Related Experiment Videos
ADMET in silico modelling: towards prediction paradise?
Han van de Waterbeemd1, Eric Gifford
1Pfizer Global Research & Development, PDM, Sandwich, Kent CT13 9NJ, UK. han_waterbeemd@sandwich.pfizer.com
Nature Reviews. Drug Discovery
|March 4, 2003
Summary
Early consideration of drug absorption, distribution, metabolism, excretion (ADME), and toxicity (T) is crucial. In silico methods enhance prediction of ADMET endpoints, accelerating drug discovery and reducing costly late-stage failures.
Area of Science:
- Drug discovery and development
- Pharmacokinetics and drug metabolism
- Toxicology and safety assessment
Background:
- Late-stage drug development failures are often caused by poor pharmacokinetics and toxicity.
- Early consideration of ADMET properties is now standard practice in drug discovery.
- High-throughput screening necessitates rapid ADMET data generation.
Purpose of the Study:
- To highlight the increasing need for early ADMET data in drug discovery.
- To introduce in silico approaches as a solution for predicting ADMET endpoints.
- To demonstrate how computational methods can accelerate the drug discovery pipeline.
Main Methods:
- Review of historical trends in drug development failures.
- Description of advancements in in vitro ADMET screening technologies.
- Explanation of the role and capabilities of in silico modeling for ADMET prediction.
Main Results:
- In silico approaches significantly improve the prediction of key pharmacokinetic and toxicity parameters.
- Computational modeling complements in vitro screening, providing deeper insights.
- The integration of in silico tools accelerates the identification of viable drug candidates.
Conclusions:
- In silico methods are essential for predicting ADMET endpoints early in drug discovery.
- These computational approaches enhance the efficiency and success rate of drug development.
- Utilizing in silico tools helps mitigate costly late-stage failures by identifying potential issues sooner.