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An Intestine/Liver Microphysiological System for Drug Pharmacokinetic and Toxicological Assessment

An Intestine/Liver Microphysiological System for Drug Pharmacokinetic and Toxicological Assessment

Published on: December 3, 2020

Predictive Systems Toxicology.

Narsis A Kiani^1,2,3, Ming-Mei Shang^4,5, Hector Zenil^4,6,5

¹Unit of Computational Medicine, Center for Molecular Medicine, Department of Medicine, Solna, Karolinska Institutet, Stockholm, Sweden. narsis.kiani@ki.se.

Methods in Molecular Biology (Clifton, N.J.)

|June 24, 2018

Summary

This summary is machine-generated.

Computational techniques enhance toxicity prediction by integrating pharmacokinetic and omics data. This approach improves drug safety assessments and personalized medicine through advanced data analysis and machine learning.

Keywords:

Algorithmic complexity Network pharmacology Omics Systems biology Toxicology

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Published on: May 10, 2016

Area of Science:

Pharmacology and Toxicology
Computational Biology
Systems Biology

Background:

Toxicity assessment has evolved from ancient dose observations to understanding host-organism interactions.
Modern drug evaluation emphasizes personalized medicine and kinetic properties.
Integrating diverse data sources is crucial for accurate toxicity prediction.

Purpose of the Study:

To review the extent to which computational techniques can improve toxicity prediction.
To explore the integration of pharmacokinetic analysis with systems biology approaches.
To discuss challenges and future directions in computational toxicology.

Main Methods:

Review of historical and recent literature on toxicity prediction.
Integration of classical pharmacokinetic analysis with systems biology.
Application of advanced statistical methods and machine learning (ML) techniques.
Utilizing multi-omics data alongside pharmacokinetic data.

Main Results:

Integrated computational approaches significantly improve toxicity prediction accuracy.
These methods provide mechanistic interpretations of toxicity and drug resistance.
Patient-specific toxicity predictions are enabled through data integration.

Conclusions:

Computational techniques, particularly integrated systems biology and ML, are vital for predicting drug toxicity.
Balancing predictive power with mechanistic interpretability is a key challenge.
Patient stratification and personalized models are essential for safe and effective drug development.