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@PHDTHESIS{Krau:668404,
author = {Krauß, Markus},
othercontributors = {Schuppert, Andreas and Mitsos, Alexander},
title = {{B}ayesian population {PBPK} approach for support of drug
development},
school = {Rheinisch-Westfälische Technische Hochschule Aachen},
type = {Dissertation},
address = {Aachen},
reportid = {RWTH-2016-06740},
pages = {1 Online-Ressource (135 Seiten) : Illustrationen,
Diagramme},
year = {2016},
note = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
University; Dissertation, Rheinisch-Westfälische Technische
Hochschule Aachen, 2016},
abstract = {Low likelihood-of-approval rates of new drugs constitute a
major problem in clinical development. Only one out of ten
development programs entering the first clinical phase
succeeds in being approved by the U.S. Food and Drug
Administration (FDA) [1]. A main challenge is thereby an
insufficient understanding and prediction of drug safety and
efficacy, leading to the withdrawal of new drug candidates
[2,3]. Here, model-based assessment of drug exposure and
response can support the development process at all stages,
starting from early preclinical to late clinical phases
[4,5]. Thus, the quantification of interindividual
variability in clinical outcomes and the identification of
related sources of such variability are of utmost importance
e.g. for individualized dosing strategies [6–8].
Furthermore, of particular interest are translational
approaches that transfer and integrate knowledge of recent
study programs or earlier steps of drug development in order
to make improved conclusions about drug behavior in
clinically-relevant populations [3,9]. In this thesis, we
present a Bayesian population physiologically-based
pharmacokinetic (PBPK) approach for assessment of
interindividual variability and clinical translation.
Therein, we combine large-scale mechanistic PBPK models
describing the behavior of drugs within the body with a
Bayesian statistical framework for efficient estimation of
the parameter space. The parameter space consists of clearly
deconvoluted physiological- and drug-specific parameters,
which facilitates the use of large amounts of prior
information about the parameters. Such prior knowledge is
updated with information extracted from experimental data by
considering the Bayesian theorem and in particular
performing a Markov chain Monte Carlo (MCMC) approach. This
allows to solve the inverse and strongly ill-posed parameter
estimation problem and at the same time preserve the
extrapolation capabilities of PBPK models. Our Bayesian
population PBPK approach represents a specifically-designed
workflow for whole-body PBPK models to take into account the
distinct properties of such models and guarantee a generic
form for broad applicability and to support translation of
knowledge. The overall framework contains i.a. an
hierarchical model to separate individual uncertainty about
the parameters from population variability, and a covariate
model to cope for systematic relationships of model
parameters to age, gender and body height. We further
provide a method for estimation of the a posteriori
parameter dependency structure at the population level. A
blockwise MCMC sampling structure reduces complexity and
accounts for the different types of parameters that are
estimated. Additionally, we present an adaptive sampling
method that combines gradient-based sampling with continuous
adaptation of the proposal scaling for a strongly improved
performance of the MCMC approach compared to standard
methods. Moreover, we establish a translational learning
workflow, where our Bayesian population PBPK approach is
iteratively conducted in several learning steps to finally
predict an unsupervised scenario, e.g. the pharmacokinetic
behavior of a diseased population after administration of a
new drug candidate.Subsequently, three application examples
represent how to support different phases of drug
development by the developed workflow. In the first example
we successfully identify clinically-relevant subgroups in a
cohort of individuals. These findings can improve safety and
efficacy assessment in a clinical phase I study. In the
second example we determine the interindividual variability
in the pharmacokinetic behavior and the underlying
physiological parameters, and improve population simulations
by adding estimated information about parameter
dependencies. We further reveal the performance of our new
adaptive MCMC approach. In the third example we predict the
pharmacokinetic behavior in a cohort of patients after
accumulation of available study data in three iterations of
our Bayesian population PBPK approach. We here successfully
demonstrate the concept of translational learning from a
phase I to a phase II study, taking into account the derived
pathophysiology of the population. Overall, these examples
indicate the capabilities of our approach in accumulation of
knowledge and extrapolation of drug behavior. Applied to
drug development programs, our method could improve clinical
trial design to increase the benefit/risk ratio of new
compounds. Our model-based concept could hence give
significant support to raise approval rates of new drugs in
the future.},
cin = {080003 / 416210},
ddc = {620},
cid = {$I:(DE-82)080003_20140620$ / $I:(DE-82)416210_20140620$},
typ = {PUB:(DE-HGF)11},
urn = {urn:nbn:de:hbz:82-rwth-2016-067402},
doi = {10.18154/RWTH-2016-06740},
url = {https://publications.rwth-aachen.de/record/668404},
}
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