QSPR in oral bioavailability: specificity or integrality?

Area of Science:

• Drug Discovery and Development
• Computational Chemistry
• Pharmacokinetics

Background:

• Technological advancements have significantly impacted the absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiles of new chemical entities (NCEs).
• Oral absorption is a critical factor in developing effective orally administered drugs, with experimental methods facing limitations.
• In silico methodologies offer a faster, cost-effective alternative for predicting absorption, but current models have limited accuracy and reliability.

Purpose of the Study:

• To review advances in quantitative structure-property relationship (QSPR) approaches for developing predictive oral bioavailability models.
• To highlight the importance of addressing data quality and enhancing the reliability of in silico predictions.
• To emphasize the need for a multi-objective approach integrating various absorption factors for early-stage drug development.

Main Methods:

• Review of existing literature on in silico models for oral bioavailability prediction.
• Focus on quantitative structure-property relationship (QSPR) methodologies.
• Discussion of challenges and strategies for improving predictive accuracy and data reliability.

Main Results:

• Significant progress has been made in applying QSPR to oral bioavailability prediction.
• Challenges remain due to data variability, lack of validation, and the complex multifactorial nature of oral absorption.
• Improving data resources and in silico model reliability is crucial for practical application.

Conclusions:

• Developing accurate in silico models for oral bioavailability is complex but essential for efficient drug discovery.
• Integrating multiple absorption parameters within a multi-objective framework is key for early-stage assessment.
• Further research into QSPR and data quality is needed to overcome current limitations.