@article{HetheyHartungWangorschetal.2021,
  author    = {Hethey, Christoph Philipp and Hartung, Niklas and Wangorsch, Gaby and Weisser, Karin and Huisinga, Wilhelm},
  title     = {Physiology-based toxicokinetic modelling of aluminium in rat and man},
  series = {Archives of toxicology : official journal of EUROTOX},
  volume    = {95},
  journal   = {Archives of toxicology : official journal of EUROTOX},
  number    = {9},
  publisher = {Springer},
  address   = {Berlin ; Heidelberg},
  issn      = {0340-5761},
  doi       = {10.1007/s00204-021-03107-y},
  pages     = {2977 -- 3000},
  year      = {2021},
  abstract  = {A sufficient quantitative understanding of aluminium (Al) toxicokinetics (TK) in man is still lacking, although highly desirable for risk assessment of Al exposure. Baseline exposure and the risk of contamination severely limit the feasibility of TK studies administering the naturally occurring isotope Al-27, both in animals and man. These limitations are absent in studies with Al-26 as a tracer, but tissue data are limited to animal studies. A TK model capable of inter-species translation to make valid predictions of Al levels in humans-especially in toxicological relevant tissues like bone and brain-is urgently needed. Here, we present: (i) a curated dataset which comprises all eligible studies with single doses of Al-26 tracer administered as citrate or chloride salts orally and/or intravenously to rats and humans, including ultra-long-term kinetic profiles for plasma, blood, liver, spleen, muscle, bone, brain, kidney, and urine up to 150 weeks; and (ii) the development of a physiology-based (PB) model for Al TK after intravenous and oral administration of aqueous Al citrate and Al chloride solutions in rats and humans. Based on the comprehensive curated Al-26 dataset, we estimated substance-dependent parameters within a non-linear mixed-effect modelling context. The model fitted the heterogeneous Al-26 data very well and was successfully validated against datasets in rats and humans. The presented PBTK model for Al, based on the most extensive and diverse dataset of Al exposure to date, constitutes a major advancement in the field, thereby paving the way towards a more quantitative risk assessment in humans.},
  language  = {en}
}
@article{HartungWahlRastogietal.2021,
  author    = {Hartung, Niklas and Wahl, Martin and Rastogi, Abhishake and Huisinga, Wilhelm},
  title     = {Nonparametric goodness-of-fit testing for parametric covariate models in pharmacometric analyses},
  series = {CPT: pharmacometrics \& systems pharmacology},
  volume    = {10},
  journal   = {CPT: pharmacometrics \& systems pharmacology},
  number    = {6},
  publisher = {Nature Publ. Group},
  address   = {London},
  issn      = {2163-8306},
  doi       = {10.1002/psp4.12614},
  pages     = {564 -- 576},
  year      = {2021},
  abstract  = {The characterization of covariate effects on model parameters is a crucial step during pharmacokinetic/pharmacodynamic analyses. Although covariate selection criteria have been studied extensively, the choice of the functional relationship between covariates and parameters, however, has received much less attention. Often, a simple particular class of covariate-to-parameter relationships (linear, exponential, etc.) is chosen ad hoc or based on domain knowledge, and a statistical evaluation is limited to the comparison of a small number of such classes. Goodness-of-fit testing against a nonparametric alternative provides a more rigorous approach to covariate model evaluation, but no such test has been proposed so far. In this manuscript, we derive and evaluate nonparametric goodness-of-fit tests for parametric covariate models, the null hypothesis, against a kernelized Tikhonov regularized alternative, transferring concepts from statistical learning to the pharmacological setting. The approach is evaluated in a simulation study on the estimation of the age-dependent maturation effect on the clearance of a monoclonal antibody. Scenarios of varying data sparsity and residual error are considered. The goodness-of-fit test correctly identified misspecified parametric models with high power for relevant scenarios. The case study provides proof-of-concept of the feasibility of the proposed approach, which is envisioned to be beneficial for applications that lack well-founded covariate models.},
  language  = {en}
}