{"doi":"10.3389/fphar.2025.1694886","title":"Strategy advancements in placental pharmacokinetics: from in vitro experiments to in silico prediction","abstract":"<jats:sec>\n                    <jats:title>Background</jats:title>\n                    <jats:p>\n                      The placental barrier is a critical interface that regulates drug transport between maternal and fetal circulation and is an important component in assessing fetal drug-exposure risk. Since pregnant women are often excluded from clinical trials, pharmacokinetic (PK) analysis data on placental drug transport remain limited. Currently,\n                      <jats:italic>in vitro</jats:italic>\n                      experiments and\n                      <jats:italic>in silico</jats:italic>\n                      simulation strategies are the primary and effective means for understanding drug transport across the placenta.\n                    </jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Method</jats:title>\n                    <jats:p>\n                      Various\n                      <jats:italic>in vitro</jats:italic>\n                      experimental methods, including cell monolayer models,\n                      <jats:italic>ex vivo</jats:italic>\n                      placental perfusion, and organ-on-a-chip platforms, along with model-based computational simulations, were systematically reviewed. The advantages, limitations, and potential future applications of these methods were evaluated.\n                    </jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Result</jats:title>\n                    <jats:p>\n                      A total of seven studies using cell models, 28 employing\n                      <jats:italic>ex vivo</jats:italic>\n                      perfusion, six utilizing placenta-on-a-chip technology, and 39 focusing on\n                      <jats:italic>in silico</jats:italic>\n                      simulations, were identified, involving 8, 34, 5, and 42 drugs, respectively. Antiviral agents, antibiotics, and opioids were the most frequently investigated drug types. Overall,\n                      <jats:italic>in silico</jats:italic>\n                      simulations informed by\n                      <jats:italic>in vitro</jats:italic>\n                      data as baseline parameters and constraints demonstrated higher predictive accuracy. Integrating multi-model data was shown to be a reliable strategy for improving the precision of placental PK studies.\n                    </jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion</jats:title>\n                    <jats:p>This review highlights the current strategies in placental PK research and supports safer drug use during pregnancy. Multi-model data integration is essential for developing reliable and quantitative fetal drug-exposure assessment frameworks, thus addressing data gaps caused by the exclusion of pregnant women from clinical trials.</jats:p>\n                  </jats:sec>","journal":"Frontiers in Pharmacology","year":2025,"id":629559,"datarank":0.20794415416798362,"base_score":1.3862943611198906,"endowment":1.3862943611198906,"self_citation_contribution":0.20794415416798362,"citation_network_contribution":0.0,"self_endowment_contribution":0.20794415416798362,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":3,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1156603,"name":"Yue Wu","orcid":"0000-0002-0084-2510","position":1,"is_corresponding":false},{"id":1630508,"name":"Siyu Zeng","orcid":null,"position":2,"is_corresponding":false},{"id":1390824,"name":"Fei Wang","orcid":"0000-0002-1122-5286","position":3,"is_corresponding":false},{"id":666731,"name":"Jiao Zhang","orcid":"0000-0002-2881-7755","position":4,"is_corresponding":false},{"id":1630509,"name":"Shiran Li","orcid":null,"position":5,"is_corresponding":false},{"id":533515,"name":"Yong Yang","orcid":"0000-0002-9928-7165","position":6,"is_corresponding":false},{"id":1183287,"name":"Yujie Yang","orcid":"0000-0002-8485-0437","position":7,"is_corresponding":false},{"id":111736,"name":"Zhimin Li","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Strategy advancements in placental pharmacokinetics: from in vitro experiments to in silico prediction","abstract":"<jats:sec>\n                    <jats:title>Background</jats:title>\n                    <jats:p>\n                      The placental barrier is a critical interface that regulates drug transport between maternal and fetal circulation and is an important component in assessing fetal drug-exposure risk. Since pregnant women are often excluded from clinical trials, pharmacokinetic (PK) analysis data on placental drug transport remain limited. Currently,\n                      <jats:italic>in vitro</jats:italic>\n                      experiments and\n                      <jats:italic>in silico</jats:italic>\n                      simulation strategies are the primary and effective means for understanding drug transport across the placenta.\n                    </jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Method</jats:title>\n                    <jats:p>\n                      Various\n                      <jats:italic>in vitro</jats:italic>\n                      experimental methods, including cell monolayer models,\n                      <jats:italic>ex vivo</jats:italic>\n                      placental perfusion, and organ-on-a-chip platforms, along with model-based computational simulations, were systematically reviewed. The advantages, limitations, and potential future applications of these methods were evaluated.\n                    </jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Result</jats:title>\n                    <jats:p>\n                      A total of seven studies using cell models, 28 employing\n                      <jats:italic>ex vivo</jats:italic>\n                      perfusion, six utilizing placenta-on-a-chip technology, and 39 focusing on\n                      <jats:italic>in silico</jats:italic>\n                      simulations, were identified, involving 8, 34, 5, and 42 drugs, respectively. Antiviral agents, antibiotics, and opioids were the most frequently investigated drug types. Overall,\n                      <jats:italic>in silico</jats:italic>\n                      simulations informed by\n                      <jats:italic>in vitro</jats:italic>\n                      data as baseline parameters and constraints demonstrated higher predictive accuracy. Integrating multi-model data was shown to be a reliable strategy for improving the precision of placental PK studies.\n                    </jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion</jats:title>\n                    <jats:p>This review highlights the current strategies in placental PK research and supports safer drug use during pregnancy. Multi-model data integration is essential for developing reliable and quantitative fetal drug-exposure assessment frameworks, thus addressing data gaps caused by the exclusion of pregnant women from clinical trials.</jats:p>\n                  </jats:sec>","is_dataset_classified":null,"base_score":1.3862943611198906,"endowment":1.3862943611198906,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"41221050","pmcid":"PMC12597936","openalex_id":"https://openalex.org/W4415567219","authors":[],"funders":[],"total_grants":0,"fwci":3.4655,"citation_percentile":0.92975847,"influential_citations":0,"citation_trend":[{"year":2026,"count":3}],"oa_status":"gold","license":"cc-by","oa_locations":[{"url":"https://public-pages-files-2025.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2025.1694886/pdf","host_type":"journal"},{"url":"https://public-pages-files-2025.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2025.1694886/pdf","host_type":"publisher"},{"url":"https://www.frontiersin.org/articles/10.3389/fphar.2025.1694886/full","host_type":"publisher"},{"url":"https://doi.org/10.3389/fphar.2025.1694886","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/41221050","host_type":"repository"},{"url":"https://doaj.org/article/ee6b2172fdb64304938355ad379023e8","host_type":"repository"},{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/12597936","host_type":"repository"},{"url":"https://europepmc.org/articles/PMC12597936","host_type":"Europe_PMC"},{"url":"https://europepmc.org/articles/PMC12597936?pdf=render","host_type":"Europe_PMC"}],"fields_of_study":["Pregnancy and preeclampsia studies","Pregnancy and Medication Impact","Preterm Birth and Chorioamnionitis"],"mesh_terms":[],"keywords":["In silico","Drug","Placenta","In vitro","SAFER","Drug discovery","Pharmacokinetics","in vitro model","In Silico Simulation","Ex Vivo Placental Perfusion","Placental Drug Transfer"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-05T19:02:57.268694Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}