Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Dose-Response Relationship: Overview01:03

Dose-Response Relationship: Overview

3.2K
Agonists can bind with and activate receptors, resulting in the formation of drug-receptor complexes. Once formed, these complexes catalyze many biochemical processes at the cellular level and subsequently induce a pharmacologic response. The degree of response is directly proportional to the fraction of activated receptors, which in turn, depends on the concentration of the drug at the receptor site as well as the sensitivity of the receptor. An increase in the administered dose contributes to...
3.2K
Agonism and Antagonism: Quantification01:14

Agonism and Antagonism: Quantification

380
When drugs are administered, they can elicit either an agonist or antagonist effect on the body. Agonism occurs when a drug activates a specific receptor, triggering a biological response. On the other hand, antagonism happens when a drug binds to the same receptors but blocks their activation, thereby preventing a biological response.
To quantify these effects, researchers use a dose-response curve, which provides valuable information about the potency and efficacy of a drug. Potency refers to...
380
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

78
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.
78
Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

266
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...
266
Dose-Response Relationship: Potency and Efficacy01:22

Dose-Response Relationship: Potency and Efficacy

4.5K
The potency of a drug is the measure of its ability to produce a biological response and can be compared by looking at the half-maximum effective concentration or EC50 values of different drugs. A lower EC50 value indicates higher potency of the drug. In the dose–response curve of two antihypertensive drugs, candesartan and irbesartan, a significant difference is observed in their EC50 values. A lower EC50 value for candesartan indicates that it is more potent than irbesartan, as it...
4.5K
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

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

138
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...
138

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Designs of the clinical trials aiming at evaluating cell and gene therapy products: A critical appraisal from a literature review.

Molecular therapy. Advances·2026
Same author

Splicing-associated network PAK1-CLK1/4-SRRM1 is a vulnerability to overcome chemoresistance in human and mouse acute myeloid leukemia.

Science translational medicine·2026
Same author

Prognostic impact of intensive chemotherapy in patients with TP53-mutated AML.

Blood cancer journal·2026
Same author

Persistent <i>BCR::ABL1</i>-positive preleukemic stem cells drive late clonal evolution in Philadelphia chromosome-positive ALL.

HemaSphere·2026
Same author

Information borrowing in phase II randomized dose-ranging clinical trials in oncology.

BMC medical research methodology·2026
Same author

Ultra-high-sensitivity next-generation sequencing-based MRD predicts outcome in intensively treated older patients with acute myeloid leukemia: results from the ALFA-1200 cohort.

Blood cancer journal·2026

Related Experiment Video

Updated: Jul 10, 2025

Diagonal Method to Measure Synergy Among Any Number of Drugs
12:08

Diagonal Method to Measure Synergy Among Any Number of Drugs

Published on: June 21, 2018

18.6K

Drug combinations screening using a Bayesian ranking approach based on dose-response models.

Luana Boumendil1, Morgane Fontaine2, Vincent Lévy1,3

  • 1Université Paris Cité, INSERM U1153, Team ECSTRRA, Paris, France.

Biometrical Journal. Biometrische Zeitschrift
|November 20, 2023
PubMed
Summary

This study introduces a novel rank-based screening method for identifying effective drug combinations, even with limited biological resources. The approach efficiently ranks potential cancer treatments, addressing challenges in dose-response analysis.

Keywords:
Bayesian modeldose-response modeldrug screeningranking

More Related Videos

High-throughput Identification of Synergistic Drug Combinations by the Overlap2 Method
07:51

High-throughput Identification of Synergistic Drug Combinations by the Overlap2 Method

Published on: May 21, 2018

11.8K
Potentiation of Anticancer Antibody Efficacy by Antineoplastic Drugs: Detection of Antibody-drug Synergism Using the Combination Index Equation
15:04

Potentiation of Anticancer Antibody Efficacy by Antineoplastic Drugs: Detection of Antibody-drug Synergism Using the Combination Index Equation

Published on: January 19, 2019

12.2K

Related Experiment Videos

Last Updated: Jul 10, 2025

Diagonal Method to Measure Synergy Among Any Number of Drugs
12:08

Diagonal Method to Measure Synergy Among Any Number of Drugs

Published on: June 21, 2018

18.6K
High-throughput Identification of Synergistic Drug Combinations by the Overlap2 Method
07:51

High-throughput Identification of Synergistic Drug Combinations by the Overlap2 Method

Published on: May 21, 2018

11.8K
Potentiation of Anticancer Antibody Efficacy by Antineoplastic Drugs: Detection of Antibody-drug Synergism Using the Combination Index Equation
15:04

Potentiation of Anticancer Antibody Efficacy by Antineoplastic Drugs: Detection of Antibody-drug Synergism Using the Combination Index Equation

Published on: January 19, 2019

12.2K

Area of Science:

  • Pharmacology
  • Biostatistics
  • Oncology

Background:

  • Drug combinations are crucial for treating complex diseases like cancer, potentially reducing drug resistance and addressing tumor heterogeneity.
  • Identifying optimal drug combinations is challenging due to high costs, limited biological material, and patient variability.
  • Existing methods struggle with resource constraints in screening numerous drug combinations.

Purpose of the Study:

  • To develop a rank-based screening approach for identifying potent drug combinations under limited biological resource conditions.
  • To establish a robust method for ranking drug combinations using a hierarchical Bayesian model and activity metrics.
  • To address the challenges of cost and sample limitations in drug combination screening.

Main Methods:

  • Utilized a hierarchical Bayesian 4-parameter log-logistic (4PLL) model to estimate dose-response curves.
  • Employed a parsimonious experimental design suitable for limited biological samples.
  • Computed activity ranking metrics including area under the dose-response curve and Bliss synergy score.
  • Incorporated posterior rank distributions and surface under the cumulative ranking curve for comprehensive ranking.

Main Results:

  • The proposed rank-based screening method demonstrated good operating characteristics in simulations.
  • The approach effectively identified promising treatments across various scenarios, including limited sample sizes and interpatient variability.
  • The method was successfully illustrated using real data from an acute myeloid leukemia combination screening experiment.

Conclusions:

  • The developed rank-based approach provides an efficient strategy for drug combination screening with limited resources.
  • This method offers a reliable way to rank potential drug combinations, aiding in the discovery of novel cancer therapies.
  • The approach is applicable to various settings with constraints on biological material and patient data, as shown in acute myeloid leukemia research.