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Computational drug disposition modeling

We use computational modeling and simulation to shed light on the cellular and molecular mechanisms of drug disposition, and to develop predictive tools for improved, more rational drug development.

Modeling is performed at multiple scales using a wide range of techniques. We use a combination of physics-based approaches (molecular dynamics, docking) and empirical structure-property relationship (QSPR) and protein structure modeling to understand the molecular interactions between drugs and cellular membranes and the function of transport proteins.

At the cellular and tissue levels, kinetic and physics-based (stochastic dynamics) techniques are used to study the impact of and interaction between different drug distribution phenomena, and physiologically-based pharmacokinetic modeling is used to connect to the in vivo clinical setting. At all levels data from experiments are integrated in the modeling, to improve predictions and to rationalize experimental observations.

Molecular dynamics simulation of a model drug penetrating an artificial cell membrane.

Molecular dynamics simulation of a model drug penetrating an artificial cell membrane.

Methods

Structure-property relationship models relate descriptions of the molecular structures of drugs to a measured property. The models can then be used to forecast properties of new molecules based solely on their molecular structure. We use such techniques to develop predictive models of, for example, cell permeability and drug interactions with transport proteins, through the use of a variety of machine learning techniques (e.g., partial least squares, random forests and support vector machines).

In a molecular docking experiment, the interactions between molecules (e.g., a drug molecule and a pharmacological target protein or a transporter) are quantified and used to predict the three-dimensional orientation of the molecules that result in optimal binding.

Molecular dynamics simulations are used to describe the motion and interaction of molecules at atomic resolution. We use molecular dynamics to, for example, quantify and visualize the transport of drug molecules across biological membranes. For simulations at greater scales, such as whole cells and multicellular tissues, we use stochastic dynamics to simulate drug distribution phenomena.

Physiologically based pharmacokinetic (PBPK) modeling is used to model and simulate drug absorption, distribution, metabolism and excretion at the organ or whole-body level, using physiologically relevant parameters (for example, transport and metabolic rates, blood flows and organ volumes). Our research into the cellular and molecular mechanisms of drug absorption and disposition feed into such models, and our data has been implemented in the major software platforms for PBPK modeling.

Contact information

Pär Matsson
Professor

E-mail: par.matsson@gu.se

Page Manager: Webbredaktör|Last update: 12/12/2019
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Denna text är utskriven från följande webbsida:
http://neurophys.gu.se/english/departments/pharmacology/pkdm/research/computational-drug-disposition-modeling/
Utskriftsdatum: 2020-04-10