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Related Concept Videos

Drug Discovery: Overview01:26

Drug Discovery: Overview

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

Impact of Pharmacokinetic–Pharmacodynamic Models: Regulatory Decisions

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 (CHF).
Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
SAR studies the intricate relationship between a drug's chemical structure and biological activity. It focuses on understanding how modifications to a drug's structure can influence its...
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
Pharmacokinetic Models: Overview01:20

Pharmacokinetic Models: Overview

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 assumptions,...
Pharmacokinetic–Pharmacodynamic Relationship: Problems01:24

Pharmacokinetic–Pharmacodynamic Relationship: Problems

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...

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Related Experiment Video

Updated: May 15, 2026

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions
08:31

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

Published on: December 1, 2020

Torching the Haystack: modelling fast-fail strategies in drug development.

Dennis W Lendrem1, B Clare Lendrem

  • 1Institute of Cellular Medicine, University of Newcastle upon Tyne, United Kingdom. dennis.lendrem@newcastle.ac.uk

Drug Discovery Today
|December 19, 2012
PubMed
Summary

Fast-fail strategies accelerate drug development by removing unsuccessful candidates early. This approach reduces time to market, lowers research and development costs, and boosts R&D productivity.

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Area of Science:

  • Drug development
  • Pharmaceutical R&D
  • Biopharmaceutical industry

Background:

  • Drug development is a lengthy and costly process with high attrition rates.
  • Identifying and discontinuing failing drug candidates early is crucial for resource optimization.

Purpose of the Study:

  • To introduce and evaluate the Quick-Kill model for drug development.
  • To demonstrate the impact of fast-fail strategies on key development metrics.

Main Methods:

  • Utilizing the Quick-Kill model to analyze drug development pipelines.
  • Incorporating cost and risk data from pharmaceutical and biopharmaceutical companies.

Main Results:

  • Fast-fail strategies significantly reduce the expected time to market for new drugs.
  • Implementation of fast-fail approaches leads to decreased expected research and development costs.
  • The adoption of fast-fail strategies enhances overall R&D productivity.

Conclusions:

  • The Quick-Kill model provides a framework for implementing effective fast-fail strategies.
  • Fast-fail approaches are essential for improving efficiency and success rates in drug development.
  • Optimizing the development pipeline through early termination of non-viable products is a key strategy for the pharmaceutical industry.