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.
In parallel to this growing challenge, technologies are also dramatically
evolving, opening doors to opportunities never seen before.
11-02-2023 3
4. • This standard way to discover new drugs is essentially by trail 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 developments of models in the pharmaceutical industry is certainly one of the significant
break throughs proposed to face the challenges of cost, speed, and quality, somewhat imitating
what happens in the aeronautics industry.
• The concepts, however, is that of adopting just another new technology, known as “modeling”.
11-02-2023 4
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
behaviour, with the use of model, 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.
11-02-2023 5
6. CONCEPTS:-
• According to Breiman , there are two cultures in the use of statistical modelling
to reach conclusions from data.
• The first culture, namely, the data modelling culture, assumes that the data are
generated by a given stochastic data model.
• Where as the order, the algorithmic modelling culture, uses algorithmic models
and treats the data mechanism as unknown.
• To understand the mechanism, the use of modelling concepts is essential.
11-02-2023 6
7. • The purpose of the model is essentially for that of translation 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.
11-02-2023 7
9. DESCRIPTIVE MODEL MECHANISTIC MODEL
1. Descriptive model or empirical model,
describes the overall behaviour of the system in
question, without making any claim about the
nature of the underlying mechanisms that produce
this behaviour.
2. Descriptive model is a generic term for activities
that create models by observation and
experiment.
1. A mechanistic model is one where the basic
elements of the model have a direct
correspondence to the underlying mechanisms in
the system being modelled.
2. A mechanistic model assumes that a complex
system can be understood by examining the
workings of its individual parts and the manner in
which they are coupled.
10. DESCRIPTIVE MODEL MECHANISTIC MODEL
3. Descriptive model operates on a simple logic: the
maker observes a close correspondence between the
behaviour of the model and that of its referent.
4. The crafting of this correspondence can be
'empirical' in a wide variety of senses: it may entail a
trial-and-error process, may be based on
computational approximation to analytic formulae .
5.In Empirical Modelling, the process of construction
is an incremental one in which the intermediate
products are artefacts that evoke aspects of the
intended (and sometimes emerging) referent through
live interaction and observation.
3. Mechanistic models typically have a tangible,
physical aspect. In that system components are real,
solid and visible.
4. These models are considered as interpretrable or
meaningful,but their inherent nature (nonlinearity
,high number of parameters) posess other challenges
,particularly once several sources of noise are also to
be adequately modelled.
5. Mechanistic models are based on an understanding
of the behaviour of a system's components.
11-02-2023 10
11. REFERENCES
• Computur applications in pharmaceutical research by john wiley and sons and sean ekins
2006.
• Internet source .
• www.slideshare.net.
11-02-2023 11
