Early model-based precision-dosing at home to guide adalimumab
therapy
Running title: early prediction of adalimumab levels
Paul A.G. de Klaver1, Ron J.
Keizer2, Rob ter Heine3, Frank
Hoentjen4, Paul J. Boekema5, Inge
Kuntzel6, Tiny Schaap7, Annick de
Vries8, Karien Bloem9, Theo
Rispens10, Lisa Smits11, Luc J.J.
Derijks12
Author’s institutional affiliations and email address:
1 Máxima Medical Center, Department of Pharmacy and Clinical
Pharmacology, Veldhoven, the Netherlands, p.deklaver@mmc.nl
2 InsightRx Inc, San Francisco, CA, US, ron@insight-rx.com
3 Radboud University Medical Center, Radboud Institute for Health
Sciences, Department of Pharmacy, Nijmegen, the Netherlands,
R.terHeine@radboudumc.nl
4 Radboud University Medical Center, Department of Gastroenterology,
Nijmegen, the Netherlands, Division of Gastroenterology, Department of
Medicine, University of Alberta, Edmonton, Canada, hoentjen@ualberta.ca
5 Máxima Medical Center, Department of Gastroenterology, Veldhoven, the
Netherlands, P.Boekema@mmc.nl
6 Máxima Medical Center, Department of Rheumatology, Eindhoven, the
Netherlands, I.Kuntzel@mmc.nl
7 Biologics Laboratory, Sanquin Diagnostic Services, Amsterdam, The
Netherlands, j.schaap@sanquin.nl.
8 Biologics Laboratory, Sanquin Diagnostic Services, Amsterdam, The
Netherlands, annick.devries@sanquin.nl
9 Biologics Laboratory, Sanquin Diagnostic Services, Amsterdam, The
Netherlands, K.Bloem@sanquin.nl
10 Biologics Laboratory, Sanquin Diagnostic Services, Amsterdam, The
Netherlands,
Department of Immunopathology, Sanquin Research, Amsterdam, The
Netherlands, and Landsteiner Laboratory, Academic Medical Centre,
University of Amsterdam, Amsterdam, The Netherlands,
T.Rispens@sanquin.nl
11 Radboud University Medical Center, Department of Gastroenterology,
Nijmegen, the Netherlands, Division of Gastroenterology,
Lisa.Smits@radboudumc.nl
12 Máxima Medical Center, Department of Pharmacy and Clinical
Pharmacology, Veldhoven, the Netherlands, l.derijks@mmc.nl
Ethical statements:
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.
Funding statement: n.a.
Conflict of interest disclosure: the authors declare that there is no
conflict of interest
Ethics approval statement: the study was approved by the local ethics
committee.
Patient consent statement: all patients provided written informed
consent.
Clinical trial registration: the trial was registered in the Netherlands
Trial Register with trial registry number NTR 7692
(www.trialregister.nl).
Keywords: adalimumab, model based precision dosing, Inflammatory bowel
disease, rheumatology
Total word count excluding summary: 2144
- What is already known about this subject
- Capillary adalimumab sampling can be done at home
- Adalimumab treatment can be optimised with therapeutic drug
monitoring
- Underdosing of adalimumab can lead to poor disease control and
non-response
- What this study adds
- Steady state adalimumab concentrations can be predicted after a
single dose
- Home capillary sampling and electronic needle container can be
combined to monitor treatment at home
- MAP Bayesian forecasting can be used for precision dosing of
adalimumab
- Abstract
- Aims
Underdosing of adalimumab can result in non-response and poor disease
control. In this study we investigated the prediction of adalimumab
levels with population pharmacokinetic model-based Bayesian forecasting
early in therapy. This way underexposed non-responders can possibly be
identified early to optimise disease control.
Methods
A literature study was performed to identify adalimumab pharmacokinetic
models. With data from a previous pharmacokinetic adalimumab study a
model was evaluated retrospectively. In the prospective phase, a
fit-for-purpose evaluation of the model was performed for rheumatologic
and inflammatory bowel disease patients with peak, trough and control
adalimumab samples obtained by a volumetric absorptive microsampling
technique and administration data from an electronic needle container.
Steady state adalimumab levels were predicted from peak and trough
levels collected after the first adalimumab administration. Predictive
performance was calculated with mean prediction error (MPE) and
normalized root mean square error (RMSE).
Results
An existing pharmacokinetic model was selected with external validation
for the prospective phase. Thirty-six patients (22 rheumatologic and 14
IBD) were included in our study. After stratification for absence of
anti-adalimumab antibodies, the calculated MPE was -2.6% and normalised
RMSE 24.0%. Concordance between predicted and measured adalimumab serum
levels falling within or outside the therapeutic window was 75%. Three
patients (8.3%) developed detectable levels of anti-adalimumab
antibodies.
Conclusion
This prospective study demonstrates that adalimumab levels at steady
state can be predicted from early samples. This concept enables early
precision dosing at home to guide therapy.
“Clinical trial registry number: Netherlands Trial Register, NTR 7692”
Keywords: model-based precision-dosing adalimumab
Introduction
Adalimumab is a fully human recombinant IgG1k monoclonal antibody
against Tumor Necrosis Factor (TNF) alpha. It is approved for moderate
to severe inflammatory bowel disease (IBD) and the rheumatic diseases
rheumatoid arthritis (RA), psoriatic arthritis (PsA), and ankylosing
spondylitis (SpA) with poor response to conventional immunosuppressants.
Adalimumab is administered subcutaneously. For RA, PsA and SpA the
licensed dose is 40 mg every other week, without induction therapy. For
IBD the licensed induction dose is either 80 mg followed by 40 mg after
two weeks or 160 mg followed by 80 mg after two weeks, the latter
induction scheme being used more frequently in clinical practice. The
licensed maintenance dose is 40 mg every other week.
Up to 30% of patients with IBD do not respond to initial treatment with
TNFα antagonists. It is important to differentiate between true primary
non-responders (pharmacodynamic failure) and underexposed non-responders
(pharmacokinetic failure), to intervene early in latter cases and adjust
dosage to the individual patient. This serves patient satisfaction,
disease control and drug expenses.
Target adalimumab trough-levels can range from 5-12 mg/L and therapeutic
drug monitoring (TDM) can be performed in routine clinical practice,
most often reactively during the maintenance phase of therapy.
Population pharmacokinetic models have been developed and could
theoretically be used for serum level prediction at steady state and
therefore early dosage prediction, but these models have not yet reached
clinical practice.
In the current study, we investigated the feasibility of predicting
adalimumab levels with population pharmacokinetic model-based Bayesian
forecasting early in therapy. This can be used to identify underdosed
non-responders as soon as possible to optimise disease control in
clinical practice.
Materials and Methods
2.1 Population pharmacokinetic model selection
A 3-step-approach as described by ter Heine et al was
used. For step 1, identification of models, a PubMed search for a
population pharmacokinetic adalimumab model was performed and FDA
registration data were evaluated. In step 2, an expert panel of
pharmacometricians and clinical pharmacologists retrospectively
evaluated the predictive performance of the pharmacokinetic models with
data from a published study with IBD patients in Máxima Medical Center
using Nonlinear Mixed Effects Modelling (NONMEM) version 7.4, executed
through the Pirana workbench. Final model selection was based on
Goodness of fit evaluation in line with best practice. Step 3 in this
strategy is described below as the prospective observational cohort
study.
2.2 Study design and population
This multicentre prospective observational cohort study aimed to collect
data from 40 patients ≥ 18 years with IBD or RA, SpA and PsA starting
with adalimumab from March 2019 up to August 2020.
Patients were recruited from Rheumatology and Gastroenterology
departments of Máxima Medical Center, Veldhoven/Eindhoven, the
Netherlands and Gastroenterology department of Radboud University
Medical Center (UMC), Nijmegen, the Netherlands. Adalimumab was dosed
according to label and local clinical care pathways.
Pregnancy, known allergy for adalimumab or excipients and previous
adalimumab use were exclusion criteria. For each drop-out a new patient
was recruited. Patients weight, gender, date of birth and indication for
treatment with adalimumab were collected.
The workflow is shown in figure 1.
2.3 Sampling
Sampling was done with a volumetric absorptive microsampling (VAMS)
method. All patients were provided with 3 sampling sets for capillary
blood microsampling at home. A sampling set consists of two 20
microliter MitraTM microsamplers (Neotyrex, Torrance,
USA) and a BD microtainer 2 mm contact-activated lancet (BD, Dublin,
Ireland). Patients were asked to perform capillary sampling at home 5
days, 13 days and 12 weeks after first adalimumab administration (Figure
1). Patients could receive sampling reminders for each sampling moment
per email or text message on request. Samples were returned and stored
under refrigerated conditions until analysis at Sanquin Diagnostic
Services (Amsterdam, the Netherlands). Patients completed the trial upon
returning the third sample.
2.4 Drug administration monitoring
All patients were required to use a an electronic needle container
(HealthBeacon Injection Care Management SystemTM,
Health Beacon Ltd, Dublin, Ireland). Electronic needle containers were
provided as part of standard care to all patients in this study. The
electronic needle container is a device intended to monitor and improve
compliance for patients on therapy with injectables. It reports the date
and time a syringe is dropped in the device after use. The electronic
needle container reports were automatically sent to Máxima Medical
Center with secure mail.
2.5 Measurement of adalimumab and anti-adalimumab antibody
concentrations