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Related Concept Videos

Pharmacokinetic Models: Overview01:20

Pharmacokinetic Models: Overview

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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.
There are three primary types of models: empirical, compartment, and physiological. Empirical models, with minimal...
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Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

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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...
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Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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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...
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Mouse Models of Cancer Study02:43

Mouse Models of Cancer Study

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Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...
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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.
46
Combination Therapies and Personalized Medicine02:50

Combination Therapies and Personalized Medicine

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Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...
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  1. Home
  3. Research Domains
  5. Biomedical And Clinical Sciences
  7. Oncology And Carcinogenesis
  9. Predictive And Prognostic Markers
  11. Model Ensembling As A Tool To Form Interpretable Multi-omic Predictors Of Cancer Pharmacosensitivity.
  1. Home
  3. Research Domains
  5. Biomedical And Clinical Sciences
  7. Oncology And Carcinogenesis
  9. Predictive And Prognostic Markers
  11. Model Ensembling As A Tool To Form Interpretable Multi-omic Predictors Of Cancer Pharmacosensitivity.

Related Experiment Video

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Model ensembling as a tool to form interpretable multi-omic predictors of cancer pharmacosensitivity.

Sébastien De Landtsheer1, Apurva Badkas1, Dagmar Kulms2,3

  • 1Department of Life Sciences and Medicine, University of Luxembourg, 2, place de l'Université, L4365 Esch-sur-Alzette, Luxembourg.

Briefings in Bioinformatics
|November 4, 2024

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a machine learning framework to predict cancer drug sensitivity using multi-omic data. The models identify key molecular markers for personalized oncology treatments.

Keywords:
CCLEcancermachine-learningpharmacosensitivity

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Area of Science:

  • Computational Biology
  • Genomics
  • Pharmacology

Background:

  • Personalized oncology relies on predicting patient response to cancer therapies.
  • Multi-omic data holds potential for identifying predictive molecular signatures.
  • Explainable predictions are crucial for clinical integration.

Purpose of the Study:

  • To develop a machine learning framework for integrating multi-omic data.
  • To predict drug sensitivity in cancer cell lines.
  • To identify explainable molecular markers for therapeutic response.

Main Methods:

  • Ensemble learning framework integrating multi-omic data.
  • Training omic-specific classifiers and a random forest meta-classifier.
  • Utilizing the Cancer Cell Line Encyclopedia (CCLE) dataset.
predictive algorithm
  • Validation using nested cross-validation.

    • Main Results:

    • Achieved Area under the Receiver-Operating Curve >79% for several compounds.
    • Identified important omic layers and novel markers for drug responsiveness.
    • Developed predictive models using subsets of transcriptional markers.

    Conclusions:

    • The proposed framework effectively predicts drug sensitivity from multi-omic data.
    • The approach offers explainable insights into molecular drivers of drug response.
    • Models show potential for clinical application in personalized cancer therapy.