Seqens eBook - 28

Solubility in Pharmaceutical R&D:
Predictions and Reality
David P. Elder, PhD, David P. Elder Consultancy
Christoph Saal, PhD, Merck KGaA

Introduction

hydrophobicity assessments, molecular surface area calculations, etc.9

Lipinski's seminal paper on experimental and computational (in silico)

fragment-based models,10 with the 3 most favored approaches being

methods to estimate the solubility and permeability of drug candidates
was published in 1997.1 The iconic "Rule of 5" predicted that absorption
was adversely impacted when the calculated LogP (cLogP) was >5, when
molecular weight (MW) was >500, when there are >5 H-bond donors or
>10 H-bond acceptors. The related concept of "drug-likeness" importantly
focuses on both potency and physicochemical attributes, using tools such
as lipophilic efficiency2 or ligand efficiency.3

In addition, other researchers have attempted to predict solubility using
computational models utilizing molecular topology,11 group contribution
approaches,12 and E-state indices.13 However, these computational
methods need to be able to cope with significant numbers of compounds
and filter out 'non-drug-like' compounds. The methods must help to
focus chemistry initiatives on programs with improved physicochemical
attributes, thereby enhancing productivity. Importantly, it should be
clearly understood that early discovery methodologies provide qualitative

Drug-likeness and related concepts have been widely used in the

and not quantitative outcomes.14

pharmaceutical industry to attempt to reduce the very high attrition

The challenges inherent in solubility prediction were graphically

rates currently seen with unprecedented pharmacological targets.
Unfortunately, both combinatorial and high-throughput chemistry tends
to favor leads with higher MW, higher cLogP and lowered solubility.1

highlighted by the recent Solubility Challenge. An academic research
group15 measured the equilibrium solubilities of 100 drug-like molecules
at a fixed temperature and ionic strength. Using this training dataset, they

Therefore, successful drug discovery strategies appear to be a balance

requested other research groups to predict, using their own preferred

between trying to optimize both "hydrophobicity-driven potency and

computational approach(es), the intrinsic solubilities of an additional 32

hydrophilicity-driven biopharmaceutics properties."4,5 Consequently, an

drug-like molecules. The training set was selected to represent a wide

over-reliance on optimizing potency to the detriment of physicochemical

range of chemical space with MW ranging from 115 (proline) to 645

properties will yield sub-optimal ADMET (absorption, distribution,

(amiodarone), that had pKas between 1 and 12. The intrinsic solubilities

metabolism, excretion, and toxicity) properties and reduce the likelihood

spanned over 7-orders of magnitude from the poorly soluble (amiodarone)

of clinical success.6 However, the lack of optimal physicochemical

to the highly soluble (acetaminophen), with a relatively even spread of

attributes can often be offset using sophisticated formulation strategies,7,8

intermediate values.

and deficiencies in these properties can often be rate limiting to the

The authors received 100+ entries to the Solubility Challenge.16 Participants

progression of drug candidates. Hence, computational methods that can
qualitatively predict physicochemical properties, eg, solubility, before a
compound is synthesized based on molecular structure, are an essential
requirement for drug discovery.

used the entire spectrum of available computational tools, and this challenge
therefore provided a holistic overview of our ability to predict aqueous
solubility. The authors could not recommend the best approach(es), rather
a number of methodologies that were equally successful at predicting

In this article, the authors will provide an overview of approaches to

aqueous solubility were identified. Some participants were surprised that

predict and measure solubility in the pharmaceutical R&D environment

the simple models were superior to the more complex methodologies.17

with the goal of providing relevant information at the appropriate stage

Faller and Ertl10 went further, claiming that the advantages of the

of the process.

complex models were debatable when compared with the simple cLogP

Relationships between Solubility and
Physicochemical Properties

correlations.18 Hewitt et al17 indicated that data quality was pivotal to
successful predictivity and even with the "high quality" dataset provided
in the "Challenge," questions were still raised about data quality, and it is
critical to understand the applicability domain (ie, the chemical space

There are significant numbers of computational methods reported in the

where the model works best). Understandably, predictions made outside

literature for predicting solubility from underlying molecular properties.

of this domain will be less reliable. Despite the impressive size of some

These include electronic and topological evaluations, hydrophilicity/

participants' training sets, (46 000 compounds of known solubility), their

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28

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