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

Pharmacogenomics: Identification of New Drug Targets01:29

Pharmacogenomics: Identification of New Drug Targets

129
Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...
129
Drug Discovery: Overview01:26

Drug Discovery: Overview

10.4K
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...
10.4K
Impact of Pharmacokinetic–Pharmacodynamic Models: Regulatory Decisions01:15

Impact of Pharmacokinetic–Pharmacodynamic Models: Regulatory Decisions

97
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...
97
Biopharmaceutical Factors Influencing Drug Product Design: Overview01:22

Biopharmaceutical Factors Influencing Drug Product Design: Overview

561
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...
561
Pharmacodynamic Models: Overview01:27

Pharmacodynamic Models: Overview

161
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...
161
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

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

733
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...
733

You might also read

Related Articles

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

Sort by
Same author

Systems Pharmacology Approach to Paroxysmal Nocturnal Hemoglobinuria: Quantitative Framework for Biomarker Dynamics across Multi-Mechanistic Therapies.

Clinical pharmacology and therapeutics·2026
Same author

Shifting a paradigm: highlights of model informed drug development in dosage selection and optimization for oncology products.

The oncologist·2026
Same author

FDA-AACR Strategies for Optimizing Dosages for Oncology Drug Products: Selecting Optimized Dosages for Registrational Trials.

Clinical cancer research : an official journal of the American Association for Cancer Research·2025
Same author

FDA-AACR Strategies for Optimizing Dosages for Oncology Drug Products: Early-Phase Trials Using Innovative Trial Designs and Biomarkers.

Clinical cancer research : an official journal of the American Association for Cancer Research·2025
Same author

FDA-AACR Strategies for Optimizing Dosages for Oncology Drug Products: Selecting Dosages for First-in-Human Trials.

Clinical cancer research : an official journal of the American Association for Cancer Research·2025
Same author

Evaluation and Mitigation of Time-Dependent Confounding Effects in Conventional Exposure-Response Analyses for Oncology Drugs.

CPT: pharmacometrics & systems pharmacology·2025
Same journal

PBPK Modeling and Clinical Data Reveal Reduced Impact of CYP3A4 and CYP2C9 Inhibitors on Elimination of Siponimod.

Clinical and translational science·2026
Same journal

Drug Interactions of Vebreltinib, a Novel Type I c-Met Inhibitor, Coadministration With Rifampin or Itraconazole in Healthy Participants.

Clinical and translational science·2026
Same journal

Correction to "Severe Thiopurine-Induced Myelosuppression in a Pediatric Acute Lymphoblastic Leukemia Patient ith the NUDT15 *1/*6 Genotype: A Brief Report".

Clinical and translational science·2026
Same journal

Model-Informed Development of a Subcutaneous Formulation of Tislelizumab: Phase 1 Pharmacokinetics in Patients With Cancer.

Clinical and translational science·2026
Same journal

EPHX2 Orchestrates Intestinal Epithelial Barrier Repair in Ulcerative Colitis: An Integrated Multi-Omics and Experimental Study.

Clinical and translational science·2026
Same journal

A Phase 1, Open-Label, Randomized Study to Investigate the Pharmacokinetics and Safety of Multiple Doses of Intranasal Naloxone in Healthy Participants.

Clinical and translational science·2026
See all related articles

Related Experiment Video

Updated: May 6, 2026

Profiling Sensitivity to Targeted Therapies in EGFR-Mutant NSCLC Patient-Derived Organoids
08:52

Profiling Sensitivity to Targeted Therapies in EGFR-Mutant NSCLC Patient-Derived Organoids

Published on: November 22, 2021

6.0K

Optimizing Oligonucleotide Therapeutics: A Model-Informed Drug Development Perspective.

Ye Yuan1, Vishnu Sharma1, Venkatesh Atul Bhattaram1

  • 1Division of Pharmacometrics, Office of Clinical Pharmacology, Center for Drug Evaluation and Research, Food and Drug Administration, Silver Spring, Maryland, USA.

Clinical and Translational Science
|May 5, 2026
PubMed
Summary
This summary is machine-generated.

Model-informed drug development (MIDD) addresses challenges in oligonucleotide therapy by bridging pharmacokinetic and pharmacodynamic gaps. This approach optimizes dosing and enhances clinical success for novel gene-targeting therapeutics.

Keywords:
ASOMIDDoligonucleotide therapysiRNA

More Related Videos

Author Spotlight: Cost-Effective Transcriptomic Drug Screening - Unlocking New Targets
06:40

Author Spotlight: Cost-Effective Transcriptomic Drug Screening - Unlocking New Targets

Published on: February 23, 2024

1.9K
Looking for Driver Pathways of Acquired Resistance to Targeted Therapy: Drug Resistant Subclone Generation and Sensitivity Restoring by Gene Knock-down
08:59

Looking for Driver Pathways of Acquired Resistance to Targeted Therapy: Drug Resistant Subclone Generation and Sensitivity Restoring by Gene Knock-down

Published on: December 11, 2017

6.5K

Related Experiment Videos

Last Updated: May 6, 2026

Profiling Sensitivity to Targeted Therapies in EGFR-Mutant NSCLC Patient-Derived Organoids
08:52

Profiling Sensitivity to Targeted Therapies in EGFR-Mutant NSCLC Patient-Derived Organoids

Published on: November 22, 2021

6.0K
Author Spotlight: Cost-Effective Transcriptomic Drug Screening - Unlocking New Targets
06:40

Author Spotlight: Cost-Effective Transcriptomic Drug Screening - Unlocking New Targets

Published on: February 23, 2024

1.9K
Looking for Driver Pathways of Acquired Resistance to Targeted Therapy: Drug Resistant Subclone Generation and Sensitivity Restoring by Gene Knock-down
08:59

Looking for Driver Pathways of Acquired Resistance to Targeted Therapy: Drug Resistant Subclone Generation and Sensitivity Restoring by Gene Knock-down

Published on: December 11, 2017

6.5K

Area of Science:

  • Pharmacology and Therapeutics
  • Genetics and Molecular Biology
  • Drug Development

Background:

  • Oligonucleotide therapies (ASOs, siRNAs, aptamers) offer novel treatment strategies by targeting gene expression.
  • Development challenges include a disconnect between drug levels in the body and effect in tissues, and limited clinical data for rare diseases.
  • Traditional methods struggle with dose optimization and efficacy prediction for these complex therapeutics.

Purpose of the Study:

  • To review the application of Model-Informed Drug Development (MIDD) in overcoming challenges specific to oligonucleotide therapeutics.
  • To highlight how MIDD facilitates dose optimization, efficacy prediction, and decision-making in oligonucleotide drug development.
  • To demonstrate the role of quantitative modeling in advancing oligonucleotide therapies and their regulatory success.

Main Methods:

  • Comprehensive literature review of MIDD applications in FDA-approved oligonucleotide therapies.
  • Analysis of how quantitative models bridge pharmacokinetic (PK) and pharmacodynamic (PD) disconnects.
  • Examination of MIDD's role in informing clinical trial design, endpoint selection, and dosing strategies.

Main Results:

  • MIDD successfully addresses the PK-PD disconnect in oligonucleotide therapies.
  • Quantitative models enable dose optimization and efficacy prediction despite limited clinical data.
  • MIDD has informed accelerated approvals, general and subpopulation dosing, and efficient clinical development.

Conclusions:

  • MIDD is crucial for overcoming unique oligonucleotide development challenges.
  • Quantitative modeling enhances the clinical and regulatory success of oligonucleotide therapeutics.
  • MIDD strategies are essential for advancing this promising therapeutic class.