Partition coefficient

A drug's distribution coefficient strongly affects how easily the drug can reach its intended target in the body, how strong an effect it will have once it reaches its target, and how long it will remain in the body in an active form.[25] Hence, the log P of a molecule is one criterion used in decision-making by medicinal chemists in pre-clinical drug discovery, for example, in the assessment of druglikeness of drug candidates.[26] Likewise, it is used to calculate lipophilic efficiency in evaluating the quality of research compounds, where the efficiency for a compound is defined as its potency, via measured values of pIC₅₀ or pEC₅₀, minus its value of log P.[27]

In the context of pharmacokinetics (how the body absorbs, metabolizes, and excretes a drug), the distribution coefficient has a strong influence on ADME properties of the drug. Hence the hydrophobicity of a compound (as measured by its distribution coefficient) is a major determinant of how drug-like it is. More specifically, for a drug to be orally absorbed, it normally must first pass through lipid bilayers in the intestinal epithelium (a process known as transcellular transport). For efficient transport, the drug must be hydrophobic enough to partition into the lipid bilayer, but not so hydrophobic, that once it is in the bilayer, it will not partition out again.[29][30] Likewise, hydrophobicity plays a major role in determining where drugs are distributed within the body after absorption and, as a consequence, in how rapidly they are metabolized and excreted.

In the context of pharmacodynamics (how the drug affects the body), the hydrophobic effect is the major driving force for the binding of drugs to their receptor targets.[31][32] On the other hand, hydrophobic drugs tend to be more toxic because they, in general, are retained longer, have a wider distribution within the body (e.g., intracellular), are somewhat less selective in their binding to proteins, and finally are often extensively metabolized. In some cases the metabolites may be chemically reactive. Hence it is advisable to make the drug as hydrophilic as possible while it still retains adequate binding affinity to the therapeutic protein target.[33] For cases where a drug reaches its target locations through passive mechanisms (i.e., diffusion through membranes), the ideal distribution coefficient for the drug is typically intermediate in value (neither too lipophilic, nor too hydrophilic); in cases where molecules reach their targets otherwise, no such generalization applies.

The hydrophobicity of a compound can give scientists an indication of how easily a compound might be taken up in groundwater to pollute waterways, and its toxicity to animals and aquatic life.[34] Partition coefficient can also be used to predict the mobility of radionuclides in groundwater.[35] In the field of hydrogeology, the octanol–water partition coefficient K_ow is used to predict and model the migration of dissolved hydrophobic organic compounds in soil and groundwater.

Hydrophobic insecticides and herbicides tend to be more active. Hydrophobic agrochemicals in general have longer half-lives and therefore display increased risk of adverse environmental impact.[36]

In metallurgy, the partition coefficient is an important factor in determining how different impurities are distributed between molten and solidified metal. It is a critical parameter for purification using zone melting, and determines how effectively an impurity can be removed using directional solidification, described by the Scheil equation.[6]

Many other industries take into account distribution coefficients, for example in the formulation of make-up, topical ointments, dyes, hair colors and many other consumer products.[37]

In this method the solid particles present into the two immiscible liquids can be easily separated by suspending those solid particles directly into these immiscible or somewhat miscible liquids.

The classical and most reliable method of log P determination is the shake-flask method, which consists of dissolving some of the solute in question in a volume of octanol and water, then measuring the concentration of the solute in each solvent.[38][39] The most common method of measuring the distribution of the solute is by UV/VIS spectroscopy.[38]

A faster method of log P determination makes use of high-performance liquid chromatography. The log P of a solute can be determined by correlating its retention time with similar compounds with known log P values.[40]

An advantage of this method is that it is fast (5–20 minutes per sample). However, since the value of log P is determined by linear regression, several compounds with similar structures must have known log P values, and extrapolation from one chemical class to another—applying a regression equation derived from one chemical class to a second one—may not be reliable, since each chemical classes will have its characteristic regression parameters.

The pH-metric set of techniques determine lipophilicity pH profiles directly from a single acid-base titration in a two-phase water–organic-solvent system.[10]^: 280–4 Hence, a single experiment can be used to measure the logarithms of the partition coefficient (log P) giving the distribution of molecules that are primarily neutral in charge, as well as the distribution coefficient (log D) of all forms of the molecule over a pH range, e.g., between 2 and 12. The method does, however, require the separate determination of the pK_a value(s) of the substance.

Polarized liquid interfaces have been used to examine the thermodynamics and kinetics of the transfer of charged species from one phase to another. Two main methods exist. The first is ITIES, "interfaces between two immiscible electrolyte solutions".[41] The second is droplet experiments.[42] Here a reaction at a triple interface between a conductive solid, droplets of a redox active liquid phase and an electrolyte solution have been used to determine the energy required to transfer a charged species across the interface.[43]

There are attempts to provide partition coefficients for drugs at a single-cell level.[44][45] This strategy requires methods for the determination of concentrations in individual cells, i.e., with Fluorescence correlation spectroscopy or quantitative Image analysis. Partition coefficient at a single-cell level provides information on cellular uptake mechanism.[45]

Standard approaches of this type, using atomic contributions, have been named by those formulating them with a prefix letter: AlogP,[51] XlogP,[52] MlogP,[53] etc. A conventional method for predicting log P through this type of method is to parameterize the distribution coefficient contributions of various atoms to the overall molecular partition coefficient, which produces a parametric model. This parametric model can be estimated using constrained least-squares estimation, using a training set of compounds with experimentally measured partition coefficients.[51][53][54] In order to get reasonable correlations, the most common elements contained in drugs (hydrogen, carbon, oxygen, sulfur, nitrogen, and halogens) are divided into several different atom types depending on the environment of the atom within the molecule. While this method is generally the least accurate, the advantage is that it is the most general, being able to provide at least a rough estimate for a wide variety of molecules.[53]

The most common of these uses a group contribution method and is termed cLogP. It has been shown that the log P of a compound can be determined by the sum of its non-overlapping molecular fragments (defined as one or more atoms covalently bound to each other within the molecule). Fragmentary log P values have been determined in a statistical method analogous to the atomic methods (least-squares fitting to a training set). In addition, Hammett-type corrections are included to account of electronic and steric effects. This method in general gives better results than atomic-based methods, but cannot be used to predict partition coefficients for molecules containing unusual functional groups for which the method has not yet been parameterized (most likely because of the lack of experimental data for molecules containing such functional groups).[21]^: 125ff[23]^: 1–193

A typical data-mining-based prediction uses support-vector machines,[55] decision trees, or neural networks.[56] This method is usually very successful for calculating log P values when used with compounds that have similar chemical structures and known log P values. Molecule mining approaches apply a similarity-matrix-based prediction or an automatic fragmentation scheme into molecular substructures. Furthermore, there exist also approaches using maximum common subgraph searches or molecule kernels.

For cases where the molecule is un-ionized:[13][14]

${\displaystyle \log D\cong \log P.}$

For other cases, estimation of log D at a given pH, from log P and the known mole fraction of the un-ionized form, ${\displaystyle f^{0}}$, in the case where partition of ionized forms into non-polar phase can be neglected, can be formulated as[13][14]

${\displaystyle \log D\cong \log P+\log \left(f^{0}\right).}$

The following approximate expressions are valid only for monoprotic acids and bases:[13][14]

${\displaystyle {\begin{aligned}\log D_{\text{acids}}&\cong \log P+\log \left[{\frac {1}{1+10^{\mathrm {p} H-\mathrm {p} K_{a}}}}\right],\\\log D_{\text{bases}}&\cong \log P+\log \left[{\frac {1}{1+10^{\mathrm {p} K_{a}-\mathrm {pH} }}}\right].\end{aligned}}}$

Further approximations for when the compound is largely ionized:[13][14]

• for acids with ${\displaystyle \mathrm {pH} -\mathrm {p} K_{a}>1}$, ${\displaystyle \log D_{\text{acids}}\cong \log P+\mathrm {p} K_{a}-\mathrm {pH} }$,
• for bases with ${\displaystyle \mathrm {p} K_{a}-\mathrm {pH} >1}$, ${\displaystyle \log D_{\text{bases}}\cong \log P-\mathrm {p} K_{a}+\mathrm {pH} }$.

For prediction of pK_a, which in turn can be used to estimate log D, Hammett type equations have frequently been applied.[57][58]

If the solubility, S, of an organic compound is known or predicted in both water and 1-octanol, then log P can be estimated as[46][59]

${\displaystyle \log P=\log S_{\text{o}}-\log S_{\text{w}}.}$

There are a variety of approaches to predict solubilities, and so log S.[60][61]

Values for log K_ow typically range between -3 (very hydrophilic) and +10 (extremely lipophilic/hydrophobic).[66]

The values listed here[67] are sorted by the partition coefficient. Acetamide is hydrophilic, and 2,2′,4,4′,5-Pentachlorobiphenyl is lipophilic.