API


be less reliable. Despite the impressive size of some participants’ training sets, (46 000 compounds of known solubility), their methods still performed poorly for both soluble and insoluble compounds.19 Interestingly, Kramer et al20


showed improved solubility predictions with


their metaClassifier approach, despite the fact that their training set was based on kinetic rather than equilibrium solubilities. They showed a high prediction accuracy for solubility (77.8%), but typically showing a high bias, possibly because their training set used small levels of dimethyl sulfoxide (DMSO) as a co-solvent and were measured at pH 7.4. However, their model correctly predicted only one-third of the insoluble compounds in the dataset. Finally, the accuracy of the in silico model needs to be greater than that of the experimental determinations.10


The biggest obstacle to accurate solubility prediction is still the unpredictable nature of the solid-state (polymorphs, solvates, salts, hydrates, co-crystals, amorphous, etc),21


and how to effectively model


enthalpy and entropy of the system, ie, moving from an ordered, structured low entropy solid state to a disordered, unstructured high entropy solution state. So far, polymorphs still cannot be reliably predicted22


by in silico tools. Yalkowski et al23


derived the General Solubility Equation (GSE) to try to


improve model solubility: LogS = -LogP -0.01 * (MPt – 25) + 0.5


(Equation 1)


where S is the intrinsic solubility, P is the octanol/water partition coefficient, and MPt is the melting point


However, the MPt term is only partially successful in addressing solid-state considerations and the impact on aqueous solubility. It is 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.


The GSE constraint of clogP of >2.5 is probably the worst-case scenario as it does not reflect the positive impact that ionization can have in


improving aqueous solubility; therefore replacing LogP with LogDpH 7.4 produces a more predictive GSE: LogSpH 7.4


= -LogDpH 7.4 Hill and Young24 -0.01 * (MPt – 25) + 0.5 (Equation 2) evaluated a large dataset (ca 20 000 compounds), where


measured LogDpH 7.4, together with calculated values for hydrophobicity (cLogP and clogD7.4), accurate kinetic solubility measurements (at


pH 7.4), MW, and the number of aromatic rings were all available. The authors showed marked differences between measured and calculated hydrophobicity with compounds of decreasing solubility. Poorly soluble compounds (<30 μM) show particularly bad correlations (R2 this improves slightly (R2


= 0.11); = 0.32) as solubility increases (30 to 200 μM),


with the “best” correlation occurring with good solubility compounds (>200 μM) compounds (R2


contention that calculated LogD7.4 (or cLogP) might be a better predictor of hydrophobicity rather than using the measured value.25


The negative impacts of aromaticity on solubility have been reported. These include the number of aromatic rings,26,27 and the percentage of sp3 molecule.28


the aromatic portion,9 hybridized atoms within the target Molecules with low lipophilicity are more likely to display


poor solubility arising from solid-state considerations, ie, “brick-dust molecules”; whereas highly lipophilic compounds are solubility limited


= 0.462). Interestingly, these data support the


due to inadequate solvation, ie, “grease ball molecules.”29


Various


scenarios were modeled and showed that for compounds with an MPt of >250°C and cLogP of >2, the GSE demonstrates that solid-state considerations will predominate (over 50%); whereas, when the cLogP is increased beyond 6, that solid-state considerations drop markedly (about 25%). Thus, planar, flat, and rigid molecules with ring systems have a high likelihood (86%) of demonstrating low aqueous solubility.29 How molecular planarity reduces solubility and how solubility can be increased by disrupting planarity has been evaluated by Ishikawa.30 This can be explained by the increased lattice energy (and MPt) owing to enhanced π–π stacking of the planar aromatic systems. Hill and Young24


of aromatic ring systems and cLogDpH 7.4


also demonstrate enhanced correlations between the number (as opposed to LogP) and


ultimately solubility. They proposed a solubility forecast index (SFI): SFI = cLogDpH 7.4


+ number of aromatic rings and accordingly their effect on solubility cannot be predicted (Equation 3)


Where SFI <5, there is typically good aqueous solubility, and they contended that each aromatic ring system is equivalent to an extra log unit of cLogDpH 7.4


marketed oral products is 1.623


. The average number of aromatic ring systems in and thus the average SFI would be 2.4.


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 compounds for which solubility is assessed determines the amount of work. Solubility could be measured for every compound which is freshly obtained or might be measured upon request. However, at this stage it is clear that solubility has to be measured for a very large number of compounds and therefore very efficient procedures have to be in place. There are 2 main purposes of measuring solubility at this stage31


:


• First, solubility provides a means to answer the fundamental question: is the compound dissolved in the assay medium or has it precipitated out if results from other assays are problematic and need to be questioned? This query becomes relevant for many types of assays, eg, biochemical and cellular assays that demonstrate activity of the compound. The same question also applies to assays that support safety testing which have been shifted more towards earlier stages of research during recent years. In this case, low solubility of a compound might yield false negatives for the respective assay and consequently hide safety related risks of a compound or a whole series or scaffold(s).


• Secondly, even at this early stage, solubility will be used as one of the many parameters relevant for compound optimization. The final goal should be envisaged to deliver compounds with sufficient solubility to realize high bioavailability and to simplify formulation development and clinical progression.


From a technical standpoint, realizing the required throughput to fulfill both objectives requires a high degree of automation. The key to this and many other assay formats is provided by using pre-dissolved compounds. Typically, 10-mmol solutions in DMSO are utilized. This avoids handling of the solid material which might not always be crystalline but could be oily, sticky, or highly electrostatic. Instead of weighing the compounds, compound handling can be carried out by simple volumetric dispensing, ie, pipetting steps. Accordingly, it becomes feasible to implement solubility determinations on robotic systems which carry out manipulation such as volumetric dispensing,


Pharmaceutical Outsourcing | 38 | March/April 2015


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