The pharmaceutical industry is undergoing a radical change in its ways of working. Directives from the US Food and Drug Administration
have raised questions about understanding the relationships between formulation and manufacturing that result in controlled
product performance. These relationships, however, can rarely be precisely quantified, and the formulation and manufacturing
must be carried out in a design space that is both multidimensional in nature and difficult to conceptualize. Attempts to
investigate these problems through the use of experimental design have generated large amounts of data, but processing these
data remains a challenge. This article introduces two new implementations of data-mining software packages (INForm and FormRules,
Intelligensys, Stokesley, UK) that are specifically tailored for the pharmaceutical formulator or process engineer to generate
understandable rules and to model and optimize the process and formulation.
Advanced technologies
Both INForm and FormRules rely on advanced computing techniques such as neural networks, fuzzy logic, and genetic algorithms.
Neural networks are mathematical constructs that are capable of learning, for themselves, the relationships within data. No assumptions need
be made about the functional form of these relationships because the neural network simply tries out a range of models to
determine one that best fits the known data. In recent years, artificial neural networks (ANNs) have increasingly and successfully
been used to model complex behavior in problems such as those found in pharmaceutical formulation and processing (1, 2).
Fuzzy logic can be implemented to allow a formulator's objectives to be described in a linguistically intuitive way. Traditional "crisp"
logic means that values must be either "true" (1) or "false" (0). Fuzzy logic, based on the theory of fuzzy sets, allows the
membership in each set to take a value between 0 and 1. For example, if a tablet disintegration time of <300 s is desired,
then a value >300 s will have a desirability of <100%, with the desirability decreasing as the disintegration time increases.
This gives a formulator considerable control over an optimization process. As the name implies, genetic algorithms use an evolutionary approach to finding the best solutions. To do this, a measure
of fitness is set up, using the desired values for each property together with its importance relative to other properties.
The optimization starts with a random trial population, and the fitness of each member in the population is assessed. New
solutions are generated from the fittest members, using mathematical operations that are analogous to reproduction and mutation,
and their fitness is assessed. In this way, the population evolves so that ultimately the fittest solution is the one that
best meets a formulator's specified needs. If there are constraints on the ingredients or processing conditions —for example,
if a particular combination of ingredients must sum to 100% —then these can be implemented easily by penalizing the fitness
of nonconforming solutions.
Combining these technologies allows the development of useful and powerful methodologies. For example, using neural networks
for modeling together with genetic algorithms for optimization (as is done within the INForm software system) allows a user
to develop a formulation or process to meet stringent, often conflicting, objectives. New methodologies such as neurofuzzy
logic (implemented in the FormRules system) have evolved. This combines the ability of neural networks to "learn" from data
with fuzzy logic's capacity to express complex concepts simply, allowing a formulator and process engineer to gain an understanding
of the underlying rules governing the formulation and process.
This article examines the applications of the software systems to formulation and processing using data taken from three examples
in the literature.
The rules that govern roller compaction
Table I: Effect of binder type and binder addition on ejection force and crushing strength.
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The first case concerns a roller-compaction process for acetaminophen tablets using data published by Turkoglu et al. (3). Because acetaminophen has poor flow and compression characteristics, a prior agglomeration process is generally used.
In Turkoglu's study, both the formulation and the process conditions were changed. In the formulation, the binder was one
of three possibilities: hydroxypropyl methyl cellulose (HPMC, Methocel, Dow Chemical Co., Midland, MI), polyethylene glycol,
or carbomer (Carbopol, Noveon, Cleveland, OH). The percentage of binder and the amount of microcrystalline cellulose (MCC)
that was added were varied. One or two passes through the roller compactor was allowed, and 42 different experiments were
measured, 30 of which were used to develop the models. These 30 experiments were used in FormRules, to determine which inputs
were most important and to investigate how they affect the measured properties: crushing strength of the tablets, friability,
ejection force, and disintegration time. With the default training parameters, good models (as assessed by an analysis of
variance statistics, which all showed the value of R2 > 0.9) were obtained for each property.
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