Computational Approaches in Preclinical Studies on Drug Discovery and Development

Area of Science:

• Pharmacology
• Computational Chemistry
• Drug Discovery

Background:

• Late-stage drug development failures are often caused by poor pharmacokinetics and toxicity.
• Increasing drug recalls necessitate enhanced safety evaluations in preclinical stages.
• Traditional in vitro and in vivo methods are effective but costly.

Purpose of the Study:

• To review the application of in silico technology for predicting drug ADMET properties in early drug discovery.
• To provide a systematic classification of databases and software for ADMET prediction.
• To discuss challenges and future prospects in computational ADMET prediction.

Main Methods:

• Introduction to two main categories of ADMET prediction: molecular modeling and data modeling.
• Systematic classification and description of commonly used databases and software for ADMET prediction.
• Focus on widely studied ADMET properties and physiologically based pharmacokinetic (PBPK) simulation.

Main Results:

• Overview of available in silico tools and models for predicting drug ADMET properties.
• Identification of key ADMET properties and PBPK simulation as critical areas of focus.
• Listing of relevant applications, prediction categories, and web tools.

Conclusions:

• In silico methods offer a cost-effective approach to predict ADMET properties early in drug discovery.
• Computational tools are vital for improving the safety and success rates of drug candidates.
• Further development and integration of in silico models are needed to address current challenges.