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

Opioid Analgesics: Synthetic and Semisynthetic Opioids01:15

Opioid Analgesics: Synthetic and Semisynthetic Opioids

687
Synthetic and semisynthetic opioids are pivotal in pain management and tackling opioid addiction. Semisynthetic opioids, including morphinans (morphine derivatives), oxycodone, oxymorphone, hydrocodone, and hydromorphone, have improved pharmacokinetic profiles compared to morphine. Additionally, heroin and 6-MAM (6-Monoacetylmorphine) show better CNS penetration than morphine due to heightened lipid solubility. Hydromorphone, a potent opioid, undergoes hepatic metabolism to form the active...
687
Analgesia and Pain Management01:25

Analgesia and Pain Management

1.2K
Pain is critical to various clinical pathologies, provoking an urgent need for effective management. Pain, whether acute or chronic, is a complex neurochemical process. Its alleviation depends on the type, with nonopioid analgesics effective for mild to moderate pain, such as musculoskeletal or inflammatory pain, while neuropathic pain responds best to anticonvulsants, tricyclic antidepressants, or serotonin/norepinephrine reuptake inhibitors. For severe acute or chronic pain, opioids may be...
1.2K
Opioid Analgesics: Morphine and Other Natural Cogeners01:20

Opioid Analgesics: Morphine and Other Natural Cogeners

618
Opioids are a class of drugs that mimic endogenous opioid peptides and act on opioid receptors, and help in pain relief. These compounds are classified as natural, synthetic, or semi-synthetic. Natural opioids, like morphine, codeine, and thebaine, are derived from the opium poppy plant (Papaver somniferum or Papaver album) and are termed opiates. Synthetic opioids are artificial, while semi-synthetic opioids combine natural and synthetic compounds. Morphine, a prototypical opioid, possesses a...
618
Opioid Receptors: Overview01:22

Opioid Receptors: Overview

3.3K
Opioid receptors, including the mu (μ, MOR), delta (δ, DOR), and kappa (κ, KOR) types, belong to the rhodopsin family of G protein-coupled receptors. These receptors are located throughout the central and peripheral nervous systems and in non-neuronal tissues such as macrophages and astrocytes. Opioid receptor ligands can be categorized into agonists or antagonists. Highly selective agonists include [d-Ala2, MePhe4, Gly(ol)5]-enkephalin or DAMGO for MOR, [D-Pen2,...
3.3K
Drug Abuse and Addiction: Pharmacological Phenomena01:15

Drug Abuse and Addiction: Pharmacological Phenomena

943
Drug dependence, abuse, and addiction are complex phenomena that can precipitate various abnormal states. Physical dependence refers to a state of pharmacological adaptation to a drug. This adaptation often results in tolerance—a reduced response to the drug after repeated administrations. When the drug use is abruptly stopped, withdrawal symptoms occur due to the body's need to readjust from the pharmacologically induced imbalance. However, tolerance and withdrawal symptoms do not...
943
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

176
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...
176

You might also read

Related Articles

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

Sort by
Same author

Employment insecurity and binge drinking in the United States: Implications of unemployment insurance.

Preventive medicine·2026
Same author

A cross-national comparison of nonmedical and medical use of psychedelic drugs in the international cannabis policy study.

The International journal on drug policy·2026
Same author

Exploring Opioid Prescriber Decision-Making: A Qualitative Study.

Pharmacoepidemiology and drug safety·2026
Same author

Cost-Effectiveness of Community Tuberculosis Screening in South Africa.

American journal of respiratory and critical care medicine·2026
Same author

Fentanyl Purity and Overdose Decline: A Reexamination of Geographic Trends.

medRxiv : the preprint server for health sciences·2026
Same author

Recent minimum wage policies and their association with suicide and poisoning deaths.

American journal of epidemiology·2026

Related Experiment Video

Updated: Nov 27, 2025

Combining Laser Capture Microdissection and Microfluidic qPCR to Analyze Transcriptional Profiles of Single Cells: A Systems Biology Approach to Opioid Dependence
09:54

Combining Laser Capture Microdissection and Microfluidic qPCR to Analyze Transcriptional Profiles of Single Cells: A Systems Biology Approach to Opioid Dependence

Published on: March 8, 2020

5.5K

Data Needs in Opioid Systems Modeling: Challenges and Future Directions.

Mohammad S Jalali1, Emily Ewing2, Calvin B Bannister2

  • 1MGH Institute for Technology Assessment, Harvard Medical School, Boston, Massachusetts; MIT Sloan School of Management, Cambridge, Massachusetts.

American Journal of Preventive Medicine
|December 4, 2020
PubMed
Summary
This summary is machine-generated.

Addressing the U.S. opioid crisis requires better data for simulation models. Key recommendations include enhanced data collection and collaboration between modelers and data experts to improve policy interventions.

More Related Videos

Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation
16:02

Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation

Published on: February 10, 2023

3.0K
Determining Pain Detection and Tolerance Thresholds Using an Integrated, Multi-Modal Pain Task Battery
09:38

Determining Pain Detection and Tolerance Thresholds Using an Integrated, Multi-Modal Pain Task Battery

Published on: April 14, 2016

13.0K

Related Experiment Videos

Last Updated: Nov 27, 2025

Combining Laser Capture Microdissection and Microfluidic qPCR to Analyze Transcriptional Profiles of Single Cells: A Systems Biology Approach to Opioid Dependence
09:54

Combining Laser Capture Microdissection and Microfluidic qPCR to Analyze Transcriptional Profiles of Single Cells: A Systems Biology Approach to Opioid Dependence

Published on: March 8, 2020

5.5K
Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation
16:02

Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation

Published on: February 10, 2023

3.0K
Determining Pain Detection and Tolerance Thresholds Using an Integrated, Multi-Modal Pain Task Battery
09:38

Determining Pain Detection and Tolerance Thresholds Using an Integrated, Multi-Modal Pain Task Battery

Published on: April 14, 2016

13.0K

Area of Science:

  • Public Health
  • Systems Science
  • Data Science

Background:

  • The opioid crisis is a significant public health issue in the U.S.
  • Simulation modeling is crucial for understanding the crisis and evaluating policy interventions.
  • Data limitations hinder the accuracy and effectiveness of opioid systems models.

Purpose of the Study:

  • To identify data needs and challenges for opioid systems modeling.
  • To synthesize discussions from a federal meeting on opioid data.
  • To interpret findings within the context of ongoing simulation modeling efforts.

Main Methods:

  • A meeting was convened with federal partners, modeling teams, and data experts.
  • Discussions were synthesized and interpreted in relation to current simulation modeling work.
  • The landscape of national quantitative data sources was identified and analyzed.

Main Results:

  • Significant issues within existing national-level quantitative data sources were identified.
  • Recommendations include fostering collaboration, enhancing data collection, bridging information gaps, and clarifying policy goals.
  • Direct interaction between modelers and data experts is crucial.

Conclusions:

  • This work highlights critical data challenges in opioid research and systems modeling.
  • Opportunities exist for modelers and government agencies to enhance opioid systems models.
  • Improved data is essential for effective policy development and intervention.