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

Decision Making: P-value Method01:09

Decision Making: P-value Method

5.8K
The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can...
5.8K
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
Methods of Medium Optimization01:28

Methods of Medium Optimization

70
Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...
70
Drug Administration and Therapy Phases: Overview01:26

Drug Administration and Therapy Phases: Overview

1.9K
Drugs, the chemical agents used in diagnosing, treating, or preventing diseases, undergo a four-phase process of development: pharmaceutic, pharmacokinetics, pharmacodynamics, and therapeutic.
The pharmaceutical phase focuses on leveraging the physicochemical properties of the drug to design and manufacture an effective product. Variants include orally administered tablets or capsules, topical creams or ointments, and parenteral-delivery solutions or emulsions.
The pharmacokinetic phase...
1.9K
Clinical Trials: Overview01:11

Clinical Trials: Overview

4.7K
Clinical development focuses on how the drug will interact with the human body and encompasses four key phases of clinical trials, each serving a specific purpose in assessing the safety and effectiveness of new drugs. These phases overlap and build upon one another. Phase I involves a small group of healthy volunteers (typically 20-80 individuals) or, in cases where significant toxicity is expected, patients with the targeted disease, such as cancer or AIDS. The volunteers are tested for...
4.7K
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

627
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
627

You might also read

Related Articles

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

Sort by
Same author

The interaction between chronic hepatitis B (CHB) and Metabolic dysfunction-associated steatotic liver disease (MASLD) in a diverse central London population.

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

Implementation of the VIVALDI Social Care data pipeline for monitoring and research of infections in care homes for older adults in the UK.

International journal of medical informatics·2026
Same author

CRT-Estimands Framework: consensus based extension of the ICH E9(R1) addendum for cluster randomised trials.

BMJ (Clinical research ed.)·2026
Same author

The impacts of introducing online postal self-sampling for sexually transmitted infections on the sustainability and equity of sexual health systems: lessons learned from a multi-method UK-wide realist evaluation.

BMC medicine·2026
Same author

Corrigendum to "Sexually transmitted infection testing and key outcomes following implementation of online postal self-sampling into sexual health services in England: a retrospective observational study of routinely collected service-level healthcare data" Lancet Reg Health Eur. 2025 Nov 29;61:101541.

The Lancet regional health. Europe·2026
Same author

On-treatment serum prostate-specific antigen and overall survival in prostate cancer (STAMPEDE platform protocol): a post-hoc analysis of data from five phase 3 trials.

The Lancet. Oncology·2026

Related Experiment Video

Updated: May 1, 2026

Procedure to Evaluate the Efficiency of Flocculants for the Removal of Dispersed Particles from Plant Extracts
10:37

Procedure to Evaluate the Efficiency of Flocculants for the Removal of Dispersed Particles from Plant Extracts

Published on: April 9, 2016

8.6K

A Quantitative Process for Enhancing End of Phase 2 Decisions.

Tony Sabin1, James Matcham2, Sarah Bray3

  • 1Tony Sabin, Amgen Ltd., Cambridge Science Park, Cambridge, UK (E-mail: tsabin@amgen.com ); MRC Clinical Trials Unit at University College London, London, UK.

Statistics in Biopharmaceutical Research
|April 1, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces a quantitative process for drug development decision-making at the end of phase 2 trials. It uses a Bayesian approach to predict phase 3 efficacy success, ensuring evidence-based choices for new drug candidates.

Keywords:
Decision makingPancreatic cancerProbability of success

More Related Videos

Generic Protocol for Optimization of Heterologous Protein Production Using Automated Microbioreactor Technology
06:24

Generic Protocol for Optimization of Heterologous Protein Production Using Automated Microbioreactor Technology

Published on: December 15, 2017

13.5K
A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM
13:54

A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM

Published on: August 18, 2023

6.4K

Related Experiment Videos

Last Updated: May 1, 2026

Procedure to Evaluate the Efficiency of Flocculants for the Removal of Dispersed Particles from Plant Extracts
10:37

Procedure to Evaluate the Efficiency of Flocculants for the Removal of Dispersed Particles from Plant Extracts

Published on: April 9, 2016

8.6K
Generic Protocol for Optimization of Heterologous Protein Production Using Automated Microbioreactor Technology
06:24

Generic Protocol for Optimization of Heterologous Protein Production Using Automated Microbioreactor Technology

Published on: December 15, 2017

13.5K
A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM
13:54

A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM

Published on: August 18, 2023

6.4K

Area of Science:

  • Pharmaceutical Sciences
  • Biostatistics
  • Drug Development

Background:

  • Phase 2 trials evaluate drug safety, tolerability, and efficacy signals.
  • Decisions for phase 3 initiation require significant resource commitment.
  • Current decision-making balances portfolio, cost, competition, and therapeutic benefits.

Purpose of the Study:

  • To present a practical quantitative process for phase 2 drug development decisions.
  • To ensure consistent, evidence-based decision-making for new drug candidates.
  • To enhance statisticians' strategic role in drug development programs.

Main Methods:

  • Implementation of a quantitative process for drugs entering phase 2.
  • Utilizing a predominantly Bayesian approach for predicting phase 3 efficacy.
  • Illustrating the process with a pancreatic cancer drug development example.

Main Results:

  • A structured, evidence-based approach for phase 2 to phase 3 transition decisions.
  • Improved consistency in evaluating drug candidates.
  • Demonstrated application of Bayesian methods for predicting phase 3 success.

Conclusions:

  • The presented quantitative process supports robust, data-driven decisions for advancing drug candidates.
  • The Bayesian approach provides a probabilistic framework for assessing phase 3 success likelihood.
  • Adoption of this process can optimize resource allocation and strategic planning in drug development.