Computer- aided formulation development - Computer-Aided Applications in Pharmaceutical Technology - page 27

water. The type of microemulsion microstructure (i.e. o/w, bi-continuous,
w/o) was differentiated by measuring the freezing peak of the water in
DSC thermograms. The data pool used to train both ANNs included the
composition of 170 microemulsion samples and DSC curves. To
determine the type of microemulsion from its composition, a feed-
forward network was programmed, with the fi nal architecture involving
4 input neurons, a single hidden layer of 12 neurons, and 5 output
neurons. To determine the type of microemulsion from its DSC curve, a
second feed-forward ANN with 1 hidden layer was constructed,
containing 100 input neurons, a single layer of 5 hidden neurons, and
5 output neurons. Both ANNs showed an accuracy of 90% in predicting
the type of microemulsion from the previously untested compositions.

2.4 Conclusion

A nonlinear mathematical approach comprising experimental design,
neural networks, GAs, and/or neuro-fuzzy logic represents a promising
tool for in silico modeling of formulation procedures in development of
emulsion and (micro)emulsion drug carriers. Although in silico
formulation is not a substitute for laboratory experiments, the results of
current efforts clearly demonstrated a potential to shorten the time
necessary to fi nd optimal quantitative and qualitative composition. Also,
this strategy is capable of generating new potential (micro)emulsion
forming systems. The upcoming step would be application of such
methodology as a tool to correlate composition/structure characteristics
with the biopharmaceutical profi les of (micro)emulsion drug delivery
systems, which is encouraging for their future development.

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2.5 References

Aboofazeli, R., Lawrence, C.B., Wicks, S.R., and Lawrence, M.J. (1994)
'Investigations into the formation and characterization of phospholipid
microemulsions. III: Pseudo-ternary phase diagrams of systems containing
water-lecithin-isopropyl myristate and either an alkanoic acid, amine,
alkanediol, polyethylene glycol alkyl ether or alcohol as cosurfactant', Int. J.
Pharm. , 111: 63-72.
Agatonovic-Kustrin, S. and Alany, R.G. (2001) 'Role of genetic algorithms and
artifi cial neural networks in predicting the phase behavior of colloidal delivery
systems', Pharm. Res. , 18(7): 1049-55.