scholarly journals Using State Transition Models To Explore How the Prevalence of Subtherapeutic Posaconazole Exposures Impacts the Clinical Utility of Therapeutic Drug Monitoring for Posaconazole Tablets and Oral Suspension

2019 ◽  
Vol 63 (12) ◽  
Author(s):  
Russell E. Lewis ◽  
Dimitrios P. Kontoyiannis ◽  
Pierluigi Viale ◽  
Eric M. Sarpong

ABSTRACT Therapeutic drug monitoring (TDM) has been recommended in guidelines for patients receiving posaconazole oral suspension, but its utility in patients receiving posaconazole tablet, which has an improved bioavailability, remains unclear. We used state transition models with first-order Monte Carlo microsimulation to re-examine the posaconazole exposure-response relationships reported in two phase III clinical trials (prophylaxis with posaconazole oral suspension, models 1 and 2) and a third multicenter observational TDM study (model 3). We simulated the impact of TDM-guided interventions to improve initial average posaconazole concentrations (Cavg) to reduce clinical failure (in models 1 and 2) and breakthrough invasive fungal disease (bIFD) in model 3. Simulations were then repeated using posaconazole tablet Cavg distributions in place of the oral suspension formulation. In all three models with posaconazole oral suspension, TDM interventions associated with maximal improvement in posaconazole Cavg reduced absolute rates of subtherapeutic exposures (Cavg < 700 ng/ml) by 25% to 49%. Predicted reductions in absolute clinical failure rates were 11% in model 1 and 6.5% in model 2 and a 12.6% reduction in bIFD in model 3. With the tablet formulation, maximally effective TDM interventions reduced subtherapeutic exposures by approximately 5% in all three models and absolute clinical failure rates by 3.9% in model 1 and 1.6% in model 2; there was a 1.6% reduction in bIFD in model 3. Our modeling suggests that routine TDM during prophylaxis with posaconazole tablets may have limited clinical utility unless populations with higher prevalence (>10%) of subtherapeutic exposures can be identified based on clinical risk factors.

2016 ◽  
Vol 51 (3) ◽  
pp. 295-296 ◽  
Author(s):  
Tarun Bastiampillai ◽  
Stephen Allison ◽  
Arun Gupta

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