RESUMO
PURPOSE: The aim of this work was to integrate the Therapeutic Drug Monitoring (TDM) with the model-informed precision dosing (MIPD) approach, using Physiologically-based Pharmacokinetic/Pharmacodynamic (PBPK/PD) modelling and simulation, to explore the relationship between amikacin exposure and estimated glomerular filtration rate (GFR) in critically ill patients with cancer. METHODS: In the TDM study, samples from 51 critically-ill patients with cancer treated with amikacin were analysed. Patients were stratified according to renal function based on GFR status. A full-body PBPK model with 12 organs model was developed using Simcyp V. 21, including steady-state volume of distribution of 0.21 L/kg and renal clearance of 6.9 L/h in healthy adults. PK parameters evaluated were within the 2-fold error range. RESULTS: During the validation step, predicted vs observed amikacin clearance values after single infusion dose in patients with normal renal function, mild and moderate renal impairment were 7.6 vs 8.1 L/h (7.5 mg/kg dose); 3.8 vs 4.5 L/h (1500 mg dose) and 2.2 vs 3.1 L/h (25 mg/kg dose), respectively. However, predicted vs observed amikacin clearance after a single dose infusion of 1400 mg in critically-ill patients with cancer were 1.46 vs 1.63 (P = 0.6406) L/h (severe), 2.83 vs 1.08 (P < 0.05) L/h (moderate), 4.23 vs 2.49 (P = 0.0625) L/h (mild) and 7.41 vs 3.36 (P < 0.05) L/h (normal renal function). CONCLUSION: This study demonstrated that estimated GFR did not predict amikacin elimination in critically-ill patients with cancer. Further studies are necessary to find amikacin PK covariates to optimize the pharmacotherapy in this population. Therefore, TDM of amikacin is imperative in cancer patients.
Assuntos
Amicacina , Neoplasias , Adulto , Humanos , Amicacina/uso terapêutico , Estado Terminal/terapia , Taxa de Filtração Glomerular , Monitoramento de Medicamentos , Neoplasias/tratamento farmacológico , Antibacterianos/uso terapêuticoRESUMO
Abstract Voriconazole increases tacrolimus blood concentration significantly when coadministrated. The recommendation of reducing tacrolimus to 1/3 in voriconazole package insert seems not to be satisfactory in clinical practice. In vitro studies demonstrated that the magnitude of inhibition depends on the concentration of voriconazole, while voriconazole exposure is determined by the genotype status of CYP2C19. CYP2C19 gene polymorphism challenges the management of drug-drug interactions(DDIs) between voriconazole and tacrolimus. This work aimed to predict the impact of CYP2C19 polymorphism on the DDIs by using physiologically based pharmacokinetics (PBPK) models. The precision of the developed voriconazole and tacrolimus models was reasonable by evaluating the pharmacokinetic parameters fold error, such as AUC0-24, Cmax and tmax. Voriconazole increased tacrolimus concentration immediately in all population. The simulated duration of DDIs disappearance after voriconazole withdrawal were 146h, 90h and 66h in poor metabolizers (PMs), intermediate metabolizers (IMs) and extensive metabolizers(EMs), respectively. The developed and optimized PBPK models in this study can be applied to assit the dose adjustment for tacrolimus with and without voriconazole.
Assuntos
Tacrolimo/agonistas , Fator de Impacto , Voriconazol/agonistas , Citocromo P-450 CYP2C19/análise , Técnicas In Vitro/métodos , Preparações Farmacêuticas/administração & dosagem , Adaptação Psicológica/classificaçãoRESUMO
The integration between physiologically-based pharmacokinetics (PBPK) models and pharmacodynamics (PD) models makes it possible to describe the absorption, distribution, metabolism and excretion processes of drugs, together with the concentration-response relationship, being a fundamental framework with wide applications in pharmacology. Nevertheless, the enormous complexity of PBPK models and the large number of parameters that define them leads to the need to study and understand how the uncertainty of the parameters affects the variability of the models output. To study this issue, this paper proposes a global sensitivity analysis (GSA) to identify the parameters that have the greatest influence on the response of the model. It has been selected as study cases the PBPK models of an inhaled anesthetic and an analgesic, along with two PD interaction models that describe two relevant clinical effects, hypnosis and analgesia during general anesthesia. The subset of the most relevant parameters found adequately with the GSA method has been optimized for the generation of a virtual population that represents the theoretical output variability of various model responses. The generated virtual population has the potential to be used for the design, development and evaluation of physiological closed-loop control systems.