6533b85efe1ef96bd12c06ac
RESEARCH PRODUCT
Population pharmacokinetic model of lithium and drug compliance assessment.
Isabel Pérez-castellóVictor Mangas-sanjuanIgnacio González-garcíaIñaki F. TrocónizIsabel González-álvarezJose Luis Marco-garbayoMarival Bermejosubject
OncologyAdultMalemedicine.medical_specialtyBipolar DisorderPopulationPopulationchemistry.chemical_elementRenal functionBiological AvailabilityLithium030226 pharmacology & pharmacy03 medical and health sciencesYoung Adult0302 clinical medicinePharmacokineticsAntimanic AgentsInternal medicineStatisticsCovariateMedicineHumansPharmacology (medical)educationBiological PsychiatryPharmacologyeducation.field_of_studyModels Statisticalmedicine.diagnostic_testDose-Response Relationship Drugbusiness.industryMiddle AgedMarkov ChainsNONMEMBioavailabilityPsychiatry and Mental healthNeurologychemistryTherapeutic drug monitoringLithium CompoundsPatient ComplianceLithiumFemaleNeurology (clinical)Drug Monitoringbusiness030217 neurology & neurosurgerydescription
Population pharmacokinetic analysis of lithium during therapeutic drug monitoring and drug compliance assessment was performed in 54 patients and 246 plasma concentrations levels were included in this study. Patients received several treatment cycles (1-9) and one plasma concentration measurement for each patient was obtained always before starting next cycle (pre-dose) at steady state. Data were analysed using the population approach with NONMEM version 7.2. Lithium measurements were described using a two-compartment model (CL/F=0.41Lh-1, V1/F=15.3L, Q/F=0.61Lh-1, and V2/F = 15.8L) and the most significant covariate on lithium CL was found to be creatinine clearance (reference model). Lithium compliance was analysed using inter-occasion variability or Markovian features (previous lithium measurement as ordered categorical covariate) on bioavailability parameter. Markov-type model predicted the lithium compliance in the next cycle with higher success rate (79.8%) compared to IOV model (65.2%) and reference model (43.2%). This model becomes an efficient tool, not only being able to adequately describe the observed outcome, but also to predict the individual drug compliance in the next cycle. Therefore, Bipolar disorder patients can be classified regarding their probability to become extensive or poor compliers in the next cycle and then, individual probabilities lower than 0.5 highlight the need of intensive monitoring, as well as other pharmaceutical care measurements that might be applied to enhance drug compliance for a better and safer lithium treatment.
year | journal | country | edition | language |
---|---|---|---|---|
2016-12-01 | European neuropsychopharmacology : the journal of the European College of Neuropsychopharmacology |