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Factors Affecting Drug Response: Overview

Factors Affecting Drug Response: Overview

When it comes to infants and young children, they are typically administered smaller doses of medication in comparison to adults. This is primarily because their organ functions still need to fully develop, meaning their bodies are not as efficient at metabolizing or eliminating drugs. Additionally, their blood-brain barrier is more permeable than in adults. As a result, high concentrations of drugs can easily penetrate the central nervous system (CNS), potentially leading to neurological...

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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...

Analysis of Population Pharmacokinetic Data

Analysis of Population Pharmacokinetic Data

Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...

Factors Affecting Drug Distribution: Miscellaneous Factors

Factors Affecting Drug Distribution: Miscellaneous Factors

Drug distribution in the human body is a complex process influenced by various individual factors, including age, pregnancy, obesity, diet, body water composition, pH levels, and specific disease conditions.
Age plays a significant role due to differences in body composition among different age groups. Infants, for instance, have a higher proportion of total body water and lower albumin levels, a protein that binds drugs in the bloodstream. This unique composition in infants enhances the...

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This...

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Improving drug response prediction by integrating multiple data sources: matrix factorization, kernel and

Betül Güvenç Paltun¹, Hiroshi Mamitsuka², Samuel Kaski²

¹Department of Computer Science, Helsinki Institute for Information Technology HIIT, Aalto University, Helsinki, Finland.

Briefings in Bioinformatics

|December 16, 2019

Summary

This summary is machine-generated.

This review explores machine learning methods for predicting cancer drug responses by integrating diverse data. It highlights challenges and advantages of combining multiple data sources for personalized medicine.

Keywords:

bioinformatics drug response prediction heterogeneous data integration machine learning personalized medicine

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Published on: June 13, 2025

Area of Science:

Computational biology
Bioinformatics
Genomics

Background:

Predicting cancer cell line drug response is crucial for personalized medicine.
Diverse cell line characteristics necessitate advanced computational approaches.
Integrating heterogeneous biological data improves drug sensitivity analyses.

Purpose of the Study:

To review recent advances in data integration-based machine learning for drug response prediction.
To categorize existing methods and discuss relevant databases.
To highlight challenges and benefits of multi-source data integration.

Main Methods:

Categorization of methods into matrix factorization-based, kernel-based, and network-based approaches.
Review of benchmark databases for drug response prediction.
Discussion of data integration challenges (structure, complexity).

Main Results:

Summarizes recent progress in machine learning for drug response prediction.
Identifies key challenges in integrating heterogeneous biological data.
Demonstrates the advantages of combining multiple data sources through experimental comparison.

Conclusions:

Data integration-based machine learning offers powerful tools for predicting drug responses.
Overcoming data integration challenges is key to advancing personalized medicine.
Combining heterogeneous data sources enhances drug sensitivity analysis accuracy.