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

Pharmacodynamic Models: Direct Effect Model and Indirect Response Model01:29

Pharmacodynamic Models: Direct Effect Model and Indirect Response Model

142
Pharmacodynamic models are essential tools in understanding the relationship between drug concentrations and their effects on biological systems. By characterizing the dynamics of drug action, these models guide dose selection, optimize therapeutic efficacy, and inform the development of new drugs. Two major classes of pharmacodynamic models include direct effect and indirect response models.Direct Effect ModelsDirect effect models describe the immediate relationship between drug concentration...
142
Biopharmaceutical Factors Influencing Drug Product Design: Overview01:22

Biopharmaceutical Factors Influencing Drug Product Design: Overview

514
Rational drug product design integrates knowledge of the drug’s physicochemical properties, formulation components, manufacturing techniques, and intended route of administration. Each factor influences the drug’s performance, including how it is released, absorbed, and eliminated in the body.The physicochemical properties of a drug—such as solubility, stability, and particle size—affect its compatibility with excipients and the choice of dosage form. Excipients, though...
514
Pharmacodynamic Models: Overview01:27

Pharmacodynamic Models: Overview

134
Pharmacodynamic (PD) responses describe the interaction between a drug and its biological target, culminating in a physiological effect. These responses can be classified into different types: continuous variables, such as blood glucose levels; categorical outcomes, like survival rates; and time-to-event metrics, such as disease progression. Understanding and modeling PD responses are critical for optimizing drug efficacy and safety.PD models describe the relationship between drug concentration...
134
Impact of Pharmacokinetic–Pharmacodynamic Models: Regulatory Decisions01:15

Impact of Pharmacokinetic–Pharmacodynamic Models: Regulatory Decisions

88
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...
88
Drug Discovery: Overview01:26

Drug Discovery: Overview

13.6K
Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...
13.6K
Pharmacokinetic Models: Overview01:20

Pharmacokinetic Models: Overview

2.8K
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...
2.8K

You might also read

Related Articles

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

Sort by
Same author

The Discovery of MORF-627, a Highly Selective Conformationally-Biased Zwitterionic Integrin αvβ6 Inhibitor for Fibrosis.

Journal of medicinal chemistry·2024
Same author

First-in-class versus best-in-class: an update for new market dynamics.

Nature reviews. Drug discovery·2023
Same author

2020 FDA approvals: momentum kept despite COVID-19, but value falls.

Nature reviews. Drug discovery·2021
Same author

Identification of a Metabolic, Transcriptomic, and Molecular Signature of Patatin-Like Phospholipase Domain Containing 3-Mediated Acceleration of Steatohepatitis.

Hepatology (Baltimore, Md.)·2020
Same author

Author Correction: The Transcriptomic Signature Of Disease Development And Progression Of Nonalcoholic Fatty Liver Disease.

Scientific reports·2020
Same author

Value of 2019 FDA approvals: back to the recent average.

Nature reviews. Drug discovery·2020

Related Experiment Video

Updated: Apr 20, 2026

Pharmacophore Modeling for Targets with Extensive Ligand Libraries: A Case Study on SARS-CoV-2 Mpro
05:50

Pharmacophore Modeling for Targets with Extensive Ligand Libraries: A Case Study on SARS-CoV-2 Mpro

Published on: September 26, 2025

2.1K

Racing to define pharmaceutical R&D external innovation models.

Liangsu Wang1, Andrew Plump2, Michael Ringel3

  • 1Merck Research Laboratories, 2015 Galloping Hill Road, Kenilworth, NJ 07033, USA.

Drug Discovery Today
|December 3, 2014
PubMed
Summary

Pharmaceutical companies are adopting external innovation strategies like academic collaboration and biotech partnerships to boost research and development (R&D) productivity and create novel therapies.

More Related Videos

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
05:10

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System

Published on: December 11, 2016

10.3K
In Vitro Three-Dimensional Sprouting Assay of Angiogenesis Using Mouse Embryonic Stem Cells for Vascular Disease Modeling and Drug Testing
08:04

In Vitro Three-Dimensional Sprouting Assay of Angiogenesis Using Mouse Embryonic Stem Cells for Vascular Disease Modeling and Drug Testing

Published on: May 11, 2021

3.5K

Related Experiment Videos

Last Updated: Apr 20, 2026

Pharmacophore Modeling for Targets with Extensive Ligand Libraries: A Case Study on SARS-CoV-2 Mpro
05:50

Pharmacophore Modeling for Targets with Extensive Ligand Libraries: A Case Study on SARS-CoV-2 Mpro

Published on: September 26, 2025

2.1K
Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
05:10

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System

Published on: December 11, 2016

10.3K
In Vitro Three-Dimensional Sprouting Assay of Angiogenesis Using Mouse Embryonic Stem Cells for Vascular Disease Modeling and Drug Testing
08:04

In Vitro Three-Dimensional Sprouting Assay of Angiogenesis Using Mouse Embryonic Stem Cells for Vascular Disease Modeling and Drug Testing

Published on: May 11, 2021

3.5K

Area of Science:

  • Pharmaceutical industry
  • Biotechnology
  • Drug discovery and development

Background:

  • Declining research and development (R&D) productivity poses a significant challenge for the pharmaceutical sector.
  • Increasing customer expectations necessitate the development of innovative and transformative therapies beyond incremental improvements.
  • The need to reduce R&D costs drives the exploration of new innovation models.

Purpose of the Study:

  • To review and compare various external innovation strategies employed by pharmaceutical companies.
  • To identify current trends in pharmaceutical external innovation.
  • To discuss factors influencing these innovation models and propose success metrics.

Main Methods:

  • Literature review of pharmaceutical industry innovation strategies.
  • Comparative analysis of different external innovation models (e.g., precompetitive collaboration, biotech partnerships, licensing, acquisitions).
  • Identification of key trends and influencing factors in external innovation.

Main Results:

  • Pharmaceutical companies are increasingly leveraging external sources for innovation to overcome R&D challenges.
  • Key strategies include collaborations with academia, nurturing biotech start-ups, and strategic licensing and acquisitions.
  • Emerging trends indicate a shift towards more proactive and diverse external innovation approaches.

Conclusions:

  • External innovation is crucial for pharmaceutical companies to enhance R&D productivity and deliver transformative medicines.
  • A combination of strategic partnerships and acquisitions is vital for a robust innovation pipeline.
  • Developing leading indicators for success in external innovation models is essential for future strategy refinement.