Evaluation of Current Amikacin Dosing Recommendations and Development of an Interactive Nomogram: The Role of Albumin.

This study aimed to evaluate the potential efficacy and safety of the amikacin dosage proposed by the main guidelines and to develop an interactive nomogram, especially focused on the potential impact of albumin on initial dosage recommendation. The probability of target attainment (PTA) for each of the different dosing recommendations was calculated through stochastic simulations based on pharmacokinetic/pharmacodynamic (PKPD) criteria. Large efficacy and safety differences were observed for the evaluated amikacin dosing guidelines together with a significant impact of albumin concentrations on efficacy and safety. For all recommended dosages evaluated, efficacy and safety criteria of amikacin dosage proposed were not achieved simultaneously in most of the clinical scenarios evaluated. Furthermore, a significant impact of albumin was identified: The higher is the albumin, (i) the higher will be the PTA for maximum concentration/minimum inhibitory concentration (Cmax/MIC), (ii) the lower will be the PTA for the time period with drug concentration exceeding MIC (T>MIC) and (iii) the lower will be the PTA for toxicity (minimum concentration). Thus, accounting for albumin effect might be of interest for future amikacin dosing guidelines updates. In addition, AMKnom, an amikacin nomogram builder based on PKPD criteria, has been developed and is freely available to help evaluating dosing recommendations.

Amikacin initial dosage in patients with hypoalbuminaemia: an interactive tool based on a population pharmacokinetic approach.

OBJECTIVES: To characterize amikacin population pharmacokinetics in patients with hypoalbuminaemia and to develop a model-based interactive application for amikacin initial dosage. METHODS: A population pharmacokinetic model was developed using a non-linear mixed-effects modelling approach (NONMEM) with amikacin concentration-time data collected from clinical practice (75% hypoalbuminaemic patients). Goodness-of-fit plots, minimum objective function value, prediction-corrected visual predictive check, bootstrapping, precision and bias of parameter estimates were used for model evaluation. An interactive model-based simulation tool was developed in R (Shiny and R Markdown). Cmax/MIC ratio, time above MIC and AUC/MIC were used for optimizing amikacin initial dose recommendation. Probabilities of reaching targets were calculated for the dosage proposed. RESULTS: A one-compartment model with first-order linear elimination best described the 873 amikacin plasma concentrations available from 294 subjects (model development and external validation groups). Estimated amikacin population pharmacokinetic parameters were CL (L/h) = 0.525 + 4.78 × (CKD-EPI/98) × (0.77 × vancomycin) and V (L) = 26.3 × (albumin/2.9)-0.51 × [1 + 0.006 × (weight - 70)], where CKD-EPI is calculated with the Chronic Kidney Disease Epidemiology Collaboration equation. AMKdose is a useful interactive model-based application for a priori optimization of amikacin dosage, using individual patient and microbiological information together with predefined pharmacokinetic/pharmacodynamic (PKPD) targets. CONCLUSIONS: Serum albumin, total bodyweight, estimated glomerular filtration rate (using the CKD-EPI equation) and co-medication with vancomycin showed a significant impact on amikacin pharmacokinetics. A powerful interactive initial dose-finding tool has been developed and is freely available online. AMKdose could be useful for guiding initial amikacin dose selection before any individual pharmacokinetic information is available.

Evaluation of renal function equations to predict amikacin clearance.

Objective: To evaluate the predictive performance of eight renal function equations to describe amikacin elimination in a large standard population with a wide range of age. Methods: Retrospective study of adult hospitalized patients treated with amikacin and monitored in the clinical pharmacokinetics laboratory of a pharmacy service. Renal function was calculated as Cockcroft-Gault with total, adjusted and ideal body weight, MDRD-4, CKD-EPI, rLM, BIS1, and FAS. One compartment model with first-order elimination, including interindividual variability on clearance and volume of distribution and combined residual error model was selected as a base structural model. A pharmaco-statistical analysis was performed following a non-linear mixed effects modeling approach (NONMEM 7.3 software). Results: 198 patients (61 years [18-93]) and 566 measured amikacin plasma concentrations were included. All the estimated glomerular filtration rate and creatinine clearance equations evaluated described properly the data. The linear relationship between clearance and glomerular filtration rate based on rLM showed a statistically significant improvement in the fit of the data. rLM must be evaluated carefully in renal failure for amikacin dose adjustment. Conclusions: Revised Lund-Malmö (rLM) and CKD-EPI showed the superior predictive performance of amikacin drug elimination comparing to all the alternative metrics evaluated.