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==Distribution coefficient and log D (logD)==
==Distribution coefficient and log D (logD)==
The '''distribution coefficient''' is the ratio of the sum of the concentrations of all forms of the compound (ionized plus un-ionized) in each of the two phases.<ref name="Scherrer"/> As such, it depends on [[pH]]. For measurements of '''distribution coefficient''', the pH of the aqueous phase is [[Buffer solution|buffered]] to a specific value such that the pH is not significantly perturbed by the introduction of the compound. The logarithm of the ratio of the sum of concentrations of the solute's various forms in one solvent, to the sum of the concentrations of its forms in the other solvent is called '''log ''D''''':
The '''distribution coefficient''' is the ratio of the sum of the concentrations of all forms of the compound (ionized plus un-ionized) in each of the two phases.<ref name="Scherrer"/><ref name="Manners_1998"?> As such, it depends on [[pH]]. For measurements of '''distribution coefficient''', the pH of the aqueous phase is [[Buffer solution|buffered]] to a specific value such that the pH is not significantly perturbed by the introduction of the compound. The logarithm of the ratio of the sum of concentrations of the solute's various forms in one solvent, to the sum of the concentrations of its forms in the other solvent is called '''log ''D''''':


:* <math>\log\ D_{\rm oct/wat} = \log\Bigg(\frac{\big[\rm{solute}\big]_{\rm octanol}^{\rm ionized}+\big[\rm{solute}\big]_{\rm octanol}^{\rm un-ionized}}{\big[\rm{solute}\big]_{\rm water}^{\rm ionized}+\big[\rm{solute}\big]_{\rm water}^{\rm un-ionized}}\Bigg)</math>
:* <math>\log\ D_{\rm oct/wat} = \log\Bigg(\frac{\big[\rm{solute}\big]_{\rm octanol}^{\rm ionized}+\big[\rm{solute}\big]_{\rm octanol}^{\rm un-ionized}}{\big[\rm{solute}\big]_{\rm water}^{\rm ionized}+\big[\rm{solute}\big]_{\rm water}^{\rm un-ionized}}\Bigg)</math>
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: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 equation|Hammett type corrections]] are included to account of [[Inductive effect|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).<ref name="Hansch">{{cite book |last = Hansch |first = Corwin |author2=Leo A| title = Substituent Constants for Correlation Analysis in Chemistry and Biology |publisher = John Wiley & Sons Ltd. |year = 1979 |location = New York |pages = 178 pages |isbn = 978-0-471-05062-9 | name-list-format = vanc | department = (secondary) }}</ref><ref name="Leo2">{{cite book |last = Leo |first = Albert |author2=Hoekman DH |author3=Hansch C | title = Exploring QSAR, Hydrophobic, Electronic, and Steric Constants |publisher = American Chemical Society |year = 1995 |location = Washington, DC |isbn = 978-0-8412-3060-6 | name-list-format = vanc | department = (secondary) }}</ref>
: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 equation|Hammett type corrections]] are included to account of [[Inductive effect|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).<ref name="Hansch">{{cite book |last = Hansch |first = Corwin |author2=Leo A| title = Substituent Constants for Correlation Analysis in Chemistry and Biology |publisher = John Wiley & Sons Ltd. |year = 1979 |location = New York |pages = 178 pages |isbn = 978-0-471-05062-9 | name-list-format = vanc | department = (secondary) }}</ref><ref name="Leo2">{{cite book |last = Leo |first = Albert |author2=Hoekman DH |author3=Hansch C | title = Exploring QSAR, Hydrophobic, Electronic, and Steric Constants |publisher = American Chemical Society |year = 1995 |location = Washington, DC |isbn = 978-0-8412-3060-6 | name-list-format = vanc | department = (secondary) }}</ref>
* Data mining prediction
* Data mining prediction
: A typical [[data mining]] based prediction uses [[support vector machine]]s,<ref name="pmid17031534">{{cite journal | vauthors = Liao Q, Yao J, Yuan S | title = SVM approach for predicting LogP | journal = Molecular Diversity | volume = 10 | issue = 3 | pages = 301–9 | date = Aug 2006 | pmid = 17031534 | doi = 10.1007/s11030-006-9036-2 | department = (primary }}</ref> [[Decision tree learning|decision trees]], or [[neural networks]].<ref name="pmid15012980">{{cite journal | vauthors = Molnár L, Keseru GM, Papp A, Gulyás Z, Darvas F | title = A neural network based prediction of octanol-water partition coefficients using atomic5 fragmental descriptors | journal = Bioorganic & Medicinal Chemistry Letters | volume = 14 | issue = 4 | pages = 851–3 | date = Feb 2004 | pmid = 15012980 | doi = 10.1016/j.bmcl.2003.12.024 | department = (primary) }}</ref> 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.
: A typical [[data mining]] based prediction uses [[support vector machine]]s,<ref name="pmid17031534">{{cite journal | vauthors = Liao Q, Yao J, Yuan S | title = SVM approach for predicting LogP | journal = Molecular Diversity | volume = 10 | issue = 3 | pages = 301–9 | date = Aug 2006 | pmid = 17031534 | doi = 10.1007/s11030-006-9036-2 | department = (primary) }}</ref> [[Decision tree learning|decision trees]], or [[neural networks]].<ref name="pmid15012980">{{cite journal | vauthors = Molnár L, Keseru GM, Papp A, Gulyás Z, Darvas F | title = A neural network based prediction of octanol-water partition coefficients using atomic5 fragmental descriptors | journal = Bioorganic & Medicinal Chemistry Letters | volume = 14 | issue = 4 | pages = 851–3 | date = Feb 2004 | pmid = 15012980 | doi = 10.1016/j.bmcl.2003.12.024 | department = (primary) }}</ref> 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 prediction
* Molecule mining prediction
: [[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 isomorphism problem|maximum common subgraph]] searches or [[Molecule mining|molecule kernel]]s.
: [[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 isomorphism problem|maximum common subgraph]] searches or [[Molecule mining|molecule kernel]]s.
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* Approximate estimation of log ''D'' (at a given pH) from log ''P'' and known [[mole fraction]] of the un-ionized form, <math>f^0</math>, in the case where partition of [[Partition coefficient#Relationship to log P (logP)|ionized forms]] into non-polar phase can be neglected:
* Approximate estimation of log ''D'' (at a given pH) from log ''P'' and known [[mole fraction]] of the un-ionized form, <math>f^0</math>, in the case where partition of [[Partition coefficient#Relationship to log P (logP)|ionized forms]] into non-polar phase can be neglected:
*: <math>\log D \cong \log P + \log\left(f^0\right)</math>
*: <math>\log D \cong \log P + \log\left(f^0\right)</math>
** approximate expressions valid for [[Acid dissociation constant|monoprotic acids and bases]] only:<ref name="Scherrer">{{cite journal | vauthors = Scherrer RA, Howard SM | title = Use of distribution coefficients in quantitative structure-activity relationships | journal = Journal of Medicinal Chemistry | volume = 20 | issue = 1 | pages = 53–8 | date = Jan 1977 | pmid = 13215 | doi = 10.1021/jm00211a010 | department = (primary }}</ref>
** approximate expressions valid for [[Acid dissociation constant|monoprotic acids and bases]] only:<ref name="Scherrer">{{cite journal | vauthors = Scherrer RA, Howard SM | title = Use of distribution coefficients in quantitative structure-activity relationships | journal = Journal of Medicinal Chemistry | volume = 20 | issue = 1 | pages = 53–8 | date = Jan 1977 | pmid = 13215 | doi = 10.1021/jm00211a010 | department = (primary }}</ref><ref name="Manners_1998">{{cite journal | vauthors = Manners CN, Payling DW, Smith DA | title = Distribution coefficient, a convenient term for the relation of predictable physico-chemical properties to metabolic processes | journal = Xenobiotica; the Fate of Foreign Compounds in Biological Systems | volume = 18 | issue = 3 | pages = 331–50 | year = 1988 | pmid = 3289270 | doi = 10.3109/00498258809041669 | deparment = (secondary) }}</ref>
*: <math>\log D_\text{acids} \cong \log P + \log\left[\frac{1}{(1+10^{pH-pK_a})}\right]</math>
*: <math>\log D_\text{acids} \cong \log P + \log\left[\frac{1}{(1+10^{pH-pK_a})}\right]</math>
*: <math>\log D_\text{bases} \cong \log P + \log\left[\frac{1}{(1+10^{pK_a-pH})}\right]</math>
*: <math>\log D_\text{bases} \cong \log P + \log\left[\frac{1}{(1+10^{pK_a-pH})}\right]</math>

Revision as of 20:26, 18 March 2016

Not to be confused with distribution constant.

In the physical sciences, a partition-coefficient (P) or distribution-coefficient (D) is the ratio of concentrations of a compound in a mixture of two immiscible phases at equilibrium. These coefficients are a measure of the difference in solubility of the compound in these two phases.

In the chemical and pharmaceutical sciences, the two phases are often restricted to mean two immiscible solvents. In this context, a partition coefficient is the ratio of concentrations of a compound in the two phases of a mixture of two immiscible liquids at equilibrium.[1] Normally one of the solvents chosen is aqueous while the second is hydrophobic such as 1-octanol.[2] Hence both the partition and distribution coefficient are measures of how hydrophilic ("water-loving") or hydrophobic ("water-fearing") a chemical substance is. Partition coefficients are useful in estimating the distribution of drugs within the body. Hydrophobic drugs with high octanol/water partition coefficients are preferentially distributed to hydrophobic compartments such as the lipid bilayers of cells while hydrophilic drugs (low octanol/water partition coefficients) preferentially are found in aqueous compartments such as blood serum.

If one of the solvents is a gas and the other a liquid, the "gas/liquid partition coefficient" is the same as the dimensionless form of the Henry's law constant. For example, the blood/gas partition coefficient of a general anesthetic measures how easily the anesthetic passes from gas to blood. Partition coefficients can also be used when one or both solvents is a solid (see solid solution).

The IUPAC has deemed the term partition coefficient is to be "obsolete,"[not verified in body] and recommends use of the most appropriate rigourous term —e.g., partition constant, defined as (KD)A = [A]org / [A]aq, where KD is the process equilibrium constant, [A] represents the concentration of solute A being tested, and "org" and "aq" refer to the organic and aqueous phases, respectively; also recommended is "partition ratio" for cases where transfer activity coefficients can be determined, and "distribution ratio" for the ratio of total analytical concentrations of a solute between phases, regardless of chemical form.[3]

Partition coefficient and log P (logP)

The partition coefficient is a ratio of concentrations of un-ionized compound between the two liquid phases. The logarithm of the ratio of the concentrations of the un-ionized solute in the solvents is called log P: When one of the solvents is water and the other is a non-polar solvent, then the log P value is also known as a measure of lipophilicity. For example, in an octanol-water system:

In the first approximation, the non-polar phase is usually dominated by the electrically neutral un-ionized form of the solute. This may not be true for the aqueous phase. To measure the partition coefficient of ionizable solutes, the pH of the aqueous phase is adjusted such that the predominant form of the compound is also un-ionized.

Generalization to ionized forms of the solute

In cases where the strong dominance of un-ionized form in the non-polar phase is no longer ensured, or where greater precision is required, one must also consider partition of all ionized forms between the two phases.[4] Let M indicate the number of ionized forms. For the I-th form (I = 1,...,M) the logarithm of the corresponding partition coefficient log PI is defined in the same manner as for the un-ionized form; e.g., in octanol-water:

For consistency, the "ordinary" (i.e., un-ionized) partition coefficient is often assigned the symbol log P0 and the index I is extended to span the 0,...,M range.

Distribution coefficient and log D (logD)

The distribution coefficient is the ratio of the sum of the concentrations of all forms of the compound (ionized plus un-ionized) in each of the two phases.[5]Cite error: A <ref> tag is missing the closing </ref> (see the help page).[6] For a given compound lipophilic efficiency is defined as the pIC50 (or pEC50) of interest minus the LogP of the compound.

Pharmacokinetics

In the context of pharmacokinetics (what the body does to 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.[7] 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.

Pharmacodynamics

In the context of pharmacodynamics (what a drug does to the body), the hydrophobic effect is the major driving force for the binding of drugs to their receptor targets.[8][9] 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.[10] Therefore the ideal distribution coefficient for a drug is usually intermediate (not too hydrophobic nor too hydrophilic).

Consumer products

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.

Agrochemicals

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.

Metallurgy

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.

Environmental

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.[11] Partition coefficient can also used to predict the mobility of radionuclides in groundwater.[12]

Distribution coefficients may be measured or predicted for compounds currently causing problems or with foresight to gauge the structural modifications necessary to make a compound environmentally more friendly in the research phase.

In the field of hydrogeology, the octanol-water partition coefficient, or Kow, is used to predict and model the migration of dissolved hydrophobic organic compounds in soil and groundwater.

Measurement

Shake flask (or tube) method

Separatory funnel, an example of a two-phase system. In a shake flask, a hydrophobic layer (often at top) and a hydrophilic (often at bottom) are established in special glassware that allows shaking, and sampling, to determine the partiion coefficient. A separatory funnel is a related example of such a two-phase system, which is used in organic synthesis. Here the funnel is the inverted, tear-shaped glass piece being held by an opening at its top, where under the hand one sees a yellow/golden hydrophobic layer above a clear aqueous, hydrophilic layer in the cone of the funnel.

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.[citation needed] The most common method of measuring the distribution of the solute is by UV/VIS spectroscopy.[citation needed]

HPLC determination

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.[13]

Pros:

  • Fast method of determination (5-20 minutes per sample)

Cons:

  • The solute's chemical structure must be known beforehand.
  • Since the value of log P is determined by linear regression, several compounds with similar structures must have known log P values.
  • Different chemical classes will have different regression parameters, hence extrapolations to other chemical classes (applying a regression equation derived from one chemical class to a second chemical class) are not reliable.

Electrochemical methods

In the recent past some experiments using 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.

  • ITIES, Interfaces between two immiscible electrolyte solutions,[14] which, for example, has been used at Ecole Polytechnique Fédérale de Lausanne.
  • Droplet experiments, which have been used by Alan Bond, Frank Marken, and the team at the Ecole Polytechnique Fédérale de Lausanne. 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.[15]

Prediction

Quantitative structure-property relationship (QSPR) algorithms (many of which have been evaluated in a recent review[16]) calculate log P in several different ways:

  • Atomic based prediction (atomic contribution; AlogP, XlogP,[17] MlogP, etc.)
A conventional method for predicting log P is to parameterize the distribution coefficient contributions of various atoms to the over-all 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.[18][19][20] 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.
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][22]
  • Data mining prediction
A typical data mining based prediction uses support vector machines,[23] decision trees, or neural networks.[24] 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 prediction
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.
  • Predicting log P from log S
If the solubility of an organic compound is known or predicted [25] in both water and 1-octanol, then you can estimate log P as
  • Approximate estimation of log D (at a given pH) from log P and known mole fraction of the un-ionized form, , in the case where partition of ionized forms into non-polar phase can be neglected:
    • further approximations for when the compound is largely ionized:
    • further approximation when the compound is largely un-ionized:
  • Prediction of pKa
    For prediction of pKa, which in turn can be used to estimate log D, Hammett type equations have frequently been applied.[27] See[28] for a recent review of newer methods.

Some octanol-water partition coefficient data

The given values[29] are sorted by the partition coefficient. Acetamide is hydrophilic and 2,2',4,4',5-pentachlorobiphenyl is lipophilic.

Component log POW T (°C)
Acetamide[30] -1.16 25
Methanol[31] -0.82 19
Formic acid[32] -0.41 25
Diethyl ether[31] 0.83 20
p-Dichlorobenzene[33] 3.37 25
Hexamethylbenzene[33] 4.61 25
2,2',4,4',5-Pentachlorobiphenyl[34] 6.41 Ambient

Values for other compounds may be found in Sangster Research Laboratories' 1989 publication[35]

See also

References

  1. ^ Leo A, Hansch C, and Elkins D (1971). "Partition coefficients and their uses". (secondary). Chem Rev. 71 (6): 525–616. doi:10.1021/cr60274a001.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  2. ^ Sangster, James (1997). Octanol-Water Partition Coefficients: Fundamentals and Physical Chemistry. (secondary). Wiley Series in Solution Chemistry. Vol. 2. Chichester: John Wiley & Sons Ltd. pp. 178 pages. ISBN 978-0-471-97397-3. {{cite book}}: Unknown parameter |name-list-format= ignored (|name-list-style= suggested) (help)
  3. ^ Wilkinson, Andrew M.; McNaught, Alan D. (1997). "Partition Coefficient". Compendium of Chemical Terminology: IUPAC Recommendations. (vanc). Oxford: Blackwell Science. doi:10.1351/goldbook. ISBN 0-86542-684-8. {{cite book}}: External link in |chapterurl= (help); Unknown parameter |chapterurl= ignored (|chapter-url= suggested) (help); Unknown parameter |name-list-format= ignored (|name-list-style= suggested) (help)
  4. ^ Pagliara A, Carrupt PA, Caron G, Gaillard P, Testa, B. (1997). "Lipophilicity Profiles of Ampholytes". (secondary). Chemical Reviews. 97: 3385–3400. doi:10.1021/cr9601019.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  5. ^ a b Scherrer RA, Howard SM (Jan 1977). "Use of distribution coefficients in quantitative structure-activity relationships". (primary. Journal of Medicinal Chemistry. 20 (1): 53–8. doi:10.1021/jm00211a010. PMID 13215.
  6. ^ Leeson PD, Springthorpe B (Nov 2007). "The influence of drug-like concepts on decision-making in medicinal chemistry". (secondary). Nature Reviews. Drug Discovery. 6 (11): 881–90. doi:10.1038/nrd2445. PMID 17971784.
  7. ^ Kubinyi H (Mar 1979). "Nonlinear dependence of biological activity on hydrophobic character: the bilinear model". (primary). Il Farmaco; Edizione Scientifica. 34 (3): 248–76. PMID 43264.
  8. ^ Eisenberg D, McLachlan AD (1986). "Solvation energy in protein folding and binding". (primary). Nature. 319 (6050): 199–203. Bibcode:1986Natur.319..199E. doi:10.1038/319199a0. PMID 3945310.
  9. ^ Miyamoto S, Kollman PA (Sep 1993). "What determines the strength of noncovalent association of ligands to proteins in aqueous solution?". (primary). Proceedings of the National Academy of Sciences of the United States of America. 90 (18): 8402–6. Bibcode:1993PNAS...90.8402M. doi:10.1073/pnas.90.18.8402. PMC 47364. PMID 8378312.
  10. ^ Pliska, Vladimir; Testa B; Van De Waterbeemd H (1996). Lipophilicity in Drug Action and Toxicology. (secondary). New York: John Wiley & Sons Ltd. pp. 439 pages. ISBN 978-3-527-29383-4. {{cite book}}: Unknown parameter |name-list-format= ignored (|name-list-style= suggested) (help)
  11. ^ Cronin D, Mark T (2006). "The Role of Hydrophobicity in Toxicity Prediction". Current Computer - Aided Drug Design. 2 (4): 405–413. doi:10.2174/157340906778992346.
  12. ^ Heuel-Fabianek B (2014). "Partition Coefficients (Kd) for the Modelling of Transport Processes of Radionuclides in Groundwater" (PDF). (primary). JÜL-Berichte, Forschungszentrum Jülich (4375): 1–51. ISSN 0944-2952.
  13. ^ Valkó K (May 2004). "Application of high-performance liquid chromatography based measurements of lipophilicity to model biological distribution". (primary). Journal of Chromatography. A. 1037 (1–2): 299–310. doi:10.1016/j.chroma.2003.10.084. PMID 15214672.
  14. ^ Ulmeanu SM, Jensen H, Bouchard G, Carrupt PA, Girault HH (Aug 2003). "Water-oil partition profiling of ionized drug molecules using cyclic voltammetry and a 96-well microfilter plate system". (primary). Pharmaceutical Research. 20 (8): 1317–22. doi:10.1023/A:1025025804196. PMID 12948031.
  15. ^ Bond AM, Marken F (1994). "Mechanistic aspects of the electron and ion transport processes across the electrode". (primary). Journal of Electroanalytical Chemistry. 372 (1–2): 125–135. doi:10.1016/0022-0728(93)03257-P.
  16. ^ Mannhold R, Poda GI, Ostermann C, Tetko IV (Mar 2009). "Calculation of molecular lipophilicity: State-of-the-art and comparison of log P methods on more than 96,000 compounds". Journal of Pharmaceutical Sciences. 98 (3): 861–93. doi:10.1002/jps.21494. PMID 18683876.
  17. ^ Cheng T, Zhao Y, Li X, Lin F, Xu Y, Zhang X, Li Y, Wang R, Lai L (2007). "Computation of octanol-water partition coefficients by guiding an additive model with knowledge". (primary). Journal of Chemical Information and Modeling. 47 (6): 2140–8. doi:10.1021/ci700257y. PMID 17985865.
  18. ^ Ghose AK, Crippen GM (1986). "Atomic Physicochemical Parameters for Three-Dimensional Structure-Directed Quantitative Structure-Activity Relationships I. Partition Coefficients as a Measure of Hydrophobicity". (primary). Journal of Computational Chemistry. 7 (4): 565–577. doi:10.1002/jcc.540070419.
  19. ^ Ghose AK, Viswanadhan VN, Wendoloski, JJ (1998). "Prediction of Hydrophobic (Lipophilic) Properties of Small Organic Molecules Using Fragmental Methods: An Analysis of AlogP and ClogP Methods". (primary). Journal of Physical Chemistry A. 102 (21): 3762–3772. doi:10.1021/jp980230o.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  20. ^ Moriguchi I, Hirono S, Liu Q, Nakagome I, Matsushita Y (1992). "Simple method of calculating octanol/water partition coefficient". (primary). Chem Pharm Bull. 40 (1): 127–130. doi:10.1248/cpb.40.127.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  21. ^ Hansch, Corwin; Leo A (1979). Substituent Constants for Correlation Analysis in Chemistry and Biology. (secondary). New York: John Wiley & Sons Ltd. pp. 178 pages. ISBN 978-0-471-05062-9. {{cite book}}: Unknown parameter |name-list-format= ignored (|name-list-style= suggested) (help)
  22. ^ Leo, Albert; Hoekman DH; Hansch C (1995). Exploring QSAR, Hydrophobic, Electronic, and Steric Constants. (secondary). Washington, DC: American Chemical Society. ISBN 978-0-8412-3060-6. {{cite book}}: Unknown parameter |name-list-format= ignored (|name-list-style= suggested) (help)
  23. ^ Liao Q, Yao J, Yuan S (Aug 2006). "SVM approach for predicting LogP". (primary). Molecular Diversity. 10 (3): 301–9. doi:10.1007/s11030-006-9036-2. PMID 17031534.
  24. ^ Molnár L, Keseru GM, Papp A, Gulyás Z, Darvas F (Feb 2004). "A neural network based prediction of octanol-water partition coefficients using atomic5 fragmental descriptors". (primary). Bioorganic & Medicinal Chemistry Letters. 14 (4): 851–3. doi:10.1016/j.bmcl.2003.12.024. PMID 15012980.
  25. ^ Buonaiuto MA, Lang AS (Dec 2015). "Prediction of 1-octanol solubilities using data from the Open Notebook Science Challenge". (primary). Chemistry Central Journal. 9 (1): 50. doi:10.1186/s13065-015-0131-2. PMID 26435734.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  26. ^ Manners CN, Payling DW, Smith DA (1988). "Distribution coefficient, a convenient term for the relation of predictable physico-chemical properties to metabolic processes". Xenobiotica; the Fate of Foreign Compounds in Biological Systems. 18 (3): 331–50. doi:10.3109/00498258809041669. PMID 3289270. {{cite journal}}: Unknown parameter |deparment= ignored (help)
  27. ^ Perrin, DD; Dempsey B; Serjeant EP (1981). pKa Prediction for Organic Acids and Bases. (secondary. London: Chapman & Hall. ISBN 0-412-22190-X. {{cite book}}: Unknown parameter |name-list-format= ignored (|name-list-style= suggested) (help)
  28. ^ Fraczkiewicz, R (2013). "In Silico Prediction of Ionization". In Reedijk, J (ed.). Reference Module in Chemistry, Molecular Sciences and Chemical Engineering [Online]. Vol. vol. 5. Amsterdam, The Netherlands: Elsevier. doi:10.1016/B978-0-12-409547-2.02610-X. {{cite encyclopedia}}: |volume= has extra text (help)
  29. ^ Dortmund Data Bank
  30. ^ Wolfenden R (Jan 1978). "Interaction of the peptide bond with solvent water: a vapor phase analysis". Biochemistry. 17 (1): 201–4. doi:10.1021/bi00594a030. PMID 618544.
  31. ^ a b Collander R, Lindholm M, Haug CM, Stene J, Sörensen NA (1951). "The partition of organic compounds. between higher alcohols and water". Acta Chem Scand. 5: 774–780. doi:10.3891/acta.chem.scand.05-0774.
  32. ^ Whitehead KE, Geankoplis CJ (1955). "Separation of Formic and Sulfuric Acids by Extraction". Ind Eng Chem. 47 (10): 2114–2122. doi:10.1021/ie50550a029.
  33. ^ a b Wasik SP, Tewari YB, Miller MM, Martire DE (1981). "Octanol - Water Partition Coefficients and Aqueous Solubilities of Organic Compounds". NBS Techn Rep. 81 (2406): S1–56.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  34. ^ Brodsky J, Ballschmiter K (1988). "Reversed phase liquid chromatography of PCBs as a basis for calculation of water solubility and Kow for polychlorobiphenyls". Fresenius Z Anal Chem. 331 (3–4): 295–301. doi:10.1007/BF00481899.
  35. ^ Sangster J. "Octanol-Water Partition Coefficients of Simple Organic Compounds" (PDF). Sangster Research Laboratories. doi:10.1063/1.555833. Retrieved 2014-12-10. {{cite web}}: |archive-date= requires |archive-url= (help); Check date values in: |archivedate= (help)

Further reading

  • Martin, Yvonne Connolly (2010). Quantitative Drug Design: A critical introduction (2nd ed.). Boca Raton: CRC Press/Taylor & Francis. ISBN 978-1-4200-7099-6.

There are many logP calculators or predictors available both commercially and for free.

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