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Developing a Physiologically-Based Pharmacokinetic Model Knowledgebase in Support of Provisional Model Construction.

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Building physiologically-based pharmacokinetic (PBPK) models is challenging. A PBPK knowledgebase using chemical analogues aids in constructing new PBPK models, saving resources and improving predictions.

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Area of Science:

  • Pharmacokinetics and Toxicological Modeling
  • Computational Chemistry
  • Systems Biology

Background:

  • Developing physiologically-based pharmacokinetic (PBPK) models is resource-intensive due to data scarcity.
  • Existing, validated PBPK models can serve as a foundation for new chemical models.

Purpose of the Study:

  • To establish a PBPK knowledgebase from existing literature.
  • To develop a chemical structure-based approach for identifying relevant PBPK models for new chemicals.
  • To demonstrate that analogue PBPK models can guide new model construction.

Main Methods:

  • Compiled a PBPK knowledgebase from 2,039 articles (1977-2013), including 307 unique chemicals.
  • Analyzed keywords related to species, gender, developmental stages, and organs.
  • Calculated a chemical correlation matrix based on molecular descriptors.
  • Selected chemicals as exact matches, close analogues, or non-analogues to target compounds.
  • Constructed new models using data from analogues and compared predictions to observed values.

Main Results:

  • A PBPK knowledgebase of 307 chemicals was created.
  • Chemical analogues were identified using a correlation matrix based on molecular descriptors.
  • Models built using analogue data showed comparable predictions to observed values.
  • The approach successfully guided the construction of PBPK models for new chemicals.

Conclusions:

  • The developed PBPK knowledgebase offers a systematic, chemical structure-based method for PBPK model development.
  • Utilizing analogue data significantly streamlines the creation of PBPK models for novel substances.
  • This approach enhances the efficiency and reliability of PBPK modeling in chemical risk assessment.