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Related Concept Videos

Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

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Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
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Mechanistic Models: Overview of Compartment Models01:21

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Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
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Pharmacokinetic Models: Overview01:20

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Pharmacokinetic models utilize mathematical analysis to achieve a detailed quantitative understanding of a drug's life cycle within the body. They are instrumental in simulating a drug's pharmacokinetic parameters, predicting drug concentrations over time, optimizing dosage regimens, linking concentrations with pharmacologic activity, and estimating potential toxicity.
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Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models00:57

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Physiological pharmacokinetic models, often called flow-limited or perfusion models, typically assume a swift drug distribution between tissue and venous blood, creating a rapid drug equilibrium. This premise is based on the idea that drug diffusion is extremely fast, and the cell membrane presents no barrier to drug permeation. In this scenario, where no drug binding occurs, the drug concentration in the tissue equals that of the venous blood leaving the tissue. This greatly simplifies the...
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Upstream Processing01:27

Upstream Processing

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Upstream processing represents a critical phase in biomanufacturing, wherein biological systems such as microorganisms, mammalian cells, or insect cells are cultivated to produce therapeutic proteins, vaccines, enzymes, or other biologically derived products. This phase encompasses all steps from the selection and genetic manipulation of the production organism to the cultivation of cells in bioreactors under tightly controlled environmental conditions.Host Selection and Genetic OptimizationThe...
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Multicompartment Models: Overview01:14

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Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
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Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes
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Managing critical materials with a technology-specific stocks and flows model.

Jonathan Busch1, Julia K Steinberger, David A Dawson

  • 1Sustainability Research Institute, School of Earth and Environment, University of Leeds , Leeds, West Yorkshire, LS2 9JT, United Kingdom.

Environmental Science & Technology
|December 17, 2013
PubMed
Summary

Low carbon infrastructure requires critical materials like lithium, risking supply disruption. Circular economy policies and advanced modeling are vital for managing these resources through reuse and recycling.

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Area of Science:

  • Materials Science and Engineering
  • Environmental Science and Policy
  • Sustainable Infrastructure Development

Background:

  • Climate change mitigation necessitates a transition to low-carbon infrastructure, introducing novel materials like lithium and rare earth metals.
  • These critical materials face risks of supply disruption, threatening infrastructure sustainability and resilience.
  • Effective circular economy policies are essential for managing these materials but require data on future demand and end-of-life recovery options.

Purpose of the Study:

  • To develop and demonstrate a novel, enhanced stocks and flows model for dynamically assessing material demands in infrastructure transitions.
  • To quantify the effectiveness of recovery strategies, including technology remanufacturing, reuse, and material recycling.
  • To inform policy decisions for sustainable management of critical materials in emerging low-carbon technologies.

Main Methods:

  • Development of an enhanced stocks and flows model with a hierarchical structure of infrastructure technologies, components, and constituent materials.
  • Dynamic assessment of material demands based on infrastructure technology roll-out scenarios.
  • Case study application to electric vehicle deployment in the UK, utilizing Department of Energy and Climate Change data.

Main Results:

  • The model quantifies material demands and recovery potential at both component reuse and material recycling levels.
  • Analysis of UK electric vehicle scenarios indicates a need for established lithium-ion battery recycling infrastructure by 2025.
  • Results suggest neodymium-iron-boron (NdFeB) motor magnets should be designed for reuse to significantly reduce primary demand.

Conclusions:

  • Implementing circular economy policies supported by advanced material flow modeling is crucial for sustainable infrastructure transitions.
  • Policy interventions are recommended to establish battery recycling and promote magnet reuse, potentially reducing primary lithium demand by 40% and neodymium by 70%.
  • The enhanced model provides a robust framework for evaluating material circularity in future infrastructure development and policy planning.