Pharmacometric Modeling in Rheumatoid Arthritis

70 Pages·2015·1.55 MB·English

Digital Comprehensive Summaries of Uppsala Dissertations
from the Faculty of Pharmacy 199
Pharmacometric Modeling in
Rheumatoid Arthritis
BRIGITTE LACROIX
ACTA
UNIVERSITATIS
ISSN 1651-6192
UPSALIENSIS
ISBN 978-91-554-9221-2
UPPSALA
urn:nbn:se:uu:diva-247917
2015
Dissertation presented at Uppsala University to be publicly examined in B42, BMC, Uppsala,
Friday, 22 May 2015 at 13:15 for the degree of Doctor of Philosophy (Faculty of Pharmacy).
The examination will be conducted in English. Faculty examiner: Dr. Ekaterina Gibiansky.
Abstract
Lacroix, B. 2015. Pharmacometric Modeling in Rheumatoid Arthritis. Digital Comprehensive
Summaries of Uppsala Dissertations from the Faculty of Pharmacy 199. 68 pp. Uppsala: Acta
Universitatis Upsaliensis. ISBN 978-91-554-9221-2.
Biologic therapies have revolutionized the treatment of rheumatoid arthritis, a common
chronic inflammatory disease, mainly characterized by the chronic inflammation of the joints.
The activity and progression of the disease are highly variable, both between subjects and
between the successive assessments for the same subject. Standardized assessments of clinical
variables have been developed to reflect the disease activity and evaluate new therapies.
Pharmacokinetics-pharmacodynamic (PKPD) models and methods for analyzing the generated
time-course data are needed to improve the interpretation of the clinical trials’ outcomes, and
to describe the variability between subjects, including patients characteristics, disease factors
and the use of concomitant treatments that may affect the response to treatment. In addition,
good simulation properties are also desirable for predicting clinical responses for various
populations or for different dosing schedules. The aim of this thesis was to develop methods and
models for analyzing pharmacokinetic and pharmacokinetic-pharmacodynamic (PKPD) data
from rheumatoid arthritis patients, illustrated by treatment with a new anti-TNFα biologic drug
under clinical development, certolizumab pegol.
Two models were developed that characterized the relationship between the exposure to the
drug and the efficacy ACR variables that represent improvement of the disease; a logistic-
type Markov model for 20% improvement (ACR20) and a continuous-type Markov model for
simultaneous analysis of 20% (ACR20), 50% (ACR50) and 70% (ACR70) improvement. Both
models accounted for the within-subjects correlation in the successive clinical assessments and
were able to capture the observed ACR responses over time. Simulations from these models of
the ACR20 response rate supported dosing regimens of 400 mg at weeks 0, 2 and 4 to achieve
a rapid onset of response to the treatment, followed by 200 mg every 2 weeks, or alternative
maintenance regimen of 400 mg every 4 weeks.
The immunogenicity induced by the biologic drug was characterized by a time to event model
describing the time to appearance of antibodies directed against the drug. The immunogenicity
was predicted to appear mainly during the first 3 months following the start of the treatment and
to be reduced at higher trough concentrations of CZP, as well as with concomitant administration
of MTX.
The full time-course of sequential events, such as dose-exposure-efficacy relations, is most
accurately described by a simultaneous analysis of all data. However, due to the complexity
and runtime limitations of such an analysis, alternatives are often used. In this thesis, a method,
IPPSE, was developed and compared to the reference simultaneous method and to existing
alternative methods. The IPPSE method was shown to provide accuracy and precision of
estimates similar to the simultaneous method, but with easier implementation and shorter run
times.
In conclusion, two PKPD models and one immunogenicity model were developed for
evaluation of the response of a biologic drug against rheumatoid arthritis that allowed accurate
analysis and simulation of clinical trial data, as well as serving as examples for how a model-
informed basis for decisions about biological drugs can be created.
Keywords: rheumatoid arthritis, PKPD, immunogenicity, ACR, IPPSE, certolizumab pegol
Brigitte Lacroix, Department of Pharmaceutical Biosciences, Box 591, Uppsala University,
SE-75124 Uppsala, Sweden.
© Brigitte Lacroix 2015
ISSN 1651-6192
ISBN 978-91-554-9221-2
urn:nbn:se:uu:diva-247917 (http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-247917)
Le voyage permet d’ouvrir une brèche et de se replacer à la bonne
échelle. Un peu comme quand on mesure un centimètre sur une carte
face à l’immensité du monde. Ce voyage a été un bon mètre-étalon pour
moi.
Zazie (Rendez-vous en terre inconnue)
The journey permits to open a window and to put ourselves on the right scale. A bit like
when measuring one centimeter on a map face to the immensity of the world.
This journey has been a good yardstick for me.
Zazie (Rendez-vous en terre inconnue)

List of Papers
This thesis is based on the following papers, which are referred to in the text
by their Roman numerals.

I Lacroix BD, Lovern MR, Stockis A, Sargentini-Maier ML,
Karlsson MO and Friberg LE.
A pharmacodynamic Markov mixed-Effects model for
determining the effect of exposure to certolizumab pegol on the
ACR20 Score in patients with rheumatoid arthritis.
Clinical Pharmacology and Therapeutics, 86:387–395 (2009)

II Lacroix BD, Karlsson MO and Friberg LE.
Simultaneous exposure-response modeling of ACR20, ACR50,
and ACR70 improvement scores in rheumatoid arthritis patients
treated with certolizumab pegol.
CPT Pharmacometrics and System Pharmacology, 3:e143. Doi:
10.1038/psp.2014.41 (2014)

III Lacroix BD, Kirby H, Oliver R, Parker G, Friberg LE and
Karlsson MO.
A time-to-event model for the immunogenicity of certolizumab
pegol in rheumatoid arthritis subjects.
In manuscript

IV Lacroix BD, Friberg LE and Karlsson MO.
Evaluation of IPPSE, an alternative method for sequential
population PKPD analysis.
Journal of Pharmacokinetics and Pharmacodynamics, 39:177–
193 (2011)
Reprints were made with permission from the respective publishers.

Contents
Introduction ................................................................................................... 11 
Rheumatoid arthritis ................................................................................. 11 
Pathogenesis and Epidemiology .......................................................... 11 
Pathophysiology and TNF-α role ........................................................ 11 
Treatment ............................................................................................. 14 
Classification and clinical assessment ................................................. 18 
Pharmacometrics ...................................................................................... 19 
Population methods ............................................................................. 20 
PKPD modeling ................................................................................... 21 
Dropout modeling ................................................................................ 22 
Categorical data modeling ................................................................... 23 
Simulations .......................................................................................... 28 
Aims .............................................................................................................. 29 
Materials and methods .................................................................................. 30 
Data .......................................................................................................... 30 
Clinical studies .................................................................................... 30 
Exposure measures .............................................................................. 32 
PD measures ........................................................................................ 34 
Covariates ............................................................................................ 34 
Modeling .................................................................................................. 35 
Software and data analysis ................................................................... 35 
Model building .................................................................................... 36 
Variability model ................................................................................. 36 
Dropout model ..................................................................................... 36 
Model evaluation ................................................................................. 37 
Model-based simulations ..................................................................... 37 
ACR20 structural model ........................................................................... 37 
ACR20/50/70 structural model ................................................................ 39 
Immunogenicity model structure .............................................................. 40 
Evaluation of IPPSE ................................................................................. 40 
PKPD approaches ................................................................................ 41 
PKPD models ...................................................................................... 41 
Model parameters and study design .................................................... 42 
Performance measures ......................................................................... 43
Results ........................................................................................................... 44 
Reduced vs. Full joint count ..................................................................... 44 
Time effect ............................................................................................... 44 
Exposure-response ................................................................................... 45 
Covariates ................................................................................................. 47 
Dropout .................................................................................................... 47 
IPPSE method .......................................................................................... 49 
Precision in parameter estimation ........................................................ 49 
Accuracy of PD parameter estimation ................................................. 49 
Estimation time .................................................................................... 50 
Estimation status .................................................................................. 50 
Discussion ..................................................................................................... 51 
Conclusions ................................................................................................... 58 
Acknowledgments......................................................................................... 59 
References ..................................................................................................... 63
Abbreviations and definitions
Ab+ Antibody positive
ACPA Antibodies to citrullinated protein antigen
ACR American college of rheumatology
ADA(s) Anti-drug antibody(ies)
AUC Area under the curve
Cavg Average concentration over the last dosing interval
CL/F Apparent clearance
Cmax Maximum serum concentration over a dosing interval
Cp Predicted individual concentration at time of PD observation
CPU Central processing unit
CTLA4–Ig Cytotoxic T lymphocyte antigen 4–immunoglobulin
CRP C-reactive protein
CV Coefficient of variation
DMARDs Disease modifying anti-rheumatic drugs
ESR Erythrocyte sedimentation rate
EULAR European league against rheumatism
Fab The fragment antigen-binding of an antibody (the region that
binds to antigens)
Fc The fragment crystallizable region of an antibody
FDA Food and Drug Administration
GM-CSF Granulocyte macrophage colony stimulating factor
HAQ-DI Health assessment questionnaire disability index
IgG Immunoglobulin class G
IIV Inter-individual variability
IL Interleukine
INF-α interferon α
IPP Method for sequential PKPD analysis using individual PK
parameters
IPPSE Method for SEQ sequential analysis using individual PK
parameters and their standard errors
LIF Leucocyte inhibitory factor
LT Lymphotoxine
LV Latent variable
M-CSF Macrophage colony stimulating factor
NSAIDs Non-steroidal anti-inflammatory drugs
OFV Objective function value
PEG Polyethylene glycol
PK Pharmacokinetic, pharmacokinetics
PD Pharmacodynamic, pharmacodynamics
PPP&D Method for SEQ PKPD analysis using population PK pa-
rameters and PK data
Q2W Every 2 weeks,
Q4W Every 4 weeks
RF Rheumatoid factor
SE Standard error
SEQ Sequential method for PKPD analysis
SIM Simultaneous method for PKPD analysis
SJC Swollen joint count
TGFβ Transforming growth factor beta
TJC Tender joint count
TNF Tumor necrosis factor
TTE Time to event
VAS Visual analog scale
VPC Visual predictive check