Computational modeling of human oral bioavailability: what will be next?

Area of Science:

• Pharmacokinetics and Drug Development
• Computational Chemistry
• Medicinal Chemistry

Background:

• Oral drug administration is preferred for convenience.
• Accurate prediction of oral bioavailability is critical in early drug discovery.
• Existing methods face challenges in predicting this complex pharmacokinetic property.

Purpose of the Study:

• To provide insight into factors influencing oral bioavailability.
• To review computational methods for predicting oral bioavailability.
• To discuss future directions in bioavailability prediction modeling.

Main Methods:

• Quantitative Structure-Property Relationship (QSPR) modeling.
• Rule-of-Thumb (RoT) approaches.
• Physiologically Based Pharmacokinetic (PBPK) modeling.

Main Results:

• Reliability of datasets and descriptor selection impact QSPR/RoT models.
• Pharmacokinetic data integrity and parameter complexity affect PBPK models.
• Hybrid QSPR-PBPK and deep-learning models show promise.

Conclusions:

• Oral bioavailability prediction is challenging due to its multi-factorial nature.
• Independent computational methods have limitations.
• Integrated and advanced computational approaches offer improved prediction capabilities.