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Related Concept Videos

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
Dosage Regimens: Partial Pharmacokinetic Parameters01:01

Dosage Regimens: Partial Pharmacokinetic Parameters

It is not uncommon for complete drug pharmacokinetic profiles to remain elusive in pharmacokinetics. This necessitates certain educated assumptions by pharmacokineticists to determine appropriate dosage regimens without comprehensive pharmacokinetic data from animal or human studies. One prevalent assumption is setting the bioavailability factor, denoted as F, to 1 or 100%. This assumption caters to the scenario where a drug doesn't achieve full systemic absorption, resulting in the patient...
Pharmacodynamic Models: Direct Effect Model and Indirect Response Model01:29

Pharmacodynamic Models: Direct Effect Model and Indirect Response Model

Pharmacodynamic models are essential tools in understanding the relationship between drug concentrations and their effects on biological systems. By characterizing the dynamics of drug action, these models guide dose selection, optimize therapeutic efficacy, and inform the development of new drugs. Two major classes of pharmacodynamic models include direct effect and indirect response models.Direct Effect ModelsDirect effect models describe the immediate relationship between drug concentration...
Pharmacokinetic–Pharmacodynamic Relationship: Problems01:24

Pharmacokinetic–Pharmacodynamic Relationship: Problems

The empirical approach to drug therapy optimization relies on correlating pharmacological response with administered dosage. Such an approach can be costly, time-consuming, and often yields poor correlation due to variables like formulation factors and drug elimination characteristics. A more precise approach correlates response with plasma drug concentration or the amount of drug in the body, rather than dosage. This is achieved through pharmacokinetic-pharmacodynamic (PK/PD) modeling, which...
Pharmacodynamic Models: Linear Concentration–Effect Model01:15

Pharmacodynamic Models: Linear Concentration–Effect Model

The linear concentration–effect model, underpinned by the principle that pharmacological effect (E) is directly proportional to plasma drug concentration (C), emerges as a pivotal simplification of the Emax model for conditions where C is significantly less than EC50. This model portrays a linear trajectory of the concentration–effect relationship when drug levels are markedly below the EC50 threshold.Despite its inherent assumption of continuous effect augmentation with increasing drug...
Pharmacodynamic Models: Link Model and Systems Pharmacodynamic Model01:14

Pharmacodynamic Models: Link Model and Systems Pharmacodynamic Model

The link model is a fundamental pharmacokinetic-pharmacodynamic (PK–PD) approach to account for delayed drug responses when the observed effect does not immediately correlate with the drug's plasma concentration peak. This delay is mathematically addressed by introducing an effect compartment concentration, Ce, which is kinetically linked to the plasma concentration, Cp, via a first-order rate constant, ke0. The linkage allows for a more accurate prediction of drug effects over time. A higher...

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Measurement of Particle Size Distribution in Turbid Solutions by Dynamic Light Scattering Microscopy
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Published on: January 9, 2017

Density functional method including weak interactions: Dispersion coefficients based on the local response

Takeshi Sato1, Hiromi Nakai

  • 1Research Institute for Science and Engineering, Waseda University, Tokyo 169-8555, Japan.

The Journal of Chemical Physics
|December 17, 2009
PubMed
Summary

A novel local response dispersion (LRD) method accurately calculates molecular dispersion energy from electron density alone. This efficient, non-empirical approach shows excellent agreement with ab initio references for weakly bound complexes.

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Area of Science:

  • Computational Chemistry
  • Quantum Chemistry
  • Materials Science

Background:

  • Accurate calculation of dispersion energy is crucial for describing intermolecular interactions in molecules.
  • Existing density functional theory with dispersion (DFT-D) methods often rely on empirical parameters or approximations.
  • A need exists for non-empirical, efficient methods to compute dispersion coefficients from first principles.

Purpose of the Study:

  • To introduce a new, non-empirical method for calculating atom-atom dispersion coefficients within density functional theory.
  • To develop a computationally efficient approach based on the local response approximation and a modified dielectric model.
  • To assess the performance of the new method when combined with long-range corrected density functional theory functionals.

Main Methods:

  • The proposed method utilizes the local response approximation (Dobson and Dinte, 1996) with a modified dielectric model (Vydrov and van Voorhis, 2009).
  • Distributed multipole polarizabilities are calculated from the local response model, enabling the computation of dispersion coefficients via a Casimir-Polder type frequency integral.
  • Atomic polarizabilities derived from this method are also employed in the damping function for short-range interactions.

Main Results:

  • The local response dispersion (LRD) method calculates dispersion energy solely from the ground-state electron density, eliminating the need for empirical constants.
  • The method is applicable to any molecular geometry and is computationally efficient.
  • When combined with the long-range corrected DFT functional (LC-BOP), the LC-BOP+LRD approach yields binding energies for the S22 weakly bound complex set that closely match ab initio references.

Conclusions:

  • The LRD method provides an accurate and efficient non-empirical way to compute dispersion coefficients for DFT-D.
  • This approach overcomes limitations of empirical DFT-D methods by relying only on the electron density.
  • The excellent agreement with ab initio results demonstrates the potential of LRD for accurate modeling of non-covalent interactions.