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

Mean free path and Mean free time01:22

Mean free path and Mean free time

5.3K
Consider the gas molecules in a cylinder. They move in a random motion as they collide with each other and change speed and direction. The average of all the path lengths between collisions is known as the "mean free path."
5.3K
Performing a Simple Data Analysis using MS-Excel Function01:17

Performing a Simple Data Analysis using MS-Excel Function

1.1K
Microsoft Excel offers a suite of functions and tools ideal for statistical analysis, making it accessible to students and researchers. This article outlines fundamental Excel functions pivotal for data analysis.
SUM: This function calculates the total sum of a range of values. It's the foundation for aggregating data, essential for determining overall trends and totals in datasets.
AVERAGE: It computes the mean value of a given set of numbers, providing a quick insight into the central...
1.1K
Path Between Thermodynamics States01:21

Path Between Thermodynamics States

4.1K
Consider the two thermodynamic processes involving an ideal gas that are represented by paths AC and ABC in Figure 1:
4.1K
Interference: Path Lengths01:10

Interference: Path Lengths

2.2K
Consider two sources of sound, that may or may not be in phase, emitting waves at a single frequency, and consider the frequencies to be the same.
Two special sources may be considered when they are in phase. This can be easily achieved by feeding the two sources from the same source. An example would be synchronizing the two speakers by feeding them with the same source, such as the sound waves produced by a tuning fork. This setup ensures that the two sources have the same frequency and are...
2.2K
Stereotype Content Model02:16

Stereotype Content Model

15.5K
The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
15.5K
Predicting Molecular Geometry02:27

Predicting Molecular Geometry

46.1K
VSEPR Theory for Determination of Electron Pair Geometries
46.1K

You might also read

Related Articles

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

Sort by
Same author

Sabatolimab (MBG453) model-informed drug development for dose selection in patients with myelodysplastic syndrome/acute myeloid leukemia and solid tumors.

CPT: pharmacometrics & systems pharmacology·2023
Same author

Current practices for QSP model assessment: an IQ consortium survey.

Journal of pharmacokinetics and pharmacodynamics·2022
Same author

A framework to guide dose & regimen strategy for clinical drug development.

CPT: pharmacometrics & systems pharmacology·2021
Same author

Estimands-What they are and why they are important for pharmacometricians.

CPT: pharmacometrics & systems pharmacology·2021
Same author

Estimating drug potency in the competitive target mediated drug disposition (TMDD) system when the endogenous ligand is included.

Journal of pharmacokinetics and pharmacodynamics·2021
Same author

Chimeric Antigen Receptor T Cell Therapies: A Review of Cellular Kinetic-Pharmacodynamic Modeling Approaches.

Journal of clinical pharmacology·2020

Related Experiment Video

Updated: Feb 10, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

1.2K

Benchmarking QSP Models Against Simple Models: A Path to Improved Comprehension and Predictive Performance.

Andrew M Stein1, Michael Looby2

  • 1Novartis Institute for Biomedical Research, Cambridge, Massachusetts, USA.

CPT: Pharmacometrics & Systems Pharmacology
|May 16, 2018
PubMed
Summary

Quantitative Systems Pharmacology (QSP) models aid biological understanding but face complexity challenges. Comparing QSP models against simpler alternatives is recommended to improve predictive performance and experimental reliability.

More Related Videos

Predictive Immune Modeling of Solid Tumors
08:50

Predictive Immune Modeling of Solid Tumors

Published on: February 25, 2020

7.6K
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

754

Related Experiment Videos

Last Updated: Feb 10, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

1.2K
Predictive Immune Modeling of Solid Tumors
08:50

Predictive Immune Modeling of Solid Tumors

Published on: February 25, 2020

7.6K
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

754

Area of Science:

  • Pharmacology
  • Systems Biology
  • Computational Biology

Background:

  • Quantitative Systems Pharmacology (QSP) models integrate biological knowledge into quantitative frameworks.
  • These models aim to enhance biological understanding and predict experimental and clinical trial outcomes.
  • Model complexity and uncertainty can compromise the goals of QSP.

Purpose of the Study:

  • To address challenges posed by complexity and uncertainty in QSP models.
  • To propose a method for assessing the predictive performance of QSP models.
  • To enhance the reliability and interpretability of QSP models in biological research.

Main Methods:

  • Developing simpler, purpose-built models for comparison.
  • Assessing the predictive performance of QSP models.
  • Utilizing comparative analysis to evaluate model accuracy and robustness.

Main Results:

  • Comparative assessment can reveal limitations in complex QSP models.
  • Simpler models can serve as benchmarks for QSP performance.
  • This approach can lead to more trustworthy QSP predictions.

Conclusions:

  • The predictive performance of QSP models should be rigorously evaluated.
  • Comparison with simpler models is a valuable strategy for validating QSP models.
  • This method can improve the utility of QSP in drug discovery and development.