Purely in silico BCS classification: science based quality standards for the world's drugs

Area of Science:

• Pharmacokinetics and Drug Development
• Computational Chemistry
• Pharmaceutical Sciences

Background:

• The Biopharmaceutics Classification System (BCS) is crucial for generic and innovative drug development.
• Accurate drug classification aids in predicting in vivo performance and optimizing formulation strategies.

Purpose of the Study:

• To provisionally classify top-selling oral drugs worldwide using in silico methods for BCS.
• To evaluate the efficacy of different in silico approaches for predicting drug permeability and solubility.

Main Methods:

• Employed three in silico methods: CLogP, ALogP, and KLogP for permeability estimation.
• Utilized a thermodynamic equation incorporating partition coefficient and melting point for solubility prediction.
• Validated methods against reference data and literature, classifying drugs from WHO and national lists.

Main Results:

• In silico permeability methods showed 69-92.9% accuracy against reference drugs.
• CLogP demonstrated the highest correlation (r²=0.97) with experimental log P values.
• 49-59% of top-selling drugs were classified as highly soluble (BCS Class 1 and 3), suitable for in vivo bioequivalence waivers.

Conclusions:

• Simple in silico methods, including KLogP, can reliably estimate drug permeability and solubility for BCS classification.
• These methods facilitate provisional drug classification from molecular formula alone, even pre-synthesis.
• Waivers for in vivo bioequivalence studies can be granted for highly soluble drugs, promoting generic drug availability and reducing development costs.