Precision Bayesian Modeling Tailors Vancomycin Dosing in Patients With Obesity

Vancomycin (Vancocin; ANI Pharmaceuticals Inc), a critical antibiotic in the treatment of severe gram-positive infections, presents distinct challenges in dosing patients with obesity. Achieving therapeutic levels is crucial for efficacy and preventing the development of resistance. Bayesian modeling has emerged as a valuable tool for optimizing vancomycin dosing, providing 24-hour area under the curve (AUC24) estimations.

Dosing Challenges in Obesity

The complexities of vancomycin dosing intensify in patients with obesity due to altered pharmacokinetics (PK) affecting drug distribution, protein binding, and clearance. In addition, patients with obesity have a high variability in PK, resulting in the need for patient-specific dosing.¹ The volume of distribution (Vd) of vancomycin tends to be larger in patients with obesity compared with patients without obesity due to the hydrophilic nature of vancomycin.^2,3 Patients with obesity also demonstrate increased blood flow, cardiac output, and blood volume, which can further explain changes in Vd.³

Vancomycin protein binding may also be altered in obesity, leading to differences in the free drug concentrations.³ The clearance of vancomycin differs in obesity due to physiological changes such as increased kidney mass and renal blood flow. Clearance tends to be greater in patients with obesity compared with matched patients without obesity.

Clinical Outcomes in Obesity

Traditional dosing strategies may result in suboptimal vancomycin exposure, leading to untoward clinical outcomes. Patients with obesity may demonstrate reduced target attainment (defined as an AUC to minimum inhibitory concentration ratio of 400-600 mg*h/L).⁴ Findings from other studies highlight the potential for drug accumulation once adipose tissue becomes saturated with vancomycin.⁵

To improve empiric maintenance dosing, current guidelines recommend alternative dosing strategies, such as allometric scaling or lower doses (mg/kg) to compensate for patient variability based on size.^1,6 Because using traditional dosing strategies in patients with obesity may result in supratherapeutic vancomycin AUC24 levels,⁷ allometrically dosed vancomycin in patients with obesity has been shown to lower incidence of supratherapeutic vancomycin levels and nephrotoxicity.^7,8

Obesity may also be associated with a higher risk of nephrotoxicity compared with patients without obesity.⁹ Findings from a recent study by Covington and Bellamy showed a lower acute kidney injury incidence with Bayesian modeling vs 2-level first order AUC calculations. Although this study did not specifically focus on patients with obesity, about one-third of patients included had obesity.¹⁰

The Role of Bayesian Modeling

Bayesian modeling has gained prominence in optimizing vancomycin dosing by providing individualized AUC24 estimates. This approach considers patient-specific factors, such as weight, renal function, and drug concentrations, to tailor dosing regimens. However, the application of Bayesian modeling in patients with obesity requires further exploration to ensure accuracy and reliability. The 2020 guidelines from the American Society of Health-System Pharmacists, Infectious Diseases Society of America, Pediatric Infectious Diseases Society, and Society of Infectious Diseases Pharmacists for therapeutic monitoring of vancomycin note that “data supporting single-concentration Bayesian modeling are limited in special populations such as patients [with critical illness], patients [with obesity], pediatric patients, and those with unstable renal function.”¹

The ideal timing and number of levels to perform therapeutic drug monitoring are another controversy in dosing patients with obesity. As such, the consensus guidelines recommend 2 vancomycin concentration levels be obtained in special populations to estimate the Bayesian AUC until more data are available.¹

Covington and Watkins’ recent study aimed to evaluate the agreement between double-concentration and single-concentration Bayesian AUC24 estimates in patients with obesity receiving vancomycin.¹¹ The retrospective within-participants cohort analysis of 31 patients revealed an overall agreement between AUC24 estimates when using double- vs single-concentration vancomycin (mean difference, 11.4 mg*h/L [95% CI, −72 to 95 mg*h/L]). However, proportional bias was detected at higher AUC24 values. Findings from this small study show the power of Bayesian monitoring and optimizing vancomycin dosing in this complicated patient population. Confirmatory research with larger sample sizes is needed.

Conclusion

Optimizing vancomycin dosing in patients with obesity is a complex task, requiring tailored approaches to account for altered PK. Bayesian modeling holds promise in providing individualized AUC24 estimates, but its application in this population warrants further investigation. Future research should focus on larger sample sizes to validate the observed proportional bias and explore optimal timing and sampling for therapeutic drug monitoring. Additionally, investigating the impact of factors such as age, comorbidities, and concurrent medications on vancomycin PK in obesity would enhance the precision of Bayesian modeling.

Covington’s next project plans to look at the optimal timing of vancomycin concentrations when using precision dosing. In particular, she plans to assess the agreement between steady-state AUC24 when using pre-steady state vs pre-steady-state plus steady-state levels. Although Bayesian dosing touts the ability to check pre-steady-state concentrations, what generated the idea for Covington’s project were encounters with pharmacists who are hesitant to trust data that are solely based on a pre-steady-state level. The future of vancomycin dosing lies in optimizing dosing regimens for each patient’s unique needs through personalized dosing algorithms and model-informed precision dosing platforms.

References

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5. Assadoon MS, Pearson JC, Kubiak DW, Kovacevic MP, Dionne BW. Evaluation of vancomycin accumulation in patients with obesity. Open Forum Infect Dis. 2022;9(10):ofac491. doi:10.1093/ofid/ofac491

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7. Wright L, Childress DT, Brown ML, Wilkerson W, Maldonado R, Durham SH. Which “weigh” to go? alternative vancomycin dosing strategies in obese patients. J Pharm Pract. 2023;36(4):870-874. doi:10.1177/08971900221087122

8. Brown ML, Hutchison AM, McAtee AM, Gaillard PR, Childress DT. Allometric versus consensus guideline dosing in achieving target vancomycin trough concentrations. Am J Health Syst Pharm. 2017;74(14):1067-1075. doi:10.2146/ajhp160260

9.Choi YC, Saw S, Soliman D, et al. Intravenous vancomycin associated with the development of nephrotoxicity in patients with class III obesity. Ann Pharmacother. 2017;51(11):937-944. doi:10.1177/1060028017720946

10. Bellamy A, Covington EW. Acute kidney injury incidence with Bayesian dosing software versus 2-level first-order area under the curve-based dosing of vancomycin with piperacillin-tazobactam. J Pharm Technol. 2023;39(4):183-190. doi:10.1177/87551225231182542

11. Covington EW, Watkins AM. Agreement between two-concentration and one-concentration area under the curve (AUC) estimates when using Bayesian modeling to dose vancomycin in patients with obesity. Ann Pharmacother. 2023;10600280231190910. doi:10.1177/10600280231190910