Statistical relationship between metabolic decomposition and chemical uptake predicts bioconcentration factor data for diverse chemical exposures

Michael A. Rowland, Hannah Wear, Karen Watanabe-Sailor, Kurt A. Gust, Michael L. Mayo

Research output: Contribution to journalArticle

Abstract

BACKGROUND: A challenge of in vitro to in vivo extrapolation (IVIVE) is to predict the physical state of organisms exposed to chemicals in the environment from in vitro exposure assay data. Although toxicokinetic modeling approaches promise to bridge in vitro screening data with in vivo effects, they are often encumbered by a need for redesign or re-parameterization when applied to different tissues or chemicals. RESULTS: We demonstrate a parameterization of reverse toxicokinetic (rTK) models developed for the adult zebrafish (Danio rerio) based upon particle swarm optimizations (PSO) of the chemical uptake and degradation rates that predict bioconcentration factors (BCF) for a broad range of chemicals. PSO reveals a relationship between chemical uptake and decomposition parameter values that predicts chemical-specific BCF values with moderate statistical agreement to a limited yet diverse chemical dataset, and all without a need to retrain the model to new data. CONCLUSIONS: The presented model requires only the octanol-water partitioning ratio to predict BCFs to a fidelity consistent with existing QSAR models. This success begs re-evaluation of the modeling assumptions; specifically, it suggests that chemical uptake into arterial blood may be limited by transport across gill membranes (diffusion) rather than by counter-current flow between gill lamellae (convection). Therefore, more detailed molecular modeling of aquatic respiration may further improve predictive accuracy of the rTK approach.

Original languageEnglish (US)
Number of pages1
JournalBMC systems biology
Volume12
Issue number1
DOIs
StatePublished - Aug 7 2018
Externally publishedYes

Fingerprint

Zebrafish
Decomposition
Decompose
Predict
Octanols
Convection
Quantitative Structure-Activity Relationship
Parameterization
Respiration
Particle swarm optimization (PSO)
Particle Swarm Optimization
Reverse
Membranes
Water
Molecular modeling
Molecular Modeling
Reparameterization
Quantitative Structure-activity Relationship
Relationships
Toxicokinetics

Keywords

  • Bioconcentration factors
  • IVIVE
  • Physiologically based toxicokinetics
  • Reverse toxicokinetics

ASJC Scopus subject areas

  • Structural Biology
  • Modeling and Simulation
  • Molecular Biology
  • Computer Science Applications
  • Applied Mathematics

Cite this

Statistical relationship between metabolic decomposition and chemical uptake predicts bioconcentration factor data for diverse chemical exposures. / Rowland, Michael A.; Wear, Hannah; Watanabe-Sailor, Karen; Gust, Kurt A.; Mayo, Michael L.

In: BMC systems biology, Vol. 12, No. 1, 07.08.2018.

Research output: Contribution to journalArticle

Rowland, Michael A. ; Wear, Hannah ; Watanabe-Sailor, Karen ; Gust, Kurt A. ; Mayo, Michael L. / Statistical relationship between metabolic decomposition and chemical uptake predicts bioconcentration factor data for diverse chemical exposures. In: BMC systems biology. 2018 ; Vol. 12, No. 1.
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