Last updated 9 October 2022
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 Auckland Starship Blood and Cancer Centre.
The opportunity for improved clinical care by using Bayesian forecasting
for dosing of busulfan was demonstrated and accepted.
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 has been designed around a database
to provide security and transportability. This approach allows patient data to
be stored remotely. It is extensible to other medicines, and associated
observation and reporting types. 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). The NextDose 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.
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reserved | Developed by Sam Holford & Nick Holford 2012-2022