Individualized Patient Care Through Model-Informed Precision Dosing: Reflections on Training Future Practitioners
- PMID: 36380020
- DOI: 10.1208/s12248-022-00769-z
Individualized Patient Care Through Model-Informed Precision Dosing: Reflections on Training Future Practitioners
Abstract
Prior to his passing, Dr. Roger Jelliffe, expressed the need for educating future physicians and clinical pharmacists on the availability of computer-based tools to support dose optimization in patients in stable or unstable physiological states. His perspectives were to be captured in a commentary for the AAPS J with a focus on incorporating population pharmacokinetic (PK)/pharmacodynamic (PD) models that are designed to hit the therapeutic target with maximal precision. Unfortunately, knowing that he would be unable to complete this project, Dr. Jelliffe requested that a manuscript conveying his concerns be completed upon his passing. With this in mind, this final installment of the AAPS J theme issue titled "Alternative Perspectives for Evaluating Drug Exposure Characteristics in a Population - Avoiding Analysis Pitfalls and Pigeonholes" is an effort to honor Dr. Jelliffe's request, conveying his concerns and the need to incorporate modeling and simulation into the training of physicians and clinical pharmacists. Accordingly, Dr. Jelliffe's perspectives have been integrated with those of the other three co-authors on the following topics: the clinical utility of population PK models; the role of multiple model (MM) dosage regimens to identify an optimal dose for an individual; tools for determining dosing regimens in renal dialysis patients (or undergoing other therapies that modulate renal clearance); methods to analyze and track drug PK in acutely ill patients presenting with high inter-occasion variability; implementation of a 2-cycle approach to minimize the duration between blood samples taken to estimate the changing PK in an acutely ill patient and for the generation of therapeutic decisions in advance for each dosing cycle based on an analysis of the previous cycle; and the importance of expressing therapeutic drug monitoring results as 1/variance rather than as the coefficient of variation. Examples showcase why, irrespective of the overall approach, the combination of therapeutic drug monitoring and computer-informed precision dosing is indispensable for maximizing the likelihood of achieving the target drug concentrations in the individual patient.
Keywords: Clinical pharmacokinetics; Individual dose optimization; Interacting Multiple Model approach; Maximum likelihood estimation; Nonparametric models.
© 2022. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.
References
-
- Yamada W, Bartroff J, Bayard D, Burke J, Van Guilder M, Jelliffe R, Leary R, Neely M, Kryschenko A, Schumitzky A. The nonparametric adaptive grid algorithm for population pharmacokinetic modeling. Technical Report TR-2014–1, USC Laboratory of Applied Pharmacokinetics and Bioinformatics, 2013. NPAG_submission_29Oct2013.dvi (lapk.org). Accessed 03/24/22.
-
- Yamada WM, Neely MN, Bartroff J, Bayard DS, Burke JV, Guilder MV, Jelliffe RW, Kryshchenko A, Leary R, Tatarinova T, Schumitzky A. An algorithm for nonparametric estimation of a multivariate mixing distribution with applications to population pharmacokinetics. Pharmaceutics. 2020;13:42. https://doi.org/10.3390/pharmaceutics13010042 . - DOI - PubMed - PMC
-
- Bayard DS, Neely M. Experiment design for nonparametric models based on minimizing Bayes Risk: application to voriconazole1. J Pharmacokinet Pharmacodyn. 2017;44:95–111. https://doi.org/10.1007/s10928-016-9498-5 . - DOI - PubMed
-
- Lindsay BG. The geometry of mixture likelihoods: a general theory. Ann Statist. 1983;11:86–94. https://doi.org/10.1214/aos/1176346245 . - DOI
-
- Mallet A. A maximum likelihood estimation method for random coefficient regression models. Biometrika. 1986;73:645–56. https://doi.org/10.1093/biomet/73.3.645 . - DOI
Publication types
MeSH terms
Grants and funding
LinkOut - more resources
Full Text Sources