The document discusses the role of statistical modeling in pharmaceutical research and development, highlighting its importance in improving quality while reducing costs and time in drug discovery. It distinguishes between descriptive and mechanistic modeling, each serving different purposes in understanding data and system behaviors. The challenges faced by the pharmaceutical industry necessitate the development of new modeling approaches to enhance the overall drug development process.

1. SUBMITTED BY
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2. Statistical modeling in pharmaceutical
research and development
Statistics:
 Statistics is a scientific study of numerical data based on natural
phenomena.
 It is also the science of collecting, organizing, interpreting and
reporting data.
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reporting data.
Pharmaceutical statistics:
 Pharmaceutical statistics is the application of statistics to matters
concerning the pharmaceutical industry.
 To compare the action of two or more different drugs or different
dosage of same drug are studied using statistical method.

3. Statistical modeling:
 The new major challenge that the pharmaceutical industry is
facing in the discovery and development of the 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 the costs and time, then its whole business model will be
expose to danger or risk.
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expose to danger or risk.
 The development of the models in the pharmaceutical industry
certainly one of the significant proposed to face the challenges of
cost, speed and quality.
 The perspective to develop new technologies give rise to hope of
establishment new model in the pharmaceutical industries
significant to overcome the challenges of cost, speed as well as
quality .

4. Modeling is nothing but the concept adopting just another new
technologies.
The concept, however, is that of adopting just another new
technology, known as modeling. In the use of statistical modeling
to achieve summary from data to cultures said by breiman.
 The first culture which is data modeling culture where data
generated by a given random data model.
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generated by a given random data model.
 The second culture which is the algorithmic modeling
culture where use of the algorithmic model.

5. Objectives of statistical modeling:
 To improve the quality as well as to reduce cost and time
in experimental cycle.
 Enhancing the model will help to get knowledge that
experiment on successive drug as well as enhancing of
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experiment on successive drug as well as enhancing of
model ability to show real condition.
 Without models, the final purpose of an experiment was
one single drug or its behavior. With the use of models,
the objective of experiment will be the drug and the
models at the same level.

6. Type of statistical modeling:
1. Descriptive modeling
2. Mechanistic modeling
1. Descriptive modeling:
 Descriptive modeling is a mathematical process that describes
real world events and the relationship between factors
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real world events and the relationship between factors
responsible for them. The goal of this model is to provide
appropriate description of data to understand the concept of
data generating mechanism and the family of model is selected
based to denote the data structure.
 Empirical models are based on direct observation,
measurement and extensive data records.

7.  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.
 Descriptive model is a generic term for activities that
create models by observation and experiment.
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Descriptive model operates on a simple logic: the
maker observes a close correspondence between the
behaviour of the model and that of its referent.

8. 2.Mechanistic modeling:
 Mechanistic models are based on the fundamental laws of natural
sciences, Physical & biochemical principles constitute the model
equations.
 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
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collaboration between scientists, specialists in the field, and
statisticians or mathematicians.
 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. Mechanistic models typically
have a tangible, physical aspect. In that system components are real,
solid and visible.

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10. Difference between descriptive and mechanistic modeling:
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