Highlights of Significant Changes in the New Vancomycin Therapeutic Monitoring Guidelines1

The key elements of the revised 2020 Vancomycin dosing guidelines include the transition from the trough to an AUC based dosing target and the utilization of Bayesian algorithm to accurately calculate daily AUC values with non-steady-state levels. Bayesian-based precision dosing platforms are essential tools to facilitate the implementation of AUC/MIC-based vancomycin dosing guidelines and optimize clinical practice. PrecisePK is a Bayesian dosing software that has been shown to predict the future vancomycin AUC most accurately in critically ill patients, who are at increased risks of treatment failure due to altered antimicrobial pharmacokinetics2, according to a recently published peer-reviewed study3. Since 1986, we have worked with several renowned institutions such as UC San Diego, UC San Francisco, Sharp Healthcare, etc.

Why does AUC targeting make sense for Vancomycin?

• AUC/MIC is the best predictor of vancomycin activity against methicillin-susceptible S.aureus(MSSA), methicillin-resistant S.aureus (MRSA), and glycopeptide-intermediate S.aureus (GISA), according to a study in neutropenic-mouse thigh conducted by Ebert and colleagues in 19874.

Figure 1:The plot above delineates the change in the colony-forming unit based on 24-H*AUC/MIC, peak/ MIC, and time above MIC. 24H*AUC/MIC correlates with vancomycin efficacy best, in which the cidal activity against MRSA is observed at 24-H AUC/MIC>400

• Trough by itself is a poor surrogate marker for AUC value as it does not correlate well to AUC. A study by Drennan5 et. al shows that different dosing frequency yield different peaks and drug exposure while resulting in the same trough level. Trough only represents one singular exposure point at the end of the dosing interval whereas AUC 24 hours show the average concentration during that time period.

Figure 2: Different dosing regimens (Q6Hvs Q12H) result in different AUC with similar trough values.

Another study by Mizokami6 [Pai et al 2014] demonstrated the relationship between trough level and AUC. Trough concentrations correlate with significant inter-and intra-variability in the AUC24 exposure; therefore, the vancomycin15-20 mg/L trough concentration range isn’t a reliable indicator of AUC/MIC BMD ratio >400 for S. aureus isolates, which is advocated as a target to achieve clinical effectiveness with vancomycin.

Figure 3

• Vancomycin pharmacokinetics is best described with a two-compartment model. The serum level graph associated with this model has a complex decay pattern that accounts for concomitant drug distribution into the peripheral compartment and elimination from the central compartment7. If one is to simply just target a trough, they are in essence, ignoring all the intricacies of the shape of the graph and the fact that it involves a dual exponential decay curve. Due to this difference in the shape, the same trough target may result in vastly different drug exposure in different populations.

Figure 4: Schematic representation of vancomycin2-compartment model

• Trough concentration when maintained above 15 to 20 mg/L is associated with increased risk of vancomycin-induced nephrotoxicity. The AUC-guided vancomycin monitoring strategy reduces the occurrence of acute kidney injury when comparing trough-guided monitoring8.

Figure 5: Forest plot indicating the risk of vancomycin-induced nephrotoxicity associated with AUC-guided monitoring strategy was significantly lower than trough-based monitoring strategy (OR=0.68, [95% CI 0.46-0.99)

How Does PrecisePK Apply Bayesian Algorithm to Compute Vancomycin Dosing?

• Vancomycin guidelines mention over 20 different population models and provide definitive dosing recommendation for three main subgroups1:

• Adult and obese population
• Pediatric population
• Neonate population

• PrecisePK takes these unique population characteristics (age, weight, kidney) and extra drug factors (critically ill, burn, etc.) into consideration when building population pharmacokinetic model.  The very first thing PrecisePK does is to auto-select the most appropriate clinically validated population PK model based   on   the   patients’   characteristics,   or   in   Bayesian   language,   most appropriate Bayesian priori. For example, critically ill adults have very large volume of distribution and supranormal drug clearance9,10
• When combining with the individual patient’s measured drug level, Bayesian posterior pharmacokinetic parameters are estimated. Precise PK offers an individual recommendation after just one serum concentration input. In a robust Bayesian program like PrecisePK, it actually does not matter when that drug level is obtained and you generally only need one level for the program to be able to hone in individual’s pharmacokinetics, thus giving a very precise dosing recommendation or AUC prediction.

Why is PrecisePK Capable of Predicting the Most Accurate AUC Among Its Peers?

• PrecisePK has been used in clinical setting for over 30 years and has helped calculate dose for over a million patients since its inception. The reason for the software’s precision in estimating the Vancomycin AUC, as observed by Turner et. al3 is due to

• Its detailed and accurate implementation of the two compartment PK models for critically ill population.
• Its robust and effective Bayesian algorithm in identifying the individual’s pharmacokinetics with just one drug level. PrecisePK could identify the exact shape the serum level graph, not just the terminal slope of the graph. This enables the accurate AUC estimation since both the shape of the graph and the terminal slope of the graph determine AUC value.