{"doi":"10.1111/bcp.15756","title":"Using seconds‐resolved pharmacokinetic datasets to assess pharmacokinetic models encompassing time‐varying physiology","abstract":"AIM: Pharmacokinetics have historically been assessed using drug concentration data obtained via blood draws and bench-top analysis. The cumbersome nature of these typically constrains studies to at most a dozen concentration measurements per dosing event. This, in turn, limits our statistical power in the detection of hours-scale, time-varying physiological processes. Given the recent advent of in vivo electrochemical aptamer-based (EAB) sensors, however, we can now obtain hundreds of concentration measurements per administration. Our aim in this paper was to assess the ability of these time-dense datasets to describe time-varying pharmacokinetic models with good statistical significance. METHODS: We used seconds-resolved measurements of plasma tobramycin concentrations in rats to statistically compare traditional one- and two-compartmental pharmacokinetic models to new models in which the proportional relationship between a drug's plasma concentration and its elimination rate varies in response to changing kidney function. RESULTS: We found that a modified one-compartment model in which the proportionality between the plasma concentration of tobramycin and its elimination rate falls reciprocally with time either meets or is preferred over the standard two-compartment pharmacokinetic model for half of the datasets characterized. When we reduced the impact of the drug's rapid distribution phase on the model, this one-compartment, time-varying model was statistically preferred over the standard one-compartment model for 80% of our datasets. CONCLUSIONS: Our results highlight both the impact that simple physiological changes (such as varying kidney function) can have on drug pharmacokinetics and the ability of high-time resolution EAB sensor measurements to identify such impacts.","journal":"British Journal of Clinical Pharmacology","year":2023,"id":349416,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":10,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9145,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1092602,"name":"Sophie L. Stocker","orcid":"0000-0002-2114-587X","position":1,"is_corresponding":false},{"id":542602,"name":"Tod E. Kippin","orcid":"0000-0003-3216-8863","position":2,"is_corresponding":false},{"id":1013164,"name":"Wendy Meiring","orcid":"0000-0002-5843-6662","position":3,"is_corresponding":false},{"id":459159,"name":"Kevin W. Plaxco","orcid":"0000-0003-4772-8771","position":4,"is_corresponding":false},{"id":1032435,"name":"Matthew H. McDonough","orcid":"0000-0001-5931-124X","position":0,"is_corresponding":true}],"reference_count":31,"raw_metadata":null,"created_at":"2026-07-19T01:12:14.919810Z","pmid":"37186478","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":[]}