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Related Concept Videos

Clearance Models: Physiological Models01:09

Clearance Models: Physiological Models

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Drug clearance is a critical pharmacokinetic process involving the irreversible removal of drugs from the body through various organs over a specified time period. Physiological models are indispensable in determining organ-specific clearance, defined by the proportion of the drug eliminated per unit of time from the organ's blood volume.
The organ's clearance rate depends on the blood flow to the organ and the extraction ratio (E). The extraction ratio describes the organ's...
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Alternative RNA splicing is the regulated splicing of exons and introns to produce different mature mRNAs from a single pre-mRNA. Unlike in constitutive splicing where a single gene produces a single type of mRNA, alternative splicing allows an organism to produce multiple proteins from a single gene and plays an important role in protein diversity.
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Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

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Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
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Null and Alternative Hypotheses01:16

Null and Alternative Hypotheses

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The actual hypothesis testing begins by considering two hypotheses. They are termed  the null hypothesis and the alternative hypothesis. These hypotheses contain opposing viewpoints.
The null hypothesis, denoted by H0 is a statement of no difference between the variables—they are not related. This can often be considered the status quo. As  a result if you cannot accept the null, it requires some action.
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Physiological Pharmacokinetic Models: Assumption with Protein Binding01:13

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Physiological models with protein binding in pharmacokinetics offer a sophisticated approach to understanding drug disposition. These models consider drug-protein interactions, enabling them to effectively predict drug concentrations in different organs and tissues. This precision aids in accurate drug dosing, providing a significant advantage over conventional models. A key process within these models is equilibration, which ensures that drug concentrations achieve a steady state within the...
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The Madagascar Hissing Cockroach as an Alternative Non-mammalian Animal Model to Investigate Virulence, Pathogenesis, and Drug Efficacy
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Bottom-up physiologically-based biokinetic modelling as an alternative to animal testing.

James C Y Chan1,2, Shawn P F Tan2,3, Zee Upton1,4

  • 1Skin Research Institute of Singapore, Agency for Science Technology and Research, Singapore.

ALTEX
|May 13, 2019
PubMed
Summary

Physiologically-based biokinetic (PBK) models using in vitro data offer a promising alternative to animal testing for chemical safety. These bottom-up models accurately predicted drug exposure, demonstrating the potential of in vitro-to-in vivo extrapolation.

Keywords:
In vitro-to-in vivo extrapolationmetabolismtransportersmechanistic scalingquantitative proteomics

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Area of Science:

  • Pharmacokinetics and Drug Metabolism
  • Toxicology and Safety Assessment
  • Computational Biology and Modeling

Background:

  • There is a critical need for non-animal methods to assess chemical pharmacokinetics and safety.
  • Physiologically-based biokinetic (PBK) modeling, utilizing in vitro data, presents a viable alternative to traditional animal testing.
  • Understanding chemical biokinetics is essential for evaluating drug efficacy and safety.

Purpose of the Study:

  • To develop and validate bottom-up physiologically-based biokinetic (PBK) models for three HMG-CoA reductase inhibitors: rosuvastatin, fluvastatin, and pitavastatin.
  • To assess the accuracy of these models in predicting systemic exposure (AUC0h-t), maximum plasma concentration (Cmax), plasma clearance, and time to reach Cmax (Tmax).
  • To evaluate the utility of quantitative proteomics-based mechanistic in vitro-to-in vivo extrapolation (IVIVE) in predicting human biokinetics.

Main Methods:

  • Constructed bottom-up PBK models using the Simcyp® Simulator, integrating in vitro metabolism and transporter data (Vmax, Jmax, Km, CLint).
  • Employed proteomics-based scaling factors to adjust for differences in transporter expression between in vitro systems and in vivo organs.
  • Performed simulations for single intravenous, single oral, and multiple oral doses of the selected statins, with additional middle-out simulations using animal distribution data.

Main Results:

  • The developed bottom-up PBK models generally predicted key pharmacokinetic parameters (AUC0h-t, Cmax, CL, Tmax) within a two-fold margin of observed data.
  • Exceptions were noted for multiple oral pitavastatin dosing and single oral fluvastatin dosing, indicating areas for model refinement.
  • Middle-out simulations using animal distribution data improved plasma-concentration time profiles but did not significantly alter predicted biokinetic parameters.

Conclusions:

  • Quantitative proteomics-based mechanistic IVIVE can accurately predict whole organ clearances, accounting for transporter downregulation in vitro.
  • Bottom-up PBK modeling, when integrated with mechanistic IVIVE, serves as a robust, animal-free alternative for predicting human biokinetics.
  • This approach supports the development of safer and more effective chemicals without reliance on animal testing.