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@PHDTHESIS{Baier:862923,
      author       = {Baier, Vanessa},
      othercontributors = {Blank, Lars M. and Küpfer, Lars},
      title        = {{P}hysiologically-based pharmacokinetic modelling for the
                      prediction of adverse drug reactions; 1. {A}uflage},
      volume       = {29},
      school       = {RWTH Aachen University},
      type         = {Dissertation},
      address      = {Aachen},
      publisher    = {Apprimus Verlag},
      reportid     = {RWTH-2023-00622},
      isbn         = {978-3-98555-135-4},
      series       = {Applied microbiology},
      pages        = {1 Online-Ressource : Illustrationen, Diagramme},
      year         = {2023},
      note         = {Druckausgabe: 2023. - Auch veröffentlicht auf dem
                      Publikationsserver der RWTH Aachen University; Dissertation,
                      RWTH Aachen University, 2022},
      abstract     = {Adverse drug reactions endanger patients’ health and pose
                      a considerable challenge to drug development and medical
                      care. Despite a variety of approaches ranging from in silico
                      up to clinical studies, predicting drug toxicity still fails
                      in many cases due to limited inter-assay or cross-species
                      translatability and the idiosyncrasy of many drug effects.
                      Thus, findings from diverse sources, such as in vitro assays
                      or animal models, need to be jointly analysed and
                      contextualised with individual patient conditions, e.g.,
                      diseases, specific genotypes, or co-medications. Thereby, a
                      systemic understanding and reliable predictions of adverse
                      reaction risks become possible. However, experiments
                      mimicking realistic patient scenarios are frequently
                      expensive, infeasible, and insufficient. Therefore,
                      integrating data from different levels into mechanistic in
                      silico models has emerged as a promising and cost-effective
                      alternative to overcome the imbalance between the lack of
                      viable and sound models and the necessity to predict adverse
                      drug reactions effectively. In this work, computational
                      modelling was applied to identify drugs with a high risk of
                      inducing hepatic adverse drug reactions as well as patients
                      prone to experience such. Predisposing patient factors
                      associated with drug toxicity were considered throughout the
                      studies to account for the idiosyncrasy of adverse drug
                      reactions. A model of bile acid circulation was developed to
                      investigate drug-induced cholestasis by coupling it to a
                      drug-specific whole-body physiologically-based
                      pharmacokinetic model. Through contextualisation of
                      physiological knowledge, pharmacokinetic data, genotype, and
                      in vitro inhibition data, the model allowed the simulation
                      of bile acid levels in healthy individuals and confirmed
                      cholestasis susceptibility for familial cholestasis
                      genotypes during cyclosporine A treatment. The further
                      integration of time-resolved expression data from a
                      drug-treated in vitro assay into the model enabled a
                      systematic categorisation of the cholestasis risk of several
                      hepatotoxic drugs. By providing a framework to benchmark
                      potentially cholestatic drugs against a reference dataset of
                      ten drugs, this approach could support the identification of
                      drug-induced cholestasis in drug development in the future.
                      Finally, to assist patient safety in clinical care,
                      computational modelling was utilised to guide a clinical
                      test strategy striving for a personalised treatment decision
                      by investigating the individual metabolic phenotype of a
                      patient. The simulations of virtual populations permitted to
                      differentiate between biometric and metabolic contributions
                      to drug exposure. Subsequently, recommendations for the test
                      strategy were derived to support optimal study design in
                      terms of sampling time points or selection of compounds. The
                      presented approaches support the early identification of
                      adverse drug reactions during drug development as well as in
                      routine health care. Thus, by elucidating the link between
                      individual patient factors and adverse drug reactions, this
                      work can be employed to increase patients’ safety and
                      optimise drug development in the future.},
      cin          = {161710 / 160000},
      ddc          = {570},
      cid          = {$I:(DE-82)161710_20140620$ / $I:(DE-82)160000_20140620$},
      typ          = {PUB:(DE-HGF)11 / PUB:(DE-HGF)3},
      doi          = {10.18154/RWTH-2023-00622},
      url          = {https://publications.rwth-aachen.de/record/862923},
}