computer aided drug delivery

1. REDUCE
COST WITH
IMPROVED
QUALITY
MINIMIZE
TIME
RESEARCH
AND
DISCOVERY
DESCRIPTIVE VERSUS MECHANISTIC MODELLING
BY SAYEDA SALMA
1ST M PHARM
DEPT OF PHARMACEUTICS
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2. INDEX
• INTRODUCTION
• OBJECTIVES
• CONCEPT
• DIFFERENCE BETWEEN DESCRIPTIVE AND MECHANISTIC
MODELLING
• EXAMPLES
• CONCLUSION
• REFERNCES
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3. INTRODUCTION
• The new major challenge that the pharmaceutical industry is facing in the
discovery and development of new drugs is to reduce costs and time
needed from discovery to market, while at the same time raising standards
of quality.
• If the pharmaceutical industry cannot find a solution to reduce both costs
and time, then its whole business model will be jeopardized.
• The market will hardly be able, even in the near future, to afford excessively
expensive drugs, regardless of their quality.
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4.  In parallel to this growing challenge, technologies are also dramatically evolving,
opening doors to opportunities never seen before.
 This standard way to discover new drugs is essentially by trial and error.
 The “new technologies” approach has given rise to new hope in that it has
permitted many more attempts per unit time, increasing proportionally, however,
also the number of errors.
 The development of models in the pharmaceutical industry is certainly one of
the significant breakthroughs proposed to face the challenges of cost, speed,
and quality, somewhat imitating what happens in the aeronautics industry.
 The concept, however, is that of adopting just another new technology , known
as “modeling”.
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5. OBJECTIVES
 The use of models in the experimental cycle to reduce cost and time and
improve quality.
 Without models, the final purpose of an experiment was one single drug or
its behavior, with the use of models, the objective of experiments will be the
drug and the model at the same level.
 Improving the model will help understanding the experiments on successive
drugs and improving the model’s ability will help to represent reality.
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6. CONCEPT
 According to Breiman , there are two cultures in the use of
statistical modeling to reach conclusions from data.
 The first culture, namely, the data modeling culture, assumes that
the data are generated by a given stochastic data model.
 whereas the other, the algorithmic modeling culture, uses
algorithmic models and treats the data mechanism as unknown.
 To understand the mechanism, the use of modeling concepts is
essential.
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7.  The purpose of the model is essentially for that of translating the
known properties about the black box as well as some new
hypotheses into a mathematical representation.
 In this way, a model is a simplifying representation of the data-
generating mechanism under investigation.
 The identification of an appropriate model is often not easy and may
require thorough investigation.
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8. DESCRIPTIVE VERSUS
MECAHNISTIC MODELLING
DIFFERENCE
Click on the globe to start
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9. DESCRIPTIVE MODELLING
 If the purpose is just to provide a reasonable description
of the data in some appropriate way without any attempt
at understanding the underlying phenomenon, that is, the
data-generating mechanism, then the family of models is
selected based on its adequacy to represent the data
structure.
 The net result in this case is only a descriptive model of
the phenomenon.
 These models are very useful for discriminating between
alternative hypotheses but are totally useless for
capturing the fundamental characteristics of a
mechanism.
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10. MECHANISTIC MODELLING
• Whenever the interest lies in the
understanding of the mechanisms of action,
it is critical to be able to count on a strong
collaboration between scientists, specialists
in the field, and statisticians or
mathematicians.
• The former must provide updated, rich, and
reliable information about the problem.
• whereas the latter are trained for translating
scientific information in mathematical
models.
MECHANISM OF
ACTION
STATISTICIANS
SCIENTIST
AND
SPECIALIST IN
THE FIELD
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11. EXAMPLE
• A first evaluation of the data can be done by running non-parametric
statistical estimation techniques like, for example, the Nadaraya–
Watson kernel regression estimate.
• These techniques have the advantage of being relatively cost-free in
terms of assumptions, but they do not provide any possibility of
interpreting the outcome and are not at all reliable when
extrapolating.
• The fact that these techniques do not require a lot of assumptions
makes them relatively close to what algorithm-oriented people try to
do.
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13. • These techniques are essentially descriptive by nature and are useful for
summarizing the data by smoothing them and providing interpolated values.
• The fit obtained by using the Nadaraya–Watson estimate on the set of data
previously introduced is represented by the dashed line figure.
• This approach, although often useful for practical applications, does not
quite agree with the philosophical goal of science, which is to understand a
phenomenon as completely and generally as possible.
• This is why a parametric mechanistic modeling approach to approximate the
data-generating process must be used.
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14. • After having used a (simple) model formulation with some plausible
meaning and a behavior matching the observed data structure, the next
step in the quest for a good model.
• The investigation of tumor growth on which we concentrate in this chapter
falls in fact into the broad topic of growth curve analysis, which is one of the
most common types of studies in which non-linear regression functions are
employed.
• Note that different individuals may have different tumor growth rates, either
inherently or because of environmental effects or treatment, This will justify
the population approach .
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15. EXAMPLE 2
• The growth rate of a living organism or tissue can often be characterized by
two competing processes.
• The net increase is then given by the difference between anabolism and
catabolism, between the synthesis of new body matter and its loss.
• Catabolism is often assumed to be proportional to the quantity chosen to
characterize the size of the living being, namely, weight or volume, whereas
anabolism is assumed to have an allometric relationship to the same
quantity.
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16. • These assumptions on the competing processes are translated into
mathematics by the following differential equation:
• where µ(t) represents the size of the studied system in function of time.
Note that this equation can be reformulated as follows:
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17. • The curve represented by this last equation is commonly named the
Richards curve.
• When K is equal to one, the Richards curve becomes the well-known
logistic function.
• If the allometric factor in the relationship representing the catabolism
mechanism is small, that is, K tends to 0, then the differential equation
becomes :
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18. • The general solution of this differential equation is now given by,
µ(t) = αexp(−exp(−γ(t − η))), and is called the Gompertz curve.
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19. CONCLUSION
• The exponential growth model can thus be now justified not only because, it
fits well the data but also because it can be seen as a first approximation to
the Gompertz growth model, which is endowed with a mechanistic
interpretation, namely, competition between the catabolic and anabolic
processes.
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20. REFERENCES
 Computer applications in pharmaceutical research by john wiley and sons
and sean ekins 2006.
 Internet sources
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21. THANK YOU
MOVING TOWARDS DEVELOPING , IMPROVEMENT AND MOST ADVANCED
CONDITIONS FOR COMPUTER AIDED PHARMACEUTICAL RESEARCH.
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