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

Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

888
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...
888
Pharmacokinetic–Pharmacodynamic Relationship: Problems01:24

Pharmacokinetic–Pharmacodynamic Relationship: Problems

50
The empirical approach to drug therapy optimization relies on correlating pharmacological response with administered dosage. Such an approach can be costly, time-consuming, and often yields poor correlation due to variables like formulation factors and drug elimination characteristics. A more precise approach correlates response with plasma drug concentration or the amount of drug in the body, rather than dosage. This is achieved through pharmacokinetic-pharmacodynamic (PK/PD) modeling, which...
50
Dosage Regimens: Partial Pharmacokinetic Parameters01:01

Dosage Regimens: Partial Pharmacokinetic Parameters

244
It is not uncommon for complete drug pharmacokinetic profiles to remain elusive in pharmacokinetics. This necessitates certain educated assumptions by pharmacokineticists to determine appropriate dosage regimens without comprehensive pharmacokinetic data from animal or human studies. One prevalent assumption is setting the bioavailability factor, denoted as F, to 1 or 100%. This assumption caters to the scenario where a drug doesn't achieve full systemic absorption, resulting in the patient...
244
Impact of Pharmacokinetic–Pharmacodynamic Models: Regulatory Decisions01:15

Impact of Pharmacokinetic–Pharmacodynamic Models: Regulatory Decisions

58
PK–PD modeling has significantly influenced FDA regulatory decisions, particularly drug approval, dosage optimization, and labeling. These models integrate pharmacokinetics (PK) and pharmacodynamics (PD) to predict drug behavior and effects, aiding in optimizing dosing regimens and enhancing the probability of clinical trial success.One notable example is Nesiritide (Natrecor®), a recombinant human brain natriuretic peptide for treating acute decompensated congestive heart failure...
58
Dosage Regimen: Individualization01:24

Dosage Regimen: Individualization

249
Individualization in dosing regimens is the customization of medication doses for individual patients. Its necessity arises from the goal of maximizing therapeutic benefits while minimizing risks. This approach is pivotal because human responses to drugs can vary widely; what is effective for one person may be inadequate or excessive for another. Interpatient (intersubject) variability refers to differences in drug responses between individuals, while intrapatient (intrasubject) variability...
249
Therapeutic Drug Monitoring: Affecting Factors01:29

Therapeutic Drug Monitoring: Affecting Factors

306
Therapeutic Drug Monitoring (TDM) is the clinical practice of measuring specific drug levels in a patient's blood or body tissues to manage and optimize therapy. TDM is crucial for drugs with narrow therapeutic windows, like warfarin and phenytoin, where incorrect doses can lead to treatment failure or severe side effects. This monitoring ensures the dosage administered is within a safe and effective range. The factors affecting therapeutic drug monitoring include:Patient-Specific Factors:a.
306

You might also read

Related Articles

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

Sort by
Same author

The deleterious effect of acetaminophen in cancer immunotherapy.

Annals of oncology : official journal of the European Society for Medical Oncology·2022
Same author

A pharmacokinetic-pharmacodynamic assessment of oral antibiotics for pyelonephritis.

European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology·2019
Same author

Underestimation of Fecal Coliform Counts in Shellfish-Growing Waters by the Spanish Official Method.

Journal of food protection·2019
Same author

Detection of Sorghum yellow banding virus Infecting Grain Sorghum in Venezuela.

Plant disease·2019
Same author

Semi-mechanistic Pharmacokinetic/Pharmacodynamic model of three pegylated rHuEPO and ior®EPOCIM in New Zealand rabbits.

European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences·2018
Same author

Combined immunotherapy encompassing intratumoral poly-ICLC, dendritic-cell vaccination and radiotherapy in advanced cancer patients.

Annals of oncology : official journal of the European Society for Medical Oncology·2018

Related Experiment Video

Updated: Mar 9, 2026

Predictive Immune Modeling of Solid Tumors
08:50

Predictive Immune Modeling of Solid Tumors

Published on: February 25, 2020

7.6K

Commentary on Pharmacometrics for Immunotherapy.

M J Garrido1,2, P Berraondo2,3, I F Trocóniz1,2

  • 1Pharmacometrics & Systems Pharmacology, Department of Pharmacy and Pharmaceutical Technology, School of Pharmacy, University of Navarra, Pamplona, Spain.

CPT: Pharmacometrics & Systems Pharmacology
|December 21, 2016
PubMed
Summary

Pharmacometrics successfully predicted dosing for PD-1 antagonists like pembrolizumab and nivolumab. However, tumor response varied, necessitating biomarkers and combination studies for better outcomes.

More Related Videos

Quantification of the Immunosuppressant Tacrolimus on Dried Blood Spots Using LC-MS/MS
08:38

Quantification of the Immunosuppressant Tacrolimus on Dried Blood Spots Using LC-MS/MS

Published on: November 8, 2015

17.5K
Immunophenotyping of Orthotopic Homograft Syngeneic of Murine Primary KPC Pancreatic Ductal Adenocarcinoma by Flow Cytometry
08:30

Immunophenotyping of Orthotopic Homograft Syngeneic of Murine Primary KPC Pancreatic Ductal Adenocarcinoma by Flow Cytometry

Published on: October 9, 2018

13.0K

Related Experiment Videos

Last Updated: Mar 9, 2026

Predictive Immune Modeling of Solid Tumors
08:50

Predictive Immune Modeling of Solid Tumors

Published on: February 25, 2020

7.6K
Quantification of the Immunosuppressant Tacrolimus on Dried Blood Spots Using LC-MS/MS
08:38

Quantification of the Immunosuppressant Tacrolimus on Dried Blood Spots Using LC-MS/MS

Published on: November 8, 2015

17.5K
Immunophenotyping of Orthotopic Homograft Syngeneic of Murine Primary KPC Pancreatic Ductal Adenocarcinoma by Flow Cytometry
08:30

Immunophenotyping of Orthotopic Homograft Syngeneic of Murine Primary KPC Pancreatic Ductal Adenocarcinoma by Flow Cytometry

Published on: October 9, 2018

13.0K

Area of Science:

  • Immunotherapy
  • Pharmacometrics
  • Oncology

Background:

  • Programmed death-1 (PD-1) antagonists, including pembrolizumab and nivolumab, have shown significant clinical success.
  • Optimizing the therapeutic use of these agents is crucial for improving patient outcomes.

Purpose of the Study:

  • To review the application of pharmacometrics in the clinical development of PD-1 antagonists.
  • To highlight challenges and future directions in PD-1 antagonist therapy.

Main Methods:

  • Review of recent clinical development data for pembrolizumab and nivolumab.
  • Application of quantitative approaches in pharmacometric modeling.

Main Results:

  • Pharmacometric methods have demonstrated success in predicting optimal dosing schedules for PD-1 antagonists.
  • Significant heterogeneity in tumor response was observed despite optimized dosing.

Conclusions:

  • Further research is needed to identify predictive biomarkers for non-response to PD-1 inhibitors.
  • Mechanism-based combination studies are warranted to enhance therapeutic efficacy.