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Structured Formulation Development
of Poorly Soluble Drugs Guided by a
Virtual Formulation Assistant
New digital tools can aid solubilization approach
selection and structured formulation development
for poorly soluble oral drugs
INTRODUCTION
Formulation of a poorly soluble active pharmaceutical ingredient (API) for oral
administration presents several challenges, particularly during early development.
The suitability of various formulations must be evaluated based on limitations
of solubility and bioavailability, appropriate excipients and manufacturing
techniques, and prototype formulations must be screened for drug load, stability,
and potential in vivo performance. This process can be costly and time consuming.
This paper reviews several systems and parameters that can facilitate this
structured formulation development process and shows how they are implemented
in BASF’s ZoomLab digital application, with particular attention to how these
tools can identify potential solubility issues and guide formulators in the rational
selection of a suitable drug formulation approach.
PHARMACEUTICAL CLASSIFICATION AND BIOAVAILABILITY
Two major classification systems can be used to identify potential limitations in
oral bioavailability of a new API. The Biopharmaceutics Classification System
(BCS) was established in 1995 by Amidon et al. (1), who based their system on the
correlation of drug dissolution and intestinal permeability with the rate and extent
of drug absorption. The oral bioavailability of a drug was estimated based on its
solubility in 250 mL of aqueous buffer solution, as well as the fraction absorbed
from the intestinal lumen. The BCS has particular applicability for regulatory
purposes; however, due to its simplicity, it has also been used for drug development
despite being overly discriminative for numerous formulation candidates. Butler
and Dressman (2) adapted the BCS for drug development in their Developability
Classification System, which is based on drug solubility in 500 mL fasted statesimulated intestinal fluid (FaSSIF), as well as effective intestinal permeability. Class I
drugs under either system are expected to have high bioavailability due to their
high solubility and high permeability, while Class IV drugs are expected to have low
Martin Hofsäss
Development, Pharma Solutions
BASF
Sponsored by
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bioavailability due to both low solubility and low permeability.
Class II drugs, with low solubility and high permeability,
and in some cases Class III drugs, with high solubility but
low permeability, require appropriate excipients to ensure
adequate oral bioavailability. Class II subclasses in the DCS
differentiate between drugs with limited solubility (Class IIb)
and those with dissolution rate-limited bioavailability
(Class IIa).
ZoomLab can calculate the DCS class for an API based on
input parameters including target product dosage strength,
FaSSIF solubility, and effective intestinal permeability.
Intestinal permeability input can be based on actual values,
either entered manually for a new API or loaded from the
drug database for existing APIs, or can be calculated based
on molecular weight, hydrogen bond donor/acceptor counts,
partition coefficient, and topological polar surface area
(TPSA) data entered manually or imported from PubChem.
For example, the antifungal drug, itraconazole, is classified by
ZoomLab as DCS Class IIb, with a calculated solubility limited
absorbable dose of 1 mg and a maximum d90 particle size
distribution cutoff-diameter of 4 microns for an adequate
dissolution rate. In addition to the DCS classification of a
given API, ZoomLab also provides general formulation advice
based on the properties of the API. In this example, ZoomLab
recommends using solubilization approaches for itraconazole
such as addition of surfactants or use of lipid-based or
amorphous solid dispersion formulations, since conventional
formulation would likely not result in sufficient bioavailability.
SOLUBILIZATION APPROACH SELECTION
Once the limitations of bioavailability have been identified
for an API, the next step in formulation is to evaluate suitable
solubilization approaches. There are many possible approaches
for poorly soluble drugs, including amorphous solid dispersions
(ASD), lipid-based formulations (LBF), complexation with
cyclodextrin, or colloidal delivery systems such as nanoparticles.
The most frequently used approaches are ASD and LBF. Many
parameters have been described in the pharmaceutical
literature that can be used to evaluate the probability of success
for ASD versus LBF given an API’s structural and chemical
properties. While specific cutoff values have been defined for
some parameters, such as a molecular weight of >300 Da for
good glass forming ability related to ASD formulations or a
melting point below 150° C for LBF, other favorable chemical
parameters have been more vaguely defined.
We have collected molecular property data for APIs that have
been successfully formulated in ASD versus LBF formulations
to examine which previously described parameters were
discriminative (FIGURE 1) and can be use in a classification
tool. Surprisingly, melting point and partition coefficient did
not differ significantly between ASD and LBF candidate drugs.
While good lipid solubility could be observed for APIs with
low melting points, more than half of the APIs in successfully
developed LBF had melting points higher than the proposed
cutoff value of 150° C. However, other parameters were more
distinctly distributed: ASD formulations tended to have higher
molecular weight and larger topological polar surface area
(TPSA) compared to LBF formulations (FIGURE 1). Estimated
solubility in soybean oil and Hansen solubility parameters
(HSP) also differed significantly between APIs formulated as
ASD versus LBF (data not shown).
Based on our findings, we have developed two classification
models for choosing between ASD and LBF techniques
(FIGURE 2). The first is a decision tree that uses recursive
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good and the classification models indicate high similarity
of physical, chemical, and computed parameters with other
APIs typically formulated as an ASD. Based on the ratio of
melting point to glass transition temperature of itraconazole,
ZoomLab recommends a moderate drug load of 35–50% by
weight to prevent accelerated recrystallization. ZoomLab can
also query its database for API with similar properties and
provide supplementary information regarding similar APIs in
successfully formulated drug products, as well as additional
information on potentially suitable manufacturing techniques.
In the case of itraconazole, there is a direct match to the
database and hot melt extrusion is suggested due to its good
glass forming ability and melting point below 180° C.
SOLUBILITY PARAMETERS
AND EXCIPIENT SELECTION
partitioning (left panel) based on HSP, molecular weight,
solubility, and molecular structure. The second is a logistic
regression model that calculates a probability score based
on the aforementioned parameters (example using TPSA in
the right panel). In these models, the properties of an API are
compared to the previously collected data and probability
of success for either formulation class is assigned based
on similarity of the API to formulated drugs in the training
data set. These probability scores are used to help ZoomLab
users make informed decisions regarding their solubilization
approach. The Decision Support for Solubilization tool
then evaluates various formulation strategies and makes a
recommendation to the user for a solubilization approach
based on experimentally determined properties such as
melting point and glass transition temperature of the API,
as well as calculated structural and chemical properties.
These can be entered either manually or may be imported
from PubChem and include parameters such as molecular
weight, hydrogen bonds, rotatable bonds, TPSA, and HSP
parameters. Using the previous example API, itraconazole,
ZoomLab suggests that its estimated solubility in lipids of only
0.3 mg/g is insufficient to achieve a reasonable drug load as
an LBF. However, its glass-forming ability is expected to be
After a solubility-enabling approach has been chosen, the next
step is the selection of excipients that allow high drug loads
and can provide good stability for the API in the formulation.
Hansen solubility parameters (HSP) (3) are widely used to
screen for polymers and solvents that are suitable for a given
API. The Hansen solubility parameter theory states that two
compounds with similar intermolecular cohesive energies
from dispersion, dipolar, and hydrogen bond interactions are
expected to be miscible or even mutually soluble (3). Solubility
parameters for an API and an excipient with an HSP distance
less than 3–5 as calculated using the Bagley method (4) are
expected to yield good miscibility. Experimental determination
of solubility parameters is laborious and time-consuming.
However, HSP may be estimated based on a drug's molecular
structure by identifying specific functional groups on a
molecule and assigning parameter values according to their
contribution to the total cohesive energy.
This process is automated in ZoomLab, using the group
contribution methods of Van Krevelen (5), Just (6), and/or
Hoy (7). The simplified molecular-input line-entry system
(SMILES) string of the API can be input manually or imported
from PubChem to generate the molecular structure and the
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corresponding parameter values of each functional group
are assigned to the total calculated HSP. Depending on the
molecular size and types of functional groups, some group
contribution methods may not be applicable. For example,
the Hoy method cannot be applied to intraconazole due to its
molecular size, but ZoomLab used the Van Krevelen and Just
methods to compute its total solubility parameter at 25.68
and 22.84, respectively. The calculated solubility parameters
can then be used to find the most suitable polymers and
solvents for an API.
The integrated ZoomLab excipient database provides a
searchable database of all excipients currently included in
ZoomLab, from both BASF and non-BASF sources. Relevant
excipients for a given formulation problem can be searched
based on the excipient’s application purpose. In upcoming
ZoomLab features, excipient properties such as HSP can be
automatically screened to find the most suitable excipient for
a given API. In the case of the example API, itraconazole, the
most promising polymer candidates are identified as cellulose
derivatives, copovidone, and povidone (HSP distances <2),
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followed by polyethylene glycols (HSP distance 2.8) based on
literature HSP data for the polymers and HSP calculated with
ZoomLab for the active ingredient. Taken together, the results
from ZoomLab and HSP comparison suggest that a potential
prototype formulation of itraconazole for further experimental
characterization in vitro would be an ASD based on cellulose
or povidone derivatives with an intraconazole load of 35–50%
by weight.
CASE STUDY: FENOFIBRATE
A second example is oral formulation of 200 mg fenofibrate,
which like itraconazole shows limited solubility in FaSSIF.
However, fenofibrate is further characterized by a lower
molecular weight and thus a lower melting point, as well as
a lower TSPA, when compared to itraconazole. Febofibrate
was also classified by ZoomLab as a DCS Class IIb compound;
however, unlike itraconazole, a larger dose of 42 mg
was predicted to be soluble in intestinal fluid, and with
micronization below 31 microns (FIGURE 3), a certain amount
of fenofibrate would even be expected to be absorbed from
the intestine in the absence of further solubilization. However,
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solubilization approaches would likely aid in maximizing
oral bioavailability, so potential approaches were suggested
by ZoomLab.
Fenofibrate has a lower melting point and glass transition
compared to itraconazole, and the contribution of dipolar
interaction forces to the HSP is also markedly lower. No
nitrogen atoms are present in the molecular structure,
which leads to a higher expected solubility in lipids. These
properties were reflected in the results of the ZoomLab
Solubilization Decision Support tool (FIGURE 4). Estimated
solubility in soybean oil was relatively high at 50 mg/g. While
glass forming ability was expected to be good, only a limited
number of APIs formulated as ASDs shared similar molecular
properties when compared with fenofibrate. Therefore,
ZoomLab recommended formulation in a lipid-based system.
This was borne out by the list of similar drug products,
where fenofibrate was found in at least two distinct LBF
formulations, but an alternative formulation as an ASD was
also present (FIGURE 4). The Solubility Parameter Calculation
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tool was used to calculate HSP for fenofibrate. The most
promising excipient candidate based on the HSP distance
approach was polylactic acid (HSP distance 0.3), but many
excipients typically used in LBF such as polyethylene glycols,
pegylated emulsifiers, and poloxamers were also expected
to be suitable (HSP distance ≤1.5). These HSP-based excipient
recommendations were consistent with excipients used in
marketed formulations of fenofibrate.
SUMMARY
Pharmaceutical classification systems and parameters
have been defined which facilitate structured development
of oral formulations for poorly soluble APIs. Digital solutions
such as ZoomLab integrate these tools using scientific
models to provide customized formulations advice related
to selection of solubility-enabling formulations and
compatible excipients for poorly soluble APIs based on
their molecular properties, without the need to disclose
proprietary information such as the chemical structure of
the API.
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REFERENCES
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