abstract
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BACKGROUND AND OBJECTIVE
Severe neutropenia is the most frequent and important toxicity of
3-weekly paclitaxel and puts patients at substantial risk of
infectious complications. It is well known that the time during
which paclitaxel plasma concentrations exceed 0.05 μmol/L
(T(C>0.05)) correlates with the extent of neutropenia. This study
was initiated to develop a dosing algorithm that would be able to
reduce severe neutropenia by targeting an individual paclitaxel
T(C>0.05) between 26 and 31 hours, and could be validated in a
prospective randomized trial by comparing it to conventional dosing
of paclitaxel.
METHODS
Paclitaxel plasma concentration-time (n = 273) and absolute
neutrophil count (ANC) data (152 of the 273 patients) were pooled
from two previous studies and submitted to population
pharmacokinetic and pharmacodynamic modelling using nonlinear
mixed-effects modelling software NONMEM® version VII. To fit the
data, we used a previously described 3-compartment model with
saturable elimination and distribution, coupled to a
semiphysiological model with a linear function to describe the
myelotoxic effect of paclitaxel (E(paclitaxel)) on circulating
neutrophils (neutropenia). Patient age, sex, body surface area
(BSA), bilirubin and renal function were tested as potential
covariates on the maximum elimination capacity of paclitaxel
(VM(EL)). Limited sampling strategies were tested on the
pharmacokinetic model for their accuracy to predict paclitaxel
T(C>0.05). Subsequently, we proposed a first-cycle dosing
algorithm that accounted for BSA, patient age and sex, while later
cycles accounted for the previous-cycle paclitaxel T(C>0.05)
(target: 26 to 31 hours) and ANC nadir to adapt the paclitaxel dose
for the next treatment cycle. To test the adequacy of the proposed
dosing algorithm, we used extensive data simulations on the final
pharmacokinetic/pharmacodynamic model, generating datasets of 1000
patients for six subsequent treatment cycles. Grade 4 neutropenia
was tested as a potential endpoint for a prospective clinical trial
and simulated for two scenarios, i.e. conventional dosing of
paclitaxel 200 mg/m(2) every 3 weeks, and personalized,
pharmacology-driven dosing as outlined above.
RESULTS
Concentration-time data for paclitaxel were adequately described by
the 3-compartment model. Also, individual ANC counts were adequately
described by the semiphysiological model using a linear function to
describe E(paclitaxel) on neutropenia. Patient age, sex, bilirubin
and BSA were significant and independent covariates on the
elimination of paclitaxel. Paclitaxel VM(EL) was 16% higher in males
than in female patients, and a 10-year increase in age led to a 13%
decrease in VM(EL). A single paclitaxel plasma concentration 24
hours after the start of infusion was adequate to predict paclitaxel
T(C>0.05) (root squared mean error [RSME] = +0.5%),
and the addition of an end-of-infusion sample did not further
improve precision (RSME = -0.6%). Data simulations on
the final pharmacokinetic/pharmacodynamic model and using the
proposed dosing algorithm resulted in a first-cycle paclitaxel dose
ranging from 150 to 185 mg/m(2) for women and from 165 to 200
mg/m(2) for men. Dose adaptations for cycles two to six ranged from
-40% to +30%, with a final median paclitaxel dose of 167 mg/m(2)
(range 76 to 311 mg/m(2)). When compared with conventional dosing
(paclitaxel 200 mg/m(2) every 3 weeks), personalized dosing reduced
grade 4 neutropenia in cycle one from 15% to 7%, and further to 4%
in cycle 2.
CONCLUSION
This study proposes a pharmacology-driven dosing algorithm of
3-weekly paclitaxel to reduce the incidence of grade 4 neutropenia.
A randomized clinical trial comparing this dosing algorithm with
conventional BSA-based dosing of paclitaxel in patients with
advanced non-small cell lung cancer is currently ongoing.
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citation
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Joerger M, Kraff S, Huitema A D R, Feiss G, Moritz B, Schellens J H
M, Beijnen J H, Jaehde U. Evaluation of a pharmacology-driven dosing
algorithm of 3-weekly paclitaxel using therapeutic drug monitoring:
a pharmacokinetic-pharmacodynamic simulation study. Clin
Pharmacokinet 2012; 51:607-17.
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