Open-source maximum a posteriori-bayesian dosing AdDS to current therapeutic drug monitoring: Adapting to the era of individualized therapy

• Published in: Pharmacotherapy Vol. 41; no. 11; p. 953
• Main Authors: Smith, Nicholas M, Chan, Arthur, Wilkinson, Laura A, Chua, Hubert C, Nguyen, Thomas D, de Souza, Harriet, Shah, Anant P, D'Argenio, David Z, Mergenhagen, Kari A
• Format: Journal Article
• Language: English
• Published: United States 01.11.2021
• Subjects: Adult; Bayes Theorem; Drug Monitoring; Humans; Obesity - drug therapy; Precision Medicine; Vancomycin - administration & dosage; MAP-Bayesian dosing; vancomycin; Staphylococcus aureus; therapeutic drug monitoring; AUC
• ISSN: 1875-9114
• DOI: 10.1002/phar.2631

Recent updates in the therapeutic drug monitoring (TDM) guidelines for vancomycin have rekindled interest in maximum a posteriori-Bayesian (MAP-Bayesian) estimation of patient-specific pharmacokinetic parameters. To create a versatile infrastructure for MAP-Bayesian dosing of vancomycin or other drugs, a freely available, R-based software package, Advanced Dosing Solutions (AdDS), was created to facilitate clinical implementation of these improved TDM methods. The objective of this study was to utilize AdDS for pre- and post-processing of data in order to streamline the therapeutic management of vancomycin in healthy and obese veterans. Patients from a local Veteran Affairs hospital were utilized to compare the process of full re-estimation versus Bayesian updating of priors on healthy adult and obese patient populations for use with AdDS. Twenty-four healthy veterans were utilized to train (14/24) and test (10/24) the base pharmacokinetic model of vancomycin while comparing the effects of updated and fully re-estimated priors. This process was repeated with a total of 18 obese veterans for both training (11/18) and testing (7/18). Comparison of MAP objective function between the original and re-estimated models for healthy adults indicated that 78.6% of the subjects in the training and 70.0% of the subjects in the testing datasets had similar or improved predictions by the re-estimated model. For obese veterans, 81.8% of subjects in the training dataset and 85.7% of subjects in the testing dataset had similar or improved predictions. Re-estimation of model parameters provided more significant improvements in objective function compared with Bayesian updating, which may be a useful strategy in cases where sufficient samples and subjects are available. The generation of bespoke regimens based on patient-specific clearance and minimal sampling may improve patient care by addressing fundamental pharmacokinetic differences in healthy and obese veteran populations.