Concentration measurements
Concentration measurements of adalimumab in patient samples were
performed by a validated ELISAs at Sanquin Diagnostic Services
(Amsterdam, the Netherlands). In short, TNFα is captured to the ELISA
plate by a coating with mouse-anti-TNFα-antibody. Hereafter adalimumab
derived from the patient samples is captured and detected by a biotin
labelled anti-idiotype polyclonal antibody in combination with HPR
coupled to streptavidin and TMB. The concentration of anti-adalimumab
antibodies (ADA) was measured by radioimmunoassay. In short, antibodies
from patient samples are captured by protein A sepharose and detected
with radio-labelled F(ab’)2 fragment of adalimumab.
Anti-adalimumab antibodies were only measured if adalimumab
concentration (back-calculated to serum) was below 5 mg/L (cascade
principle). The concentrations were back-calculated to serum
concentrations by taking into account the exact volume of the absorbent
MitraTM tip in combination with the volume of elution
buffer and a fixed haematocrit value of 0.42. For all underfilled
samples, for which the correction factor was unknown, the potassium
concentration was measured in eluates of completely filled tips and in
eluates of the underfilled tips of the same patient. The blood volume
present in the eluate was calculated with these potassium concentrations
and used for calculation of adalimumab concentration.
2.6 Fit-for-purpose evaluation and statistical analysis
The predictive performance of the selected models to predict the steady
state adalimumab serum concentration at 12 weeks from the measurements
at day 5 and day 13 was investigated. The adalimumab concentration
measured in the eluate of the MitraTM tip performed at
week 12 was considered the true serum level value and compared with the
individual model-predicted value. The primary outcome of this analysis
was a precise and accurate prediction defined as mean prediction error
(MPE) and normalised root mean square error (RMSE) < 25%. We
defined normalized RMSE as RMSE divided by range (maximal dependent
variable minus minimal dependent variable). Additionally, we calculated
normalised RMSE defined as RMSE divided by average true values for all
patients without detectable ADA. The 95% Confidence intervals (CI) are
defined as 1.96 x standard error (SE) for MPE. Standard
error for RMSE is defined as \(\sqrt{1/2n}\text{\ x}\) normalised RMSE,
where n represents the degrees of freedom.
The clinical applicability of early prediction of steady state
adalimumab levels was evaluated by dividing all predictions into four
classes: true positive (prediction and measured value within therapeutic
range), true negative (prediction and measured value outside therapeutic
range), false positive (prediction in therapeutic range, measured value
outside therapeutic range), false negative (prediction outside
therapeutic range, measured value in therapeutic range).
Secondary outcome of this analysis was fitting a new model to the
collected pharmacokinetic data. The pharmacokinetic parameters were
estimated with NONMEM version 7.4 (ICON plc, Dublin, Ireland) and PsN
version 5.2.6. (https://github.com/UUPharmacometrics/PsN) Diagnostic
plots were prepared in R (R Foundation for Statistical Computing,
Vienna, Austria). Model predictive ability was assessed using the
proseval tool in PsN.
2.7 Ethical considerations
The study was approved by the local ethics committee and all patients
provided written informed consent. The trial was registered in the
Netherlands Trial Register with trial registry number NTR 7692
(www.trialregister.nl).
Results
3.1 Population pharmacokinetic model selection
Based on the literature search and the external evaluation of existing
models with our retrospective dataset, the model by Ternant et alwas selected for use in this prospective analysis. Prediction corrected
visual predictive check (VPC) used for the goodness of fit evaluation
for this model is shown in figure 2. Other VPCs of the model as well as
the model code are shown in the appendix.
3.2 Patients
A total of 56 patients were included in the trial. Drop-out rate in this
trial was 20 patients (36%). Data of 36 patients were included in the
prospective analysis. Inclusion was stopped at 36 patients because of
the COVID pandemic. Twenty-two patients carried a diagnosis of rheumatic
disease and 14 IBD. Baseline characteristics of patients included in the
analysis are shown in table 1.
3.3 Fit-for-purpose evaluation
The predictive performance analysis resulted in an MPE of 294% (95% CI
261% to 326%) and a normalised RMSE of 80% (95% CI 61% to 99%).
When stratified for absence of ADA, the MPE was-2.6% (95% CI -3.9% to
-1.4%) and normalised RMSE 24.0% (95% CI 18.4% to 29.6%).
When calculating normalised RMSE defined as RMSE divided by average true
values for patients without measured ADA, we found an RMSE of 42.5%
(95% CI 37.5% to 47.6%)
Clinical applicability evaluation resulted in 75% true predictions.
Full results from the clinical applicability evaluation are shown in
table 2.
The results of parameter estimation based on the newly collected
adalimumab levels and ADA titers collected in this study are shown in
table 3.
3.3 Immunogenicity
Three patients in our cohort developed ADA at steady state 12 weeks
after start of adalimumab therapy. None of these patients had received
biologicals before and none of these patients were on combination
therapy with other immunosuppressive drugs.
3.4 Feasibility at home
The combination of an electronic needle container and capillary blood
microsampling enabled us to remotely monitor patient’s medication
treatment. Exclusion from the analysis was mostly caused by home
sampling errors by a minority of patients resulting in samples
unsuitable for concentration measurement. Other reasons were needle drop
registration issues with health beacon occurred and some patients failed
to provide a complete set of three samples. These issues should be
addressed to increase feasibility at home.
Discussion
In this study we demonstrated the possibility of predicting steady state
adalimumab concentrations, based on early single peak and trough levels
only, resulting in a correct prediction (therapeutic – subtherapeutic)
in the vast majority of cases without ADA. After stratification for ADA
our primary outcome measures for bias and precision were met for
patients without ADA. It should be noted that ADA development is not
predictable in clinical practice. The application of MAP Bayesian
forecasting early in therapy in combination with an electronic needle
container and home capillary sampling is unique and enables us to fully
remotely monitor the patient’s medication treatment at home from
pharmacokinetic point of view. Self-management can be of value for
patients with chronic conditions on adalimumab treatment to reduce the
number of visits to the clinic.
The foremost clinical implication of our study is the possibility of an
early adalimumab dose optimisation for patients with predicted
subtherapeutic levels. Since we did not measure clinical response, our
prediction does not account for non-response due to other reasons.
This study shows that the population pharmacokinetic model selected
(which is based on adalimumab concentrations measured in serum) could be
used in combination with a VAMS method with capillary blood for
adalimumab sampling. This makes sampling more accessible for patients.
This method has been compared to venepuncture for adalimumab and has
been studied in IBD patients at home before with reliable results.
A drawback of the current VAMS technique is underfilling of the tips. In
case of underfilling, it is a challenge to calculate the concentration
that equals the serum concentration. For patients with at least one
correctly filled sample, other underfilled samples were corrected for
volume by potassium levels in both the correctly filled sample and the
underfilled sample(s). Patients with potassium-corrected samples did not
perform worse in our model then patients uncorrected samples, although
this could not be statistically proven due to the small number of
patients. For future research with home monitoring of anti-TNF serum
concentrations, a more robust sampling method (e.g. wet blood collection
with microsampling tubes) is recommended to avoid these sampling and
correction issues.
We used an electronic needle container to collect data on timing of
adalimumab administration. Unfortunately, the electronic needle
container was not able to generate a report in all cases. Therefore, on
a few occasions interpolations for the timing of adalimumab
administration were necessary. We do not expect this will influence the
outcome of our study since adalimumab has a long terminal elimination
half-life and it concerned only a single administration in a series of
administrations. For implementation of our adalimumab monitoring
concept, other systems such as mobile health apps may be good
alternatives.
Conclusion
In this study we have demonstrated prospectively that our model is
fit-for-purpose for early prediction of adalimumab levels at steady
state. This concept enables early precision dosing at home to guide
therapy.
Acknowledgements: We thank Wil Adriaans, Louise Merry-Meier and
Antoinette Piepenbrock-van Schooten for their efforts for the inclusion
of patients in this trial.
Conflict of interest disclosure: the authors declare that there is no
conflict of interest
Funding information: This study was funded by Máxima Medical Center
Data availability statement: raw data were generated at Máxima Medical
Center and Radboud University Medical Center. Derived data supporting
the findings of this study are available from the corresponding author
[PK] on request.
References
Tables
Table 1:
Patient demographics at baseline