Comparative Evaluation of the Predictive Performances of Three Different Structural Population Pharmacokinetic Models To Predict Future Voriconazole Concentrations

Abstract

i Bayesian methods for voriconazole therapeutic drug monitoring (TDM) have been reported previously, but there are only sparse reports comparing the accuracy and precision of predictions of published models. Furthermore, the comparative accuracy of linear, mixed linear and nonlinear, or entirely nonlinear models may be of high clinical relevance. In this study, models were coded into individually designed optimum dosing strategies (ID-ODS) with voriconazole concentration data analyzed using inverse Bayesian modeling. The data used were from two independent data sets, patients with proven or suspected invasive fungal infections (n ‫؍‬ 57) and hematopoietic stem cell transplant recipients (n ‫؍‬ 10). Observed voriconazole concentrations were predicted whereby for each concentration value, the data available to that point were used to predict that value. The mean prediction error (ME) and mean squared prediction error (MSE) and their 95% confidence intervals (95% CI) were calculated to measure absolute bias and precision, while ⌬ME and ⌬MSE and their 95% CI were used to measure relative bias and precision, respectively. A total of 519 voriconazole concentrations were analyzed using three models. Voriconazole is a triazole antifungal that exhibits broad-spectrum activity and is a first-line agent for the treatment of Candida sp. infections (1), invasive aspergillosis (2), and other serious fungal infections. With increasing numbers of at-risk immunocompromised populations, such as those undergoing solid-organ transplantation or those with HIV infections, the incidence of invasive mycoses is on the rise (3, 4). Despite the advent of newer antifungals, Aspergillus sp. and Candida sp. infections have exhibited high mortality rates of 60% and 30%, respectively (5, 6).Recent published studies of voriconazole have shed light on the clinical relevance of therapeutic drug monitoring (TDM) for optimization of dosing based on voriconazole's highly variable pharmacokinetics (PK) and the resultant poor predictability of plasma concentrations (7,8). Subtherapeutic concentrations have been linked to higher failure rates in patients with life-threatening invasive fungal infections, and supratherapeutic concentrations are associated with neurological and hepatic toxicity (9-19). Voriconazole is primarily metabolized by CYP2C19, which commonly exhibits genetic polymorphism, leading to variable PK and leaving certain populations susceptible to decreased metabolism and increased plasma concentrations of voriconazole (20-23). Patients of Asian descent with polymorphisms in CYP2C19 have up to a 20% incidence of being poor metabolizers while this value is up to 5% for Caucasian and African American individuals (24). Poor metabolizers can have a PK exposure up to four times higher than that of homozygous comparators.Nonlinearity in voriconazole PK relating to saturable clearance mechanisms has been reported (8), which together with its extensive variability makes dosing profoundly challenging, especially when higher doses are used. Conve...

Voriconazole: A Review of Population Pharmacokinetic Analyses

“…Regardless of the patient populations, voriconazole CL was described as a linear process in most of the studies (n = 11), which was inconsistent with the nonlinear pharmacokinetic characteristics related to saturable CL mechanisms. In fact, this finding was supported by the results of a comparative study conducted by Farkas et al, [33] who evaluated the accuracy and precision of the predictions of three different structural models (linear, nonlinear, or mixed linear and nonlinear) for voriconazole and found that the linear model was the most accurate. The favorable performance of the linear model might be explained by the applied doses of voriconazole.…”

Impact of CYP2C19 Genotype and Liver Function on Voriconazole Pharmacokinetics in Renal Transplant Recipients

“…To our knowledge, this is the first PPK study of VRC in RTRs. Recently, 17 – 20 a 1-compartment model was reported to describe the PPK characteristic of VRC in patients. However, there have been conflicting data that support a 2-compartment model with Michaelis clearance.…”

“…16 A study showed that a linear elimination model would appropriate to VRC serum concentration compared with other linear models. 17 The 1-compartment model with first-order absorption and elimination was fitted with VRC pharmacokinetic model and the absorption rate was fixed to a value of 1.1 h. 18 – 20 …”

Fuzzy Evaluation of Pharmacokinetic Models

“…Most of the time, the process to find the final PopPK model that better represents the data behavior requires the development of several PopPK models. It is common to use interchangeable software programs like R or SAS to apply various evaluation criteria to select which PopPK model is “better” among all the possible PopPK models [5–7]. These evaluation criteria range from graphical analysis to statistical methods [8].…”