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Development of level A in vitro - in vivo correlation.
Inputs for the network training were in vitro
dissolution samples, whereas in vivo dissolution
profi les calculated by numerical deconvolution for
each volunteer individually were used as outputs.
GRNN
Parojˇi ´
et al., 2007
MLP
De Matas
et al., 2008
Prediction of relative lung bioavailability and
clinical effect of salbutamol when delivered to
healthy volunteers and asthmatic patients from
dry powder inhalers (DPIs). Training of the
ANN network was performed using in vitro
aerodynamic characteristics of the formulation
and demographic data of volunteers/patients as
input parameters, whilst in vivo data (urinary
excretion of the drug and its metabolite) were
network outputs.
Prediction of kinetics of doxorubicin release from
sulfopropyl dextran ion-exchange microspheres.
Three independent variables, drug loading level,
concentration of NaCl, and CaCl 2 in the release
medium were used as the ANN inputs and the
fractional release of doxorubicin at four different
time points as the outputs.
MLP, HNN
Li et al.,
2005
Prediction of drug release profi les in transdermal
iontophoresis. Neural network inputs were the
process conditions of pH, ionic strength, and
current, as well as the time point. The output was
the predicted permeation rate of the drug
(diclofenac sodium).
RBFNN
Lim et al.,
2003
Development and optimization of pharmaceutical processes
Modeling of fl uidized-bed granulation process.
The independent input variables were inlet
air temperature atomizing air pressure and
binder solution amount, whereas the responses
used were mean granule size and granule
friability.
MLP
Murtoniemi
et al., 1994
￿
￿
￿
Modeling of fl uidized-bed drying process. Inputs
for the network training were time, bed
temperature, inlet air temperature, and moisture
of the solid in the fl uidized bed at time t . The
output is a single node containing the moisture of
the solid in the fl uidized bed at time t .
MLP
Castellanos
et al., 2002
( Continued )
 
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