Seqens eBook - 29
SOLUBILITY
methods still performed poorly for both soluble and insoluble compounds.19
The negative impacts of aromaticity on solubility have been reported.
Interestingly, Kramer et al20 showed improved solubility predictions with
These include the number of aromatic rings,26,27 the aromatic portion,9
their metaClassifier approach, despite the fact that their training set was
and the percentage of sp3 hybridized atoms within the target molecule.28
based on kinetic rather than equilibrium solubilities. They showed a high
Molecules with low lipophilicity are more likely to display poor solubility
prediction accuracy for solubility (77.8%), but typically showing a high bias,
arising from solid-state considerations, ie, "brick-dust molecules"; whereas
possibly because their training set used small levels of dimethyl sulfoxide
highly lipophilic compounds are solubility limited due to inadequate
(DMSO) as a co-solvent and were measured at pH 7.4. However, their model
solvation, ie, "grease ball molecules."29 Various scenarios were modeled
correctly predicted only one-third of the insoluble compounds in the
and showed that for compounds with an MPt of >250°C and cLogP of >2,
dataset. Finally, the accuracy of the in silico model needs to be greater than
the GSE demonstrates that solid-state considerations will predominate
that of the experimental determinations.10
(over 50%); whereas, when the cLogP is increased beyond 6, that solid-
The biggest obstacle to accurate solubility prediction is still the
state considerations drop markedly (about 25%).
unpredictable nature of the solid-state (polymorphs, solvates, salts,
hydrates, co-crystals, amorphous, etc),21 and how to effectively model
Thus, planar, flat,
and rigid molecules with ring systems have a high likelihood (86%)
of demonstrating low aqueous solubility.29 How molecular planarity
enthalpy and entropy of the system, ie, moving from an ordered, structured
reduces solubility and how solubility can be increased by disrupting
low entropy solid state to a disordered, unstructured high entropy
planarity has been evaluated by Ishikawa.30 This can be explained by the
solution state. So far, polymorphs still cannot be reliably predicted22 and
increased lattice energy (and MPt) owing to enhanced π-π stacking of
accordingly their effect on solubility cannot be predicted by in silico tools.
the planar aromatic systems. Hill and Young24 also demonstrate enhanced
Yalkowski et al23 derived the General Solubility Equation (GSE) to try to
improve model solubility:
LogS = -LogP -0.01 * (MPt - 25) + 0.5
correlations between the number of aromatic ring systems and cLogDpH 7.4
(as opposed to LogP) and ultimately solubility. They proposed a solubility
forecast index (SFI):
(Equation 1)
where S is the intrinsic solubility, P is the octanol/water partition coefficient,
and MPt is the melting point
SFI = cLogDpH 7.4 + number of aromatic rings
(Equation 3)
Where SFI <5, there is typically good aqueous solubility, and they
contended that each aromatic ring system is equivalent to an extra
However, the MPt term is only partially successful in addressing
solid-state considerations and the impact on aqueous solubility. It is
log unit of cLogDpH 7.4. The average number of aromatic ring systems in
marketed oral products is 1.623 and thus the average SFI would be 2.4.
also evident from the GSE that logP is the major variable in Equation 1.24
Indeed, medicinal chemists can more easily influence logP than MPt (more
difficult to predict or to control, and as a consequence, MPt is not typically
measured) and therefore optimizing LogP tends to be the main focus in
some discovery organizations. Typical marketed drugs have cLogPs of
2.5, and it is probably not a coincidence that this also corresponds to the
upper limit of good solubility as predicted by the GSE.25 Unfortunately,
poor aqueous solubility is, therefore, the logical outcome of introducing
overly hydrophobic character into potential drug candidates.
Approaches to Measuring Solubility
during Different Phases of Research
and Development
After the compound has been synthesized and is physically available,
solubility will be measured the first time. Concepts and workflows
to obtain measured solubility at this stage vary from organization to
organization as in comparison to in silico assessment: the number of
The GSE constraint of clogP of >2.5 is probably the worst-case scenario
compounds for which solubility is assessed determines the amount of
as it does not reflect the positive impact that ionization can have in
work. Solubility could be measured for every compound which is freshly
improving aqueous solubility; therefore replacing LogP with LogDpH 7.4
obtained or might be measured upon request. However, at this stage
produces a more predictive GSE:
it is clear that solubility has to be measured for a very large number of
LogSpH 7.4 = -LogDpH 7.4 -0.01 * (MPt - 25) + 0.5
(Equation 2)
Hill and Young evaluated a large dataset (ca 20 000 compounds), where
24
compounds and therefore very efficient procedures have to be in place.
There are 2 main purposes of measuring solubility at this stage31:
*
measured LogDpH 7.4, together with calculated values for hydrophobicity
First, solubility provides a means to answer the fundamental
question: is the compound dissolved in the assay medium or has
(cLogP and clogD7.4), accurate kinetic solubility measurements (at pH
7.4), MW, and the number of aromatic rings were all available. The
it precipitated out if results from other assays are problematic and
authors showed marked differences between measured and calculated
need to be questioned? This query becomes relevant for many types
hydrophobicity with compounds of decreasing solubility. Poorly soluble
of assays, eg, biochemical and cellular assays that demonstrate
compounds (<30 μM) show particularly bad correlations (R2 = 0.11); this
activity of the compound. The same question also applies to
improves slightly (R = 0.32) as solubility increases (30 to 200 μM), with
assays that support safety testing which have been shifted more
the "best" correlation occurring with good solubility compounds (>200
towards earlier stages of research during recent years. In this case,
μM) compounds (R = 0.462). Interestingly, these data support the
low solubility of a compound might yield false negatives for the
contention that calculated LogD7.4 (or cLogP) might be a better predictor
respective assay and consequently hide safety related risks of a
of hydrophobicity rather than using the measured value.25
compound or a whole series or scaffold(s).
2
2
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Seqens eBook
Table of Contents for the Digital Edition of Seqens eBook
Contents
Seqens eBook - 1
Seqens eBook - Contents
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