hello by
either supplementing from the university medical college crackle bolt
a little attention to the paper entitled in we should be the relationship to generalize
model based on artificial on it
if you should be able correlation i d c is classical approach it's a mathematical
model
designed to predict the in-vehicle measured from energy profile based on the in vitro measure
dissolution provide of that particular trial of interest
this formula is used by pharmaceutical industry for the costs at purposes
because once it is established it allows to reduce the demand of prospective buyers
this is the classical approach of i'm a satellite based on instructions of it
you have or is it linear problem were drug fraction of sort of be said
and via and independent variable dry fraction within b
for the last are you have the results ready for computations measured in the lower
bound for czech
for the for you don't have text is its measurement of this profile therefore you
need to create based on numerical approaches one of them the most commonly used it's
the deconvolution
and the deconvolution methods unfortunately in order to be precise and accurate demands an additional
in table five which is usually derived from the
intravenous restriction particular drug
this situation
it's not very comfortable for pharmaceutical industry goes
first it requires an additional by a slight and second us for certain cases
okay
i know administration is sometimes impostor
for particular so we would like to propose some solution distribution based on artificial neural
networks
computational intelligence tools
eight to create very complicated relationships
and therefore to provide direct relationship idea of our model without a deconvolution phase and
these obstacles
so that we shall do not show how we do
we put two into the input of the neural network to dissolution provide accompanied by
the chemical formula of the active pharmaceutical entreaty and icsi and encoded in so called
monocular descriptors
and also we introduce some parameters of the dissolution test
to the output in order to train the one that's work we put a presented
a corresponding from canada provides derived from each range
this is an example all the prediction power system an average prediction
we can see it's not it's not perfectly precise
it's still able to need a job of course of the of the kinetic profile
and it is also a able to predict the range of considerations
which is very important feature the small
for more details i would like to invite you to drive design development and their
actual
i we should have really thank you for that image