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Models are provided that use different assumptions for predicting the Bayesian dose. One has the suffix “_AVG” and the other does not. The models are the same for estimation of the individual parameters and use both between subject variability (BSV) and between occasion variability (BOV) when BOV was in the original model used for development. An occasion is defined by any dose which has concentrations measured before the next dose. The difference is in how the parameters are used for predicting the dose. The model without “_AVG” uses both BSV and BOV to predict doses. The model with “_AVG” uses only BSV. This means that models without “_AVG” may include an estimate of occasion to occasion differences in the parameters e.g. due to disease progression affecting clearance which is not in the model as a predictable effect. The model with “_AVG” uses just BSV which is equivalent to averaging the BOV random effects. This may give a more stable dose prediction but does not include any real changes that might have occurred from occasion to occasion. It is your choice to decide which model to use depending on what you know about the patient status and how it might have changed from occasion to occasion. The warfarin model does not BOV affecting the parameters so the results will be the same for warfarin with both models with and without the “_AVG” suffix.
Target dose predictions are now based on covariates (fixed effects) at the end of dosing intervals rather than the start. This should give better predictions when covariates change during a dosing interval such as the empirical time varying clearance of busulfan.
Dose prediction using Bayesian forecasting has been proven to save lives. Evans et al. demonstrated a larger increase in survival for childhood acute lymphoblastic leukaemia, by individualising methotrexate dose, than any other single drug treatment (Evans, Relling et al. 1998). Despite its striking improvement on 5-year survival, it is hardly used because of the difficulties of access to, and usability of, Bayesian forecasting software. Dose individualisation has also been shown to increase survival after busulfan conditioning by maintaining population concentrations within a narrow safe and effective range (Bleyzac, Souillet et al. 2001; Bolinger, Zangwill et al. 2001; Booth, Rahman et al. 2007; Bartelink, Bredius et al. 2009; Abbasi, Vadnais et al. 2011; Hempel and Trame 2011; Long-Boyle et al. 2015). In addition to the busulfan application, LabPlus and clinical staff needed a tool to help make the best use of concentration and biomarker measurements for dose individualisation. Warfarin dose predictions using NextDose with INR measurements have been shown to be accurate across a wider range of daily doses (especially over 7 mg/day) (Holford, Ma, Tsuji 2018) than other Bayesian algorithms (Saffian et al. 2016). NextDose was developed to meet the clinical need and promote more widespread dose individualisation.
In January 2012, busulfan analysis and reporting with NextDose was demonstrated to the Clinical Director and lab technicians of LabPlus as well as pharmacists, nurses, haematologists and oncologists associated with the SBCC. The clear opportunity for improved clinical care by using Bayesian forecasting for dosing of busulfan was demonstrated.
These Bayesian forecasting methods have not been available, in a clinically useful form, in Auckland until 2012 and it was decided that NextDose should be adopted initially for busulfan dosing. Methods for other drugs such as methotrexate have been added when suitable models have been developed. Its application to methotrexate dosing may be clinically important because of the major improvement in survival that has been shown elsewhere (Evans, Relling et al. 1998). NextDose will continue to be modified and extended in collaboration with LabPLUS and hospital clinical staff. Data collected will be used to improve the models over time.
NextDose has been designed around a database to provide security and transportability. This approach allows patient data to be stored remotely (to meet hospital security requirements) or locally within the NextDose application. It is extensible to other medicines, and associated observation and reporting types, and has already been extended to support methotrexate dosing. NextDose consists of three software abstraction layers to provide a clear separation between the user interface, model controllers and the modelling software. This modular approach allows the use of different modelling software via the same intuitive interface, which should make these tools more accessible and useable in a clinical environment.
The original model used for busulfan was based upon a modification of the PK model developed by FDA (Booth, Rahman et al. 2007). This modification employed a maturation function to help predict doses in infants. In 2014 the model was updated based on a large data set collected in infants, children and adults (McCune et al. 2014). The model was further refined in 2017 to improve the empirical prediction of clearance changes with time..
The observations (red) show duplicate concentration measurements. The population prediction (green) shows the profile predicted based on the patient’s age, weight and other characteristics. The individual prediction is based on a weighted combination of the population prediction, and observations, and represents the best estimation of the patient’s actual concentration-time profile. If there is a delay between the end of infusion and ideal peak sample time, the Bayesian method allows extrapolation to the infusion end time, which is vital for area-under-curve based dosing targets.
Abbasi, N., B. Vadnais, et al. (2011). "Pharmacogenetics of Intravenous and Oral Busulfan in Hematopoietic Cell Transplant Recipients." The Journal of Clinical Pharmacology 51(10): 1429-1438.
Bartelink, I. H., R. G. M. Bredius, et al. (2009). "Association between busulfan exposure and outcome in children receiving intravenous busulfan before hematologic stem cell transplantation." Biology of Blood and Marrow Transplantation 15(2): 231-241.
Bleyzac, N., G. Souillet, et al. (2001). "Improved clinical outcome of paediatric bone marrow recipients using a test dose and Bayesian pharmacokinetic individualization of busulfan dosage regimens." Bone Marrow Transplant 28(8): 743-751.
Bolinger, A. M., A. B. Zangwill, et al. (2001). "Target dose adjustment of busulfan in pediatric patients undergoing bone marrow transplantation." Bone Marrow Transplant 28(11): 1013-1018.
Booth, B. P., A. Rahman, et al. (2007). "Population pharmacokinetic-based dosing of intravenous busulfan in pediatric patients." J Clin Pharmacol 47(1): 101-111.
Evans, W. E., M. V. Relling, et al. (1998). "Conventional compared with individualized chemotherapy for childhood acute lymphoblastic leukemia." New England Journal of Medicine 338(8): 499-505.
Hempel, G. and M. N. Trame (2011). "Therapeutic drug monitoring of busulfan." Clin Chem 57(4): 643-644.
Holford N, Ma G, Tsuji Y. Using biomarkers to predict the target dose of warfarin and linezolid. PAGE. 2018;27[www.page-meeting.org/?abstract=8562].
Long-Boyle JR, Savic R, Yan S, Bartelink I, Musick L, French D, et al. Population pharmacokinetics of busulfan in pediatric and young adult patients undergoing hematopoietic cell transplant: a model-based dosing algorithm for personalized therapy and implementation into routine clinical use. Ther Drug Monit. 2015;37(2):236-45.
McCune JS, Bemer MJ, Barrett JS, Scott Baker K, Gamis AS, Holford NHG. Busulfan in Infant to Adult Hematopoietic Cell Transplant Recipients: A Population Pharmacokinetic Model for Initial and Bayesian Dose Personalization. Clin Cancer Res. 2014;20(3):754-63.
Saffian SM, Duffull SB, Roberts RL, Tait RC, Black L, Lund KA, et al. Influence of Genotype on Warfarin Maintenance Dose Predictions Produced Using a Bayesian Dose Individualization Tool. Ther Drug Monit. 2016;38(6):677-83.
NextDose 1.5.1 | Copyright 2018 All rights reserved | Developed by Sam Holford & Nick Holford