openalex.org/S4210213891","display_name":"Machine Learning and Knowledge Extraction","issn_l":"2504-4990","issn":["2504-4990"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning and Knowledge Extraction","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2504-4990/7/2/47/pdf?version=1748006990","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5111213367","display_name":"Anastasios Nikolopoulos","orcid":"https://orcid.org/0009-0009-0828-315X"},"institutions":[{"id":"https://openalex.org/I200777214","display_name":"National and Kapodistrian University of Athens","ror":"https://ror.org/04gnjpq42","country_code":"GR","type":"education","lineage":["https://openalex.org/I200777214"]}],"countries":["GR"],"is_corresponding":false,"raw_author_name":"Anastasios Nikolopoulos","raw_affiliation_strings":["Department of Pharmacy, School of Health Sciences, National and Kapodistrian University of Athens, 15784 Athens, Greece"],"raw_orcid":"https://orcid.org/0009-0009-0828-315X","affiliations":[{"raw_affiliation_string":"Department of Pharmacy, School of Health Sciences, National and Kapodistrian University of Athens, 15784 Athens, Greece","institution_ids":["https://openalex.org/I200777214"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5016554880","display_name":"Vangelis Karalis","orcid":"https://orcid.org/0000-0003-0492-0712"},"institutions":[{"id":"https://openalex.org/I200777214","display_name":"National and Kapodistrian University of Athens","ror":"https://ror.org/04gnjpq42","country_code":"GR","type":"education","lineage":["https://openalex.org/I200777214"]},{"id":"https://openalex.org/I8901234","display_name":"Foundation for Research and Technology Hellas","ror":"https://ror.org/052rphn09","country_code":"GR","type":"facility","lineage":["https://openalex.org/I8901234"]}],"countries":["GR"],"is_corresponding":true,"raw_author_name":"Vangelis D. Karalis","raw_affiliation_strings":["Department of Pharmacy, School of Health Sciences, National and Kapodistrian University of Athens, 15784 Athens, Greece","Institute of Applied and Computational Mathematics, Foundation for Research and Technology Hellas (FORTH), 70013 Heraklion, Greece"],"raw_orcid":"https://orcid.org/0000-0003-0492-0712","affiliations":[{"raw_affiliation_string":"Department of Pharmacy, School of Health Sciences, National and Kapodistrian University of Athens, 15784 Athens, Greece","institution_ids":["https://openalex.org/I200777214"]},{"raw_affiliation_string":"Institute of Applied and Computational Mathematics, Foundation for Research and Technology Hellas (FORTH), 70013 Heraklion, Greece","institution_ids":["https://openalex.org/I8901234"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5016554880"],"corresponding_institution_ids":["https://openalex.org/I200777214","https://openalex.org/I8901234"],"apc_list":{"value":1400,"currency":"CHF","value_usd":1515},"apc_paid":{"value":1400,"currency":"CHF","value_usd":1515},"fwci":0.9651,"has_fulltext":true,"cited_by_count":2,"citation_normalized_percentile":{"value":0.79492542,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":"7","issue":"2","first_page":"47","last_page":"47"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.9973000288009644,"subfield":{"id":"https://openalex.org/subfields/2718","display_name":"Health Informatics"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.9973000288009644,"subfield":{"id":"https://openalex.org/subfields/2718","display_name":"Health Informatics"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T13280","display_name":"Biomedical and Engineering Education","score":0.9850999712944031,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12859","display_name":"Cell Image Analysis Techniques","score":0.9815999865531921,"subfield":{"id":"https://openalex.org/subfields/1304","display_name":"Biophysics"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.7539136409759521},{"id":"https://openalex.org/keywords/bioequivalence","display_name":"Bioequivalence","score":0.7528196573257446},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.6528230905532837},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5795238018035889},{"id":"https://openalex.org/keywords/generative-adversarial-network","display_name":"Generative adversarial network","score":0.5424159169197083},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.44980940222740173},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.38958826661109924},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.34720462560653687},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.1687926948070526},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.13144519925117493}],"concepts":[{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.7539136409759521},{"id":"https://openalex.org/C42404028","wikidata":"https://www.wikidata.org/wiki/Q864940","display_name":"Bioequivalence","level":3,"score":0.7528196573257446},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.6528230905532837},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5795238018035889},{"id":"https://openalex.org/C2988773926","wikidata":"https://www.wikidata.org/wiki/Q25104379","display_name":"Generative adversarial network","level":3,"score":0.5424159169197083},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.44980940222740173},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38958826661109924},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.34720462560653687},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.1687926948070526},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.13144519925117493},{"id":"https://openalex.org/C98274493","wikidata":"https://www.wikidata.org/wiki/Q128406","display_name":"Pharmacology","level":1,"score":0.0},{"id":"https://openalex.org/C181389837","wikidata":"https://www.wikidata.org/wiki/Q461809","display_name":"Bioavailability","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.3390/make7020047","is_oa":true,"landing_page_url":"https://doi.org/10.3390/make7020047","pdf_url":"https://www.mdpi.com/2504-4990/7/2/47/pdf?version=1748006990","source":{"id":"https://openalex.org/S4210213891","display_name":"Machine Learning and Knowledge Extraction","issn_l":"2504-4990","issn":["2504-4990"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning and Knowledge Extraction","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:e175695afddc475d81f0c1441399e353","is_oa":true,"landing_page_url":"https://doaj.org/article/e175695afddc475d81f0c1441399e353","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Machine Learning and Knowledge Extraction, Vol 7, Iss 2, p 47 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/make7020047","is_oa":true,"landing_page_url":"https://doi.org/10.3390/make7020047","pdf_url":"https://www.mdpi.com/2504-4990/7/2/47/pdf?version=1748006990","source":{"id":"https://openalex.org/S4210213891","display_name":"Machine Learning and Knowledge Extraction","issn_l":"2504-4990","issn":["2504-4990"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning and Knowledge Extraction","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4410628604.pdf","grobid_xml":"https://content.openalex.org/works/W4410628604.grobid-xml"},"referenced_works_count":42,"referenced_works":["https://openalex.org/W2038287973","https://openalex.org/W2049509720","https://openalex.org/W2466033159","https://openalex.org/W2896893468","https://openalex.org/W2911835721","https://openalex.org/W2940562610","https://openalex.org/W2953532875","https://openalex.org/W2962411443","https://openalex.org/W2975317124","https://openalex.org/W2988347076","https://openalex.org/W3092253034","https://openalex.org/W3094001604","https://openalex.org/W3106033826","https://openalex.org/W3109650690","https://openalex.org/W3118650257","https://openalex.org/W3121512587","https://openalex.org/W3135096391","https://openalex.org/W3137814255","https://openalex.org/W3140760072","https://openalex.org/W3158500763","https://openalex.org/W3202819594","https://openalex.org/W3205073382","https://openalex.org/W3215969524","https://openalex.org/W4205164650","https://openalex.org/W4224255787","https://openalex.org/W4226323522","https://openalex.org/W4283330395","https://openalex.org/W4290975288","https://openalex.org/W4385420510","https://openalex.org/W4387457231","https://openalex.org/W4388622621","https://openalex.org/W4391898704","https://openalex.org/W4396742831","https://openalex.org/W4396898756","https://openalex.org/W4399055412","https://openalex.org/W4399802736","https://openalex.org/W4399934508","https://openalex.org/W4402670893","https://openalex.org/W4403956415","https://openalex.org/W4407725330","https://openalex.org/W4408635724","https://openalex.org/W6869610026"],"related_works":["https://openalex.org/W2018887812","https://openalex.org/W2044291706","https://openalex.org/W2145901263","https://openalex.org/W2888032422","https://openalex.org/W2996316059","https://openalex.org/W4377980832","https://openalex.org/W2897769091","https://openalex.org/W2845413374","https://openalex.org/W3005996785","https://openalex.org/W4235873501"],"abstract_inverted_index":{"This":[0,195,213],"study":[1,83,204,214],"introduces":[2],"artificial":[3],"intelligence":[4],"as":[5,161,163],"a":[6],"powerful":[7],"tool":[8],"to":[9,25,159,220],"transform":[10],"bioequivalence":[11],"(BE)":[12],"trials.":[13],"We":[14],"apply":[15],"advanced":[16],"generative":[17,76],"models,":[18],"specifically":[19],"Wasserstein":[20],"Generative":[21],"Adversarial":[22],"Networks":[23],"(WGANs),":[24],"create":[26],"virtual":[27,120,177],"subjects":[28,114],"and":[29,122,148,183,209,224],"reduce":[30],"the":[31,55,73,101,111,117,127,139,142,145,153,166,192,216],"need":[32],"for":[33,116],"real":[34],"human":[35],"participants":[36],"in":[37,79,132,228],"generic":[38],"drug":[39],"assessment.":[40],"Although":[41],"BE":[42,80,93,180,203,229],"studies":[43],"typically":[44],"involve":[45],"small":[46],"sample":[47,109,205],"sizes":[48],"(usually":[49],"24":[50],"subjects),":[51],"which":[52,157],"may":[53],"limit":[54],"use":[56],"of":[57,75,88,100,113,119,126,141,165,191,218],"AI-generated":[58],"populations,":[59],"our":[60],"findings":[61],"show":[62,72],"that":[63,164,173,186],"these":[64],"models":[65],"can":[66],"successfully":[67],"overcome":[68],"this":[69,82],"challenge.":[70],"To":[71],"utility":[74],"AI":[77],"algorithms":[78],"testing,":[81],"applied":[84],"Monte":[85],"Carlo":[86],"simulations":[87],"2":[89,91],"\u00d7":[90],"crossover":[92],"trials,":[94],"combined":[95],"with":[96,144,179],"WGANs.":[97],"After":[98],"training":[99],"WGAN":[102],"model,":[103],"several":[104],"scenarios":[105],"were":[106],"explored,":[107],"including":[108],"size,":[110],"proportion":[112],"used":[115],"synthesis":[118],"subjects,":[121],"variabilities.":[123],"The":[124,170],"performance":[125,143],"AI-synthesized":[128],"populations":[129,178],"was":[130],"tested":[131],"two":[133],"ways:":[134],"(a)":[135],"first,":[136],"by":[137,151],"assessing":[138],"similarity":[140,184],"actual":[146],"population,":[147],"(b)":[149],"second,":[150],"evaluating":[152],"statistical":[154],"power":[155],"achieved,":[156],"aimed":[158],"be":[160],"high":[162],"entire":[167],"original":[168,193],"population.":[169,194],"results":[171],"demonstrated":[172],"WGANs":[174,219],"could":[175],"generate":[176],"acceptance":[181],"percentages":[182],"levels":[185],"matched":[187],"or":[188],"exceeded":[189],"those":[190],"approach":[196],"proved":[197],"effective":[198],"across":[199],"various":[200],"scenarios,":[201],"enhancing":[202],"sizes,":[206],"reducing":[207],"costs,":[208],"accelerating":[210],"trial":[211],"durations.":[212],"highlights":[215],"potential":[217],"improve":[221],"data":[222],"augmentation":[223],"optimize":[225],"subject":[226],"recruitment":[227],"studies.":[230]},"counts_by_year":[{"year":2026,"cited_by_count":2}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}
