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mole ratio. The high positive charge (>1) of the drug leads to high
encapsulation effi ciency. If the charge is ≤1, then low gradient
magnitudes (≤100) lead to low loading effi ciency. If the gradient
magnitude is above 100, then we check the nonpolar/polar surface
area ratio of the molecule. Low salt concentration inside the
liposomes (≤100 mM) or low solubility (≤1.9 mM) leads to medium
loading. Many other rules can be interpreted from the tree (Figure 5.7).
Example 3
(Shao et al., 2007a)
Decision trees were used to gain information from an immediate
release tablet formulation data base, relating formulation ingredients
(silica aerogel, magnesium stearate, microcrystalline cellulose,
and sodium carboxymethylcellulose) and process variables
(dwell time and compression force) to tablet properties (tensile
strength, disintegration time, friability, capping, and drug dissolution
at various time intervals). Decision trees were generated with the
C5.0 algorithm (available at
www.rulequest.com
). Figure 5.8
represents a decision tree constructed in order to elucidate the
infl uence of the formulation and processing parameters on tablet
tensile strength. There are two main branches divided by the tablet
machine variable 'dwell time', which has been selected as the most
important factor for tablet tensile strength during the process of tree
construction. Each branch has a number of sub-branches representing
various decision making routes, where the dependent variable (tablet
tensile strength) is defi ned as either 'Low' or 'High' at a leaf node.
Tablet tensile strength has been discretized into two categories (Low
and High) according to values lower and higher than the median
value. Numbers are associated to leaf nodes in the form of (
n
) or
(
n/m
), where
n
is the number of data examples in the training set that
are mapped to this leaf, and
m
is the number of those classifi ed
incorrectly by this particular decision making route. Factor
n/m
indicates the confi dence of the decision made upon that
particular route:
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