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those strictly relying on diffusion as a drug delivery mechanism. By
tailoring the structure (geometry and dimensionality) of a hydrogel,
it is possible to obtain specifi c drug release profi les. Also, initial
drug concentration inside the gel structure critically infl uences
its release. It is assumed that drug molecules diffuse randomly
throughout the hydrogel lattice. By using GAs, it was possible to
study several thousand combinations of hydrogel structures, in
order to rank them according to their usefulness in generating
desired drug release rates. Since the aim was to produce constant
release profi les (over specifi ed time intervals), the following fi tness
parameter was calculated:
[5.24]
In this equation,
ΔM
=
M
max
−
M
(
τ
ϕ
) is the maximum value of
the drug release rate from a simulated hydrogel minus the value of
the release rate at upper time
τ
ϕ
(smaller values of
ΔM
indicate a more constant release rate over that period of time).
〈
M
〉
is the average drug release rate and
γ
is an adjustment
parameter. GA begins by initialization of the fi rst generation of
solutions and calculation of fi tness parameter
F
for each individual.
In principle, it is possible to select an appropriate fi tness parameter
in order to achieve any desired functional form for the release
profi le
M(t)
. It was shown that the fi tness parameter
converges with increasing numbers of generations. Each
generation is built using genetic characteristics from the best
parents of the previous generation following the rules of the
algorithm.
The fi rst concern was optimization of the placement of gel
fi ber structures that serve as an obstacle for drug diffusion. An
example of ring-like fi ber structures optimization is represented in
Figure 5.14.
Two parents are combined, using a cross-over technique, and
their traits are used in the next generation. Once an offspring is
generated, it is introduced to a certain percentage of mutations.
Mutations represent changes from fi ber to void space (and
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