{"doi":"10.1515/dmdi-2020-0135","title":"Genotype-driven pharmacokinetic simulations of warfarin levels in Puerto Ricans","abstract":"Objectives The inter-individual variability of warfarin dosing has been linked to genetic polymorphisms. This study was aimed at performing genotype-driven pharmacokinetic (PK) simulations to predict warfarin levels in Puerto Ricans. Methods Analysis of each individual dataset was performed by one-compartmental modeling using WinNonlin®v6.4. The k e of warfarin given a cytochrome P450 2C9 (CYP2C9) genotype ranged from 0.0189 to 0.0075 h-1. K a and V d parameters were taken from literature. Data from 128 subjects were divided into two groups (i.e., wild-types and carriers) and statistical analyses of PK parameters were performed by unpaired t-tests. Results In the carrier group (n=64), 53 subjects were single-carriers and 11 double-carriers (i.e., *2/*2, *2/*3, *2/*5, *3/*5, and *3/*8). The mean peak concentration (Cmax) was higher for wild-type (0.36±0.12 vs. 0.32±0.14 mg/L). Likewise, the average clearance (CL) parameter was faster among non-carriers (0.22±0.03 vs. 0.17±0.05 L/h; p=0.0001), with also lower area under the curve (AUC) when compared to carriers (20.43±6.97 vs. 24.78±11.26 h mg/L; p=0.025). Statistical analysis revealed a significant difference between groups with regard to AUC and CL, but not for Cmax. This can be explained by the variation of k e across different genotypes. Conclusions The results provided useful information for warfarin dosing predictions that take into consideration important individual PK and genotyping data.","journal":"Drug Metabolism and Personalized Therapy","year":2020,"id":117167,"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":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.8522,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":547306,"name":"Camila de las Barreras","orcid":null,"position":1,"is_corresponding":false},{"id":547307,"name":"Gledys Reynaldo","orcid":null,"position":2,"is_corresponding":false},{"id":546699,"name":"Leyanis Rodríguez‐Vera","orcid":"0000-0003-1688-7008","position":3,"is_corresponding":false},{"id":546700,"name":"Cornelis P. Vlaar","orcid":"0000-0001-5145-8300","position":4,"is_corresponding":false},{"id":547308,"name":"Vilmali Lopez Mejias","orcid":null,"position":5,"is_corresponding":false},{"id":546701,"name":"Jean‐Christophe M. Monbaliu","orcid":"0000-0001-6916-8846","position":6,"is_corresponding":false},{"id":546702,"name":"Torsten Stelzer","orcid":"0000-0003-3881-0183","position":7,"is_corresponding":false},{"id":430431,"name":"Víctor Mangas‐Sanjuán","orcid":"0000-0002-3388-5023","position":8,"is_corresponding":false},{"id":262348,"name":"Jorgé Duconge","orcid":"0000-0002-5955-3449","position":9,"is_corresponding":false},{"id":547305,"name":"Stephanie Reyes-González","orcid":null,"position":0,"is_corresponding":true}],"reference_count":16,"raw_metadata":null,"created_at":"2026-07-18T23:13:51.309289Z","pmid":"32809952","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":[]}