Thinking About Aqueous Solvation

Given that it was International Women's Day yesterday, I'll open the the post (and blogging for 2025) with a photo of a gravestone at St James' Church in Bramley (Hampshire).

In the current post I’ll be taking a look at some aspects of aqueous solvation and Richard Wolfenden’s 1983 “Waterlogged Molecules” article (W1983) is still worth reading today (as an aside, Prof Wolfenden will turn ninety in May of this year and hopefully mentioning this won't put what is called "goat mouth" in my native Trinidad and Tobago on him as I did for Oscar Niemeyer with the words "ele vive ainda" while studying Portuguese in 2012). As noted in W1983 the formation of a target-ligand complex requires partial desolvation of both target and ligand:

When biological compounds combine, react with each other, or change shape in watery surroundings, solvent molecules tend to be reorganized in the neighborhood of the interacting groups.

Formation of a target-ligand can also be seen as an “exchange reaction” and this point is very well made in SGT2012:

Molecular binding in an aqueous solvent can be usefully viewed not as an association reaction, in which only new intermolecular interactions are introduced between receptor and ligand, but rather as an exchange reaction in which some receptor–solvent and ligand–solvent interactions present in the unbound state are lost to accommodate the gain of receptor–ligand interactions in the bound complex.

In HBD3 I briefly discuss ‘frustrated hydration’ as a phenomenon that could be exploited in drug design and I’ll quote from the Summary section of W1983:  

When two or more functional groups are present within the same solute molecule, their combined effects on its free energy of solvation are commonly additive. Striking departures from additivity, observed in certain cases, indicate the existence of special interactions between different parts of a solute molecule and the water that surrounds it.

I’ll try to explain how this could work for ligand design and let’s suppose that we have two polar atoms that are close together in the binding site. The proximity of the polar atoms in the binding site means that water molecules forming ideal interactions with the polar atoms in the binding sites are also likely to be close together. However, the mutual proximity of the water molecules can lead to unfavourable interactions between the water molecules which ‘frustrate’ the (simultaneous) hydration of the two polar atoms in the binding site. Now if we design a ligand with two polar atoms positioned to form good interactions with polar atoms in the binding site it is likely that these will also be in close proximity and that their hydration will be similarly frustrated. I would generally anticipate that frustration of hydration will not be handled well by implicit solvent models (RT1999 | FB2004 | CBK2008 |  KF2014)  or computational tools such as WaterMap that calculate energetics for individual water molecules (especially in cases where the two hydration sites cannot be simultaneously occupied).

To illustrate frustration of hydration I’ve taken a graphic from a talk from 2023. The unfavorable interactions between solvating water molecules that frustrate hydration are shown as red double-headed water molecules (in some cases these interactions will be repulsive to the extent that only one of the hydration sites can be occupied at a time). You’ll also notice two thick green lines in the right hand panel and these show secondary interactions that stabilize the bound complex. Secondary interactions of this nature were discussed in a molecular recognition context in the JP1990 study and the observation (see A1989) that pyridazine is a better hydrogen bond acceptor (HBA) than its pK_a would have you believe can be seen in a similar light.  Secondary interactions like these only enhance affinity when the proximal polar atoms are of the same ‘type’ (the proximal polar atoms in the 1,8-naphthyridine are both HBAs) and we should anticipate that the secondary interactions for the contact between pyrazole and the ‘hinge’ of a tyrosine kinase will be deleterious for affinity. In contrast to secondary interactions, frustration of hydration can be beneficial for affinity even when the proximal polar atoms are of opposite types, as would be the case for an HBA that is near to a hydrogen bond donor (HBD).     

While it is clearly important to account for aqueous solvation when using physics-based approaches for prediction of binding affinity, passive permeability and aqueous solubility, the measurement of gas-to-water transfer free energy is not exactly routine (I’m not aware that any companies offer measurement aqueous solvation energy as a service nor do I believe that this is an activity that would readily funded). Measurements for aqueous solvation energy reported in the literature tend to be for relatively volatile compounds and I’ll direct readers to the C1981, W1981 and A1990 studies.

A view is that I've held for many years is that a partition coefficient could be used as an alternative to gas-to-water transfer free energy for studying aqueous solvation. It's also worth noting that when we think about desolvation in drug design we're often considering the energetic cost of bringing polar atoms into contact with non-polar atoms (as opposed to transferring the polar atoms to gas phase). Partition coefficient measurement is a lot more routine than solvation free energy measurement and most drug discovery scientists are of aware that the octanol/water partition coefficient (usually quoted as its base 10 logarithm logP) is an important design parameter. However, the octanol/water partition coefficient is not useful for assessing aqueous solvation because the hydroxyl group of octanol can form hydrogen bonds with solutes and the water-saturated solvent is actually quite 'wet' (the DC1992 study reports that the room temperature solubility of water in octanol is 2.5 M). If we’re going to use partition coefficient measurements for studying aqueous solvation then I would argue that we should make these measurements with a saturated hydrocarbon such as cyclohexane or hexadecane that lacks hydrogen bonding capability.

Here’s another slide from that 2023 talk showing that pyridine is lipophilic for octanol/water but hydrophilic for hexadecane/water. The difference in the logP values for a solute is sometimes referred to as ΔlogP (it is equivalent to the hexadecane/water logP value with both solvents water-saturated) and can be considered to quantify the solute’s ability to form hydrogen bonds (see Y1988 | A1994 | T2008). I'll mention in passing that ΔlogP measurements with toluene as the less polar organic solvent have been used to study intramolecular hydrogen bonding (see S2013 | C2016 | C2018).     

It should be stressed  that people have been thinking about using different organic solvents for partition coefficient measurement for a lot longer than me. My view, expressed in K2013, is that the justification in H1963 for using octanol was partly based on a misinterpretation of Collander's C1951 study. I really like this quote from Alan Finkelstein's 1976 article (as an aside the partition coefficient literature is not exactly awash with alkane/water logP measurements for amides and the article reports measured values of the hexadecane/water partition coefficient for acetamide, formamide, urea, butyramide and isobutyramide): 

It has long been fashionable to worry about which organic solvent (and polarity) is the best model for the lipoidal region of a particular cell membrane (Collander, 1954). These solvents have ranged from isobutanol (the most polar) to olive oil (the least polar). I have never understood the point of this. If the lipoidal region of the plasma membrane is a lipid bilayer, then clearly the appropriate model solvent is hydrocarbon. For artificial bilayers this is obviously so. I chose n-hexadecane as the particular hydrocarbon, because its chain length is comparable to that of the fatty acid residues in most phospholipids, and it is conveniently available.

I also need to mention the B2016 study (Blind prediction of cyclohexane–water distribution coefficients from the SAMPL5 challenge) since the the cyclohexane/water distribution coefficient was used as a surrogate for gas-to-water transfer free energy in the challenge:

The inclusion of distribution coefficients replaces the previous focus on hydration free energies which was a fixture of the past five challenges (SAMPL0-4) [1 | 2 | 3 | 4 | 5 | 6 | 7]. Due to a lack of ongoing experimental work to generate new data, hydration free energies are no longer a practical property to include in blind challenges. It has become increasingly difficult to find unpublished or obscure hydration free energies and therefore impossible to design a challenge focusing on target compounds, functional groups or chemical classes.

I consider initiatives such as the SAMPL5 cyclohexane/water distribution challenge to be valuable for assessing model predictivity in an objective and transparent manner. Generally, I would avoid including logD measurements for compounds that are significantly ionized under experimental conditions because these require that account be taken of ionization when making predictions (better to measure logD at a pH at which ionizable functional groups are not significantly ionized). While challenges such as SAMPL5 are certainly valuable for assessment of predictivity of models, I consider them less useful in model development which requires measured data for structurally-related compounds. 

The isosteric pairs 1/2  and 3/4 shown in the graphic below will give you an idea of what I'm getting at. The predicted pK_BHX values taken from K2016 suggest that 1 is less polar than than its isostere 2 and I'd expect 3 to be more polar than 4.

While the three N-butylated purines shown in the graphic below are not strictly isosteric I would consider it valid to interpret the cyclohexane/water logP values taken from S1998 as reflecting differences in hydrogen bond acceptor strength.

This is a good point at which wrap up and, given the fundamental importance of aqueous solvation in biomolecular recognition and drug design, I see tangible advantages in having a large body of measured data in the public domain. My view is that to measure gas-to-water transfer free energy for significant numbers of compounds of interest to drug discovery scientists would be both technically demanding and unlikely to get funded although I would be delighted to be proven wrong on either point. This means that we need to learn to use other types of data in order to study aqueous solvation and my view is that an alkane/water partition coefficient would be the best option. Using alkane/water partition coefficients as an alternative to gas-to-water transfer free energies for studying aqueous solvation would also enable enthalpic (see RT1984) and volumetric aspects of aqueous solvation to be investigated more easily.     

More hydrogen bonding asymmetries

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I examined an article on the polarized nature of protein-ligand binding interfaces previously and promised that I'd discuss a completely different type of hydrogen bond asymmetry which is based not on structure but on energetics. Readers may be familiar with hydrogen bond (HB) acidity and basicity which may be quantified by measurement of 1:1 association constants for hydrogen bonded complexes in non-hydrogen bonding solvents (e.g. carbon tetrachloride). Here are three references (1 | 2 | 3) and I'll also mention that molecular electrostatic potential (MEP) can be used for prediction of both HB acidity and HB basicity.

As discussed in this article, measurements of HB acidity and basicity have their limitations when trying to use them to understand and predict solvation behavior in aqueous media. First, measuring the association constant for a 1:1 complex does not tell us what will happen when two water molecules simultaneously donate hydrogen bonds to the oxygen atom of a carbonyl group. Second, the measured association constants cannot be used to compare HB acceptors with HB donors. This may seem a perverse sort of thing to want to do but one of the things that drug designers are interested in is the ease of dragging different HB donors and acceptors out of water.

Lake Liadskoye

Prediction of alkane/water partition coefficients (logPalk) has been a long standing interest (1 | 2 | 3) of mine for a number of years. It turns out that analysis of logPalk values measured for structurally prototypical model compounds can tell us quite a lot about what happens when you drag individual HB donors and acceptors out of water. The analysis is based on the observation of a very strong correlation between molecular surface area (MSA) and logPalk. The figure below shows the response of logPalk to MSA for saturated hydrocarbons, aliphatic alcohols (single hydroxyl group) and aliphatic diols. The lines of fit are essentially parallel and equally spaced which suggests that the effect on logPalk of adding a hydroxyl group to a saturated hydrocarbon or to an aliphatic alcohol is constant. This suggests treating polar groups as perturbations of saturated hydrocarbons for prediction of logPalk and analysis of data like what is shown in Figure 1 can be used to parameterize the perturbations for different polar groups. The approach, described in this article is to first calculate logPalk for a hypothetical saturated hydrocarbon with the same MSA as the compound of interest and then to sum the parameters for the polar groups in the molecular structure to account for the introduction of these polar groups. 

Figure 1. Relationship between alkane/water logP and molecular surface area (MSA) for saturated hydrocarbons, saturated alcohols and saturated diols. Neither of the aliphatic diols (1,4-butanediol and 1,6-hexanediol) would be expected to form intramolecular HBs in water.  

I think we'll need more data (especially for heterocycles and species with intramolecular HBs) to make this approach to prediction of logPalk generally useful.  However, the size of the effect on logPalk of introducing an HB acceptor or donor into a saturated hydrocarbon does tell us how strongly the HB donor or acceptor interacts with water. It was actually this article which was published after our article on logPalk prediction that got me thinking along these lines. In our article, we showed how polarity can be defined for HB acceptors and donors and calculated from measured alkane/water partition coefficients. Polarity defined in this manner brings HB donors and acceptors onto the same scale and allows us to explore another type of hydrogen bonding asymmetry.

Insects exploiting surface tension in Belovezhskaya Pushcha

For HB acceptors, the approach is simple. First you need to identify appropriate model compounds for which logPalk has been measured. These have only the HB acceptor functional group of interest, saturated carbon and hydrogen in their molecular structures. Next, calculate logPalk for a saturated hydrocarbon with the same MSA as that for the model compound (use line for saturated hydrocarbons in Figure 1 to do this) and subtract the measured logPalk value from the calculated value. Things are a bit more complicated for HB donors because you can't usually have these without an HB acceptor (this is the 'baggage' I discussed in the previous post) and you need to deal with these on a case-by-case basis. For example, you might estimate the polarity of an amide NH by subtracting the polarity of the tertiary amide group from that of the secondary amide group.  Here's a table of polarity estimates for some hydrogen bond acceptors and donors (our article explains how these were derived).  

Table 1. Polarity of HB acceptors and donors estimated from measured alkane/water partition coefficient and molecular surface area

The results in Table 1 show that HB donors are typically more easily pulled out of water than HB acceptors and this can be seen as another hydrogen bonding asymmetry. This appears to go against the folklore that HB donors are somehow worse than HB acceptors from the perspective of drug-likeness. The polarity values for the NH (0.8) and carbonyl O (6.8) of the amide group may have some relevance to protein folding. This a good place to wrap up and I'll conclude by noting that, in the supplemental information for our article, you'll find an archive that contains files (in plain text format) of measured values for logPalk, hydrogen bond basicity and pKa that we extracted from the literature (DOI links are included). Here are some more photos from Belarus.  

Até mais!

Flora and fauna of Belovezhskaya Pushcha

From schwefeläther to octanol

So in this blog post, written specially for #RealTimeChem week on an #OldTimeChem theme, I'll start ten years after the Kaiser's grandmother became Queen of the United Kingdom of Great Britain and Ireland.  I first came across Ernst Friherr von Bibra while doing some literature work for an article on predicting alkane/water partition coefficients and learned that he was from an illustrious family of Franconian Prince-Bishops. Von Bibra certainly seems to have been a colorful character who is said to have fought no less than 49 duels as a young man. Presumably these were not the duels to the death that did for poor Galois and Pushkin but more like the München frat house duels that leave participants with the dueling scars that München Fräuleins find so irresistible.

So that's how I learned about von Bibra and it was his 1847 study with Harless 'Die Ergebnisse der Versuche über die Wirkung des Schwefeläthers' (The results of the experiments on the effect of the sulfuric ether) that we cited. Schwefeläther is simply diethyl ether and so named because in 1847 you needed to make it from ethanol and sulfuric acid. Von Bibra was a pioneer in the anesthesia field and proposed that anesthetics like ether exerted their effects by dissolving the fatty fraction of brain cells. Now it's easy in 2015 to scoff at this thinking but remember that in 1847 nobody knew about cell membranes or molecules and you couldn't just pick up the phone and expect the ether to arrive the next day. Put another way, if it was 1847 and I was in the laboratory (or kitchen?) gazing at a bowl of brains and a bottle of ether, I would probably have come to a similar conclusion.

What we know now is that anesthetics (and other drugs) dissolve IN lipids as opposed to dissolving THE lipids. Nobody in 1847 knew about partition coefficients and Walther Nernst didn't articulate his famous distribution law (Verteilung eines Stoffes zwischen zwei Lösungsmitteln und zwischen Lösungsmittel und Dampfraum. Z Phys Chem 8:110–139) until 1891 by which time the Kaiser had already handed Bismarck his P45. Within ten years Ernest Overton and Hans Meyer had shown incredible foresight in using amphibians as animal models and the concept of the cell membrane would soon be introduced.

Before moving on, let's take a look at the 'introduction to partition coefficients' graphic below in which the aqueous phase is marked by a the presence of fish (they're actually piranhas and have have graced my partition coefficient powerpoints since my first visit to Brazil in 2009). We would describe the compound on the left as lipophilic because its neutral form prefers the organic phase to water and, for now, I'm not going to be too specific about exactly what that organic phase is. The compound on the right prefers to be in the water so we describe it as hydrophilic.  The red molecule on the left represents an ionized form of the compound on the left and typically these don't particularly like to go into the organic phase (especially not without counter ions for company).  A compound that prefers to to be in the organic phase can still be drawn into the aqueous phase by increasing the extent to which it is ionized (e.g. by decreasing pH if the compound is basic).

  

Now I'd like to introduce Runar Collander (whom many of you will have heard of) and Calvin Golumbic (whom few of you will have heard of).  Let's first take a look at Golumbic's 1949 study of the effects of ionization on the partitioning of phenols between water and cyclohexane.  Please observe the responses to pH in Fig 1 in that article but also take a look at equation 5 which accounts for self-association in the organic phase and the discussion about how methyl group ortho the phenolic hydroxyl compromises the hydrogen bonding of that hydroxyl group and has observable effects on the partition coefficient.

Collander's study (The Partition of Organic Compounds Between Higher Alcohols and Water) explores how differences in the organic solvent affect partitioning behaviour of solutes.  Collander presented evidence for strong linear relationships between partition coefficients measured using different alcohols (see Fig 1 in his article) although he notes that compounds with two or more hydrophilic groups in their molecular structure tend to deviate from the trend. Now take a look at Fig 2 in Collander's study which shows a plot of octanol/water partition coefficients against their ether/water equivalents. Now the correlation doesn't look so strong although relationships within chemical families appear to be a lot better. In particular, amines appear to be more soluble in octanol than they are in ether and Collander attributes this to the greater acidity of octanol. 

When reading these articles from over sixty years ago, I'm struck by the way the authors ratioanalize their observations in physical terms.  Don't be misled by what we would regard as the obscure use of language (e.g. Collander's "double molecules"  and Golumbic's description of partition coefficients as "true") because the conceptual and linguistic basis of chemistry in 2015 is richer than it was when these pioneering studies were carried out.  How these pioneers would have viewed some of the more mindless metrics by which chemists of 2015 have become enslaved can only be speculated about.

So I'm almost done but one last character in this all-star cast has yet to make his entrance and that, of course, is Corwin Hansch. Most drug discovery scientists 'know' that octanol 'defines' lipophilicity and only a small minority actually question the suitability of octanol for this purpose or even ask how this situation came to be. In order to address the second question, let's take a look at what Hansch et al have to say in this 1963 article,

"We have chosen octanol and water as a model system to approximate the effect of step I on the growth reaction in much the same fashion as the classical work of Meyer and Overton rationalized the relative activities of various anesthetics. This assumption is expressed in 2 where P is the partition coefficient (octanol-water) of the auxin.

          A = f(P)                              (2)

Collander has shown that the partition coefficients for a given compound in two different solvent systems (e.g., ether-water, octanol-water) are related as in 3.

          log P1 = a log P2 + b        (3)

This would also indicate, as does the Meyer-Overton work, that it is not unreasonable to use the results from one set of solvents to predict results in a second set". 

Now if you take another look at Collander's article, you'll see that he only claims that a linear relationship exists between partition coefficients when the organic phase is an alcohol. Collander's Fig 2 seems to suggest that Hansch et al's equation 3 cannot be used to relate octanol/water and ether/water partition coefficients. Can Collander's study be used to justify what appears to be a rather arbitrary choice of octanol as a solvent for partition coefficient measurements? That question, I will leave to you, the reader but, if you're interested, let me point you towards a short talk that I did recently at Ripon College. 

See you next year at #RealTimeChem week and don't forget to take a look at Laura's nails which get my vote for highlight of the week.

Efficiency can also be lipophilic

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In the previous post, I questioned the validity of scaled ligand efficiency metrics (LEMs) such as LE.  However, LEMs can also be defined by subtracting the value of the risk factor from activity and this has been termed offsetting.  For example, you can subtract a measure of lipophilicity from pIC₅₀ to give functions such as:

pIC₅₀ – logP

pIC₅₀ – ClogP

pIC₅₀ – logD(pH)

As you will have gathered from the previous post, I am not a big fan of naming LEMs. The reason for this is that you often (usually?) can’t tell from the definition exactly what has been calculated and I think it would be a lot better if people were forced (journal editors, you can achieve something of lasting value here) to be explicit about the mathematical function(s) with which they normalize activity of compounds.  In some ways, the problems are actually worse when activity is normalized by lipophilicity because a number of different measures of lipophilicity can be used and because differences between lipophilicity measures are not always well understood (even by LEM ‘experts’).  Does LLE mean ‘ligand-lipophilicity efficiency’ or 'lipophilic ligand efficiency’?  When informed that a compound has an LLE of 4, how can I tell whether it has been calculated using logD (please specify pH), logP or predicted logP (please specify prediction method since there several to choose from)?

Have a look at this figure and observe how the three parallel lines respond the offsetting transformation (Y => Y-X).  The line with unit slope transforms to a line of zero slope (Y-X is independent of X) while the other two lines transform to lines of non-unit slope (Y/X is dependent on X).  This figure is analogous to the one in the previous post that showed how three parallel lines transformed under the scaling transformation. 

It’s going to be helpful to generalize Lipophilic Efficiency (LipE) so let’s do that first:

LipE_gen = pIC₅₀  - (l ´ ClogP)

Generalizing LipE in this manner shows us that LipE (l = 1) is actually quite arbitrary (in much the same way that a standard or reference state is arbitrary and one might ask whether l = 0.5 might not be a better LEM.  Note that a similar criticism was made of Solubility Forecast Index in the Correlation Inflation Perspective. One approach to validating an LEM would be to show that it actually predicted relevant behavior of compounds. In the case of LEMs based on lipophilicity, it would be necessary to show that the best predictions were observed for l = 1.  Although one can think of an LEMs as simple quantitative structure activity relationships (QSARs), LEMs are rarely, if ever, validated in a way that QSAR practitioners would regard as valid.  Can anybody find a sentence in the pharmaceutical literature containing the words ‘ligand’, ‘efficiency’ and ‘validated’?  Answers on a postcard...

Offset LEMs do differ from scaled LEMs and one might invoke a thermodynamic argument to justify the use of LipE as an LEM.  In a nutshell it can be argued that LipE is a measure of the ease of moving a compound from a non-polar environment to its binding site in the protein.  There are two flaws in this argument which were discussed in our LEM critique which will be open access for another 10 days or so.  Firstly, when ligand binds in an ionized form, lipophilicity measures do not quantify the ease of moving the bound form from octanol to water because ionized forms of compounds do not usually partition into octanol to a significant extent. Secondly, octanol/water is just one of a number of partitioning systems and one needs to demonstrate that lipophilicity derived from it is optimal for definition of an LEM.  The figure below shows how logP can differ when measured in alternative partitioning systems and you should be aware of an occasionally expressed misconception that the relevant logP values simply differ by a constant amount.

One solution to the problem is to model pIC₅₀ as a function of your favored measure of lipophilicity and use the residuals to quantify the extent to which activity beats the trend in the data.  This is what exactly what I suggested  in the previous post as an alternative to scaling activity by risk factors such as molecular weight or heavy atoms and the approach can be seen as bringing these risk factors and lipophilicity into a common data-analytical framework.   Even if you don’t like the idea of using the residuals, it is still useful to model the measured activity because a slope of unity helps to validate LipE (assuming that you’re using ClogP to model activity).   Even if the slope of the line of fit differs from unity, you can set  l to its value to create a lipophilic efficiency metric that has been tuned to the data set that you wish to analyze.

This is a good point at which to wrap up.  As noted (and reiterated) in the LEM critique, when you use LEMs, you're making assumptions about trends in data and your perception of the system is distorted when these assumptions break down.  Modelling the data by fitting activity to risk factor allows you use the trends actually observed in the data to normalize activity.  That’s just about all I want to say for now and please don’t get me started on LELP…

Prediction of alkane/water partition coefficients

Those of you who follow this blog will know that I have a long standing interest in alkane/water partition coefficient and I’d like to tell you a bit about the ClogP_alk model for predicting these from molecular structure that we published during my time in Brasil. Some years ago we explored prediction of ΔlogP (logP_oct - logP_alk) from calculated molecular electrostatic potentials and this can be thought of as treating the alkane/water partition coefficient as a perturbation of the octanol/water partition coefficient.  One disadvantage of this approach is that it requires access to logP_oct and I was keen to explore other avenues.  The correlation of logP_alk with computed molecular surface area (MSA) is excellent for saturated hydrocarbons and I wondered if this class of compound might represent a suitable reference state for another type of perturbation model.  Have a look at Fig 1 which shows plots of logP_alk against MSA for saturated hydrocarbons (green), aliphatic alcohols (red) and aliphatic diols (blue).  You can see how adding a single hydroxyl group to a saturated hydrocarbon shifts logP_alk down by about 4.5 units and adding two hydroxyl groups shifts logP_alk further still.

 

The perturbations are defined substructurally using SMARTS notation. Specifically, each perturbation term consists of a SMARTS definition for the relevant functional group and a decrement term (e.g. 4.5 units for alcohol hydroxyl).  The model also allows functional groups to interact with each other.  For example, an intramolecular hydrogen bond ‘absorbs’ some of a molecule’s polarity and manifests itself as an unexpectedly high logP_alk value.  Take a look at this article if you’re interested in this sort of thing.  The interaction terms can be thought of as perturbations of perturbations. The ClogP_alk model is shown in Fig 2.

The performance of the model against external test data is shown in Figure 3.  There do appear to be some issues with some of the data and measured values of logP_alk were found to differ by two or more units for some compounds (Atropine, Propanolol, Papavarine).  Also there are concerns about the self-consistency of the measurements for Cortexolone, Cortisone and Hydrocortisone. Specifically, the logP_alk of Cortexolone (-1.00) is actually lower than that for its keto analogue Cortisone (-0.55).

 

 

The software was built using OpenEye programming toolkits (OEChem and Spicoli) and you’ll find the source code and makefiles in the supplementary information with all the data used to parameterize and test the models. It’s not completely open source because you’ll need a license from OpenEye to actually run the software.  However, the documentation for the toolkits is freely available online and you may be even able to get an evaluation license to see how things work.  You’ll also find the source code for SSProFilter in the supplemental material and this is an improved (it also profiles) version of the Filter program that I put together with the Daylight toolkit back in 1996. Very useful for designing screening libraries and you might want to take a look at this post on SMARTS from a couple of years ago.

There's some general discussion in the article that is not specific to the ClogP_alk model and I'll mention it briefly since I think this is relevant to molecular design. Those of you who believe that the octanol/water partition coefficient is somehow fundamental might like to trace how we ended up with this particular partitioning system.  We also address the question of whether logP or logD is the more appropriate measure of lipophilicity measure and some ligand efficiency stuff from an earlier post makes its journal debut.  

That’s about all I wanted to say for now and I’ll finish by noting that the manuscript was originally submitted to another journal but that's going to be the subject of a post all of its very own...

Literature cited

Toulmin, Wood, Kenny (2008) Toward prediction of alkane/water partition coefficients. J Med Chem 51:3720-3730 DOI

Kenny, Montanari, Prokopczyk (2013)ClogP_alk: A method for predicting alkane/water partition coefficient. JCAMD 27:389-402 DOI

Imidazole lipophilicity revisited

It's almost three months since I arrived in Brasil to spend year in São Carlos at Universidade de São Paulo. One of the advantages of being back in academia is that access to literature is a lot better and I managed to dig up some cyclohexane/water partition coefficients for a couple of the compounds that featured last year's Lipophilicity Teaser. While octanol/water doesn't appear to 'see' the hydrogen bond donor that is unmasked by moving the methyl group, the cyclohexane/water partitioning system certainly does. Readers with an interest in Physical-Organic Chemistry might like to think about how tautomerism might affect partition coefficients. There are also a couple of lipophilicity-based items (logP versus logD for ADMET discussion and poll on correlation of pharmacological promiscuity) that are current in the FBDD LinkedIn group so why not drop by and join the fun there.

 

Literature cited

Abraham, Chadha, Whiting & Mitchell, Hydrogen bonding. 32. An analysis of water-octanol and water-alkane partitioning and the Δlog P parameter of Seiler. J. Pharm. Sci. 1994, 83, 1085-1100. DOI

Radzicka & Wolfenden, Comparing the polarities of the amino acids: side-chain distribution coefficients between the vapor phase, cyclohexane, 1-octanol, and neutral aqueous solution. Biochem. 1988, 27, 1664-1670. DOI

Lipophilicity teaser

This post got prompted one by Dan at Practical Fragments and I'm going to ask you to first take a look at that and at the comments. Now I'd like you to look at some measured octanol/water logP values that I pulled from the Sangster Research Laboratories logPow database. The question I'd like to put to you is whether you think that these measured logP values truly reflect the energetic costs of moving the different isomeric methylimidazoles from water to a truly non-polar environment like a hydrophobic binding pocket in a protein.



Let's take a look these figures. The least lipophilic compound of the set is N-methylimidazole in which the hydrogen bond donor of imidazole has been capped, although the partition coefficients for the three compounds are all very similar. It seems that the octanol/water partitioning system just doesn't seem to 'see' the hydrogen bond donors of the 2-methyl and 4/5-methyl isomers.

Octanol has a hydroxyl group and, in the context of a shake-flask logP determination, gets pretty wet (~2M), making it a unconvincing model for the hydrophobic core of a lipid bilayer or a hydrophobic binding pocket. In contrast, alkanes lack hydrogen bonding capability which also means that they also dissolve less water. The catch is that alkane/water partition coefficients are more difficult to measure than their octanol/water equivalents since polar solutes are poorly soluble in alkane solvents.

The difference between octanol/water and alkane/water logP values for a compound (often termed ΔlogP) is one measure of the polarity of the compound. The octanol/water logP of phenol is 1.5 and it would be reasonable to describe it as lipophilic. However in the alkane/water system the situation is reversed and the logP of -0.6 would lead to phenol being described as hydrophilic.

I'll leave things here for now because this post is really just a teaser and I will be returning to the theme in more depth in the future. If you're interested in finding out more take a look at my harangue from the March 2011 PhysChem Forum at Syngenta and the article that goes with it. I'd also recommend reading this review by Wolfenden if you're interested in the relevance of alkane/water logP values to protein structure and function.

Literature cited

Toulmin, Kenny & Wood, Toward prediction of alkane/water partition coefficients. J. Med. Chem. 2008, 51, 3720-3730. DOI

Wolfenden, Experimental Measures of Amino Acid Hydrophobicity and the Topology of Transmembrane and Globular Proteins. J. Gen. Physiol. 2007, 129, 357-362. DOI