Partition coefficient vs. binding constant: How best to assess molecular lipophilicity

Area of Science:

• Pharmacokinetics and Drug Discovery
• Physical Chemistry
• Analytical Chemistry

Background:

• The partition coefficient (P) is a key descriptor for molecular lipophilicity, crucial for understanding drug behavior.
• Current methods often use linear models to describe both partitioning and binding, potentially leading to inaccuracies.
• Accurate lipophilicity assessment is vital for drug design and predicting absorption, distribution, metabolism, and excretion (ADME) properties.

Purpose of the Study:

• To compare solute partitioning between two fluid media with solute binding to a surface.
• To evaluate the correctness of using the prevailing linear definition of partition coefficient (P) for both phenomena.
• To identify conditions under which partitioning and binding models yield comparable results.

Main Methods:

• Comparison of ideal solute distribution (partitioning) with solute association to a surface (binding).
• Analysis using nonlinearized formulae for partitioning (Eq. (9)) and binding (Eq. (11)).
• Investigation of factors influencing model agreement, such as solute concentration and stoichiometry.

Main Results:

• Solute partitioning and binding fundamentally differ and require distinct analytical equations.
• Linear models and approximate equations can introduce significant errors (>10^3×) in lipophilicity descriptor values.
• Model agreement is achieved only under specific conditions, including dilute solute preparations and 1:1 stoichiometry.
• Binding models often yield lower results than partitioning models due to saturation effects, which linear models fail to account for.

Conclusions:

• The prevailing linear approach to partition coefficient (P) estimation is inadequate for accurately describing both partitioning and binding.
• Nonlinearized equations are essential for precise lipophilicity assessment, improving the reliability of logP predictions.
• Addressing these discrepancies can help reduce the scattering of experimental logP data and enhance drug discovery efforts.